Where the buck stops

Nothing can be soundly understood

If daylight itself needs proof.

-- Imām al-Ḥaddād (trans. Moṣṭafā al-Badawī), "The Sublime Treasures: Answers to Sufi Questions"

Unmixing the unmixable

What is more important in determining an (individual) organism's phenotype, its genes or its environment? Any developmental biologist knows that this is a meaningless question. Every aspect of an organism's phenotype is the joint product of its genes and its environment. To ask which is more important is like asking, Which is more important in determining the area of a rectangle, the length or the width? Which is more important in causing a car to run, the engine or the gasoline? Genes allow the environment to influence the development of phenotypes. -- Tooby and Cosmides

Analogy Rot

It is the most common way of trying to cope with novelty: by means of metaphors and analogies we try to link the new to the old, the novel to the familiar. Under sufficiently slow and gradual change, it works reasonably well; in the case of a sharp discontinuity, however, the method breaks down: though we may glorify it with the name 'common sense', our past experience is no longer relevant, the analogies become too shallow, and the metaphors become more misleading than illuminating. -- E. W. Dijkstra

Out of the Boxes

Just as there are odors that dogs can smell and we cannot, as well as sounds that dogs can hear and we cannot, so too there are wavelengths of light we cannot see and flavors we cannot taste. Why then, given our brains wired the way they are, does the remark, "Perhaps there are thoughts we cannot think," surprise you? -- Richard Hamming

Uniforms

In many ways nonsense is a more effective organizing tool than the truth. Anyone can believe in the truth. To believe in nonsense is an unforgeable demonstration of loyalty. It serves as a political uniform. And if you have a uniform, you have an army. -- Mencius Moldbug

Science vs. Scientists

Let me differentiate between scientific method and the neurology of the individual scientist. Scientific method has always depended on feedback [or flip-flopping as the Tsarists call it]; I therefore consider it the highest form of group intelligence thus far evolved on this backward planet. The individual scientist seems a different animal entirely. The ones I've met seem as passionate, and hence as egotistic and prejudiced, as painters, ballerinas or even, God save the mark, novelists. My hope lies in the feedback system itself, not in any alleged saintliness of the individuals in the system. -- Robert Anton Wilson

Mapping

No map represents all of its intended territory [...] Every map is at least a map of the map-maker (his assumptions, world-view...)

**

A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness. If the map could be ideally correct, it would include, in a reduced scale, the map of the map; the map of the map of the map; and so on, endlessly, a fact first noticed by [Josiah] Royce. If we reflect upon our languages, we find that at best they must be considered only as maps. A word is not the object it represents; and languages exhibit also this peculiar self-reflexiveness, that we can analyze languages by linguistic means.

**

If words are not things, or maps are not the actual territory, then, obviously, the only possible link between the objective world and the linguistic world is found in structure, and structure alone. The only usefulness of a map or a language depends on the similarity of structure between the empirical world and the map-languages. If the structure is not similar, then the traveler or speaker is led astray, which, in serious human life-problems, must become always eminently harmful. If the structures are similar, then the empirical world becomes 'rational' to a potentially rational being, which means no more than that verbal, or map-predicted characteristics, which follow up the linguistic or map-structure, are applicable to the empirical world.

** 

[...] a language, any language, has at its bottom certain metaphysics, which ascribe, consciously or unconsciously, some sort of structure to this world. Our old mythologies ascribed an anthropomorphic structure to the world, and, of course, under such a delusion, the primitives built up a language to picture such a world and gave it a subject-predicate form.

 

-- Science and Sanity, Alfred Korzybski

Intelligence and Wisdom

I want here to make a long aside on intelligence. I have met far too many people, particularly among the colleged elite (note: if you have a college degree, you are a kind of elite; most people do not have one), who treat intelligence as a moral virtue or – God help us – the only moral virtue. This is extraordinarily foolish and I use that word (as you will see) carefully. Intelligence, as a trait, is a mix of inborn factors and perhaps early upbringing – again, I don’t want to descend into the swamps here; the key thing is that by the time we are mature enough to understand it, it isn’t susceptible to much change. Consequently, intelligence isn’t a virtue (in the moral sense) at all but simply an attribute about a person, like an attractive face, red hair, height and so on. Being intelligent does not make one a better person; it is merely the luck of birth. It carries all of the moral virtue of being good at basketball or League of Legends; less, really, since one has to show discipline and practice in those things (though for the already intelligent, there is a strong element of intellectual training necessary to really harness that inborn trait, much like natural talent at sports or e-sports). If you had the luck to be born smart, you ought – in my view – to feel obliged to give back just as if you had had the luck to have been born rich or beautiful.

Here I think it is crucial to separate intelligence from wisdom; the moral virtue lies in the latter. Intelligence is one’s ability to think through complex problems; it is an inherent ability with no moral value (but it does, of course, have use-value). Wisdom concerns one’s judgement and consequent code of conduct. I have seen too many students berate themselves – often quite cruelly – as being ‘stupid,’ because they have made some mistake (in situations, by the by, where I can be almost perfectly certain that said students, by virtue of being in my classroom, were in the top 25%, probably the top 15%, of the intelligence distribution). And on the one hand, I cringe because the self-criticism I hear from them is one that elevates intelligence to a moral virtue (they are a ‘bad person’ for being ‘stupid’) and at the same time an in-born, immutable trait that they cannot change. They are declaring (they think) not only that they have no worth (which is not true) but also the impossibility of worth. But of course they haven’t been stupid, but rather they have been foolish. The difference is that wisdom and foolishness is about choices and judgement; we can make the choice to be wiser in the future. A single foolish decision doesn’t make a fool. We have not yet found a way to make a stupid person intelligent, but we have refined many paths for the foolish person to reach to wisdom; indeed, all children are fools and must become wise as they mature.

It may be the case that it is easier for intelligent people to be wise, because they can more rapidly puzzle out life’s problems and find helpful solutions; I am unconvinced, having known a great many terribly smart, terribly foolish people (I mean, I went to graduate school in history – none of us there could have had very much sense) who in their foolishness thought they were smart enough to live without wisdom. It is certainly the case that there are great stores of wisdom, quite clearly labeled as such, available to anyone without the ability or inclination to puzzle out the basic principles of wisdom on their own. To be honest, I would advise the intelligent to use those stores as well; attempting to think one’s way to wisdom is a path full of peril, hubris and error. I have met many people who achieved a real measure of wisdom through these stores and who were often quite a bit wiser than some of the super-smart people I have known (and, as an aside, being an academic plays absolute havoc with your ability to assess normal intelligence when your entire peer-group is very smart; I suggest avoiding ever descending entirely into an academic bubble – maintain non-academic friends!).

Intelligence is an important, but quite frankly, overrated thing in our society; in almost any relationship, we ought to prefer the wise person to the intelligent one. link Bret Devereaux  

Language Corrections

Essentially PC [Politic Correctness] is a correction from language that developed in an oppressive context and achieved mainstream usage, Doublespeak must be corrected in order to understand what is really being said. Both concepts can be said to use euphemism as their mechanism, but there's an important distinction. PC uses value neutral terms to replace inherently and unfairly derogatory ones. Doublespeak uses ironic terms to hide their motives and cynically cast them as the opposite. If I speak of sex workers rather than "whores" it is not because I am trying to hide any truth about them. It is that the "acceptable" term holds no deeper truth and only insult. If I speak of "creative bookkeeping" rather than theft, I am trying to hide or minimize guilt. @absurdistwords

The Razor

Generations of writers opined vaguely that 'simple hypotheses are more plausible' without giving any logical reason for it. We suggest that this should be turned around: we should say rather that 'more plausible hypotheses tend to be simpler'. An hypothesis that we consider simpler is one that has fewer equally plausible alternatives. (p.606)

Actual scientific practice does not really obey Ockham's razor, either in its previous 'simplicity' form or in our revised 'plausibility' form. As so many of us have deplored, the attractive new hypothesis or model, which accounts for the facts in such a neat, plausible way that you want to believe it at once, is usually pooh-poohed by the official Establishment in favor of some drab, complicated, uninteresting one; or, if necessary, in favor of no alternative at all. The progress of science is carried forward mostly by the few fundamental dissenting innovators, such as Copernicus, Galileo, Newton, Laplace, Darwin, Mendel, Pasteur, Boltzmann, Einstein, Wegener, Jeffreys – all of whom had to undergo this initial rejection and attack. In the cases of Galileo, Laplace, and Darwin, these attacks continued for more than a century after their deaths. This is not because their new hypotheses were faulty – quite the contrary – but because this is part of the sociology of science (and, indeed, of all scholarship). In any field, the Establishment is seldom in pursuit of the truth, because it is composed of those who sincerely believe that they are already in possession of it. Progress is delayed also by another aspect of this. Scholars who failed to heed the teachings of William of Ockham about issues amenable to reason and issues amenable only to faith, were – and still are – doomed to a lifetime of generating nonsense. (p.613) Probability Theory, The Logic of Science, E.T.Jaynes

Reifying Ghosts

Belief in the existence of 'stochastic processes' in the real world; i.e. that the property of being 'stochastic' rather than 'deterministic' is a real physical property of a process, that exists independently of human information, is another example of the mind projection fallacy: attributing one's own ignorance to Nature instead. The current literature of probability theory is full of claims to the effect that a 'Gaussian random process' is fully determined by its first and second moments. If it were made clear that this is only the defining property for an abstract mathematical model, there could be no objection to this; but it is always presented in verbiage that implies that one is describing an objectively true property of a real physical process. To one who believes such a thing literally, there could be no motivation to investigate the causes more deeply than noting the first and second moments, and so the real processes at work might never be discovered. (p.506) Probability Theory, The Logic of Science, E.T.Jaynes

scientific discovery is not a one-step process

To counter this universal tendency of the untrained mind to see causal relations and trends where none exist, responsible science requires a very skeptical attitude, which demands cogent evidence for an effect; particularly one which has captured the popular imagination. Thus we can easily understand and sympathize with the orthodox conservatism in accepting new effects. There is another side to this; skepticism can be carried too far. The orthodox bias against a real effect does help to hold irresponsibility in check, but today it is also preventing recognition of effects that are real and important. 

The history of science offers many examples of important discoveries that had their origin in the perception of someone who saw a small unexpected thing in his data, that an orthodox significance test would have dismissed as a random error. Jeffreys (1939, p. 321) notes that there has never been a time in the history of gravitational theory when an orthodox significance test, which takes no note of alternatives, would not have rejected Newton's law and left us with no law at all. Nevertheless, Newton's law did lead to constant improvements in the accuracy of our accounting of the motions of the moon and planets for centuries, and it was only when an alternative (Einstein's law) had been stated fully enough to make very accurate known predictions of its own that a rational person could have thought of abandoning Newton's law. The discovery of argon by Lord Rayleigh and of cosmic rays by Victor Hess are examples that come to mind immediately. Of course, they did not jump to sweeping conclusions from a single observation, as do the disaster-mongers; rather, they used the single surprising observation to motivate a careful investigation that culminated in overwhelming evidence for the new phenomenon. 

It is fortunate that physicists and astronomers do not, in practice, use orthodox significance tests; their own innate common sense is a safer and more powerful reasoning tool. In other fields we must wonder how many important discoveries, particularly in medicine, have been prevented by editorial policies which refuse to publish that necessary first evidence for some effect, because the one data set that the researcher was able to obtain did not quite achieve an arbitrarily imposed significance level in an orthodox test. This could well defeat the whole purpose of scientific publication; for the cumulative evidence of three or four such data sets might have yielded overwhelming evidence for the effect. Yet this evidence may never be found unless the first data set can manage to get published. How can editors recognize that scientific discovery is not a one-step process, but a many step one, without thereby releasing a new avalanche of irresponsible, sensational publicity seekers? The problem is genuinely difficult, and we do not pretend to know the full answer. (p.504ff) Probability Theory, The Logic of Science, E.T.Jaynes

Perennial Madness

Man is surely mad. He cannot make a worm; yet he makes Gods by the dozen -- Montaigne

the pull and push of ethical systems

Intuitively, there are two basic desiderata for a system of ethics:

• that the system prescribes behaving well toward other people, or prescribes behaving with respect for certain principles; and
• that the system provides a rationally compelling reason to behave as it prescribes.
  

Satisfying either of these is notoriously easy; satisfying both at once is notoriously hard. Nozick (1981) calls these desiderata the pull and push, respectively, of ethical systems, and points out that the main theoretical problem is to connect the two. Historically, humankind has often resorted to fantasies that bridge the gap: divine incentives (usually deferred to a supposed afterlife), karma (what goes around supposedly comes around), reincarnation (in a form that depends on your prior conduct), or an exaggeration of the extent to which one’s conduct causes reciprocity. Good and Real, Gary Drescher

the switch cannot flip itself

Ultimately, all science is correlation. No matter how effectively it may use one variable to describe another, its equations will always ultimately rest upon the surface of a black box. (Saint Herbert might have put it most succinctly when he observed that all proofs inevitably reduce to propositions that have no proof.) The difference between Science and Faith, therefore, is no more and no less than predictive power. Scientific insights have proven to be better predictors than Spiritual ones, at least in worldly matters; they prevail not because they are true, but simply because they work. 

 

***

We know what rapture is: a glorious malfunction, a glitch in the part of the brain that keeps track of where the body ends and everything else begins. When that boundary dissolves the mind feels connected to everything, feels literally at one with the universe. It’s an illusion, of course. Transcendence is experience, not insight.

***

Neurons do not fire spontaneously, only in response to external stimuli; therefore brains cannot act spontaneously, only in response to external stimuli. No need to wade through all those studies that show the brain acting before the conscious mind “decides” to. Forget the revisionist interpretations that downgrade the definition from free will to will that’s merely unpredictable enough to confuse predators. It’s simpler than that: the switch cannot flip itself.

 

citações do livro Echopraxia, Peter Watts

Sunk costs

No matter how far you've gone down the wrong road, turn back. -- Turkish proverb

Models and Truth

The most common misunderstanding about science is that scientists seek and find truth. They don't — they make and test models. [...] Building models is very different from proclaiming truths. It's a never-ending process of discovery and refinement, not a war to win or destination to reach. Uncertainty is intrinsic to the process of finding out what you don't know, not a weakness to avoid. Bugs are features — violations of expectations are opportunities to refine them. And decisions are made by evaluating what works better, not by invoking received wisdom. [...] it doesn't require professional training to make mental models — we're born with those skills. What's needed is not displacing them with the certainty of absolute truths that inhibit the exploration of ideas. Making sense of anything means making models that can predict outcomes and accommodate observations. Truth is a model. -- Truth is a Model Neil Gershenfeld

Not always a lapalissade

“Should we trust models or observations?” In reply we note that if we had observations of the future, we obviously would trust them more than models, but unfortunately observations of the future are not available at this time. -- Knutson and Tuleya, Journal of Climate, 2005.

Epistemological short-circuits

I cannot therefore sidestep in any way the famous, but so badly formulated question: "Is probability subjective or objective?" In fact there is not, nor can there be, any such thing as probability in itself. There are only probabilistic models. In other words, randomness is in no way a uniquely defined, or even definable property of the phenomenon itself.

In a serious work, written by a competent author, on a scientific subject, an expression such as "this phenomenon is due to chance" constitutes simply, in principle, an elliptic form of speech. It really means "everything occurs as if this phenomenon were due to chance," or, to be more precise: "To describe, or interpret or formalize this phenomenon, only probabilistic models have so far given good results." It is then only a statement of fact. Eventually, the author may add: "And it does not appear to me that this situation is likely to change in the foreseeable future." There is then a personal stand, a methodological choice, which opens certain possibilities to research, but closes others. One understands that the author, who knows his subject, since he has practiced it for many years, wishes to spare his colleagues the trouble of entering a blind alley, thus saving them precious time.

 

There is a risk, because after all there is no reason to believe that tomorrow or in ten years time another researcher will not publish a deterministic theory which will explain beautifully and completely the phenomenon at hand, and we shall have perhaps missed the boat by following the implicit advice of our author: it is a real risk, but one which is normal and inherent to the practice of scientific work, since we must in any case make methodological choices.

 

In this purely operational sense, recourse to chance, that is, in reality, the decision to use probabilistic models, is perfectly legitimate and does not take us outside the framework of objectivity. Sometimes, however, and probably more often in works of popularization and philosophical synthesis than in purely scientific works, we are presented with quite a different interpretation. It is suggested, nay even affirmed, that Chance (sometimes with a capital c) acts decisively in its own right on the course of events. This is no longer a methodological choice. Chance is now hypothesized, supplied with positive attributes, set up as a deus ex machina. Under these circumstances, to attribute the phenomenon to Chance, is equivalent to attributing it to Providence, and both are foreign to scientific methodology.

Illegitimate use of scientific concepts beyond the limits within which they have an operative meaning is nothing else but a surreptitious passage into metaphysics.

Any given model, however well tested and corroborated, always necessarily contains theorems which do not correspond any more to empirical formulations, which cannot be controlled, and are not even controllable, beyond a certain limit. There always exists a threshold of realism, beyond which a mathematician can certainly pursue happily his deductions, but which a physicist must respect, lest he obtain first uncontrolled formulations, and later uncontrollable ones, that is, formulations which lack any objective meaning. In other words, they are "metaphysical" in the sense given to this word in the usage of objective science.

[...] nobody has ever applied either the theory of probability, or for that matter any other mathematical theory, to reality. One can only "apply" to reality real (physical, technical, etc.) operations, not mathematical operations. The latter only apply to mathematical models of the same nature as themselves. In other words, it is always to probabilistic models, and only to them, that we apply the theory of probability. [...] There is no probability in itself. There are only probabilistic models. The only question that really matters, in each particular case, is whether this or that probabilistic model, in relation to this or that real phenomenon, has or has not an objective meaning. As we have seen, this is equivalent to asking whether the model is falsifiable.

[...] sometimes we choose a unique and well-defined probability P from the beginning, and we then say that the model is completely specified; sometimes, however, we retain some room for maneuvering by just choosing a family P(a,b,...) of probabilities depending on a small number of parameters a,b,... In the latter case, we say that we have only chosen the type of model, and that we have left the problem of its specification, that is of the choice of the numerical values which should be attributed to the parameters a,b,... to a later stage. According to the viewpoint of "orthodox" statisticians the choice of the type of model constitutes a "hypothesis," while the problem of the specification of the numerical values of the parameters is called "statistical inference," or "estimation" of these parameters. Since this terminology carries with it implicit presuppositions concerning the real and objective existence of these parameters, we shall use the neutral and purely descriptive term of choice

The essential point, from the point of view of methodology, is to carefully distinguish between the two completely different roles we attribute to the same symbol p. On the one hand, p is a parameter of the model. On account of this, it may (or may not) have an objective meaning, and the assertion "p = 1/2" may (or may not) be falsifiable, that is, objective or empirical. On the other hand, the same symbol appears in the equality P(X10 = 1) = p. Here we are stating that the probability of success at the 10-th throw (of the present game) has a fixed numerical value, e.g. p=1/2: a singular and undecidable statement, which is therefore certainly devoid of any objective meaning, like all statements relating to the probability of a unique event.

The defining criterion, (in the strict sense) for the objectivity of a probabilistic model would thus be as follows: we agree to declare a model falsified if an event of zero probability (in the model) actually occurs (in reality).

According to the viewpoint of "orthodox" statistics, the most important problem one must then solve is that of "statistical inference", that is, the "estimation" of the unknown intensity theta of the Poisson process [modeling some forest dynamics]. For once this parameter is known one can calculate all other characteristics of the process. This point of view attributes, implicitly, a real and objective existence to the intensity parameter 'e': namely that even if our information allows us to arrive at no more than an approximate estimate of the "true" value of 'e', this does not change the fact that the latter exists somewhere in nature, and could be precisely measured, given perfect information. But in reality it is not at all certain that this contention has an operational sense: for in order to determine 'e' precisely, the forest would have to extend to infinity (and remain Poisson) while its real size is in fact limited. The presumed obviousness of the existence of 'e' is based on a summary identification of the model (the Poisson process) with reality (the forest). Such a confusion, which is quite common among statisticians, is essentially an epistemological short-circuit. For however well a model is adapted to its object, we never have a guarantee that all its characteristics will faithfully mirror objective properties of reality which can be put in a one-to-one correspondence with them.

The preceding analysis has highlighted three steps which have very different epistemological standings:

 

(1) there is first an epistemological choice: it has been decided to use probabilistic techniques to represent the phenomenon (the forest). This is a decision, not a hypothesis. It is a constitutive decision. (It "constitutes" the forest as an object of study, it defines the general framework within which we shall operate and determines the choice of the tools we use.) It is not an experimentally verifiable hypothesis (for it is neither true nor false to say that "this forest is a realization of a stochastic process", since there is no conceivable experiment or observation that could refute this proposition). At this level, we shall speak of a constitutive model (here a probabilistic one).

 

(2) We next encounter a hypothesis about the physical nature of the phenomenon studied - such as spatial homogeneity, absence or influence between neighboring regions - which leads to the choice of a generic model: the process is a Poisson process. Contrary to the preceding choice (which can only be justified by its efficiency and by the successes it leads to, and on which one can only pass judgement in the long run, after having dealt with a large number of cases) this second choice follows from a physical hypothesis which can be objectively tested. It can, therefore, be supported or rejected by the experimental data either through statistical tests, which can be very easily devised for this very simple particular case, or through some other method, including the judgement of the practitioner who knows that forest well. Resorting to the practitioner's intuition has no mystical connotations whatever to it; it simply reflects the epistemological priority that we give to reality over the mathematical or rather statistical model we have chosen to describe it.

 

We cannot overstress the capital importance of this step. For it is essentially here that we incorporate in the model hypotheses that have an objective meaning and that carry with them positive information which is not contained in the raw data. It is only because of this positive contribution that we can (apparently) extract from the data more than they really contain (i.e. a prediction as well as an estimation variance). The counterpart of this small-scale miracle is that our model is now vulnerable, and that our predictions can now be contradicted by experiment if the hypotheses on which we base them are not objectively valid. It should be made clear here that it is not enough to check (e.g. through tests) that these hypotheses are compatible with the data, that is, that they are verified over the sampled plots. We must also assume that they remain valid over the regions that have not been sampled, and we shall only know this after the fact. The choice of the model constitutes therefore an anticipatory hypothesis and always introduces a risk of radical error. This is why it is imperative that it takes into account not only the numerical data, but also all other available sources of in- formation (general knowledge about this type of phenomenon, the experience of practitioners, etc...).

 

In order to better localize the input of positive information and the consequent vulnerability of the model, it is often advantageous to distinguish two steps in the choice of the generic model: choice of a generic model in the wide sense, then choice of a particular type within this model. In the present case, the generic model (in the wide sense) would be for example "a stationary point process." The word "point" means that we have decided, as a first approximation, to treat each tree as a point in a plane. This is a (constitutive) decision that does not entail any input of positive information and therefore introduces no risk of experimental rejection. On the other hand, the word "stationary" is associated with a hypothesis of spatial homogeneity and entails an input of positive information as well as a vulnerability, but to a relatively small extent. By model type we shall mean a model that needs only the numerical value of a small number of parameters in order to be completely specified. Here the model type chosen is a "Poisson process" with only one indeterminate parameter. The choice of this type implies a very strong hypothesis, namely the lack of interaction between neighboring regions. It introduces a large amount of information, which enables us, among other things, to calculate the estimation variance. And it therefore introduces further risks of experimental rejection. This is a general rule: very often it is the choice of a type within the generic model that constitutes the crucial decision, the one that opens the largest number of operational possibilities, but also inevitably introduces the greatest risk of error.

 

(3) Finally the last step is the choice of the specific model or, as we shall also say, the specification of the model (here the choice of the numerical value for 'so many trees per hectare'). While Mathematical Statistics attributes an absolutely vital role to this third aspect of "model choice," to which it refers as statistical inference (i.e. numerical estimation of the parameters), it will only play a very minor role, or even none at all, here since the essential results (estimation and variance) will be expressed, in the final analysis, in terms of the experimental data alone, in a form which exploits choices (1) and (2) (and particularly the choice of the Poisson type) but not at all choice (3) of the numerical value [of the parameter]. For in reality it is only for terminological convenience and to clarify our ideas that we carry out the specification of the model, while choice (1) (the epistemo-logical choice) and choice (2) (choice of the generic model and its type) together with the numerical information provide us with an operational basis which is sufficient for solving the problem we are dealing with.

 

The precise distinction between these three aspects of model choice may appear elementary and uninteresting in a case as simple as that of the Poisson forest. But this will change rapidly as we examine more complicated models. Once the choice of the constitutive model (the epistemological decision) has been agreed upon, the crucial problem remains choice number (2), that of the generic model and more particularly that of model type. For once we have adopted a model that is suited to the experimental data, it is usually not difficult to specify it, if we really want to, on the basis of these same data. 

 

The important problem is therefore not that of statistical inference, but the choice of the generic model and of its type. Let us note carefully the fact that the problem is not just to "test a hypothesis" against the given data. This viewpoint, which is that of orthodox statistics, disregards the core of the problem. It is not only agreement with the available data that we need, but also the much stronger hypothesis that the chosen type is also compatible with the data which are as yet unavailable, that is, the unknown or inaccessible parts of the phenomenon. It is the latter hypothesis which explains the fertility of the method, and also, as we have seen, its vulnerability. Compatibility with the data is of course necessary, but it is never sufficient to insure us against an always possible disagreement with what is not given (and this is precisely what we are trying to estimate). Estimating and Choosing, Georges Matheron

the uninsurable future

In Limits to Growth, the groundbreaking study of future resource and capital flows, the authors noted that what will ultimately halt growth is not lack of resources, but the dedication of all investable capital to maintaining the existing system. This will leave no capital for expanding the size of the economy. Rising insurance and rebuilding costs [from climate change, from aggregate infrastructure complexity] are becoming a major destination for capital that would otherwise go towards producing economic growth. This is moving us ever closer to outcome suggested by the Limits to Growth researchers. -- Climate change and the uninsurable future Kurt Cobb 

As soon as we die, we enter into fiction

Evidence is always partial. Facts are not truth, though they are part of it – information is not knowledge. And history is not the past – it is the method we have evolved of organising our ignorance of the past. It’s the record of what’s left on the record. It’s the plan of the positions taken, when we to stop the dance to note them down. It’s what’s left in the sieve when the centuries have run through it – a few stones, scraps of writing, scraps of cloth. It is no more “the past” than a birth certificate is a birth, or a script is a performance, or a map is a journey. It is the multiplication of the evidence of fallible and biased witnesses, combined with incomplete accounts of actions not fully understood by the people who performed them. It’s no more than the best we can do, and often it falls short of that. -- why I became a historical novelist Hilary Mantel

The Best Things in Life

 

Biological Causes

Modern biology is characterized by a number of ideological prejudices that shape the form of its explanations and the way its researches are carried out. One of those major prejudices is concerned with the nature of causes. Generally one looks for the cause of an effect, or even if there are a number of causes allowed, one supposes that there is a major cause and the others are only subsidiary. And in any case, these causes are separated from each other, studied independently, and manipulated and interfered with in an independent way. Moreover, these causes are usually seen to be at an individual level, the individual gene or the defective organ or an individual human being who is the focus of internal biological causes and external causes from an autonomous nature.

This view of causes is nowhere more evident than in our theories of health and disease. Any textbook of medicine will tell us that the cause of tuberculosis is the tubercle bacillus, which gives us the disease when it infects us. Modern scientific medicine tells us that the reason we no longer die of infectious diseases is that scientific medicine, with its antibiotics, chemical agents, and high-technology methods of caring for the sick, has defeated the insidious bacterium. What is the cause of cancer? The cause is the unrestricted growth of cells. That runaway growth, in turn, is a consequence of the failure of certain genes to regulate cell division. So we get cancer because our genes are not doing their business. It used to be that people thought that viruses were a major cause of cancer, and a great deal of money and time has been spent looking for the viral causes of cancer in humans without success. Biology has moved on from the time when viruses were all the rage to a time when genes are much more trendy.

Alternatively, there are environmental insult theories of the causes of cancer. Cancers are caused, we are told, by asbestos or by PVC or by a host of natural chemicals over which we have no control, and although they are present in very low concentrations, we are exposed to them over our whole lives. So, just as we will avoid dying from tuberculosis by dealing with the bug that causes it, so we will avoid dying from cancer by getting rid of particularly nasty chemicals in the environment. It is certainly; true that one cannot get tuberculosis without a tubercle bacillus, and the evidence is quite compelling that one cannot get the cancer mesothelioma without having ingested asbestos or related compounds. But that is not the same as saying that the cause of tuberculosis is the tubercle bacillus and the cause of mesothelioma is asbestos. What are the consequences for our health of thinking in this way? Suppose we note that tuberculosis was a disease extremely common in the sweatshops and miserable factories of the nineteenth century, whereas tuberculosis rates were much lower among country people and in the upper classes. Then we might be justified in claiming that the cause of tuberculosis is unregulated industrial capitalism, and if we did away with that system of social organization, we would not need to worry about the tubercle bacillus. When we look at the history of health and disease in modern Europe, that explanation makes at least as good sense as blaming the poor bacterium. -- Biology as Ideology, Richard Lewontin

Dogmas Everywhere

We're all familiar with prudishness as it applies to sexual ethics: the prude thinks certain sex acts are immoral, even between happily consenting adults.  They also hold that sex work is inherently degrading, and that others should not be allowed to offer monetary compensation in exchange for one's sexual labour.  The prude is not willing to tolerate others engaging in consensual and mutually beneficial exchanges in this arena if they don't stem from what the prude regards as the "right" motivations and take place within "approved" institutional arrangements (e.g. marriage).  It's a deeply illiberal perspective that has thankfully fallen out of favour in recent decades.  We may, of course, have reasonable concerns about the exploitation of sex workers in practice.  But it's increasingly recognized that the best response to such practical concerns is to improve the options available to those in desperate circumstances, not to deprive them of (what they evidently regard as) their current best option.  So I think it's fair to say that liberals have won out over sexual prudes in our current cultural milieu.

Sadly, the reverse appears true within the arena of research ethics.  Research prudes think that certain kinds of medical research (e.g. involving voluntary infection) are unethical, even when all involved are happily consenting adults.  They disapprove of offering monetary compensation to research participants, to make participation worth one's while when it otherwise would not be.  They are not willing to tolerate others' engaging in consensual and mutually beneficial research arrangements if participation doesn't stem from what the research prude regards as the "right" (i.e. non-financial) motivations.  It's a deeply illiberal view that unfortunately still predominates, with cultural bastions like the New York Times routinely dismissing controversial ("queer") research possibilities as "unethical", without argument.  We may, of course, have reasonable concerns about the exploitation of research participants in practice.  But it's depressing how hastily people assume that the best response to such concerns is to paternalistically deprive others of an option that they might well have reasonably preferred over their available alternatives.

Perhaps the most important difference between the two arenas is that the research prude's illiberalism is vastly more harmful.  Medical research has immense positive externalities.  So preventing it has immense negative externalities.  You're not just harming the would-be research participants (not to mention undermining their autonomy), you're also harming all those who end up suffering from medical conditions that could have been cured or prevented had the research gone ahead.  Missed opportunities are rarely salient, and so do not provoke the outrage that they truly deserve.  But on any reasonable estimate, the death toll of research prudishness is surely monstrous. -- Against Prudish Research Ethics, Richard Y Chappell

Bullshit Jobs

The United States plays a key role in our story. Nowhere was the principle that all wealth derives from labor more universally accepted as ordinary common sense, yet nowhere, too, was the counterattack against this common sense so calculated, so sustained, and so ultimately effective. By the early decades of the twentieth century, when the first cowboy movies were being made, this work was largely complete, and the idea that ranch hands had once been avid readers of Marx would have seemed as ridiculous as it would to most Americans today. Even more important, this counteroffensive laid the groundwork for the apparently bizarre attitudes toward work, largely emanating from North America, that we can still observe spreading across the world, with pernicious results.

Would-be capitalists were not granted the right to create limited-liability corporations unless they could prove doing so would constitute a clear and incontestable "public benefit" (in other words, the notion of social value not only existed but was inscribed in law) [...] much of this anticapitalist feeling was justified on religious grounds; popular Protestantism, drawing on its Puritan roots, not only celebrated work, but embraced the belief that, as my fellow anthropologists Dimitra Doukas and Paul Durrenberger have put it, "work was a sacred duty and a claim to moral and political superiority over the idle rich"  [...] work was both a value in itself and the only real producer of value.

In the immediate wake of the Civil War all this began to change with the first stirrings of large-scale bureaucratic, corporate capitalism. The "Robber Barons," as the new tycoons came to be called, were at first met (as the name given them implies) with extraordinary hostility. But by the 1890s they embarked on an intellectual counteroffensive [...] The promulgation of consumerism also coincided with the beginnings of the managerial revolution, which was, especially at first, largely an attack on popular knowledge. Where once hoopers and wainwrights and seamstresses saw themselves as heirs to a proud tradition, each with its secret knowledge, the new bureaucratically organized corporations and their "scientific management" sought as far as possible to literally turn workers into extensions of the machinery, their every move predetermined by someone else.

Why was this campaign so successful? Because it cannot be denied that, within a generation, "producerism" had given way to "consumerism," the "source of status," as Harry Braverman put it, was "no longer the ability to make things but simply the ability to purchase them", and the labor theory of value—which had, meanwhile, been knocked out of economic theory by the "marginal revolution"—had so fallen away from popular common sense that nowadays, only graduate students or small circles of revolutionary Marxist theorists are likely to have heard of it. Nowadays, if one speaks of "wealth producers," people will automatically assume one is referring not to workers but to capitalists.

This was a monumental shift in popular consciousness. What made it possible? It seems to me that the main reason lies in a flaw in the original labor theory of value itself. This was its focus on "production" [...] which was always conceived primarily as male work — as a matter of making and building things, or perhaps coaxing them from the soil, while for women "labor" was seen primarily and emblematically as a matter of producing babies. Most real women’s labor disappeared from the conversation [...] "Caring labor" is generally seen as work directed at other people, and it always involves a certain labor of interpretation, empathy, and understanding. To some degree, one might argue that this is not really work at all, it’s just life, or life lived properly—humans are naturally empathetic creatures, and to communicate with one another at all, we must constantly cast ourselves imaginatively into each other’s shoes and try to understand what others are think ing and feeling, which usually means caring about them at least a little—but it very much becomes work when all the empathy and imaginative identification is on one side. The key to caring labor as a commodity is not that some people care but that others don’t; that those paying for "services" (note how the old feudal term is still retained) feel no need to engage in interpretive labor themselves.

[...] as many feminist economists have pointed out, all labor can be seen as caring labor, since even if one builds a bridge, it’s ultimately because one cares about people who might wish to cross the river. As the examples I cited at the time make clear, people do really think in these terms when they reflect on the "social value" of their jobs.

To think of labor as valuable primarily because it is "productive," and productive labor as typified by the factory worker, effecting that magic transformation by which cars or teabags or pharmaceutical products are "produced" out of factories through the same painful but ultimately mysterious "labor" by which women are seen to produce babies, allows one to make all this disappear. It also makes it maximally easy for the factory owner to insist that no, actually, workers are really no different from the machines they operate. Clearly, the growth of what came to be called "scientific management" made this easier; but it would never have been possible had the paradigmatic example of "worker" in the popular imagination been a cook, a gardener, or a masseuse. -- Bullshit Jobs, David Graeber

The THOG Problem

 

Image: Wikimedia Commons 

You are shown four symbols

1. a black square
2. a white square
3. a black circle
4. a white circle

and told by the experimenter "I have picked one colour (black or white) and one shape (square or circle). A symbol that possesses exactly one of the properties I have picked, is called a THOG. The black circle is a THOG. For each of the other symbols, are they a) definitely a THOG, b) undecidable, or c) definitely not a THOG?" wikipedia (also cf. Futility Closet)

 

 

Cromwell

 'Do you think I am saved?' he says. 'I am covered in lamp black and my hands smell of coin, and when I see myself in a glass I see grime – I suppose that is the beginning of wisdom? About my fallen state, I have no choice but agree. I must meddle with matters that corrupt – it is my office. In the golden age the earth yielded all we required, but now we must dig for it, quarry it, blast it, we must drive the world, we must gear and grind it, roll and hammer and pulp it. There must be dinners cooked, Rob. There must be slates chalked, and ink set to page, and money made and bargains struck, and we must give the poor the means to work and eat.' -- The Mirror and the Light, Hillary Mantel

Small Telescopes

It is generally very difficult to prove that something does not exist; it is considerably easier to show that a tool is inadequate for studying that something. With a small-telescopes approach, instead of arriving at the conclusion that a theoretically interesting effect does not seem to exist, we arrive at the conclusion that the original evidence suggesting a theoretically interesting effect exists does not seem to be adequate. --  Uri Simonsohn

My Time

The modern morality of “You're on my time [...] is the indignity of a man who feels he's being robbed. A worker's time is not his own; it belongs to the person who bought it. Insofar as an employee is not working, she is stealing something for which the employer paid good money (or, anyway, has promised to pay good money for at the end of the week). By this moral logic, it's not that idleness is dangerous. Idleness is theft. This is important to underline because the idea that one person's time can belong to someone else is actually quite peculiar. Most human societies that have ever existed would never have conceived of such a thing. As the great classicist Moses Finley pointed out: if an ancient Greek or Roman saw a potter, he could imagine buying his pots. He could also imagine buying the potter — slavery was a familiar institution in the ancient world. But he would have simply been baffled by the notion that he might buy the potter’s time. Bullshit Jobs, David Graeber

Malthus

Thunberg put her finger right on one of the key drivers of the problem: the fairytale of eternal economic growth. Many of us mistake that problem as a problem with capitalism, but really it’s more of a malthusian dynamic than that: you simply cannot and should not assume that population and industrialization can ramp indefinitely. Usually when I say something like that, someone will swan in and declare that Malthus was wrong and that we can handle a much larger population, if we’re smart about it and use resources wisely, etc. But the problem with that is that the larger your population is, the more damaging it’s going to be when something goes wrong and breaks those assumptions. I’m speaking here from my history as a descendant of Irish migrants who fled to the US because there was too much dependence on the potato. Potatoes were what brought my Norwegian ancestors over, too. Potatoes were a miracle food at the time and allowed some parts of the planet to expand their carrying capacity. not accounting for what might happen if the carrying capacity suddenly dipped because of British greed and airbone fungus. We can grow the population much larger than it is, sure, but what are the failure modes when the feedback loops get tighter and tighter. During WWII, the Bengal Famine [wik] was not a result of a drought (as it has been whitewashed to be) but rather a supply-chain management problem where the British thought they’d do well to hedge their bets against a nazi blockade by taking Bengal’s rice crop. 2.1 million people died of hunger, probably not realizing that it was Winston Churchill who had knifed them. As the population goes up, the catastrophic results of that sort of hiccup goes up, too. -- Marcus J. Ranum