An introduction to ethics

Table of contents

  • What is ethics?
  • Reading

    What is ethics?

    Ethics is approximately about the questions to do with the nature, content, and application of morality, and so is the study of morality in general.

    Questions of moral language, psychology, phenomonenology, epistemology, and ontology typically fall under metaethics.

    Questions of theoretical content, what makes something right, wrong, good, bad, obligatory, or supererogatory typically fall under normative ethics.

    Questions of conduct related to specific issues in the real world to do with business, professional, social, environmental, bioethics, and personhood typically fall under applied ethics. These can be things like abortion, euthanasia, treatment of non-human animals, marketing, and charity.

    Ethics has been divided traditionally into three areas concerning how we ought to conduct ourselves.

    Meta-ethics (Metaethics)

    Metaethics is occasionally referred to as a “second-order” discipline to make a distinction between itself and areas that are less about questions regarding what morality itself is. Questions about the most plausible metaphysical report of moral facts or the link between moral judgment, motivation, and knowledge are questions can be described as such, and so are metaethical questions. There are several rough divisions that have been created to introduce metaethics adequately. Either of these distinctions should be sufficient for getting a distant sense of what metaethics is.

    Metaethics as the systematic analysis of moral language, psychology, and ontology

    In Andrew Fisher’s Metaethics: An Introduction, an intro book Fisher at one point playfully thought of as “An Introduction to An Introduction to Contemporary Metaethics,” we get this:

    Looking at ethics we can see that it involves what people say: moral language. So one strand of metaethics considers what is going on when people talk moral talk. For example, what do people mean when they say something is “wrong”? What links moral language to the world? Can we define moral terms?

    Obviously ethics also involves people, so metaethicists consider and analyse what’s going on in peoples’ minds. For example, when people make moral judgements are they expressing beliefs or expressing desires? What’s the link between making moral judgements and motivation?

    Finally, there are questions about what exists (ontology). Thus meta-ethicists ask questions about whether moral properties are real. What is it for something to be real? Could moral facts exist independently of people? Could moral properties be causal?

    Metaethics, then, is the systematic analysis of:

    (a) moral language; (b) moral psychology; (c) moral ontology. This classification is rough and does not explicitly capture a number of issues that are often discussed in metaethics, such as truth and phenomenology. However, for our purposes we can think of such issues as falling under these broad headings.

    Metaethics as concerned with meaning, metaphysics, epistemology and justification, phenomenology, moral psychology, and objectivity

    In Alex Miller’s Contemporary Metaethics: An Introduction (the book Fisher playfully compared his own introduction to), Miller provides us with perhaps the most succinct description of the three:

    [Metaethics is] concerned with questions about the following:

    (a) Meaning: what is the semantic function of moral discourse? Is the function of moral discourse to state facts, or does it have some other non-fact-stating role? (b) Metaphysics: do moral facts (or properties) exist? If so, what are they like? Are they identical or reducible to natural facts (or properties) or are they irreducible and sui generis? (c) Epistemology and justification: is there such a thing as moral knowledge? How can we know whether our moral judgements are true or false? How can we ever justify our claims to moral knowledge? (d) Phenomenology: how are moral qualities represented in the experience of an agent making a moral judgement? Do they appear to be ‘out there’ in the world? (e) Moral psychology: what can we say about the motivational state of someone making a moral judgement? What sort of connection is there between making a moral judgement and being motivated to act as that judgement prescribes? (f) Objectivity: can moral judgements really be correct or incorrect? Can we work towards finding out the moral truth? Obviously, this list is not intended to be exhaustive, and the various questions are not all independent (for example, a positive answer to (f) looks, on the face of it, to presuppose that the function of moral discourse is to state facts). But it is worth noting that the list is much wider than many philosophers forty or fifty years ago would have thought. For example, one such philosopher writes:

    [Metaethics] is not about what people ought to do. It is about what they are doing when they talk about what they ought to do. (Hudson 1970)

    The idea that metaethics is exclusively about language was no doubt due to the once prevalent idea that philosophy as a whole has no function other than the study of ordinary language and that ‘philosophical problems’ only arise from the application of words out of the contexts in which they are ordinarily used. Fortunately, this ‘ordinary language’ conception of philosophy has long since ceased to hold sway, and the list of metaethical concerns – in metaphysics, epistemology, phenomenology, moral psychology, as well as in semantics and the theory of meaning – bears this out.

    Two small notes that might be made are:

    “Objectivity” is standardly taken to mean mind-independence. Here, it almost seems as if it’s cognitivism that the author is describing, but it’s made clear by the author noting that (f) presupposes facts that when Miller says “correct,” Miller means “objectively true.” This is a somewhat unorthodox usage, but careful reading makes it clear what Miller is trying to say.

    “Moral phenomenology” is often categorized as falling under normative ethics as well, but this has little impact on the veracity of this description of metaethics.

    Applied ethics

    Applied ethics is concerned with what is permissible in particular practices. In Peter Singer’s Practical Ethics, Singer provides some examples of what sorts of things this field might address.

    Practical ethics covers a wide area. We can find ethical ramifications in most of our choices, if we look hard enough. This book does not attempt to cover this whole area. The problems it deals with have been selected on two grounds: their relevance, and the extent to which philosophical reasoning can contribute to a discussion of them.

    I regard an ethical issue as relevant if it is one that any thinking person must face. Some of the issues discussed in this book confront us daily: what are our personal responsibilities towards the poor? Are we justified in treating animals as nothing more than machines- producing flesh for us to eat? Should we be using paper that is not recycled? And why should we bother about acting in accordance with moral principles anyway? Other problems, like abortion and euthanasia, fortunately are not everyday decisions for most of us; but they are issues that can arise at some time in our lives. They are also issues of current concern about which any active participant in our society’s decision-making process needs to reflect.


    This book is about practical ethics, that is, the application of ethics or morality — I shall use the words interchangeably — to practical issues like the treatment of ethnic minorities, equality for women, the use of animals for food and research, the preservation of the natural environment, abortion, euthanasia, and the obligation of the wealthy to help the poor.

    So what does the application of ethics to practical issues look like?

    We can take a look at two of the issues that Singer brings up — abortion and animal rights — to get a sense of what sort of evidence might be taken into consideration with these matters. Keep in mind that this is written with the intention of providing a sense of how discussions in applied ethics develop rather than a comprehensive survey of views in each topic.


    In Rosalind Hursthouse’s Virtue Theory and Abortion, Hursthouse gives a summary of the discussion on abortion as to do with the struggle between facts about the moral status of the fetus and women’s rights.

    As everyone knows, the morality of abortion is commonly discussed in relation to just two considerations: first, and predominantly, the status of the fetus and whether or not it is the sort of thing that may or may not be innocuously or justifiably killed; and second, and less predominantly (when, that is, the discussion concerns the morality of abortion rather than the question of permissible legislation in a just society), women’s rights.

    Judith Jarvis Thomson, in A Defense of Abortion, Thomson addresses a common version of the former consideration, refuting the slippery slope argument.

    Most opposition to abortion relies on the premise that the fetus is a human being, a person, from the moment of conception. The premise is argued for, but, as I think, not well. Take, for example, the most common argument. We are asked to notice that the development of a human being from conception through birth into childhood is continuous; then it is said that to draw a line, to choose a point in this development and say “before this point the thing is not a person, after this point it is a person” is to make an arbitrary choice, a choice for which in the nature of things no good reason can be given. It is concluded that the fetus is, or anyway that we had better say it is, a person from the moment of conception. But this conclusion does not follow. Similar things might be said about the development of an acorn into an oak trees, and it does not follow that acorns are oak trees, or that we had better say they are. Arguments of this form are sometimes called “slippery slope arguments”–the phrase is perhaps self-explanatory–and it is dismaying that opponents of abortion rely on them so heavily and uncritically.

    Nonetheless, Thomson is willing to grant the premise, addressing instead whether or not we can make the case that abortion is impermissible given that the fetus is, indeed, a person. Thomson thinks that the argument that fetuses have the right to life and that right outweighs the right for the individual carrying the fetus to do as they wish with their body is faulty, but notes a limitation.

    But now let me ask you to imagine this. You wake up in the morning and find yourself back to back in bed with an unconscious violinist. A famous unconscious violinist. He has been found to have a fatal kidney ailment, and the Society of Music Lovers has canvassed all the available medical records and found that you alone have the right blood type to help. They have therefore kidnapped you, and last night the violinist’s circulatory system was plugged into yours, so that your kidneys can be used to extract poisons from his blood as well as your own. The director of the hospital now tells you, “Look, we’re sorry the Society of Music Lovers did this to you–we would never have permitted it if we had known. But still, they did it, and the violinist is now plugged into you. To unplug you would be to kill him. But never mind, it’s only for nine months. By then he will have recovered from his ailment, and can safely be unplugged from you.” Is it morally incumbent on you to accede to this situation? No doubt it would be very nice of you if you did, a great kindness. But do you have to accede to it? What if it were not nine months, but nine years? Or longer still? What if the director of the hospital says. “Tough luck. I agree, but now you’ve got to stay in bed, with the violinist plugged into you, for the rest of your life. Because remember this. All persons have a right to life, and violinists are persons. Granted you have a right to decide what happens in and to your body, but a person’s right to life outweighs your right to decide what happens in and to your body. So you cannot ever be unplugged from him.” I imagine you would regard this as outrageous, which suggests that something really is wrong with that plausible-sounding argument I mentioned a moment ago.

    In this case, of course, you were kidnapped, you didn’t volunteer for the operation that plugged the violinist into your kidneys.

    Thomson goes on to address this limitation and goes back and forth between the issue of the fetus’s and carrier’s rights, but Hursthouse (see above) rejects this framework, noting in more detail that we can suppose that women have a right to abortion in a legal sense and still have to wrestle with whether or not abortion is permissible. On the status of fetuses, Hursthouse claims this too can be bypassed with virtue theory.

    What about the consideration of the status of the fetus-what can virtue theory say about that? One might say that this issue is not in the province of any moral theory; it is a metaphysical question, and an extremely difficult one at that. Must virtue theory then wait upon metaphysics to come up with the answer?


    But the sort of wisdom that the fully virtuous person has is not supposed to be recondite; it does not call for fancy philosophical sophistication, and it does not depend upon, let alone wait upon, the discoveries of academic philosophers. And this entails the following, rather startling, conclusion: that the status of the fetus-that issue over which so much ink has been spilt-is, according to virtue theory, simply not relevant to the rightness or wrongness of abortion (within, that is, a secular morality).

    Or rather, since that is clearly too radical a conclusion, it is in a sense relevant, but only in the sense that the familiar biological facts are relevant. By “the familiar biological facts” I mean the facts that most human societies are and have been familiar with-that, standardly (but not invariably), pregnancy occurs as the result of sexual intercourse, that it lasts about nine months, during which time the fetus grows and develops, that standardly it terminates in the birth of a living baby, and that this is how we all come to be.

    It is worth noting that Hursthouse’s argument more centrally gives her conception of what virtue ethics ought to look like rather than how we should go about abortion, and so to avoid it clouding her paper, she never takes any stance on whether one should think abortion is or is not permissible.

    Thomson’s argument appears to be rather theory-agnostic whereas Hursthouse is committed to a certain theory of ethics. A third approach is intertheoretical, an example of which can be found in Tomasz Żuradzki’s Meta-Reasoning in Making Moral Decisions under Normative Uncertainty. Here, Żuradzki discusses how we might deal with uncertainty over which theory is correct.

    For example, we have to act in the face of uncertainty about the facts, the consequences of our decisions, the identity of people involved, people’s preferences, moral doctrines, specific moral duties, or the ontological status of some entities (belonging to some ontological class usually has serious implications for moral status). I want to analyze whether these kinds of uncertainties should have practical consequences for actions and whether there are reliable methods of reasoning that deal with the possibility that we understand some crucial moral issues wrong.

    Żuradzki at one point considers the seemingly obvious “My Favorite Theory” approach, but concludes that the approach is problematic.

    Probably the most obvious proposition how to act under normative uncertainty is My Favorite Theory approach. It says that “a morally conscientious agent chooses an option that is permitted by the most credible moral theory”


    Although this approach looks very intuitive, there are interesting counter-examples.

    Żuradzki also addresses a few different approaches, some of which seem to make abortion impermissible so long as there is uncertainty, but perhaps this gives a good idea of three approaches in applied ethics.

    Animal rights

    In the abortion section, the status of the fetus falls into the background. Thomson says even given a certain status, the case against abortion must do more, Hursthouse says the metaphysical question can be bypassed altogether, and Żuradzki considers how to take multiple theories about an action into account. But it seems this strategy of moving beyond the status of the patient in question cannot be done when it comes to the question of how we ought to treat non-human animals, for there’s no obvious competing right that might give us pause when we decide not to treat a non-human animal cruelly. In dealing with animal rights, then, it appears we are forced to address the status of the non-human animal, and there seem to be many ways to address this.

    In Tom Regan’s The Case for Animal Rights, Regan, who agrees with Kant that those who are worthy of moral consideration are ends-in-themselves, thinks what grounds that worthiness in humans is also what grounds that in non-human animals.

    We want and prefer things, believe and feel things, recall and expect things. And all these dimensions of our life, including our pleasure and pain, our enjoyment and suffering, our satisfaction and frustration, our continued existence or our untimely death – all make a difference to the quality of our life as lived, as experienced, by us as individuals. As the same is true of those animals that concern us (the ones that are eaten and trapped, for example), they too must be viewed as the experiencing subjects of a life, with inherent value of their own.

    Christine Korsgaard, who also agrees with a Kantian view, argues against Regan’s view and thinks non-human animals are not like humans. In Fellow Creatures: Kantian Ethics and Our Duties to Animals, Korsgaard makes the case that humans are rational in a sense that non-human animals are not, and that rationality is what grounds our moral obligations.

    an animal who acts from instinct is conscious of the object of its fear or desire, and conscious of it as fearful or desirable, and so as to-be-avoided or to-be-sought. That is the ground of its action. But a rational animal is, in addition, conscious that she fears or desires the object, and that she is inclined to act in a certain way as a result.


    We cannot expect the other animals to regulate their conduct in accordance with an assessment of their principles, because they are not conscious of their principles. They therefore have no moral obligations.

    Korsgaard, however, still thinks this difference that makes the sense in which humans and non-human animals should be considered fundamentally distinct still leaves room for animals to be worthy of moral consideration.

    Because we are animals, we have a natural good in this sense, and it is to this that our incentives are directed. Our natural good, like the other forms of natural good which I have just described, is not, in and of itself, normative. But it is on our natural good, in this sense, that we confer normative value when we value ourselves as ends-in-ourselves. It is therefore our animal nature, not just our autonomous nature, that we take to be an end-in-itself.


    In taking ourselves to be ends-in-ourselves we legislate that the natural good of a creature who matters to itself is the source of normative claims. Animal nature is an end-in-itself, because our own legislation makes it so. And that is why we have duties to the other animals.

    So Regan thinks that we can elevate the status of non-human animals up to something like the status of humans, but Korsgaard thinks there is a vast difference between the two categories. Before we consider which view is more credible, we should consider an additional, non-Kantian view which seems to bypass the issue of status once more.

    Rosalind Hursthouse (again!), in Applying Virtue Ethics to Our Treatment of the Other Animals, argues that status need not be relevant for roughly the same reasons as the case of abortion.

    In the abortion debate, the question that almost everyone began with was “What is the moral status of the fetus?”


    The consequentialist and deontological approaches to the rights and wrongs of the ways we treat the other animals (and also the environment) are structured in exactly the same way. Here too, the question that must be answered first is “What is the moral status of the other animals…?” And here too, virtue ethicists have no need to answer the question.

    So Hursthouse once again reframes the argument and grounds her argument in terms of virtue.

    So I take the leaves on which [Singer describes factory farming] and think about them in terms of, for example, compassion, temperance, callousness, cruelty, greed, self-indulgence—and honesty.

    Can I, in all honesty, deny the ongoing existence of this suffering? No, I can’t. I know perfectly well that althrough there have been some improvements in the regulation of factory farming, what is going on is still terrible. Can I think it is anything but callous to shrug this off and say it doesn’t matter? No, I can’t. Can I deny that the practices are cruel? No, I can’t.


    The practices that bring cheap meat to our tables are cruel, so we shouldn’t be party to them.

    Żuradzki’s argument in Meta-Reasoning in Making Moral Decisions under Normative Uncertainty becomes relevant once more as well. In it, he argues that if between the competing theories, one says something is wrong and one says nothing of the matter, it would be rational to act as if it were wrong.

    Comparativism in its weak form can be applied only to very specific kinds of situations in which an agent’s credences are not divided between two different moral doctrines, but between only one moral doctrine and some doctrine (or doctrines) that does not give any moral reasons. Its conclusion says that if some theories in which you have credence give you subjective reason to choose action A over action B, and no theories in which you have credence give you subjective reason to choose action B over action A, then you should (because of the requirements of rationality) choose A over B.

    Once again, we see a variety of approaches that help give us a sense of the type of strategies that applied ethicists might use. Here, we have arguments that accept and reject a central premise of the debate, an argument that bypasses it, and an argument that considers both views. Some approaches are theory-specific, some are intertheoretical, and while it was not discussed here, Singer’s argument from marginal cases is theory-neutral.

    Other issues will differ wildly, they will rely on different central premises, have arguments such that intertheoretical approaches are impossible, or have any number of other variations on the similarities and differences between the discussions on the two topics just discussed. However, this gives some idea, hopefully enough to build on if one chooses to look deeper into the literature, of how discussions in the area of applied ethics go about.

    Normative ethics

    Normative ethics deals very directly with the question of conduct. Much of the discipline is dedicated to discovering ethical theories capable of describing what we ought to do. But what does ought mean? In different contexts, while ought tends to deal with normativity and value, it does not always deal with ethics. The oughts that link aesthetics and normativity are not obviously the same as the oughts that we’re dealing with here. The questions of what oughts exist in normative ethics have a great deal to do with concepts like what is “permissible” or “impermissible,” what is “right” or “wrong,” or what is “good” and “bad.” It should be contrasted with how people do act, as well as the moral code of some person or group. These are not what normative ethics is about, but rather what genuinely is correct when it comes to how we ought to live our lives. For now, we can roughly divide the main theories of this area into three categories, though these are not the only categories: consequentialism, deontology, and virtue theory. As noted, there are other theories, and there are even other problems in normative ethics as well, but these three types of theories will be detailed below as well as what we should take from an understanding of the three categories.

    Ethics as grounded in outcomes: Consequentialism

    Consequentialism is a family of theories that are centrally concerned with consequences. Consequentialism, in ordinary practice, is used to refer to theories rooted in classical utilitarianism (even when the theory is not utilitarianism itself), ignoring certain theories that also seem grounded solely in consequences such as egoism. The aforementioned classical utilitarianism that serves as the historical and conceptual root of this discussion entailed a great deal of claims, laid out in Shelly Kagan’s Normative Ethics:

    that goodness of outcomes is the only morally relevant factor in determining the status of a given act. the agent is morally required to perform the act with the best consequences. It is not sufficient that an act have “pretty good” consequences, that it produce more good than harm, or that it be better than average. Rather, the agent is required to perform the act with the very best outcome (compared to alternatives); she is required to perform the optimal act, as it is sometimes called. the agent is morally required to performed the act with the best consequences. The optimal act is the only act that is morally permissible; no other act is morally right. Thus the consequentialist is not making the considerably more modest claim that performing the act with the best consequences is—although generally not obligatory—the nicest or the most praiseworthy thing to do. Rather, performing the optimal act is morally required: anything else is morally forbidden. the right act is the act that leads to the greatest total amount of happiness overall. the consequences [are evaluated] in terms of how they affect everyone’s well-being…

    And of course, these can be divided even further, but what’s salient is there appear to be a great many more claims entailed in this classical form of utilitarianism than one might think first glance: classical utilitarianism is an agent-neutral theory in which acts that actually result in the optimal amount of happiness for everyone is obligatory. By understanding all of these points, we can understand how consequentialism differs from this classical utilitarianism and thus what it means to be consequentialist.

    The limits of contemporary consequentialism

    Many of these claims don’t seem necessary to the label “consequentialism” and give us an unnecessarily narrow sense of what the word could mean.

    It seems desirable to want to broaden the scope of the term then, and in fact, this hasn’t only been done simply to help understand consequentialism, but to defend against criticisms of consequentialism. In Campbell Brown’s Consequentialize This, we get a brief description of one motivation behind radical consequentializing:

    You—a nonconsequentialist, let’s assume—begin with your favorite counterexample. You describe some action…[that] would clearly have the best consequences, yet equally clearly would be greatly immoral. So consequentialism is false, you conclude; sometimes a person ought not to do what would have best consequences. “Not so fast,” comes the consequentialist’s reply. “Your story presupposes a certain account of what makes consequences better or worse, a certain ‘theory of the good,’ as we consequentialists like to say. Consequentialism, however, is not wedded to any such theory…In order to reconcile consequentialism with the view that this action you’ve described is wrong, we need only to find an appropriate theory of the good, one according to which the consequences of this action would not be best. You say you’re concerned about the guy’s rights? No worries; we’ll just build that into your theory of the good. Then you can be a consequentialist too.”

    So, Brown says, this is what has just occurred:

    Instead of showing that your nonconsequentialism is mistaken, the consequentialist shows that it’s not really nonconsequentialism; instead of refuting your view, she ‘consequentializes’ it. If you can’t beat ’em, join ’em. Better still, make ’em join you.

    Is this a good strategy? Brown thinks not, for it weakens the consequentialist’s claim.

    It might succeed in immunizing consequentialism against counterexamples only at the cost of severely weakening it, perhaps to the point of utter triviality. So effortlessly is the strategy deployed that some are led to speculate that it is without theoretical limits: every moral view may be dressed up in consequentialist clothing…But then, it seems, consequentialism would be empty—trivial, vacuous, without substantive content, a mere tautology. The statement that an action is right if and only if (iff) it maximizes the good would entail nothing more substantive than the statement that an action is right iff it is right; true perhaps, but not of much use.

    So not too broad, not too narrow, and not too shifty. We want some sort of solid and only sufficiently broad meaning to jump from. Brown goes on to define what he thinks consequentialism minimally is and three limits must be placed upon it.

    whatever is meant by ‘consequentialism’, it must be intelligible as an elaboration of the familiar consequentialist slogan “Maximize the good.” The non-negotiable core of consequentialism, I shall assume, is the claim that an action is right, or permissible, iff it maximizes the good. My strategy is to decompose consequentialism into three conditions, which I call ‘agent neutrality’, ‘no moral dilemmas’, and ‘dominance’ As usually defined, a theory is agent-relative iff it gives different aims to different agents; otherwise it’s agent-neutral. By a moral dilemma, I mean a situation in which a person cannot avoid acting wrongly…Consider, for example, a theory which holds that violations of rights are absolutely morally forbidden; it is always wrong in any possible situation to violate a right. Suppose, further, that the catalog of rights endorsed by this theory is such that sometimes a person cannot help but violate at least one right. Then this theory cannot be represented by a rightness function which satisfies NMD, and so it cannot be consequentialized. [Dominance] may be the least intuitive of the three. It requires the following. Suppose that in a given choice situation, two worlds x and y are among the alternatives. And suppose in this situation, x is right and y wrong. Then x dominates y in the following sense: y cannot be right in any situation where x is an alternative; the presence of x is always sufficient to make y wrong.

    And there we have it, a definition of consequentialism. Not only that, but this definition is formalized in the paper as well. Can we safely say, then, that this is the definition of consequentialism? The most comprehensive, elucidating, uncontroversial in the field? Certainly not! In fact, it leaves out several significant forms of consequentialism, but this formulation of consequentialism captures many concepts important consequentialism, sufficient for further discussion over the three families. This disagreement over the definition might bring a new set of worries to the mind of any reader. The problem of disagreement will be discussed in another section.

    Ethics as grounded in moral law: Deontology

    Deontology is another family of theories whose definition can wiggle through our grasp (there’s a pattern here to recognize that will become important in a later section). Once more, Shelly Kagan’s Normative Ethics offers us a definition of deontology as it is used in contemporary discourse: a theory that places value on additional factors that would forbid certain actions independently of whether or not they result in the best outcomes.

    In defining deontology, I have appealed to the concept of a constraint: deontologists, unlike consequentialists, believe in the existence of constraints, which erect moral barriers to the promotion of the good…it won’t quite do to label as deontologists all those who accept additional normative factors, beyond that of goodness of results: we must add further stipulation that in at least some cases the effect of these additional factors is to make certain acts morally forbidden, even though these acts may lead to the best possible results overall. In short, we must say that deontologists are those who believe in additional normative factors that generate constraints.

    Kagan goes on to explain why of the various definitions, this one is best. That explanation will not be detailed here, but let’s keep this tenuously in mind as we dive into one of the deontological theories to give us a sense of what deontology entails. It would be absurd if these constraints were arbitrary, nothing more than consequentialism combined with “also, don’t do these specific things because they seem icky and I don’t like them,” so we will take a look at one of the prominent deontological theories: Kantianism.

    Kant’s First Formula

    In Julia Driver’s Ethics: The Fundamentals, Driver introduces us to deontology through Kant’s moral theory, saying this of the theory:

    Immanuel Kant’s theory is perhaps the most well-known exemplar of the deontological approach…whether or not a contemplated course of action is morally permissible will depend on whether or not it conforms to what he terms the moral law, the categorical imperative.

    There’s a tone here that seems noticeably different from consequentialist talk. Permissibility as conforming to moral law could still be consequentialist if that law is something like “maximize the good,” but this description seems to indicate something else. To figure this out, we need an explanation of what “the categorical imperative” means. In Christine Korsgaard’s Creating the Kingdom of Ends:

    Hypothetical imperatives [are] principles which instruct us to do certain actions if we want certain ends…


    Willing something is determining yourself to be the cause of that thing, which means determining yourself to use the available causal connections — the means — to it. “Willing the end” is already posited as the hypothesis, and we need only analyze it to arrive at willing the means. If you will to be able to play the piano, then you already will to practice, as that is the “indispensably necessary means to it” that “lie in your power.” But the moral ought is not expressed by a hypothetical imperative. Our duties hold for us regardless of what we want. A moral rule does not say “do this if you want that” but simply “do this.” It is expressed in a categorical imperative. For instance, the moral law says that you must respect the rights of others. Nothing is already posited, which can then be analyzed.

    We now have a fairly detailed description of what the distinction between a hypothetical and categorical imperative is, with fine examples to boot. Note that already, it’s clear this theory can’t be consequentialized according to Brown, but we must go further to remove any doubt as a result of controversy over Brown’s formulation. Korsgaard goes on to explain what is necessarily entailed as a part of the categorical imperative in her description of Kant’s first formula.

    If we remove all purposes — all material — from the will, what is left is the formal principle of the will. The formal principle of duty is just that it is duty — that it is law. The essentially character of law is universality. Therefore, the person who acts from duty attends to the universality of his/her principle. He or she only acts on a maxim that he or she could will to be universal law (G 402).


    But how can you tell whether you are able to will your maxim as a universal law? On Kant’s view, it is a matter of what you can will without contradiction…you envision trying to will your maxim in a world in which the maxim is universalized — in which it is a law of nature. You are to “Ask yourself whether, if the action which you propose should take place by a law of nature of which you yourself were a part, you could regard it as possible through your will” (C2 69)

    Already, upon encountering this first formulation of the categorical imperative, we have now well established that any limit on consequentialization would leave Kant’s moral theory able to resist it. For one, the rightness or wrongness of actions is conforming to moral law such that the outcomes are no longer centrally a point of consideration. This does not mean we have deprived ethics of consequences, as Kagan points out in Normative Ethics:

    [the goodness of outcomes]

    is a factor I think virtually everyone recognizes as morally relevant. It may not be the only factor that is important for determining the moral status of an act, but it is certainly one relevant factor.

    Kantianism is notwithstanding deciding the status of actions not on the sole basis of outcomes. As well, it fails Brown’s dominance formulation.

    The two other formulas are not within the scope of this section, nor is evidence for Kant’s theory. The purpose of detailing Kantianism at all was to demonstrate deontology as conforming to moral law in a manner distinct from consequentialism. As well, it is sufficient to remind ourselves that there is a massive amount of evidence for each of these types of theories without having to detail it in this section for this theory in particular. As well, there are other types of deontological theories, also with a great deal of evidence. Scanlon’s moral theory and Ross’s moral theory are other prominent examples of deontology.

    We are now left with a fairly strong sense of what deontological theories look like. There is some imprecision in that sense, this will be discussed in another section. For now, we must move on to address virtue ethics.

    Ethics as grounded in character: Virtue Ethics

    Virtue ethics, the final family of theories described in the section on normative ethics, is predictably concerned primarily with virtue and practical intelligence.


    A virtue is described as lasting, reliable, and characteristic in Julia Annas’s Intelligent Virtue:

    A virtue is a lasting feature of a person, a tendency for the person to be a certain way. It is not merely a lasting feature, however, one that just sits there undisturbed. It is active: to have it is to be disposed to act in certain ways. And it develops through selective response to circumstances. Given these points, I shall use the term persisting rather than merely lasting. Jane’s generosity, supposing her to be generous, persists through challenges and difficulties, and is strengthened or weakened by her generous or ungenerous responses respectively. Thus, although it is natural for us to think of a virtue as a disposition, we should be careful not to confuse this with the scientific notion of disposition, which just is a static lasting tendency…


    A virtue is also a reliable disposition. If Jane is generous, it is no accident that she does the generous action and has generous feelings. We would have been surprised, and shocked, if she had failed to act generously, and looked for some kind of explanation. Our friends’ virtues and vices enable us to rely on their responses and behaviour—to a certain extent, of course, since none of us is virtuous enough to be completely reliable in virtuous response and action.


    Further, a virtue is a disposition which is characteristic—that is, the virtuous (or vicious) person is acting in and from character when acting in a kindly, brave or restrained way. This is another way of putting the point that a virtue is a deep feature of the person. A virtue is a disposition which is central to the person, to whom he or she is, a way we standardly think of character. I might discover that I have an unsuspected talent for Sudoku, but this, although it enlarges my talents, does not alter my character. But someone who discovers in himself an unsuspected capacity to feel and act on compassion, and who develops this capacity, does come to change as a person, not just in some isolated feature; he comes to have a changed character.

    Virtue ethics, then, is centered around something that is roughly this concept. Note that any plausible theory is going to incorporate all of the concepts we’ve gone over on normative ethics. We can go back to Kagan’s Normative Ethics from above, where he notes the relevancy of consequences in every theory.

    all plausible theories agree that goodness of consequences is at least one factor relevant to the moral status of acts. (No plausible theory would hold, for example, that it was irrelevant whether an act would lead to disaster!)

    Similarly, other theories will have an account of virtue, as Jason Kawall’s In Defense of the Primacy of the Virtues briefly describes:

    Consequentialists will treat the virtues as character traits that serve to maximize (or produce sufficient quantities of) the good, where the good is taken as explanatorily basic. Deontologists will understand the virtues in terms of dispositions to respect and act in accordance with moral rules, or to perform morally right actions, where these moral rules or right actions are fundamental. Furthermore, the virtues will be considered valuable just insofar as they involve such tendencies to maximize the good or to perform right actions.

    So it is important to stress then that virtue is the central concept for virtue ethics, and is no more simply a theory that makes relevant an account of virtue any more than consequentialism is any theory that makes relevant an account of consequences. One way we can come to understand virtue ethics better is by understanding a specific kind of virtue ethics, theories which satisfying four conditions laid out by Kawall:

    (i) The concepts of rightness and goodness would be explained in terms of virtue concepts (or the concept of a virtuous agent).

    (ii) Rightness and goodness would be explained in terms of the virtues or virtuous agents.

    (iii) The explanatory primacy of the virtues or virtuous agents (and virtue concepts) would reflect a metaphysical dependence of rightness and goodness upon the virtues or virtuous agents.

    (iv) The virtues or virtuous agents themselves – as well as their value – could (but need not) be explained in terms of further states, such as health, eudaimonia, etc., but where these further states do not require an appeal to rightness or goodness.

    It should be emphasized again that this describes only some theories in this family, but they are good theories to focus on because much of the discussion around these theories would be representative of discussion around virtue ethics in general.

    It is worth stressing that not all theories that could plausibly be understood as forms of virtue ethics would satisfy the above conditions; the current goal is not to defend all possible virtue ethics. Rather, we are examining what might be taken to be among the more radical possible forms of virtue ethics, particularly in treating the virtues as explanatorily prior both to rightness and to goodness tout court. Why focus on these more radical forms? First, several prominent virtue ethics can be understood as satisfying the above conditions, including those of Michael Slote, Linda Zagzebski, and, perhaps (if controversially), Aristotle’s paradigmatic virtue ethics. Beyond this, many of the arguments presented here could be taken on board by those defending more moderate forms of virtue ethics, such as Rosalind Hursthouse or Christine Swanton (against those who would attempt to argue for the explanatory primacy of the right or of the good, for example). Thus the range of interest for most of these arguments will extend beyond those focusing on the more radical approaches.

    Practical intelligence

    Practical intelligence can be described much more briefly to get a sense of its meaning across. In Rosalind Hursthouse’s Applying Virtue Ethics to Our Treatment of the Other Animals, we get a brief description of the role of practical intelligence.

    Of course, applying the virtue and vice terms correctly may be difficult; one may need much practical wisdom to determine whether, in a particular case, telling a hurtful truth is cruel or not, for example…

    Julia Annas elaborates to greater detail in “Intelligent Virtue”:

    The way our characters develop is to some extent a matter of natural endowment; some of us have traits ‘by nature’—we will tend to act bravely or generously without having to learn to do so, or to think about it. This is ‘natural virtue’, which we have already encountered. Different people will have different natural virtues, and one person may be naturally endowed in one area of life but not others—naturally brave, for example, but not naturally generous. However, claims Aristotle, this can’t be the whole story about virtue. For one thing, children and animals can have some of these traits, but in them they are not virtues. Further, these natural traits are harmful if not guided by ‘the intellect’, which in this context is specified as practical wisdom or practical intelligence (phronesis). Just as a powerfully built person will stumble and fall if he cannot see, so a natural tendency to bravery can stumble unseeingly into ethical disaster because the person has not learned to look out for crucial factors in the situation. Our natural practical traits need to be formed and educated in an intelligent way for them to develop as virtues; a natural trait may just proceed blindly on where virtue would respond selectively and in a way open to novel information and contexts.

    Ethics as maximizing happiness: Utilitarianism

    In the famous Trolley problem philosopher Philippa Foot introduced in the 1960s, you have the ability to pull a lever to divert a train from running over five tied-up people lying on the tracks. If you pull the lever, the trolley will be redirected onto a side track, and the five people on the main track will be saved. However, there is a single person lying on the side track.

    According to classical utilitarianism, pulling the lever would be permissible and more moral. English philosophers Jeremy Bentham and John Stuart Mill introduced utilitarianism as the sole moral obligation to maximize happiness. As an alternative to divine, religious theories of ethics. Utilitarianism suffers from the idea of “utility monsters,” individuals who would have much more happiness (and therefore utility) than average. This would cause actions to skew towards and exploit maximizing the monster’s happiness in such a way that others would suffer. Since philosopher Robert Nozick introduced the “utility monster” idea in 1974, it has been discussed in politics as driving the ideas of special interest groups and free speech – as though securing these interests would serve the interests of the few experiencing much more happiness than the general population.

    Are these taxonomic imperfections bad? How do we get over vague definitions?

    It might be tempting to read all of this and think there’s some sort of difficulty in discussing normative ethics. In general, academic discourse does not hinge on definitions, and so definitions are not a very large concern. And yet, it might appear upon reading this that ethics is some sort of exception. When philosophers talk about adaptationism in evolution or causation in metaphysics, the definitions they provide seem a lot more precise, so why is ethics an exception?

    The answer is uninterestingly that ethics is not an exception. It is important to avoid confusing what has been read here as some sort of fundamental ambiguity in these theories. Consider Brown’s motive for resisting consequentialization as a response to Dreir’s motive for consequentialization.

    I’ll close by drawing out another moral of my conclusion, related to something Dreier says. Dreier’s motivation for consequentializing is that he wants to overcome a certain “stigma” which he says afflicts defenders of “common sense morality” when they try to deny consequentialism. To deny consequentialism, he says, they must claim that we are sometimes required to do less good than we might, but that claim has a “paradoxical air.” So defenders of commonsense morality, who deny consequentialism, are stigmatized as having a seemingly paradoxical position.


    Dreier thinks the way to avoid the stigma is to avoid denying consequentialism. If we consequentialize commonsense morality, then defenders of commonsense morality need not deny consequentialism. If I’m right, however, this way of avoiding the stigma doesn’t work…

    Note that this is entirely orthogonal to the plausibility of any particular theory. Whatever stigmas exist makes no difference on whether or not some particular theory happens to be correct. It may prove useful to helping beginners gain a sense of what they’re talking about, but beyond pedagogical utility, it’s disputed that this distinction actually tells us, at a very fundamental level, what these theories are all about.

    In Michael Ridge’s Reasons for Action: Agent-Neutral vs. Agent-Relative, Ridge points out one of the alternative distinctions that might have a more prominent role in describing what fundamentally distinguishes these theories.

    The agent-relative/agent-neutral distinction is widely and rightly regarded as a philosophically important one.


    The distinction has played a very useful role in framing certain interesting and important debates in normative philosophy.

    For a start, the distinction helps frame a challenge to the traditional assumption that what separates so-called consequentialists and deontologists is that the former but not the latter are committed to the idea that all reasons for action are teleological. A deontological restriction forbids a certain sort of action (e.g., stealing) even when stealing here is the only way to prevent even more stealing in the long run. Consequentialists charge that such a restriction must be irrational, on the grounds that if stealing is forbidden then it must be bad but if it is bad then surely less stealing is better than more. The deontologist can respond in one of two ways. First, they could hold that deontological restrictions correspond to non-teleological reasons. The reason not to steal, on this account, is not that stealing is bad in the sense that it should be minimized but rather simply that stealing is forbidden no matter what the consequences (this is admittedly a stark form of deontology, but there are less stern versions as well). This is indeed one way of understanding the divide between consequentialists and deontologists, but the agent-relative/agent-neutral distinction, and in particular the idea of agent-relative reasons, brings to the fore an alternative conception. For arguably, we could instead understand deontological restrictions as corresponding to a species of reasons which are teleological after all so long as those reasons are agent-relative. If my reason not to steal is that I should minimize my stealing then the fact that my stealing here would prevent five other people from committing similar acts of theft does nothing to suggest that I ought to steal.


    If Dreier is right [that in effect we can consequentialize deontology] then the agent-relative/agent-neutral distinction may be more important than the distinction between consequentialist theories and non-consequentialist theories.

    The section goes on to detail several ways we can look at this issue so we can understand the importance of this distinction and what it can tell us about the structure and plausibility of certain theories. So while the typical division between consequentialist, deontological, and virtue ethical theories can be superficially valuable to those getting into ethics, it is important to not overstate the significance of these families and their implications.


    Normative ethics

    Includes a minimal definition of normative ethics as a whole.

    In this entry, Ridge lays out another way of categorizing theories in normative ethics in an accessible manner.

    Issues in normative ethics

    • Christopher Heathwood Welfare. 2010.
    • Roger Crisp Stanford Encyclopedia of Philosophy entry on Well-being. 2017.
    • Michael Zimmerman Stanford Encyclopedia of Philosophy entry on Intrinsic vs. Extrinsic Value. 2014.
    • Dana Nelkin Stanford Encyclopedia of Philosophy entry on Moral Luck. 2013.
    • Stephen Stich, John Doris, and Erica Roedder Altruism. 2008.
    • Robert Shaver Stanford Encyclopedia of Philosophy entry on Egoism. 2014.
    • Joshua May Internet Encyclopedia of Philosophy entry on Psychological Egoism. 2011.


    About the best introduction that one can find to one of the consequentialist theories: utilitarianism.

    An introduction to the debate over utilitarianism.

    An influential work that lays out a decent strategy for keeping consequentialist theories of ethics distinct from other theories.

    • Walter Sinnott-Armstrong’s Stanford Encyclopedia of Philosophy entry on Consequentialism. 2015. A
    • William Haines Internet Encyclopedia of Philosophy entry on Consequentialism. 2006.
    • Chapter 3 and 4 of Driver (see above). 2006.


    A good introduction to and strong defense of Kantianism.

    Rawls’s revolutionary work in both ethics and political philosophy in which he describes justice as fairness, a view he would continue to develop later on.

    A significant improvement and defense of one of the most influential deontological alternatives to Kantianism: Rossian deontology.

    Scanlon, one of the most notable contributors to political and ethical philosophy among his contemporaries, provides an updated and comprehensive account of his formulation of contractualism.

    • Larry Alexander and Michael Moore Stanford Encyclopedia of Philosophy entry on Deontological Ethics. 2016.
    • Chapter 5 and 6 of Driver (see above). 2006.

    Virtue ethics

    Hursthouse’s groundbreaking and accessible work on virtue theory.

    Meta-ethics (Metaethics)

    This is probably a more difficult read than the others, but it is incredibly comprehensive and helpful. There are many things in this handbook that I’ve been reading about for a long time that I didn’t feel confident about until reading this. Certainly worth the cost.

    Moral judgement

    A must read for those who want to engage with issues in moral judgment, functioning both as a work popularly considered the most important in the topic as well as a great introduction.

    • Chapter 3 of Miller (see above). 2013.
    • Connie S. Rosati Stanford Encyclopedia of Philosophy entry on Moral Motivation. 2016.

    Moral responsibility

    Moral realism and irrealism

    A very popular Philosophy Compass paper that lays out very simply what moral realism is without arguing for or against any position.

    An obligatory text laying out the popular companions in guilt argument for moral realisms.

    • Smith (see above). 1998.
    • Enoch (see above). 2011.
    • Chapter 8, 9, and 10 of Miller (see above). 2013.
    • Shafer-Landau (see above). 2005.
    • Katia Vavova Debunking Evolutionary Debunking. 2013.

    Here, Vavova provides a very influential, comprehensive, and easy to read overview of evolutionary debunking arguments, in which she also takes the liberty of pointing out their flaws.

    Korsgaard’s brilliant description, as well as her defense, of a form of Kantian constructivism.

    Research Ethics


    National Center for Professional and Research Ethics (NCPRE) –

    National Science Foundation Office of Inspector General –

    Office for Human Research Protections (OHRP) –

    Office of Research Integrity (ORI) –

    Online Ethics Center for Engineering and Research –

    Project for Scholarly Integrity –

    Resources for Research Ethics Education –

    Email lists

    RCR-Instruction, Office of Research Integrity – send a request to to subscribe


    Accountability in Research –

    Ethics and Behavior –

    Journal of Empirical Research on Human Research Ethics –

    Science and Engineering Ethics –

    News publications

    The Chronicle of Higher Education –

    Nature –

    Science –

    The Scientist –

    Ethical theory

    Frankena, William K. 1988. Ethics. 2nd ed. Prentice-Hall, Inc.

    Rachels, James, and Stuart Rachels. 2009. The Elements of Moral Philosophy. 6th ed. McGraw-Hill Companies.


    Beach, Dore. 1996. Responsible Conduct of Research. John Wiley & Sons, Incorporated.

    Bebeau, Muriel J., et al. 1995. Moral Reasoning in Scientific Research: Cases for Teaching and Assessment. Poynter Center for the Study of Ethics and American Institutions. Source: Order or download in PDF format at

    Bulger, Ruth Ellen, Elizabeth Heitman, and Stanley Joel Reiser, eds. 2002. The Ethical Dimensions of the Biological and Health Sciences. 2nd ed. Cambridge University Press.

    Elliott, Deni, and Judy E. Stern, eds. 1997. Research Ethics: A Reader. University Press of New England. See also Stern and Elliott, The Ethics of Scientific Research.

    Erwin, Edward, Sidney Gendin, and Lowell Kleiman, eds. 1994. Ethical Issues in Scientific Research: An Anthology. Garland Publishing.

    Fleddermann, Charles B. 2007. Engineering Ethics. 3rd ed. Prentice Hall.

    Fluehr-Lobban, Carolyn. 2002. Ethics and the Profession of Anthropology: Dialogue for Ethically Conscious Practice. 2nd ed. AltaMira Press.

    Goodstein, David L. 2010. On Fact and Fraud: Cautionary Tales from the Front Lines of Science. Princeton University Press.

    Harris, Charles E., Jr., Michael S. Pritchard, and Michael J. Rabins. 2008. Engineering Ethics: Concepts and Cases. 4th edition. Wadsworth.

    Israel, Mark, and Iain Hay. 2006. Research Ethics for Social Scientists: Between Ethical Conduct and Regulatory Compliance. SAGE Publications, Limited.

    Johnson, Deborah G. 2008. Computer Ethics. 4th ed. Prentice Hall PTR.

    Korenman, Stanley G., and Allan C. Shipp. 1994. Teaching the Responsible Conduct of Research through a Case Study Approach: A Handbook for Instructors. Association of American Medical Colleges. Source: Order from

    Loue, Sana. 2000. Textbook of Research Ethics: Theory and Practice. Springer.

    Macrina, Francis L. 2005. Scientific Integrity: Text and Cases in Responsible Conduct of Research. 3rd ed. ASM Press.

    Miller, David J., and Michel Hersen, eds. 1992. Research Fraud in the Behavioral and Biomedical Sciences. John Wiley & Sons, Incorporated.

    Murphy, Timothy F. 2004. Case Studies in Biomedical Research Ethics. MIT Press.

    National Academy of Sciences. 2009. On Being a Scientist: A Guide to Responsible Conduct in Research. 3rd edition. National Academy Press. Source: Order from

    National Academy of Sciences. 1992. Responsible Science, Vol. 1: Ensuring the Integrity of the Research Process. Source: Order from

    National Academy of Sciences. 1992. Responsible Science, Vol. 2: Background Papers and Resource Documents. Source: Order from

    Oliver, Paul. 2010. The Students’ Guide to Research Ethics. 2nd ed. McGraw-Hill Education.

    Orlans, F. Barbara, et al., eds. 2008. The Human Use of Animals: Case Studies in Ethical Choice. 2nd ed. Oxford University Press.

    Penslar, Robin Levin, ed. 1995. Research Ethics: Cases and Materials. Indiana University Press.

    Resnik, David B. 1998. The Ethics of Science: An Introduction. Routledge.

    Schrag, Brian, ed. 1997-2006. Research Ethics: Cases and Commentaries. Seven volumes. Association for Practical and Professional Ethics. Source: Order from

    Seebauer, Edmund G., and Robert L. Barry. 2000. Fundamentals of Ethics for Scientists and Engineers. Oxford University Press.

    Seebauer, Edmund G.. 2000. Instructor’s Manual for Fundamentals of Ethics for Scientists and Engineers. Oxford University Press.

    Shamoo, Adil E., and David B. Resnik. 2009. Responsible Conduct of Research. Oxford University Press.

    Shrader-Frechette, Kristin S. 1994. Ethics of Scientific Research. Rowman & Littlefield Publishers, Inc.

    Sieber, Joan E. 1992. Planning Ethically Responsible Research: A Guide for Students and Internal Review Boards. SAGE Publications, Inc.

    Sigma Xi. 1999. Honor in Science. Sigma Xi, the Scientific Research Society. Source: Order from

    Sigma Xi. 1999. The Responsible Researcher: Paths and Pitfalls. Sigma Xi, the Scientific Research Society. Source: Order from or download in PDF format at

    Steneck, Nicholas H. 2007. ORI Introduction to the Responsible Conduct of Research. Revised ed. DIANE Publishing Company. Source: Order from or download in PDF format at

    Stern, Judy E., and Deni Elliott. 1997. The Ethics of Scientific Research: A Guidebook for Course Development. University Press of New England. See also Elliott and Stern, eds., Research Ethics: A Reader.

    Vitelli, Karen D., and Chip Colwell-Chanthaphonh, eds. 2006. Archaeological Ethics. 2nd ed. AltaMira Press.

A Comparison of Copper in the U.S.

As humanity’s oldest metal, copper comes in many forms. People have used copper for thousands of years. When the ancient Romans mined the element “cyprium” from Cyprus, the metal soon became known in English as “copper.” 

Copper is produced and consumed in many forms, from the lining of electrical motors to the coating of pennies. Thanks to its high thermal and electrical conductivity, the material is frequently used in telecommunication technologies and as a building material.

The process of copper production includes mining, refining, smelting, and electrowinning. Through smelting and electrolytic refining, engineers and scientists transform mined ores to copper cathodes. Cathodes are thin sheets of pure copper used as raw material for processing the metal into high-quality products. 

Using data available to the public from the U.S. Geological Survey, the copper market has changed to society’s needs over the past years. 

The four major types of copper are mined copper, secondary copper, refined copper and refined electrowon copper. Secondary copper comes from recycled and scrap materials such as tubes, sheets, cables, radiators and castings, as well as from residues like dust or slag. 

Engineers and scientists transform mined pure copper metal and copper from concentrated low-grade ores through smelting and electrolytic refining in creating copper cathodes. Acid leaching of oxidized ores produces more copper.

Thanks to the chemical and physical properties of copper, the material is suitable for electrical and thermal conductivity. Copper’s high ductility and malleability give it key roles in industrial applications of coil wining, power transmission and generation and telecommunication technologies.

The different methods of processing copper have remained constant for the most part between 1990 and 2010. The data is from “U.S. Mineral Dependence—Statistical Compilation of U.S. and World Mineral Production, Consumption, and Trade, 1990–2010” by James J. Barry, Grecia R. Matos and W. David Menzie. The rise in refined copper reflects market trends for the rising demand for refined copper, according to a report in Oxide and sulfur ores generally have between 0.5 and 2.0% copper. The process involves concentrating the ore to remove gangue and other materials.

Differences between reported and apparent processed copper consumption in the U.S. have decreased from 2005 to 2009. Copper consumption itself has dropped.

The various types of copper produced by the U.S. have remained constant over the time period. 

Mined copper has remained the dominant copper produced around the world, though refined copper has come close or equal to it from 1996 to 2001. Refined electrowon copper has steadily surpassed secondary copper over the time period, too. 

The epistemology and metaphysics of causality

The epistemology of causality

There are two epistemic approaches to causal theory. Under a hypothetico-deductive account, we hypothesize causal relationships and deduce predictions based on them. We test these hypotheses and predictions by comparing empirical phenomena and other knowledge and information on what actually happens to these theories. We may also take an inductive approach in which we make a large number of appropriate, justified observations (such as a set of data) from which we can induce causal relationships directly from them.

Hypothetico-Deductive discovery

The testing phase of this account of discovery and causality uses the views on the nature of causality to determine whether we support or refute the hypothesis. We search for physical processes underlying the causal relationships of the hypothesis. We can use statistics and probability to determine which consequences of hypotheses are verified, like comparing our data to a distribution such as a Gaussian or Dirichlet one. We can further probe these consequences on a probabilistic level and show that changing hypothesized causes can predict, determine, or guarantee effects.

Philosopher Karl Popper advocated this approach for causal explanations of events that consist of natural laws, which are universal statements about the world. He designated initial conditions, single-case statements, from which we may deduce outcomes and form predictions of various events. These case initial conditions call for effects that we can determine, such as whether a physical system will approach thermodynamic equilibrium or how a population might evolve under the influence of predators or external forces. Popper delineated the method of hypothesizing laws, deducing their consequences, and rejecting laws that aren’t supported as a cyclical process. This is the covering-law account of causal explanation.

Inductive learning

Philosopher Francis Bacon promoted the inductive account of scientific learning and reasoning. From a very high number of observations of some phenomenon or event with experimental, empirical evidence where it’s appropriate, we can compile a table of positive instances (in which a phenomenon occurs), negative instances (it doesn’t occur), and partial instances (it occurs to a certain degree). This gives a multidimensionality to phenomena that characterize causal relationships from both a priori and a posterior perspectives.

Inductivist artificial intelligence (AI) approaches have in common the feature that causal relationships can be determined from statistical relationships. We assume the Causal Markov condition holds of physical causality and physical probability. This Causal Markov Condition plays a significant deterministic role in the various features of the model and the events or phenomena it predicts. A causal net must have the Causal Markov Condition as an assumption or premise. For structural equation models (SEM), Causal Markov Conditions result from representations of each variable as a function of its direct causes and an associated error variable with it. We assume probabilistic independence of each error variable. We then find the class of causal models or a single best causal model with probabilistic independences that are justified by the Causal Markov Condition. They should be consistent with independences we can infer from the data, and we might also make further assumptions about the minimality (no submodel of the causal model also satisfied the Causal Markov Condition), faithfulness (all independences in the data are implied via the Causal Markov Condition), linearity (all variables are linear functions of their direct causes and uncorrelated error variables). We may also define causal sufficiency, whether all common causes of measured variables are measured, and context generality, every individual or node in the model has causal relations of the population. These two features let us describe models and methods of scientific reasoning as causal in nature and, from there, we may apply appropriate causal models such as Bayesian, frequentist, or similar methods of prediction. We may even illustrate a causal diagram or model elements under various conditions such as those given by independence or constraints on variables.

This way, in the intercorrelatedness of the graph or model, we can’t change the value of a variable without affecting the way it relates to other variables, but there may conditions in which we construct models that have autonomous nodes or variables. The way these features and claims of inductivist AI interact with another is subject to debate by the underlying assumptions, justification, and methods of reasoning behind these models.

Metaphysics of causality

We can pose questions about the mathematization of causality even with the research and methods that have dominated the work on probability and its consequences. We can speculate what causality is and the opinions on the nature of causality as they relate to the axioms and definitions that have remained stable in the theories of probability and statistics.

We can elaborate three types of causality approaches. The first is that causality is only a heuristic and has no role in scientific reasoning and discourse, as philosopher Bertrand Russel argued. Science depends upon functional relationships, not causal laws. The second position is that causality is a fundamental feature of the world, a universal principle. We should, therefore, treat it as a scientific primitive. This position evolved out of conflict with purported philosophical analyses that appealed to asymmetry of time (that it moves in one direction) to explain the asymmetry of causation (that they move in one direction and one direction only). This raises concerns of how to interpret time in terms of causality. The third is we can reduce causal relations to other concepts that don’t involve causal notions. Many philosophers support this position, and, as such, there are four divisions within this position.

The first schism we discuss is that causality is a relation between variables that are single-case or repeatable according to the interpretation of causality in question. We interpret causality as a mental in nature given that causality is a feature of an agent’s epistemic state and physical if it’s a feature of the external world. We interpret it as subjective if two agents with the same relevant knowledge can disagree on a conclusion of the relationships with both positions correct, as though they were a matter of arbitrary choice. Otherwise we interpret it as objective. The subjective-objective schism raises issues between how different positions would be regarded as correct and what determines the subjective element or role subjectivity plays in these two different positions.

The second partition is the mechanistic account of causality – that physical processes link cause and effect. We interpret causal statements as giving information about these processes. Philosophers Wesley Salmon and Phil Dowe advocate this position as they argue causal processes transmit or have a conserved physical quantity to them. We may describe the relation between energy and mass (E = mc²) as causal relations from start (cause) to a finish (effect). One may argue against this position on the grounds that these relations in science have no specific direction one way or another and are symmetrical and not subject to causality. It does, however, relate single cases linked by physical processes even if we can induce causal regularities or laws from these connections in an objective manner. If two people disagree on the causal connections, one or both are wrong.

This approach is difficult to apply. The physics of these quantities aren’t determined by the causal relations themselves. The conservation of these physical quantities may suggest causal links to physicists, they aren’t relevant in the fields that emerge from physics such as chemistry or engineering. This would lead one to believe the epistemology of the causal concepts are irrelevant to their metaphysics. If this were the case, the knowledge of a causal relationship would have little to do with the causal connection itself.

The third subdivision is probabilistic causality in which we treat causal connections with probabilistic relationships of variables. We can debate which probabilistic relationships among variables of probabilistic causality determine or create causal relationships. One might say the Principle of Common Cause (if two variables are probabilistically dependent, then one causes the other or they’re effects of common causes that make them independent from one another). Philosopher Hans Reichenbach applied this to causality to provide a probabilistic analysis of time in its single direction. More recent philosophers use the Causal Markov Condition as a necessary condition for causality with other less central conditions. We normally apply probabilistic causality to repeatable variables such that probability handles them, but critics may argue the Principle of the Common Cause and the Causal Markov Conditions have counterexamples showing they don’t hold in under all conditions.

Finally, the fourth subclass is the counterfactual account, as advocated by philosopher David Lewis. In this way, we reduce causal relations to subjunctive conditions such that an effect depends causally on a cause if and only iff (1) if the cause were to occur, then the effect would occur (or its chance to occur would raise significantly) and (2) if the cause didn’t occur then the effect wouldn’t occur. The transitive closure of the Causal Depedendence (that a cause will either increase the probability of a direct effect or, if it’s a preventative, make the effect less likely, as long as the effect’s other direct causes are held fixed) holds. The causal relationships are what goes on in possible worlds that are similar to our own. Lewis introduced counterfactual theory to account of the causal relationships between single-case events and causal relationships that are mind-independent and objective. We may still press this account by arguing that we have no physical contact with these possible worlds or that there isn’t an objective way to determine which worlds are closest to our own or which worlds we should follow and analyze in determining causality. The counterfactualist may respond that the worlds we choose are the ones in which the cause-and-effect relationship occurs as closer to our own world and, from there, determine which appropriate world is closest to our own.

Contextual Emergence

What is contextual emergence?

The patterns that emerge from Conway’s Game of Life do so depending on the underlying theory.

Contextual emergence is a specific kind of relationship between different domains of scientific descriptions of particular phenomena. Although these domains are not ordered strictly hierarchically, one often speaks of lower and higher levels of description in which emergence occurs. From the lower levels (L), more fundamental in a certain sense, phenomena emerge in higher levels (H) in more complex phenomena. Strings of DNA in a genome may correspond to different transcripts on an transcriptome level for an individual. Chaotic conditions may emerge from certain differential equations subject to certain constraints. This complexity depends on the conditions of the context. Hence, contextual emergence.

Contextual emergence involves well-defined relationships between different levels of complexity. We can use a two-step procedure to create a systematic, formal way that an individual description (Li) creates a statistical description (Ls) among the lower level. This process can lead us to describe individuals at a higher level (Hi). We iterate this process (Li -> Ls -> Hi) through sets of descriptions connected with one another to reveal what emerges at higher levels.

During this method, we identify equivalence classes of individual states that are indistinguishable with respect to a certain property of the entire system. We can realize different statistical states in Ls by individual states in Li. Each state has limited knowledge, but, together, we can create probability distributions represent the statistical states Ls. This could be how spike signals from neural circuits encode for higher-level functions in the brain.

A property dualist position would also recognize three features of this emergence. The emergent property at the higher level Hi must have real instances, remain co-occurrent with some property or complex feature recognized in the lower level, and this property can’t be reduced to any property postulated by or definable within the lower level.

Then, we can assign individual states at the higher level H to coextensional statistical states at level L. We use a top-down constraint. This needs information about the higher description to choose a context setting the framework for the set of observable properties at level H created from L. We can implement stability criteria at level L such that the appropriate context emerges at level H. The stability refers to the ability for the features of the system to remain valid even under small changes. This includes equilibrium states of gas systems and homeostatic relationships between units of biological mechanisms such as glycolysis. We may also define stability as systems that have boundaries maintained under the dynamics specified for it We may choose to confine ourselves to certain electrochemical properties that emerge from membrane dynamics in synaptic networks. This allows the emergent properties to remain well-defined from the contextual topology of L. It also tells us which properties of L are relevant to the contextual emergence of H.

This interplay between upward and downward strategies lets the system remain self-consistent. Moving from a higher context to a lower one requires the stability conditions to lead to lower-level partitions of the system while moving to a higher context means the statistics of lower-level states extend to higher-level individual states we can observe.

Philosopher Aristotle argued emergent structures arise when their constituents interact in an interdependent manner, but others may argue that emergence may occur even if the parts act independently of one another or even be autonomous. In either case, to echo the theory of Gestalt, the whole is greater than the sum of its parts.

Point mechanics to statistical mechanics to thermodynamics

We can even demonstrate the relationship between different fields of science through contextual emergence. Moving from classical point mechanics, involving forces due to gravitational effects and electromagnetism, to statistical mechanics to thermodynamics illustrates this phenomena. From point mechanics to statistical mechanics particles or other individual units (Li) form ensemble distributions which can be studied using statistics. We can define many-particle systems with statistical ensemble descriptions (Ls) like momenta or energy of distributions, such as the Maxwell-Boltzmann distribution for N particles. From there, we can find mean kinetic energy, Gibbs free energy, entropy, and other statistical quantities.

We can observe expectation values of momenta distributions of particle ensembles to calculate temperature of the system as a higher-level function (Hi) on the assumption the system is in equilibrium. The zeroth law of thermodynamics does not come from statistical mechanics, but from thermodynamics. Other features such as irreversibility and adiabatic nature emerge as well. We can characterize this thermal equilibrium (Hi) using Kubo-Martin-Schwinger (KMS) states, defined by the condition that characterizes the structural stability of a KMS state against local perturbations or changes. This leads to stationarity, ergodicity, and mixing using the zeroth law of thermodynamics to define the system as stable. We can also use the second law of thermodynamics to express the stability in maximization of entropy for thermal equilibrium states.

The first step of the contextual emergence process (Li -> Ls) describes statistical states from the individual states, and the second gives individual thermal states from statistical mechanical states. Other examples may include emergence of geometric optics from electrodynamics, electrical engineering features from electrodynamics, chirality from quantum mechanics, and diffusion or friction of a quantum particle in a thermal medium. Neuroscientists have even found use in contextually emerging cognitive states from neural correlates.

Hodgkin-Huxley equations

The Hodgkin-Huxley equations that describe generation and propagation of action potential form a system of four ordinary nonlinear differential equations: an electric conductance equation for transmemberane currents and three master equations for the opening kinetics of sodium and potassium channels. These lower-level stochastic (using Markov processes as transition probabilities) phenomena lead to higher-level descriptions of ion channel function to characterize a deterministic dynamic system. We can treat ion channels as macro-molecular quantum objects with the Schrödinger equation for many particles. The Schrödinger equation describes a highly entangled state of electrons and atomic nuclei as a whole, and, on a molecular level, the structure of a closed or open pore of an ion channel through the Born-Oppenheimer approximation separates electronic and nucleonic wave functions. Then, we can use the electronic quantum dynamics in a constrained rigid nucleonic frame that has a classical spatial structure. This stochastic spatial structure gives the equations of the Hodgkin-Huxley system as a contextually emergent phenomenon.

Mental states emerging from neuroscience

To realize mental states from neural states, we specify the L level as neuron states of neural assemblies in the brain with respect to H, a class of mental states that reflects the situation under study. We may use experimental protocols that include a task for subjects to define mental states while recording brain states. We may use individual neuron properties Li to find Ls such that statistical states have equivalence classes of those individual states. The differences must be irrelevant with respect to the higher level H. Philosopher David Chalmers said a neural correlate of a conscious mental state can be multiply realized by “minimally sufficient neural subsystems correlated with states of consciousness” in “What is a neural correlate of consciousness?”

We can look at phenomenal families, sets of mutually exclusive phenomenal mental states that jointly partition a space of mental states. Creature consciousness can give us refined levels of phenomenal states of background consciousness (awake, dreaming, etc.), wake consciousness (perceptual, cognitive, affective, etc.), perceptual consciousness (visual, auditory, tactile, etc.), and visual consciousness (color, form, location, etc.). With one of these contexts, we choose stability criterion at Ls that has complicated neurodynamics to find robust, proper statistical states.

We may describe L-dynamics and H-dynamics meshing with one another if coarse graining and time evolution commute with one another. We create meshes, parts of space differentiated by complexes of cells between the two levels, that follow from higher-level stability criterion. The coarse graining means fine details of the system can be smoothed over, as entropy of the system increases, such that we can make predictions about the system as a whole.

Contextual emergence could help artificial intelligence approach its potential while accounting for the inherent, intrinsic differences between science and philosophy. We may model the mind as a contextual emergent phenomena of the neurophysiology of the brain. As we learn about the world, we can account for emergent phenomena when addressing issues in science and philosophy, and AI would benefit from these methods of understanding. AI could avoid the issues of reductionism using higher-level emergent behavior resulting from neural networks in the human brain. Backpropagation of neural networks lets us optimize the gap between reality and models they represent using feedback loops with optimal weights of individual neurons when optimized for emergent details. The same way a human can differentiate between a drawing of an lion and a photograph of a lion itself using the emergent phenomena of visual images that appear together to create a lion, intelligent machines can embrace contextual emergence to view the work with inquisitive wonder and curiosity to learn. Instead of having to show a computer hundreds of thousands of images of a lion to teach them how to identify a lion, they can realize a lion in another context, such as lines of a piece of artwork, through the emergent properties of a drawing of a lion itself.

Emergence in AI can account for emotional reactions and instincts by evolving using stochastic emergent phenomena the same way human intelligence has evolved. We may address the role emotions and biases play in decision-making and intelligence, as described by psychologists Daniel Kahneman, Amos Tversky, and Gerd Gigerenzer.

We can represent proper cells with basins of attraction and chaotic attractors with coarse-grained generating partitions. These partitions of the system lead to Markov chains with a rigorous theoretical constraint for the proper definition of stable mental states. The mathematical techniques come from ergodic theory and symbolic dynamics.

The emergence of mental states from electroencephalogram (EEG) dynamics shows that data from subjects with EEG data from sporadic epileptic seizures can correlate with mental states of the seizures themselves. Using a 20-channel EEG recording, we get a 20-dimension state space that we reduce to a lower number through principal component restrictions. We find a homogeneous grid of cells to set up a Markov transition matrix that reflects the EEG dynamics using fine-grained auxiliary partition. Then, this matrix gives eigenvalues that characterize time scales for which the dynamics can be ordered by size. The eigenvectors span an eigenvector space such that the measure principal component states form a simplex. The three leading eigenvalue give a neural state representation that has a 2-simplex with three vertices, or a triangle. We can further classify neural states by distance from the vertices of the simplex to clusters of neural data. In the principal component state space, the clusters appear as non-intersecting convex sets between mental states. We may also use recurrence structure analysis to partition the state space into recurrent clusters such that they overlap from the recurrence plot of the dynamical system. We figure out the metastable states and transitions between them using a Markov chain with one distinguished transient state and other states representing the metastable states in the dynamics.


Philosopher Daniel Dennett describes the intentional stance of the prediction of a system’s behavior too complex to be treated as either a physical or designed system. Intentional systems behave in predicted ways by ascribing beliefs and desires to their internal states. From thermostats to chess computers, we can make predictions of a system with necessary and sufficient conditions. The system’s dynamics have to be non-trivial, so this excludes linear systems with periodic oscillations or damped relaxations. We construct an intentional hierarchy from general case of nonlinear nonequilibrium dissipative systems to more specific intentional systems. A physical system’s physical nature is necessary for being a nonlinear dissipative nonequilibrium system while a nonlinear dissipative nonequiliibrium nature is necessary for an intentional system. An intentional system is necessary for being a true believer, according to Dennett. Sufficient conditions in the intentional hierarchy implement contextual stability conditions.

The transition from equilibrium thermodynamics to fluid dynamics represents phenomenal laws of fluid dynamics (like the Navier-Stokes equation) emerging from statistical mechanics under the assumption of local equilibrium. Sufficient boundary conditions give rise to self-organization, such as through “magnetic snakes.” We give a rationality constraint for optimal dissipation of pumped energy, and true believers emerge contextually as intentional systems under mutual adoption of the intentional stance.

The representational thought may reference aboutness, and the intentional approach concerns the contentfulness or meaningfulness of representational states. We may create a network theory of meaning that emerges from the semantics of a system. Philosopher Karl Popper argued against reductionism on the grounds there’s a world of abstract, nonphysical objects we interact with when we reason, discover proofs, speculate consequences, use language, and think about mathematics and philosophy. This autonomous reality (known as World 3, with World 1 being physical laws and World 2 as mental events and processes) we find dispositions to verbal behavior and wiring in the brain. Popper implies it’s more understandable how nonphysical states interact with intelligibilia than how neural states might.

Symbolic grounding

The symbolic grounding problem is the problem fo assigning meaning to symbols on purely syntactic grounds. Cognitivists such as philosophers Jerry Fodor and Zenon Pylyshyn have described this problem. It can also describe how the question of how conscious mental states can be characterized by neural correlates. The relation between analog and digital systems such that syntactic digital symbols relate to the analog behavior of a system they describe symbolically needs to be further examined through dynamical automata. Piecewise linear time-discrete maps over a two-dimensional state space assume the interpretation as symbolic computers through a rectangular partition of the unit square. A single point trajectory is not fully interpretable as symbolic computation. We need higher-level macrostates from ensembles of state space points, or probability distributions of points, that evolve under the dynamics.

Writer Beim Graben showed only uniform probability distributions that have rectangular support exhibit a stable dynamics can be interpreted as computation. The huge space of possible probability distributions can be contextually restricted to a subclass of uniform probability distributions to create meaningfully grounded symbolic processes. Symbolic grounding is contextually emergent.

Mental causation

Describing the mind as causally relevant in a physical world introduces the problem of mental causation, the question of how mental phenomena can be highly significant in psychology and cognitive neuroscience. It means creating a notion of agency that includes the causal efficacy of mental states. This causal efficacy of mental phenomena seems inconsistent with vertical (interlevel, synchronic) determination of the mental state by neural correlates. Philosopher Jaegwon Kim argued supervenience (also known as exclusion) describes the problem that mental states are either causally inefficacious or have the threat of overdetermining neural states. Either mental events play nor horizontally determining causal role at all or they’re the causes of the neural bases of their relevant horizontal mental effects. Contextual emergence through different levels of complexity means the conflict between horizontal and vertical determination of mental events isn’t an issue. We can define proper mental states from dynamics of an underlying neural system through statistical neural states on proper partitions with individual mental states.

This construction implies that the mental dynamics and the neural dynamics, related to each other by a so-called intertwiner, are topologically equivalent. Instead of some mutually exclusive duality of the mental and the neural, we have a monistic idea that they are part of one and the same concept, albeit related to one another
in a significant way. We can describe it using dual-aspect monism using symmetry breakdown conceptually prior to the opposite of generalization. When symmetries between entities restore themselves, we observe the similarities brought upon by the symmetries and generate equivalence classes of increasing size that can describe contextually emergent phenomena. Given properly defined mental states, the neural dynamics gives rise to a mental dynamics that is independent of those neurodynamical details that are irrelevant for a proper construction of mental states. Mental states can be causally and horizontally related to other mental states, and they neither cause their vertical neural determiners nor cause the horizontal effects of the neural determiners. This resolve the problem of mental causation in a deflationary manner. Vertical and horizontal determination don’t compete against one another. They work cooperatively.

Mental causation is a horizontal relation between previous and future mental states with effectiveness given by the vertical relation (the downward relation of neural states from higher-level mental constraints). Psychophysical neutral elementary entities are composed to sets of such entities that depend on the composition of these sets in a way they acquire mental or physical properties. The psychophysically neutral domain does not have elementary entities waiting to be composed, but, rather, has one overarching whole to be decomposed into its parts. The mental and material from a psychophysical neural whole causes a contextual emergence that requires a new technical explanation and a metaphysical one.

The technical framework refers to the contextual emergence of multiplicity from unity. The “primordial” decomposition of an undivided whole generates different domains that gives rise to differentiations, such as the mind-matter distinction. The psychophysical neutral reality is the trivial, completely symmetric partition in which nothing is distinguished from one another. We can decompose this to give rise to more and more refined partitions in which symmetries are broke and equivalence classes become smaller and smaller. Phenomenal families of mental states emerge.

On a metaphysical level, mental and physical epistemic limits describe the undivided whole as an ontic (physical factual existence) dimension. They reminisce of philosopher Plato’s abstract perfect ideas and philosopher Immanuel Kant’s things-in-thesmelves (empirically inaccessible in principle and specifically mute). The mind-matter problem causes an emergence of mind-matter correlations as direct and immediate consequence of the ontic, undivided whole that can’t be further divided without introducing more distinctions. Many describe determinism as a feature of ontic descriptions of states and observables while stochasticity uses epistemic descriptions.

Mathematical models of classical point mechanics are most common examples of deterministic descriptions and three properties of them are important. (1) The differential dynamics mean the system’s evolution obeys a differential equation in a space of ontic states. (2) The unique evolution of the system means initial and boundary conditions give a unique trajectory. (3) The value determinateness assumes that any state can be described with arbitrarily small error. These three features define a hierarchy for the contextual emergence of deterministic descriptions assuming (1) is a necessary condition for determinism, (2) can be proven under sufficient condition that trajectories created by a vector field obeying (1) pass through points whose distance is stable under small perturbations. We assume (2) for almost every initial condition as a necessary condition of determinism that defines a phase flow with weak causality. To prove (3), we need strong causality as a sufficient condition. The deterministic dynamics of Kolmogorov flow implement microscopic chaos as a stability condition. It’s also possible a continuous stochastic process that fulfills the Markov criterion can lead to a deterministic “mean-field equation.”

Different descriptive levels can correlate with different degrees of granularity. Lower-level descriptions address systems in terms of micro-properties while more global macro-properties account for higher-level descriptions. Philosophy Bas van Fraassen noted the explanatory relativity, in which explanations are not only
relationships between theories and facts, but three-place relations between theories, facts, and contexts. Contexts determine relevance of explanation backed by relevance criteria for reproducibility in science, especially in interdisciplinary fields such as bioinformatics or computational neuroscience. This gives a framework for discussing contextual emergence alongside theories and facts as they relate to explanations. We consider the granularity of descriptions that we observe when descriptive levels transform between one another and their associated granularities by the interlevel relation of contextual emergence. This gives a formally sound and empirically applicable procedure to construct level-specific criteria for relevant observables across disciplines.

Reductionism and ontology

It may seem appealing to reduce every system down to its fundamental components and conclude that every empirical phenomena in science or other disciplines is only applied mathematics. But this misses out on the features of the whole that emerge in the contexts of the higher layers which cannot be reduced. Consciousness among neural and mental correlates of different states provide one example, but we only need to look at any example, such as the emergence of transcriptome interactions from how a genome itself structures itself, to realize that these properties come about only at the higher levels, and, therefore, involve phenomena that are not completely reducible to mathematics. Biologist Peter Corning argued in “The Re-Emergence of “Emergence”: A Venerable Concept in Search of a Theory” that whole systems produce unique combined effects that may involve the context between and the interactions with the system and its environment.

Contextual emergence has been originally conceived as a relation between levels of descriptions, not levels of nature: It addresses questions of epistemology rather than ontology. In agreement with Esfeld, who advocated that ontology needs to regain more significance in science, it would be desirable to know how ontological considerations might be added to the picture that contextual emergence provides.

Various granularity degrees raises questions of descriptions with finer grains as they relate to the fundamental nature of systems when compared to coarser grains. The majority of scientists and philosophers of science answer believe this, so there’s one fundamental ontolgoy that elementary particle physics result from reducing other descriptive levels. This reductive premise produced critical assessments and alternative proposals. Philosopher Willard Van Oramn Quine introduced the ontological relatively that, if there is one ontology that fulfills a given descriptive theory, there is more than one. Philosopher Hilary Putnam developed a related kind of ontological relativity, first called internal realism, and later referred to as pragmatic realism.

We may apply Quine’s ideas to concrete scientific descriptions, their relationships with one another, and their referents. A descriptive framework can be ontic or epistemic depending on which other framework it relates to. An engineer may consider wires of an electrical circuit to be ontic, but a solid-state physicist may consider them epistemic. We can use the relevance criteria to distinguish between context-specific descriptions and avoid pitfalls of reductionism. We create a subtle and more flexible framework while still restricting ourselves to the premises and limits of the contextually emergent model.

Strong and weak emergence

Weak emergence involves emergent properties that computer simulations can control such that the interacting cells of the system retain their independence. Other emergent properties, irreducible to the system’s constituent parts, are strong. Both are supervenient and involve novel properties as the system grows, but the distinction introduces a scale-dependency to observable phenomena.

A Computational Theory of Mind

Brains are only like computers in a specific abstract sense. We can take apart this analogy in the context of the brain-computer analogy to determine knowledge for philosophy, neuroscience, artificial intelligence, and other research areas. It’s very harmful in many ways to treat the nervous system as the hardware in such a way that we need to understand the cognitive science as software when we don’t understand the limitations of such a metaphor. Any theory of anatomical connection we demonstrate in vertebrate nervous systems may give us a basic description of what happens at each stage, but don’t tell us how a given input relates to a certain output. Instead, they obfuscate the description of the brain by using unnecessary comparisons to explain phenomena that are better off explained by describing the phenomena directly and precisely.

An output of a computer depends on its program, input, and functional stages that lead to the output. We can theorize and speculate on artificial and biological computers by using this analogy with other phenomena such as artificial neural networks in computer science and mathematics or biological computers among the brains of different organisms. These computers show connections between the disciplines underlying computation with its theory from statistical mechanics and thermodynamics. We can use ideas from information theory, entropy dynamics, and constraint problems on the resulting artificial and biological computers.

Classicalism vs connectionism

The computational theory of mind is the leading contemporary version of the representational theory of mind, in which we use mental structures to represent mental processes. The computational theory of mind tries to explain all psychological states in terms of mental representations. Philosopher Stephen Stich argued cognitive psychology doesn’t and shouldn’t taxonomize mental states by their semantic properties. Those semantic properties are determined by the extrinsic properties of a mental state. Stich proposes a Syntactic Theory of the mind, arguing the semantic properties of mental states don’t have an explanatory role in the mental states. The Syntactic Theory of Mind uses computational theories of psychological states that only concern with the formal properties of the objects the state relate to. We use semantically evaluable objects with the computations of mental processes. Computational theory of mind proponents disagree on how personal-level representations (thoughts) and process (inferences) in the brain are realized. Classical Architecture proponents (classicists) such as Turing, Fodor, Pylyshyn, Newell, and Simon, believe mental representations are symbolic structures that have semantically evaluable constituents. Mental processes are rule-governed manipulations of them that are sensitive to their constituent nature. Connectionist Architecture proponents (connectionists) like McCulloch, Pitts, Rumelhart, and McClelland believe mental representations are realized by activation patterns in simple processors (nodes). These mental processes are made of the spreading activation of these patterns. The nodes aren’t semantically evaluable typically. One may argue that localist theories are neither definitive nor representative of the connectionist program.

Classicists want to find mental properties similar to language. Fodor’s Language of Thought Hypothesis (LOTH) uses mental symbols to make up the neural basis of a thought like a language. In the LOTH, the potential infinity of complex representational mental states comes from primitive representational states that form using recursive formation rules. We use a combinatorial structure to account for productivity and systematicity of the system of mental representations. We explain the properties of thought using the content of representational units and their combinability into contentful complexes. The semantics of language and thought is compositional.

Connectionists want to consider the architecture of the brain, networks of interconnected neurons. This architecture can’t carry out classical serial computations, but, instead, parallel computations lack semantic compositionality nor are semantically evaluable the way classicists argue. Representation is distributed, not local (unless it’s computationally basic). Connectionists argue information processes in these networks resembles human cognitive functioning. Connectionist networks trained by exposure to objects learn and distinguish. Some argue connectionism means there aren’t propositional attitudes. LOTH-style representation may, on the other hand, be necessary for the general features of connectionist architectures.

Stich believed mental processes are computational, but these computations aren’t sequences of mental representations. Other philosophers accept mental representation, but deny that the computational theory of mind gives the correct account of mental states and processes. Writer Tim Van Gelder doesn’t believe psychological processes are computational. Instead, dynamic cognitive systems give rise to states that are quantifiable of a complex system of the nervous system, the body, and the environment in which they are created. Cognitive processes aren’t rule-governed by discrete symbolic states. Instead, they’re continuous, evolving total states of dynamic systems by mutually determining states of the system’s components. The dynamic system leads to representation that is information-theoretic through state variables or parameters.

Philosopher Steven Horst wrote that computational models are useful in scientific psychology, but they don’t give us a philosophical understanding of intentionality of commonsense mental states. The computational theory of mind tries to reduce the intentionality of states to the intentionality of the mental symbols, but the relevant notion of symbolic content is bound by the notions of convention and intention. Horst believed the computational theory of the mind uses the very properties that it is supposed to reduce things to as a circular argument that need to be reduced themselves.


If we treat propositional attitudes with intentionality as a physical properties, we can build a computer with states that have genuine intentionality. But no computer model that stimulates human propositional attitudes will have genuine intentional states. Intentionality of propositional attitudes isn’t a physical property.

We may consider the network theory of meaning (or holistic theory or conceptual-role theory) such that the meaning of an expression plays a role in its internal representational economy. This way it relates to sensory input and behavioral output. Meaning is relational as an expression’s meaning is a function of its inferential and computational role in a person’s internal system. A robot that behaves like a human is still subject to the question of whether those thoughts it generates have the same meaning that represent our own meaning. Assigning meaning to the internal states of a robot would be applying a double standard arbitrarily with no useful purpose. The robot’s internal machinery doesn’t change that it believes, wants, and understands things. The robot’s intentional states depend on how complex its internal informational network of states it has.

We need altogether a better theory of representation in organisms much the same way we have theoretical definitions and ideas of what molecules, proteins, and neutrons are. We can also study the mind as it relates to the computer by differentiating between understanding its design and its function. Though we can perform actions such thinking, feeling, and arguing without knowing exactly the neuroscience of our brains, we can also use a computer for, more or less, what a computer is designed to do without knowing exactly how a computer. Albeit, we must know some computer basics such as turning on a computer by pressing a button as well taking care of our brains by taking care of our bodies, we must also account for intentionality in understanding why intentions works, rather than simply knowing that we have intentions and following in blind dogma.

Levels of organization

The brain-computer analogy presents a problem of complexity that we know we have in the brain as that relates to organization of a computer. The semantic, syntactic, and mechanistic levels introduce issues with the level of the algorithm and the structural implementation of those features. Neurobiological theory challenges the way of specifying the organizational description. The levels of membrane, cell, synapse, cell assembly, circuit, and behavior can be argued as levels, but even within them we have different partitions of the levels of themselves. We can also determine levels by the research methods such as how through learning and memory we can take a cellular approach to show modifications in presynaptic neurotransmitter releases in habituation. Which level is functional and which level is structural is difficult to determine, too.

Mental state semantics

According to the computational theory of mind, the mind operates on symbols and uses symbolic representations to represent mental states. We discuss the meaning of these symbols as the semantics and the relationships between them as the syntax. We may argue that more complicated mental states come from these basic symbolic “words” of the language of thought. The hypothesis that there’s a language of thought encoded within our brains is not obvious, nor is it agreed upon by everyone. There are many competing hypotheses and theories to how the logical form fo propositions relate to the structural form of the mental states that correspond to them. If we take an intentional stance to the mind (that we treat the object that has a behavior we want to predict as a rational agent that has beliefs, desires, and similar mental states that exhibit intentionality), we can uncover objective, real patterns of the world, and this is an empirical claim we can determine beyond the skepticism associated with it. Philosopher Daniel Dennett argued any object or system whose behavior we predict with this strategy is a believer. A true believer, Dennett argued, is an intentional system whose behavior we can reliably predict with the intentional stance. Our brains have somehow handled the statistical combinatorial explosion that accompanies its own complex nature such that we can use billions of cells in networks with one another, and the only representational system we have upon which to model is human language. We haven’t imagined any plausible alternatives in such detail as we do our own language.


A calculator’s representation and rules for manipulating representations can explain its behavior much the same way we describe how and why people do what they do. Philosopher Zenon Pylyshyn said we explain why a machine does something with certain interpretations of the symbols in a domain. Psychologcial theory would cross-classify categories of neurophysiology theory that would make neurophysiological generalizations miss important relations that are only describable at the level at which representations are referred to. The psychological maps only would map onto an indefinite mix of neurobiological categories.

Connectionism (Parallel distributed processing)

As philosopher Paul Churchland has argued, we may use connectionism or parallel distributed processing (PDP) in figuring out the computational operations in nervous systems in such a way we may use computer models of parallel distributed systems to generate the appropriate phenomena on a higher level (cognitive science, psychology, etc.) from basic processes (neuroscience, physics, etc.).

Tensor network theory

Neuroscientists began the theory began on the cerebellum because it has a limited number of neuron types that are each distinct on a physiological level and connected in a specific way that the cerebellar cortex produces the Purkinje cell with two different cell systems as input. Using wiring diagrams of cerebellar neurons to describe the connections accept input and result output in a parallel manner. We have a trade-off between detail to understand the system with how the array itself processes information. Through tensor network theory we attempt to use principles from mathematics, physics, and computer science in understanding how these systems may model the nervous system. We can create a schematic neuron to find out more about the patterns of neurons arranged in mathematical arrays. Though the model may be limited by the assumptions of casual theory and epistemic concerns of the phenomena we attempt to describe, it’s a nice heuristic to see something we wouldn’t otherwise see through single-cell data. We may use concepts from linear algebra and statistics to create output vectors in a coordinate system such that the corresponding tensor matrix governs the transformation of ensembles from input-output relationships by the corresponding reference frame. The spiking frequency defines a point on an axis of the coordinate system with the output a vector in the space of the output neurons. We may generalize a tensor mathematical to transform vectors into other vectors such that we address the basic problem of functionalist sensorimotor control as going from one different coordinate system to another.

When we figure out what the mind-brain does, then how it might implement various functions in a top-down manner among different levels of science, the theorizing is highly constrained, yet very well-informed, by the data of the level at which we implement. But, with tensor network theory, we wouldn’t label these processes as top-down, but, rather, from lower-level fundamental processes to higher-level descriptions.

We use a tensor transformer to transform in a way we still need: to transform vectors in sensory space to vectors in motor space. We may deform one phase space to get an object in the other one using representations as positions in phase space and computations as coordinate transformations between phase spaces. The Pellionisz-Llinás approach uses sensorimotor problems constrained by realistic creatures as a method of reducing at bottom the problem of making coordinate transformations between phase spaces. In tensor network theory, we look for functional relationships between connected cell assemblies and investigate them for properties relevant to phase spaces much the same way a computer or artificially intelligent machine searches for solutions among sentence-related criteria. Such AI would require this knowledge to determine what to do.

Tensor network theory still needs to unify results across the disciplines of cognitive science, psychology, and neuroscience in such a way that we can construct a universalized, common set of rules with coherent explanations that we can experimentally test and verify. Attempts to describe the vestibulu-ocular reflex, the method of determining movement from visual image stimuli, using semicircular canals of the vestibular system, we further imagine each eyeball detecting the images and communicating to those receptors. This system needs to determine how muscles contract so the eyes move in a way to reflect the head movements. The corresponding tensor approach would imagine the system converting a head position vector into a vector that describe muscle positions. The transformation from vestibular to oculomotor, according to the Pellionisz-Llinás hypothesis, takes a premotor vector intoa motor vector. The vestibulur organ, we can show, has a set of positions it prefers that we can call an eigenposition.

We further pose Churchland’s phase-space sandwich hypothesis that describes spatial organization of maps layer so that the corresponding neurons may perform any transformation from two dimensions to two dimensions. The maps representing phase spaces aren’t literally stacked upon one another. They may remain spatially distant from each other. With the topology of the cortical area, we still have to answer whether tensor network theory can account for neuroplasticity. Covariant proprioception vectors can give feedback about motor performance which can further provide information of transformations of the cerebellar matrix. The matrix would then turn into a state such that its eigenvectors are identical so that they are the “correct” coordinate transformation. Climbing fibers of the cerebellum may provide a pathway for reverbative feedback that modifies transformational properties of the cerebellar network. This is found in AI that use relaxation algorithms.

Mental states

If we determine how behavior related to cognition and complexity emerge from the basic neurophysiological theories that govern sensorimotor control, we can determine the nature and dynamics of cognition. We may construct representations at abstract levels of organization that correspond to cognitive activity as the way sentiential representations act according to logical rules. Phase spaces may recognize certain features as humans do, such as eyes of faces or shapes of animals. We may describe phase spaces in such a way that they’re occupied by these sensory stimuli. Using the cones of photoreceptors’ reflectances responsible for color, we can demonstrate a computational problem of how to represent a unique color with a triplet of reflectance values.

Parallel models

Sequential models can be powerful, but AI researchers have shown their ineffectiveness in simulation of fundamental cognitive processes in areas of pattern recognition and knowledge storage and retrieval. The differences between human brains and computer science phenomena only furthers these issues. Humans and computers use very different methods of storing memory as well as methods of connectivity among humans neurons against artificial ones.

The Hinton-Sejnowski visual recognition system uses a network of two sets of binary units: one for detecting input from external stimuli and the other for connecting detectors to nondetecting units. These networks determine the truth and validity of hypotheses by gauging which units fire and which don’t. It performs a cooperative search in which these assemblies vote for various outcomes and the one with the most votes wins. The relationships between various hypotheses depend upon synaptic weights using probability functions and distributions. They also perform relaxations that cool the system such that it may take different molecular organizations in an annealing process. During this process the crystalline structures have a global energy minimum that parallels adding noise to the system. From these fluctuations in noise, the system breaks out of superficial minimima. The Metropolis-Hastings algorithm lets us gauge locally improbably hypotheses such that they may win over other hypotheses.

To make the model reflect empirical data in neuroscience, we must show it accounts for processing of various neurobiological pathways. Computer vision models need to account for contours of perception as well as emergent phenomena such as recognizing how a property of an image emerges from various structures working in a dynamic, systemic manner of the visual image itself. Connectionists could update their brain-computer models using evolution the same way sensorimotor mechanisms have to suit a simultaneous solution in visual recognition.

We distinguish between different levels of description of computational processes. These levels have certain reducible relationships among them in which we can make varying levels of commitment to the reductionism between them. The theory of symbolic computational functionalism of the computational theory of mind (known as computationalism) lets minds manipulate discrete, defined symbols to model discrete, defined logical structures and computer languages. A human mind may be a deterministic finite state automata under this theory, and the theory is independent of implementation. Even if different beings have different physical structures of themselves, they may have similar or the same mental states. Philosopher Patricia Churchland and neuroscientist Terrence Sejnowski have criticized that the implementation is important, especially as lower theoretical levels (such as neuroscientific phenomena) are significant to higher ones. Opponents may also argue that the representations of computationalism don’t tell us anything more than the non-representational descriptions do. Using representation may just amount to an unnecessary model or analogy that only steers us away from the precise, defined meaning of the world.

The computationalist may respond she doesn’t want to make a physiologically accurate human mind model, but wants to find intelligent features for any agent. In AI, one might want to solve a problem in computational space that doesn’t represent human features. She may also respond that representational theories note when the features of representation, such as the similarity between representations and their objects and how accurate they are, in such a way that the representational theory is more effective, valid, and justified than non-representational theories.

We may account for the intentional nature of basic emotions even if they have a physiological component to them, such as changes in facial expression or bodily mechanisms. Weak content cognitivism, the belief that emotions are or are caused by propositional attitudes, may attack this relationship of emotions to a bodily response, but the relationship of emotions to beliefs doesn’t mean all emotions are caused by propositional attitudes like beliefs. A computational theory of mind should account for emotional effects and similar affects that influence perception and judgement. But the changes in emotions don’t seem discrete as though there were differences in logical systems as we described with the Hinton-Sejnowski theory or with tensor network theory. Emotions form a continuous gradient that doesn’t seem to arise from a sort of combinatorial engine that the computationalist theory would argue. We would need a semantic activation model that adheres to principles of symbolic computational functionalism as well.

The connectionist model describes effects of some emotions, but doesn’t model emotion itself. To allow semantic activation models to use emotions in a cognitive position would mean that emotions, in some sense, are the same as similar cognitive categories such as “visual stimuli” or “beliefs.” The other features of emotion, though, semantic activation models need to describe implementation-dependent details of the model itself.

The computationalist position also has issues with how to model affects, such as those of basic emotions, independently of cognition yet still play a role in rational human behavior. The computationalist may be inclined to treat emotions as external or even unnecessary to their models. Computationalists also can’t account for the effects of basic emotions on perception and categorization using their current models. These emotions themselves may be more fundamental to those perceptions and categories that we form, given their unique nature on intellectual perception.

Neural circuitry

We may imagine the brain as a computer through neural circuitry excitation/inhibition ratios as a property for cognitive function in cortical circuits. Research in circuit function on synaptic parameters in memory and decision-making can give us parameter spaces to reduce NMDAR conductance strengths from excitatory pyramidal neurons to inhibitory interneurons or excitatory pyramidal neurons. We may apply dopamine neuronal activity using a bifurcation diagram. In math, we generally use bifurcation plots to study dynamical system behavior with respect to parameter variations or similar perturbations. We may use Ohm’s law to relate current, potential, capacitance, and resistance among membrane channel dynamics. The dopamine neuron uses ionic currents using the Hodgkin-Huxley models. We can use these fundamentals to create circuit models of neuronal activity using population firing rates to calculate dopamine efflux in the nucleus accumbens.

Functional connectivity

Functional connectivity (FC) is the statistical correlation of neural activity to two different regions. We find evidence for this at the micro-circuit level (the relationship between structure and function through anatomical and neurophysiological research techniques). We can integrate information across brain networks using large-scale brain connectivity at finer temporal and spatial resolution. If we introduce spatiotemporal models of resting-state networks, we can analyze the time frequency of these networks using wavelet analysis, sliding-windows, and similar methods of describing temporal correlations between the networks.

FC is similar to functionalism in that we’re defining our representations in terms of their functions. Functionalism holds that qualitative states (e.g., pain) are functional states of a system, interrelated to inputs, outputs, and other internal states. For this reason, cognitive models of the mind have used FC in their explanations. If we had a neuroscientific system that realizes the same set of functional states a person, it still has the problem of liberalism and chauvinism, philosopher Ned Block argued. Liberalism is the problem a mentality theory faces when it attributes mentality to systems that don’t have it, such as behaviorism, Block believed. Functional connectivity in neuroscience must address the objection against functionalism of how mentality theories attribute mentality to systems without it. A behavioral disposition may be necessary for the possession of a certain mental state, but it isn’t sufficient. Chauvinism is the problem that a theory withholds attributing mentality to systems that seem to possess it. Block argued type physicalism falls to chauvinism because it’s the view that mental state types are equivalent to physical state types.

We may talk about the mental state of pain caused by sitting on a tack that causes behaviors such as loud cries and other mental states such as anger. We define these functional definitions (of analytic functionalism) using causal roles that are analytic and a priori truths about the other mental states alongside their propositional attitude. Identities are necessary and not subject to empirical observation. Psychofunctinoalism, on the other hand, uses empirical observation (in an posteriori manner) and experimentation to determine which mental state terms and concepts are contingent to their observations.

Structural connectivity

Structural connectivity (SC) are the long-range anatomical connections among brain areas through white-matter fiber projections. We use fiber tracking using bounded diffusion of molecules in water to create non-invasive connectivity maps. In the past scientists used diffusion tensor imaging (DTI), we track neural fibers, but more recent studies have used advances in graph theory for much more research on topological features in brain connectivity.

We can characterize the relationship between FC and SC as the former relying on connections between areas and the latter the physical characteristics of the fibers. Effective connectivity (EC) characterizes the interactions between visual processing regions (a psychophysiological interaction analysis) using structural equation modeling (SEM) based on minimization of predicted and observed dependent variables. EC also refers to the broader definition of SC that captures the features that shape connectivity like synaptic strengths, neurotransmitter concentrations, and neural excitability. Through both model-driven and data-driven approaches (the former generation signals under assumptions and the latter using statistics, information theoretical measures, or phase relationships to extract EC), we can infer EC and the topology of these networks. Using binary graphs, path length measures, clustering coefficients, and other ideas from graph theory alongside results from diffusion-based tractography, we can show the resting-state networks in various regions of the brain. Scientists have introduced Network Based Statistics for comparing whole-brain connectivity between different groups of connections.

We use the covariance between populations of neural activity with the Jacobian of the system of equations describing the neural activity in each node. For an input covariance matrix, we can describe the covariance between neural populations. The Kuramoto network model uses the global graph metrics of schizophrenia patients to account for the neurophysiological impairment to describe resting-state network activity between topological properties in schizophrenia. We may use either noise-driven spontaneous dynamics and complex interactions between phase-oscillators (with coupling, delays, and noise) to introduce a dynamic nature to the model, but these two factors contradict one another. The former implies temporal correlations in spontaneous activity emerge from uncorrelated noise propagation through connections while the latter uses complex interactions of oscillatory activities in regions of the brain. We may use a supercritical Hopf bifurcation to reconcile the two using synchronized networks and their corresponding temporal variations. From this, the Kolomogorov-Smirnov distance between empirical and simulated FC dynamic distributions is optimal at this critical point and more sensitive to deviations from the critical point.

Reinforcement learning

Reinforcement learning is emerging a dominant computational paradigm for modeling psychological and neural aspects of affectively charged decision-making tasks. The Markovian assumption lets us use decision-making models that describe how nervous tissue carries out perceptual inference. The Markovian assumption lets us use Markov models such that the various states that they use to describe processes are independent of the states that came before it. Hopfield neural networks alongside the work of Hinton-Sejnowski would let computational models use rules such as the Bush-Mosteller rule (learning based on trial-based differences between predictions and outcomes) or the Sutton-Barto approach (Monte Carlo methods and temporal-difference learning in artificial neural networks). We can introduce the temporal difference error such that the agent in the system chooses an action that maximizes a temporal reward. When diffusion ascending systems of nervous systems could use temporal difference learning as a general way biological systems could learn to value states. We can used a modified form of Hebbian learning such that it depends on incorrect prediction of the future to reinforce a bidirectional synaptic change. These Hebbian synapses could then store predictions of the future in a way that accounts for the actions of dopamine neurons.

Optimizing procedures

We may use optimizing methods from mathematics, physics, and computer science in neuroscience. If we assume artificial neural networks are similar to biological ones, we may use error minimization as an optimization procedure. The way we adjust parameters and weights we may analyze the computations of a neural system in how it generate ideas from the organization of a network. We may use backpropagation in creating models that have the capacities of a biological neural network, and speculate on how networks function in a computational theory of mind. The nervous systems of the brain have too many parameters to all be entirely controlled by genetics, neurodevelopment involves a massive synaptogenesis that grow using optimization processes, some parameters are used for feedback to adapt behavior to circumstances, and natural selection optimizes nervous systems in such a way that we may regard the nervous system’s selective pressures as error-minimizing.

The neural circuit in visual tracking of moving objects uses many unknown parameters and specific weights. We can construct a network by fixing the known parameters and train it on input and output to determine the unknown parameters. The probability inference methods depend on the degree of similarity between artificial and biological networks. We may use models to generate hypotheses because the nervous system evolution may be described with a cost function and artificial models use backpropagation to search through possibilities.


As 18th-century German philosopher Immanuel Kant said, studying concepts of the mind without empirical science is empty and studying science without philosophy is blind. Understanding how the brain works means going from simulating in a computer to making synthetic brains. We see how models interact with the actual world (whether they simulate the world or directly use it), determine which real-world parameters are relevant to our models, and extend models to cover all levels of organization. We wrestle with reduction, causation, and other phenomena through both science and philosophy.

Don’t Read this Book if you want Solutions in Life

Cartoonist Randall Munroe shares satirical advice about the world for anyone curious. The creator of the popular webcomic xkcd has come up with solutions to life’s problems. Results may vary.

Credit: Randall Munroe

Let’s say you wanted to find alternative methods to power your house. Given that the average American house uses about $1,000 per year on electricity, you turn to nature for answers. Creating an electric generator from the movement of Tectonic plates would provide a simple solution to natural electricity. If you lived on a fault line, you can figure out the force the ground exerts over a distance. Multiply this force by the distance to get energy. You decide to build a pair of giant pistons connected to the Earth’s crust. As shown above. As the pistons compress a reservoir of fluid between them, the pressure builds up to drive a turbine.

After giving this advice in his book, “How To: Absurd Scientific Advice for Common Real-World Problems,” cartoonist Randall Munroe admits the system would be “ridiculous and technically infeasible for a lot of reasons,” including cost and size. Yet these explanations make science enjoyable and entertaining no matter what your background is. Munroe’s book explores silly solutions to the most mundane problems in life such as boiling the Kansas river using teakettles so you can cross it, using butterflies to transport data or using liquid nitrogen to create snow when you want to ski. He uses scientific evidence and reasoning to back up his solutions but remains playful in explaining them, no matter how absurd they are. Setting things on fire to generate power and charge your phone can be a lifehack. Or just arson.   

A Gift to be Simple

Einstein is often paraphrased as saying, “everything should be made as simple as possible, but no simpler.” Regardless of how close this aphorism was to what Einstein actually said, simplicity is important in conveying information efficiently. Too much simplification can lead to poor representations of the universe. Munroe understands this and runs with it. His writing on simple solutions to life’s problems is friendly, lighthearted and approachable for all audiences. Much like his previous book, “Thing Explainer: Complicated Stuff in Simple Words,” he knows he can engage a broad audience through the simplicity of science. 

Though he seeks to entertain, Munroe remains cognizant about discerning seriousness from satire. He treats the reader like an intelligent being capable of understanding these tones and styles of writing. Even when he uses equations to calculate speed, force and other physical quantities, he presents them in a bite-sized, descriptive that’s easy to digest and follow. You can read the entire book in a single sitting because the explanations flow so naturally and fluidly in each chapter. Reading the book bit by bit, though, may help you become more curious about the world around you as you study Munroe’s explanations closely.

Laughing at Life

Munroe’s book is entertaining in an absurd, surreal way. He treats humans like a specimen under a microscope with enough sarcasm, wit and dry humor to keep you laughing throughout. His humor is more cultural as a satire on the rest of society – even in a self-aware sense. Making fun of the universe is how you understand it better. 

Still, some may find the humor isn’t meant for them. Munroe’s style of explaining can come across as pretentious and condescending. Readers may find that explaining simple things that they already understand only serves to show how smart Munroe is as though Munroe were some authoritative voice over all scientific knowledge. Others may find the book’s content short and thin even with thirty chapters. Some of the explanations may seem undeveloped. But the book’s personable, tongue-in-cheek nature leaves it free of presumptuous claims of the reader’s intelligence. 

The irony that an engineer would write such a treasonous attack on normalcy and established methods of scientific reasoning may put a smug, smirk on your face. But the book’s value goes beyond a few chuckles. Munroe’s humor instills curiosity and wonder of the world and how bizarre it can be. 

As Mythbusters co-host Adam Savage said he would reject reality and substitute his own, much of science and engineering come down to complicated, elaborate interpretations and explanations. You can make friends by physically running into them or jump off a mountain if you want to jump really high. Everything is up to interpretation. Munroe’s wit will let you better understand the craziness of the universe itself. Pick it up and give it a read for the sake of mad science itself. Then dispose of it by shooting it towards the sun. 

Raising the Alarm: Rhetoric on Climate Change

Shock! We realize the severe to protect the rights of individuals displaced by rising sea levels, storms, wildfires, floods and everything else brought upon by the nature of climate change.

Journalist David Wallace-Wells elucidates the assumptions, contexts, themes and other underlying features behind arguments on the future of Earth in his book “The Uninhabitable Earth: Life After Warming.” As though we were on a highway to Hell, the American journalist’s says, to avoid the doomsday scenarios of climate change spanning economic and political crises, we need a carbon tax, a method to fight against dirty energy, innovative agricultural techniques and overall funding for promoting green energy capturing waste carbon dioxide.

A Friendly Warning

As though you were meeting with him for coffee, Wallace-Wells’ writing is accessible and understandable. It lets the leader let feel at ease and understood despite the near-alarmist content of the book. Even though much of this book is content that has already been written, this book sets itself apart from others by being so frank, direct, and almost a detached objective look that Wallace-Wells takes as a journalist. As Aristotle wrote in Rhetoric, Book III, “For it is not enough to know what we ought to say; we must also say it as we ought.” Wallace-Wells provides a stunning re-contextualizing of future research, conversations and other features of existence due to climate change. The reader will feel empowered in her future ways of analyzing climate change rhetoric. It leaves the reader armed with the ability to formulate and analyze arguments on the nature of moral responsibility and power to make a difference in the world. 

The book also serves as an equalizer between contrary points of view on the issues of climate change. Wallace-wells’ writing encompasses so many perspectives to provide an accurate, multidimensional moral landscape of the issues of climate change. This makes the political message more powerful and persuasive in turning heads and changing minds. As Wallace-Wells says we have a tendency to be complacent even though we’re scared about the future of the Earth. Through comparisons and analogies, he forms predictions of how our actions affect the planet. By 2050, there will be more plastic than fish in the oceans. Even the everyday examples of our actions, such as a flight from London to New York destroying three square meters of Arctic ice, will leave you thinking twice about your role and responsibility in these global issues.

“Oh, the Humanities!”

Wallace-Wells explores many possibilities and options as he formulates his arguments. He draws comparisons from literature, history, philosophy and other disciplines in addition to science-backed conclusions. Through this, Wallace-Wells avoids pitfalls of reductionism that would come with relying on science alone. Instead of treating the issue of climate change as simply a mathematics problem with an optimal solution that we must use, it’s much more speculative. To address the crime, poverty, disease and economic collapse, he humanizes climate refugees and everyone else that shares our planet. He writes in a way we remember the fundamental ideals, values and principles we must protect. The reader may find herself in awe at how the dystopian futures found in works of “climate fiction” (or “cli-fi”) make the truth appear stranger than fiction. 

Digging deeper into the language of climate change, Wallace-Wells identifies terminology like “climactic regime,” for alleviating the effects of climate change. He uses these terms including “climate fatalism” and “ecocide” in characterizing the debates surrounding these issues. “Human futilitarianism” describes the psychoanalytic nature of climate despair, as writers Sam Kriss and Ellie Mae O’Hagan have said: 

The problem, it turns out, is not an overabundance of humans but a death of humanity. Climate change and the Anthropocene are a triumph of an undead species, a mindless shuffle towards extinction, but this is only a lopsided imitation of what we really are. This is why politics depression is important: zombies don’t feel sad, and they certainly don’t feel helpless; they just are. Political depression is, at root, the experience of a  creature that is being prevented from being itself; for all its crushing ness, for all its feebleness, it’s a cry of protest. Yes, political depressives feel as if they don’t know how to be humans buried in the despair and self-doubt is an important realization. If humanity is the capacity to act meaningfully within our surroundings, then we are really, or not yet, human.

Either way, the planet won’t grow colder or the planet won’t grow older.

What makes us special

Short answer: thinking. Why? Turning to analytic philosophy, you’ll find reasons stretching across consciousness and souls in why thinking makes us special. Evolutionary scientists explain how cognition and the ability to reflect, contemplate and ponder let humans overcome obstacles and struggle against nature. Thought transcending the surroundings of the world around us into truth, validity and other principles of reason seems nowhere in nature and, instead, only in our minds. “I think, therefore, I am human” resonates. Israeli philosopher Irad Kimhi begs to differ. That humans separate themselves from nature using thought is not only misguided but leads to false conclusions throughout philosophy, Kimhi argues in “Thinking and Being.”

Pre-Socratic philosopher Parmenides argued it’s impossible to think or say what is not. In his poem “On Nature,” he meant that what is not is nothing. To think nothing is to not think at all, and the “not”-ness of thought doesn’t differentiate it from nature and the universe itself. To think that the Earth is flat is to think from nothing in the world because there is nothing in the world that would let you think that. Though nothingness would continue in debates among thinkers including French philosopher Jean-Paul Sartre’s argument that our nothingness gives rise to consciousness, Parmenides’ reasoning that thought cannot follow from nothing doesn’t seem so appealing.

We think about what is “not” all the time. Negating anything to figure out what something isn’t is key in many lines of reasoning to figure what something is. Rejecting hypotheses and determining truth mean testing theory and detecting falsehood. But, even if we rejected Parmenides’s conclusion, we still need to figure out how to think of the “not.” Kimhi says understanding the nature of thought reveals why it doesn’t make humans so special after all.

How the sophist differs from a real philosopher, explored through Plato’s dialogue Sophist, that the Eleatic Stranger and Theaetetus discuss how discovering falsehoods let you figure out who we are. What makes thought special to the sophist is categorizing and systematizing what something is through clarifying what it is not until you figure out what it is. Thinking about what something is not is eliminates the confusion. Sophistry, then, is a productive art, the Eleatic Stranger concludes, involving imitating and copy-making to deceive and communicate with insincerity.

Philosophy in the analytic tradition means overcoming confusion similar to the way sciences do. German philosopher Gottlob Frege and British mathematician-philosopher Betrand Russell established its methods through logic. Yet the principles of logic and the appeal to science have, Kimhi believes, locked away thought’s specialness from philosophy. Frege’s belief that thought itself is fundamentally the same as nature meant thought exists independent of humans. These “propositions” stand on their own, lending credence to the idea that thought itself is part of nature just the same way “The Earth is flat” is false. Thinking, then, doesn’t set humans apart from the universe. When a philosopher debates Parmenidean’s question, her thoughts of what is “not” are false, not nothing.

Kimhi believes, however, Frege’s method of thinking about propositions is flawed. Kimhi’s argument rests on the negation of propositions. If she wanted to argue that it is raining, a philosopher could draw a picture of the sky and say “Things are as this picture shos.” To indicate that it is not raining, though, she couldn’t just draw a sky without rain. She would need the picture of rain and say “Things are not as this picture shows.” The picture, a metaphor for the proposition, needs this negation to clarify so you might conclude the picture itself, like a proposition, doesn’t say anything about how things are. Propositions mean nothing by themselves as far as stating things about the world. Kimhi attacks this idea, and believes that the picture expressing both the affirmation and the negation means a proposition says things are a certain way without having someone assert them. The same way we can’t say “Yes” or “No” to a claim without having the claim be there to begin with, Kimhi argues the propositions Frege promotes cannot be.

From a scientific perspective, if nature were an investigation of things that, by themselves have no meaning, then meaning itself is not part of nature. As Kimhi explains, thought’s place in the world doesn’t follow as separating humans from nature. Thoughts can be asserted and unasserted as a philosopher can say “It is raining, and it is not raining,” but there must be something both propositions have in common. Thinking, Kimhi believes, means representing how things are by combining elements like “the Earth” or “raining,” but the ability to put these elements together is also thinking of what these things aren’t. The difference between “It is raining” and “It is not raining” comes from our ability to think of it raining right now. Negating the claim doesn’t add any content to the thought. The two claims have a repeatable sign in common between them.

Kimhi further argues that, the same way negating a thought doesn’t add content to it, attributing thoughts to people doesn’t add content either. Though the judgments between “It is raining” and “It is not raining” differ, the claim is either affirmed or denied. Language doesn’t convey things in the world, but conveys the different ways we claim those things in the world. Thought itself is unique this way. The human capacity for language is part of the capacity to think. Language is the method of understanding the world and sets humans apart from everything else.

I sit and meditate on what makes us who we are. That thought runs so close to language makes intuitive sense. Language is the foundation for communication and expression. It’s role is inherent and to remove language from thought would be to lose thought itself. I worry that separating thinking from nature doesn’t do justice to the question Parmenides raised.

Though thinking isn’t something in nature, Kimhi believes, the linguistic form of human life constitutes thinking. Different from the austerity of “I” in German Idealism, philosophy is the apprehension of humans creatures of nature and thinkers not of nature. Thinking of what is not, though, remains a puzzle, but, by Kimhi’s views of thought, it doesn’t arise. Philosophy progresses through getting rid of confusion in clarifying what we already knew in some way or another.

The link between cognition and emotion

It’s easy to think of cognition and emotion as separate from one another, but research in cognitive science and neuroscience have suggested the two are more closely linked than we’d like to believe. Cognition can be defined as activities related to thought processes that let us gain knowledge about the world while emotions would be what we feel that involve physiological arousal, evaluation of what we experience, how our behavior expresses them, and the conscious experience of emotions themselves. To understand how cognition and emotion interact with one another in the brain, we may view cognitive behaviors neuroscientific phenomena as the result of both cognition and emotion, rather than simply one or the other. With research spanning philosophy, cognitive science, and neuroscience, emotions are no longer considered antagonistic to reason the way ancient Greek and Roman scholars treated them. Now, philosophers are much more inclined to view them closely linked through ideas such as reason being a slave to passion or reason giving way to passion through subjective experience.

Evidence of the mere-exposure effect, that people prefer things merely because they’re more familiar with them, in 1980 by psychologists William Raft Kunst-Wilson and R. B. Zajonc and as well as other findings in behavioral research shifted debates to focus on affect as a feature primary to yet independent of cognition. It could be related to unconscious processing and subcortical activity with cognition related to conscious processing and cortical involvement. 

Researchers generally agree on what constitutes cognition. Cognition, including memory, attention, language, problem-solving, and planning, often involve controlled neurological processes that respond to stimuli in the environment. This may include maintaining information while an external stimulus attempts to distract the mind. When cells in the dorsolateral prefrontal cortex of a monkey maintains information in the mind for brief periods of time, we can describe this link as a neural correlate for the cognitive process. With functional MRI (fMRI), we can identify which part of the brain are involved in these cognitive processes. Emotion, on the other hand, is much more subject to debate among scientists and philosophers. 

Emotions are arguably the most important part of our mental life to maintain quality and meaning of existence. We find meaning in emotions and rely on them to make sense of the world, sometimes in ways cognitive processes don’t offer. When researching emotion, some incorporate drive, motivation, and intention behind them as part of these states of mind.

Other researchers may use emotions in the conscious or unconscious assessment of events such as a feeling of disgust in the mouth. Subcortical parts of the brain such as the amygdala, ventral striatum, and hypothalamus are often linked to emotions. These brain structures are conserved through evolution and operate in a fast, sometimes automatic way. Still, how the different parts of the complex circuitry of the brain can mediate specific emotions is under research and debate. Neuropsychologists, neurologists and psychiatrists are only recently understanding the role of emotional processing in more complicated brain functions like decision-making and social behavior. 

But there’s much more to emotions than the physical phenomena in the brain. 

Imagine coming across a terrifying bear while hiking. In our most immediate reaction of fear, we can evaluate the situation (the bear is dangerous), a bodily change (increase heart rate), a phenomenological perception (feeling unpleasant), an expression of fear (eyelids raised and mouth open), a behavior component (wanting to run away), and a mental evaluation (focused attention on our surroundings). The phenomenological part involves our subjective experience as we respond to the world around us. All of these features come together in our emotions and can be debated to different degrees of necessity and sufficiency to emotions. On top of that, emotions may be directed towards objects with our intention (such as feeling angry at someone rather than just feeling anger on its own) and can shave motivation with respect to behavior (such as acting out of anger). Researchers have also debated whether emotions describe ourselves or emotions express ourselves imperatively. They’ve debated how the brain implements different types of emotions and how neural mechanisms describe emotional phenomena. 

Cognitive theories of emotions that have become popular in the latter half of the 20th century can be differentiated between constitutive and causal theories. Constitutive theories use emotions as cognitions or evaluations, while for causal theories, emotions are caused by cognitions or evaluations. For example, being frightened by a grizzly bear involves a judgement that the bear is scary. The fear may be the judgement itself or the result of the judgement. They let us differentiate between the complicated interactions of cognition emotion such as determining whether someone’s anger in response to a situation is the result of a cognitive evaluation of the situation or a reaction that’s more natural and automatic. In the mid-twentieth century, philosophers C. D. Broad and Errol Bedford emphasized constitutive approaches to emotion which would become dominant in philosopher while causal ones more popular in psychology. These philosophers argued that, if emotions had intentionality, there would be internal standards of appropriateness to which an emotion is appropriate. These cognitive evaluations, identifying emotions with judgements, have been used by philosophers such as Robert Solomn, Jerome Neu, and Martha Nussbaum since then. Identifying emotions with judgements, judgementalism, have been pivotal in cognitive theories of emotions.

Judgementalism in this way, however, doesn’t explain how emotions motivate, the subjective phenomenal experience of emotions, how one can experience an emotion with being able to identify a judgement with it, or a “recalcitrance to reason,” how we experience emotions even when they go against judgements that contradict them. Judgementalists may counter these issues by determining what judgements emotions are such as “enclosing a core desire,” as Solomon has argued, to let them motivate or “dynamic”, as Nussbaum has argued, so they may account for these issues. Through these methods, they may involve accepting how the world seems even with contradictory judgements. 

Other work in the 1960s showed how the cognitive component of emotions directly interacted with the physical bodily changes that occur alongside them. Psychologists Stanley Schachter and Jerome Singer developed a theory of emotion, known as the two-factor theory or Schachter-Singer theory, in which emotion is how we cognitively evaluate our bodily response to emotions. Injecting participants with epinephrine to arouse their subjects, the participants were told the drug would improve their eyesight with some of them additionally being told about the side effects. When witnessing other people act either happily or angrily, the participants who didn’t know about the side effects were more likely to feel either happier or angrier than the ones who were. The two theorized that, if people experienced an emotion without an explanation, they’d label their feelings using the feelings in the moment, suggesting participants without an explanation were susceptible to the emotional influences of others. The theory has faced criticism that it confuses emotions with how we label them such that we need complete knowledge of our emotions to label them as well as difficulty in explaining how we may experience emotions even before we think of them. Research in neuroscience has shown thinking about stimuli in ways to increase the emotion may boost prefrontal or amygdala activity while decreasing the emotion may reduce it. 

Integrating data and research from various parts of the brain, as they can provide the basis for cognitive phenomena, would illustrate a greater picture of emotion and cognition. There are many structures involved in functions and many functions for the individual structures of the brain. These neuron computations that underlie those phenomena also have affective and cognitive components, as described by cognitive scientists and philosophers. Viewing the relationship between emotion and cognition as a tug-of-war between the two doesn’t accurately capture the relationship between emotions and how we thinking about them. A combination of research in neuroscience, cognitive science, and philosophy would do justice. 

Time and Dreams in Political Unrest

With every tick of the clock I

awake, escape the shock as I

exit the dark of my dreams, as Jung would

remark. Yet not understood.
Now I ain’t sayin’ she’s a Heidegger, but she ain’t messin’ with no alt-right thinkers.

They say time flies. With age, the days feel shorter. Life speeds up, and it doesn’t slow down. The years start coming, and they don’t stop coming. However we look at it, we can understand how our perception has sped up in making these observations. It may be the result of memory. Every moment that passes and feels faster in our lives lets us view the present and the near-present with greater and and greater detail while losing the memories of what has gone long ago. We watch time speed up as we remember less.

Writing and other forms of immortalizing our words can fight against this. Whether its art, music, poetry or any other way of recording the tangible and conceivable into permanence, we can escape the fleeting visions of this world. As though we were waking up from a dream and recounting what had just happened, we can recognize dream states are part of our reality as Heidegger’s “Being-there” of the Dasein would describe.

The Dasein is what makes our existence more than a point in space-time that brings being from nothing. With death distinguishing existence, Dasein is the “being-toward-death” that gives our lives temporality. When Heidegger examined classical metaphysics with the hopes of creating a new ontological philosophy, he differentiated between the being and reality. All things have being while reality does not exist. Reality does not have the awareness of the world around it, and existing is what lets us determine what lies beyond ourselves. He described the technological advances of the 1930s and 1940s as threatening the world of ideas – poetry, intellectual thought, forms of art, and whatever we need to preserve who we are. Humanity becomes an object with an instrumetnal purpose through information and communication. Appreciating art and posing questions of who we are counteract these forces.

Much the same way Dennett wrote about his own dreams taking a long time, yet, in retrospect, seemed to have not taken any time at all, we may hypothesize that there is no dream experience. Instead, when we awaken, our memory banks play the dreams to us. Heidegger might respond to this claim by arguing that the times of dreams are consistent with the experience of dreams themselves.

With time moving faster, the present and the near-present become punctuated by events with less and less time between them. We find disparate events – whether its a meme about raiding Area 51 or the dispersion of fake news – coming and moving closer to one another. Our near-present perception enters a hypersensitive state that responds to the chaos and frenzy, and we can pick our poison: international turmoil, threats to the planet’s climate, the rise of fringe political groups, or whatever keeps us from falling asleep, as though we were trying to wake up from a nightmare. Even something as benign as a mock competitions between YouTube channels can turn messy when a shooter tells his audience to “subscribe to PewDiePie” before massacring a mosque.

It’s possible, though, that things had always been like this. The rise of Nazism during Heidegger’s time would lead historians to associate the philosopher and his views with the fascist movement. Heidegger watched rationalism, scientism, and market-centric forces overtake wonder, liberation, and freedom. Machines themselves reduced humans to the darkness they had created, and the fascists began attacking the mind-body dualism of Jews and liberals. The alt-right echoes Heidegger’s yearning for certainty and fixed values in modern life as well as nationalism and the interconnectedness of humans and the land. Trump’s former chief strategist Steve Bannon held up a biography of Heidegger and said “That’s my guy,” when he was interviewed by Der Spiegel.

Heidegger soon denounced Nazism. After he saw Hitler’s worship of efficiency and mythologized machines as though they were part of nature itself – part of who we are and how things should be – he condemned the anti-intellectualism running rampant. The racism and anti-Semitism followed an “I do not think, therefore I am,” inversion of Descartes’s famous proclamation.

When Horace wrote Caelum non animum mutant qui trans mare currunt (“those who rush across the sea change the sky above them, not their soul”), our souls still desire a connection to something permanent and fixed. Even Aristotle’s observation that we can only benefit from studying ethics when we already have “noble habits,” the philosopher must already have an idea of what she wants to learn. Heidegger believed that the philosopher with a main idea that she is a rooted being, tied to time and place and living within and through a land and language, her only interest is that she was born, worked, and died.

If only modern political discourse could heed the guidance of Aristotle. The philosopher’s first treatise on politics described a middle class that would lead to liberalist ideals by later intellectuals like Locke. The free rule because of their virtue and responsibility to rule. The commitment to philosophical thought, at the very least, eases the burden of time.