Category Archives: Epistemology

by | April 13, 2013

God and evolution 3: The problem of purpose A

The problem of purpose

When Darwin published the Origin, he was lauded by his Christian friend and correspondent Asa Gray, who wrote:

“…Darwin’s great service to Natural Science in bringing back to it Teleology: so that instead of Morphology versus Teleology, we shall have Morphology wedded to Teleology”

Darwin replied soon after in a letter:

What you say about Teleology pleases me especially and I do not think anyone else has ever noticed the point.

Gray later wrote a long essay arguing for an evolutionary teleology.  The term teleology means the study of the purposes of things, and we can just replace it with “purpose”.

Under Aristotle’s philosophy, there were four “causes” (or accounts, aitia) of why things occurred. They were: the material it was made of (material account), the thing that made it change (efficient account), the form it had (formal account) and what it was for (final account). Thus, a house might be made of brick, be built by artisans, have the shape of a box, and be for living in. Explanations in terms of “final causes” or goals or purposes were the stuff of science for the next two thousand years.

At the beginning of modern science, though, Francis Bacon wrote:

… the research into Final Causes, like a virgin dedicated to God, is barren and produces nothing.

Bacon’s barren virgin theme became the standard for most sciences thereafter, except in biology. Living things were thought by all to have purposes. The famous philosopher Immanuel Kant even went so far as to declare that there would never be a Newton of a blade of grass, because living things had “purposiveness” (Zweckmassigkeit) which could not be explained in physical terms.

The natural theology movement from the 17th to the 19th centuries attempted to explain living things in terms of the purposes for which they were made by God, and from this to uncover the aims God had. Unlike the modern intelligent design movement, rather than working from the appearance of design to proving God’s existence, they reasoned from the functions and roles of things to God’s nature and providence. This culminated in the work of authors like William Paley, who found design in everything based on the assumption of God’s goodness and plan.

There are two kinds of purpose in the tradition. One is the external purpose of God or Nature. The other is the internal purpose that things, especially living things, might have in their nature (entelechy), and drives them to their natural end. To illustrate the difference, external purpose might bring order to an otherwise unruly nature, by command or imposition. If the world tends to be chaotic, then God gives it a purpose by imposing harmony and order, a view that had a lot of traction in the early and medieval period of Christianity.

Internal purpose, however, implies that things are innately going to fulfil their purpose without any guidance. Evolution might be progressive towards some “Omega point” if living things have internal purpose. This view, too, has forerunners in the middle ages, and found its best expression in the work of Teilhard de Chardin, a Jesuit theologian of the early 20th century.

Both of these kinds of purpose are widely accepted in various theistic theologies. But the standard view, expressed by Aquinas, is that internal purpose is what God gave to things:

The natural necessity inherent in things that are determined to one effect is impressed on them by the Divine power which directs them to their end, just as the necessity which directs the arrow to the target is impressed on it by the archer, and does not come from the arrow itself. There is this difference, however, that what creatures receive from God is their nature, whereas the direction imparted by man to natural things beyond what is natural to them is a kind of violence. Hence, as the forced necessity of the arrow shows the direction intended by the archer, so the natural determinism of creatures is a sign of the government of Divine Providence.

External purpose is not the primary reason things have purpose, but a secondary reason. Roughly, if God has to intervene, then that is because he needs to cause something that would not have otherwise occurred.

The purposes of selection

Despite Darwin’s approval of Gray’s comments, natural selection was profoundly problematic for believers. It implied that the appearance of purpose in the living world was a byproduct on an unintelligent designer. Selection was simply a physical process that resulted in organisms and organs that were fit for the environment in which they lived. In short, Kant’s purposiveness was a byproduct of unpurposeful processes. Many believers felt this removed the natural world from God’s plan. It meant that God was no longer needed to explain why the world had harmony: things that were not harmonious (adapted) tended to die out.

So the real issue was this: natural selection involved purpose after and not before the adapted part evolved. Forethought implies God’s design, but if purposes can be evolved themselves, this means that what has happened had no general purpose, just lots of local little ones, and they might in fact be competing as well.

In short, natural selection delivers neither internal nor external purpose, because it doesn’t imply that the purpose is the result of a plan or goal. Progress towards goals therefore becomes a limited and immediate thing, not generaliseable.

In modern philosophy of biology, this gets called “teleonomic”, in which the purpose or meaning of some trait or gene has the “purpose” of doing what it does because it allows the organism that carries the trait or gene to survive and reproduce. The other two kinds of purpose are called teleology, as mentioned, and teleomatics. Teleology is purpose driven, teleomatics is rule following (law-driven behaviour) and teleonomy is purpose seeking. We can illustrate this with a diagram:

Teleonomy

Where the pre-scientific, sometimes called “Aristotelian”, view held that the laws of nature (teleomatic processes) were the result of purpose, and so things had innate purposes (teleonomics), the Darwinian and modern view seems to hold that the laws come first, then things evolve that have purpose-like behaviours such as functioning parts, and then, and only then, are there goal seeking things like humans with plans and intentions. Purposes become a fact of nature, shrunk down from the global nature of things to a small part of things. Organisms have purposes because they evolved them, and there is no place in this conception of nature for teleology to be the driving force of the world. At least, that is how it is seen by those who think Darwinian evolution presents a problem for belief. This includes not only theists, but those who think theism is incompatible with science, such as atheists who are exclusivists, that is, think that science precludes belief in gods.

In the next post, I will look at how some theists have dealt with this issue.

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Filed under Epistemology, Evolution, Living with Evolution, Philosophy, Religion

by | February 28, 2013

Knowing things in semantic space

For those who know this stuff, this will be quite banal, but I recently gave it to a non-philosopher science grad, and she found it useful, so some of you might too.

What is it to know something in a fallible world? A world where nobody (nobody who is talking to us, anyway, and we still wouldn’t know whether to trust them – such as God – if they did) can know things certainly. How can we say that we do know things? I have a mental picture I’d like to share with you.

Many people these days talk about “semantic spaces”. A recent rather elegant paper even mapped brain responses in terms of semantic spaces, visualising the resulting brain activities as a WordNet diagram. But what is a semantic space? I think of it as a literal space – a Cartesian graph of n dimensions, one for each semantic variable. Another way to put this is to see it as an axis on a graph where commitments vary along some scale. It might be simple: yes/no. It might be quantitative in some range. It might be qualitative. Or it might even be a function of the credence one puts into some claim. So long as it can vary, it can form a space.

If these semantic variables are independent of each other, then they form a volume. Suppose we have three variables – colour, size and shape. Colour is the standard spectrum. Size is, well, size. Shape might be along some taxonomy of shapes ranging from smooth to rough. The details don’t matter.

Semantic space1

A semantic space of three dimensions.

Now I ask, “what do I know about this object O?” I might not have a clear belief about the exact shade, but I know it’s blueish, so I exclude all parts of the spectrum scale that aren’t blueish. I have achieved an increase in specificity – I am more certain of the colour of this object O than I was to begin with. All regions of the space that aren’t blueish are excluded. I have learned something.

Semantic space2

A semantic space restricted to blueish properties.

Notice that most of the possible states of the semantic space are now excluded. Now suppose I note that the object O is rough, not smooth. I reduce the possible states even further. I can say I know more as more space is excluded:

Semantic space3

Roughness plus blueish properties

Finally, I realise the size of the object, more or less. I know have excluded most of the space, and have a restricted range of semantic coordinates – properties – for O:

Semantic space4

Roughness plus blueish plus median size properties

I now know a lot about O compared to when I began. If I can find precise semantic properties for O, then I can reduce that space down to a single coordinate:

Semantic space5

A precise specification of the properties of the object

Now I know O very well indeed.

This is of course a simplification. For a start, there is always some uncertainty, either because the metric is not discrete (that is, because it is impossible to be precise; consider the problem of Heisenberg uncertainty, for example) or because the possibility of error and other cognitive biases leaves me unable to assert pure certainty about the properties of O, no matter how strongly I feel that I have nailed it.

Science is like this. We learn about the world by various means. One is measurement and observation (assuming that observation is not just a form of measurement). The more precise our observations, the smaller the uncertainty we have about the thing observed. Another is somewhat less direct: theoretical models reduce the possible states of affairs we represent in our semantic spaces. However, ultimately a theory that precisifies our knowledge claims is always in the final analysis about measurements (apart form a priori sciences like logic and mathematics).

However, there remains another question: how do we construct the axes of our semantic spaces in the first instance? They are neither given by God, nor logic, nor evolution. And the answer is to step up a level and treat the axes themselves as coordinates in a higher space of possible semantic spaces. We eliminate regions of those spaces by finding out what fails to work. Metaspaces (sets of dimensions of semantic variables) are narrowed down much the same way as the spaces themselves are, by trial and error. This is why we have different notions of elements than the Greeks or the Indus Valley sages for example, and why we no longer seek to anthropomorphise nature. Those ideas did not deliver success at knowledge acquisition.

Scientific theories themselves evolve as we find out what contrasts, what dimensions, fail to capture the way the world is. Instead of a semantic ascent, where in order to assert the truth of a statement P, we need to have a metalanguage in which “P” (the sentence that asserts that P is true) can be said to be true, we begin with a conceptual metaspace and evolve it to better precision. It is semantic descent. We step down, revise and reframe, until we have something that works and matches the observed world.

This permits us to say also that we know something better than we did before – as when we say that we knew more when we thought the sun, not the earth, was the centre of the universe, even though the sun isn’t either. To put the sun at the centre of the universe eliminates all the states of affairs forced on us by the geocentric universe, even if Copernicus retained epicycles and an absolute universe. We later modified this to eliminate circular orbits, epicycles and eventually an absolute space. Each step precisifies our semantic coordinates (or corrects them) in line with what is observed.

So when next you say you know something, consider what is being eliminated from your conceptual, that is to say, semantic, space of possibilities.

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Filed under Epistemology, Philosophy, Science

by | February 21, 2013

Education, Journalism and Science

My last rant was perhaps somewhat intemperate. Carl Zimmer, who along with Ed Yong I really respect as a science journalist, tweeted it with the line:

@carlzimmer: Man, @john_s_wilkins does not like newspapers. 

This is not quite true. I like some newspapers. I do not like the newspaper industry. I worked in various media positions for thirty years. In that time I have seen the best and the worst of journalism (and Carl and Ed are the best). The point of the Twain quote in the last post was that only one in fifty “newspapers of the average pattern” was a virtue. The standard justification for a free press is that they are mostly okay. They mostly aren’t.

But this does not detract from the very good work done on occasion or by good magazines like National Geographic. It is possible to report science without dumbing down or misrepresenting. Carl once interviewed me about a subject I spent ten years working on, species concepts, and his piece in Scientific American (a patchy magazine sometimes) covered the territory well and without distortion.

So what was I getting at? Very simply this: if you want an informed population, put not your faith in the mass media, but in education. No amount of good or ordinary science journalism will improve the public understanding of science. This is hardly a novel view, and it is largely the consensus view in science communication studies.

But let us first ask what legitimate functions science journalism does play, and how it can be done well. First of all, what is meant by the phrase “science journalism”? This covers, in my view, everything from garish front page stories about the latest “breakthrough” in cancer research and “genes for” this or that, through to well written books like Brian Switek’s Written in Stone, or Richard Conniff’s The Species Seekers, to name two recent excellent books. Carl himself has one or two excellent books, including his recent Evolution or A Planet of Viruses (still waiting for the review copy ;-) ). What differentiates bad from good science journalism?

In my mind, the difference lies between “gee whiz” and “this is why”. Science is not a list of discoveries or results; it is a process of discovery and getting results. There is reasoning and work involved, and if you don’t understand the principles behind the reports, you don’t really understand the reports. Any book that just says “scientists have discovered that…” is bad journalism. It tells you something, of course, but doesn’t give you understanding. Good journalism (in science or any other field) tells you why things are what they are and how they came to be that way. It involves narratives, of course, and I never said that narratives, where they are called for, are bad. But good journalists tell narratives where they are required, and not merely for the sake of having a narrative.

For example, there is a narrative, beginning with Arrhenius in the late 19th century, about how we got to understand global warming. But if the goal is to provide understanding of global warming, all that history and personal development is simply drama for its own sake. If you want to understand climate and the reasons why we think the earth is warming, instead focus on the models of energy sinks and sources, ocean transport, the hydrological cycle, etc. The story merely gets in the way. A good journalist will tell only so much of the story as is needed to explain these facts and inferences. A bad journalist will ignore the facts and inferences for the story and personalities, simplifying down to stupidity the actual science, or even just dropping it altogether. As Einstein once wrote:

Anyone who has ever tried to present a rather abstract scientific subject in a popular manner knows the great difficulties of such an attempt. Either he succeeds in being intelligible by concealing the core of the problem and by offering the reader only superficial aspects or vague allusions, thus deceiving the reader by arousing in him the deceptive illusion of comprehension; or else he gives an expert account of the problem, but in such a fashion that the untrained reader is unable to follow the exposition and becomes discouraged from reading any further. If these two categories are omitted from today’s popular scientific literature, surprising little remains. [Quoted in Fahnestock  1986: 276, from 1948]

So what must a good science journalist do? If they are not to write an academic tome, they must select and report what they think is relevant and important, but whatever else they do, they absolutely must report facts. There is no need to make them dramatic if they aren’t. The reader can be asked to do a bit of work. As Terry Pratchett once said, education is Lying to Children, simplifying and paring away complexity, and then adding it back later as the students advance. A science journalist must Lie to the Reader to an extent, but not by adducing opinions from the ignorant in order to maintain interest, nor by lazily using tropes like “gene for”, but by fairly and clearly reporting on the, you know, science.

The industry doesn’t support that. Few are able to make a living like Carl or Ed, researching, talking to the scientists carefully and extensively and not merely a ten minute chat to get some pull quotes to fit a story they already have written in their head, nor just topping and tailing press releases (often written by ex-journalists now posing as university public relations experts) and putting a byline on them.

How does education get around this set of limitations? In an ideal world, by building on increasing understanding of the processes – the methods and reasoning styles – of the actual science. Instead I see evidence that too many pre-university curricula are based around passing exams, which is to say, focussing on the results. However, we know how to educate, even if we don’t do it properly a lot of the time. Educators do not need my advice, but they do need me and everyone else who gives the policy makers their marching orders to support extra funds and resources to do it.

And there’s the problem right there. We have been so acculturated into expecting the media to educate us in an entertaining fashion that we have increasingly defunded and removed opportunities for good science education, and moved to “infotainment” and high technology in schools. We do not know how ignorant we are, and so we do not ask the policy makers to support education properly. Instead we think that by adding another computer based technique we can solve the problem amusingly, with drama, to pique interest.

Another rant I shall make one day is on the industrial nature of education today (shades of Illich!), but the point now is that we are misled by media to think media is the solution, when it is the problem. How to do this better? Stop thinking that communication is the solution to the misunderstanding of science. Start teaching better.

Next, I shall issue a solution to world peace…

Reference

Fahnestock, Jeanne. 1986. Accommodating Science: The Rhetorical Life of Scientific Facts. Written Communication 3 (3):275-296.

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Filed under Education, Epistemology, Journalism, Rant

by | February 15, 2013

My latest paper

Science & Education, February 2013, Volume 22, Issue 2, pp 221-240

Biological Essentialism and the Tidal Change of Natural Kinds

Abstract

The vision of natural kinds that is most common in the modern philosophy of biology, particularly with respect to the question whether species and other taxa are natural kinds, is based on a revision of the notion by Mill in A System of Logic. However, there was another conception that Whewell had previously captured well, which taxonomists have always employed, of kinds as being types that need not have necessary and sufficient characters and properties, or essences. These competing views employ different approaches to scientific methodologies: Mill’s class-kinds are not formed by induction but by deduction, while Whewell’s type-kinds are inductive. More recently, phylogenetic kinds (clades, or monophyletic-kinds) are inductively projectible, and escape Mill’s strictures. Mill’s version represents a shift in the notions of kinds from the biological to the physical sciences.

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Filed under Epistemology, Metaphysics, Natural Classification, Systematics

by | February 3, 2013

The relation of classification to abductive reasoning

Deer tracks in the snow

Deer tracks in the snow.

In my last post, commentator DiscoveredJoys raised the question of abductive reasoning and how it relates to my claim that classification is basically pattern recognition. It’s a fair question. First I’ll repeat my response, and then go into it a little more.

In my view, abduction is larger in scope than pattern recognition (PR). PR provides the foundation, but abductive reasoning leaps (usually on the basis of very few observations) to a causal argument or inference, while I am merely talking about the PR itself. PR presents the explicandum for inductive, abductive and deductive explanation.

So I have a much smaller target here. However, I should have thought (and written) about abductive reasoning more. Let me now. Abduction is sometimes called “inference to the best explanation”. Recognition of species, for example, is not, I think, explanatory, but it sets up a problem for the pattern recogniser: why is that pattern there? The usual answer (leapt to immediately on the basis of prior knowledge) is that there is some reproductive power that makes progeny resemble parents. This is the abduction, not the recognition of a pattern. It is the “best explanation” based on a host of prior assumptions and knowledge claims. If we had never seen a living thing (if we were a Matrix style computer), we might not leap to the explanation, but I think we would still recognise the pattern.

Of course the economic argument would not apply, and would rely upon other criteria of salience (maybe the Matrix needs to categorise objects that have functional roles in the simulation).

First of all, what is abduction? The Stanford entry is quite complete and comprehensible (see this also), but basically it is leaping to an explanation from a single or few observations. It is called by the late Peter Lipton Inference to the Best Explanation (IBE). IBE is a principle that you should choose to explain an observation based on the best causal explanation, the most likely based on background knowledge. My commenter suggested that pattern recognition is a form of IBE. I think it is not.

For a start, to make a pattern recognition-based classification does not require positing an explanation. It requires explanation once you have one, which is to say, it sets up an explicandum. To make an IBE, one needs already to know enough to make some explanations more likely than others. Lipton (1991) calls this assessing the “loveliness” of competing hypotheses. But while pattern recognition involves prior knowledge – of the domain and its general properties, mostly what to look for – it doesn’t involve assessing the loveliness of hypotheses. Instead it involves assessing the salience of differing stimuli.

In order to make an IBE, you have to recognise things. Take for example an IBE about what made footprints in the snow. First you have to recognise the pattern of footprints. This is something you have learned to do, not least by making footprints. Second, in order to make an IBE that a deer made these particular tracks, you need to recognise the difference between bipedal and quadrupedal tracks (gotten from years of observing them), and between claws and hoofs (likewise) and so on. With all that categorical apparatus in play, you “leap” to the hypothesis that of the likely animals in the area, it was a deer, not a cat or horse.

But classification is different to that, at least initially when the domain under investigation (DUI) is unexplored. You know about the wider domains in which the DUI is situated, so you are primed to see some sorts of things. But you get an idea of what is in the DUI by looking, a lot. Experience trains you to see patterns, and then, and only then, can you make IBEs. Hence my response above.

There are those who think taxa are explanations. One author, Kirk Fitzhugh (2005, 2009) thinks species are explanations, a view I cannot make sense of. An explanation of why a species is a species is something independent of recognising the species. Others have argued that phylogenies are explanations or hypotheses, in a Popperian fashion. Again, I cannot make sense of this. In the case of phylogenies, the explanation is the theory of common descent (or, in some cases, lateral transfer and introgression through hybridisation), but the phylogenies themselves are patterns in data. If a systematist works out a phylogeny of a group, then there is an IBE of common ancestry (or perhaps a Bayesian inference, which is distinct in the eyes of IBE advocates from abductive inferences), but common ancestry is not the same thing as working out the phylogeny, again, at least initially. Then background information can come into play to revise and refine the phylogenetic systematics, for instance by using molecular clocks or distributional properties, but again, these are further inferential activities to classification.

The relations of different kinds of cognitive activities here are not simple. While it helps us to classify them as distinct activities, in practice we shift and change from one to another, or do them simultaneously. Science is not done by recipe. However, it pays to be clear about the differences between them.

References

Fitzhugh, Kirk. 2005. The inferential basis of species hypotheses: the solution to defining the term ‘species’. Marine Ecology 26 (3-4):155-165.
———. 2009. Species as Explanatory Hypotheses: Refinements and Implications. Acta Biotheoretica 57 (1):201-248.

Lipton, Peter. 1991. Inference to the best explanation. London: Routledge.

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Filed under Epistemology, Natural Classification, Philosophy, Science, Species and systematics, Systematics

by | January 27, 2013

Pattern recognition: neither deduction nor induction

NewImage

It occurs to me as I read Rosenberg’s Philosophy of Science (2005), that we tend in that field to classify epistemic activities into two kinds: induction (about which we have many arguments as to its warrantability) and deduction (with many arguments about its applicability). But I believe there is something else that we do to learn about what exists in the world. In my forthcoming book, The Nature of Classification, coauthored with Malte Ebach, I argue that this is classification, but typically classification is seen as either of the other two kinds of inference. I think it is a third kind.

So what happens when we classify in the absence of theory? We aren’t yet inductively constructing theory, and we aren’t able to deduce from theory (since there isn’t any yet) the classes of objects in the domain we are investigating. We argue that what is happening here is pattern recognition (Bishop 1995). We are classifier systems. It is one of the distinguishing features of neural network (NN) systems such as those between our ears that they will classify patterns. They do so in an interesting fashion. Rather than being cued by theory or explanatory goals, NNs are cued by stereotypical “training sets”. In effect, in order to see patterns, you need to have prior patterns to train your NN.

Where do these come from? I think that there are several sources. One is evolution: we are observer/classifier systems of a certain kind. This gives us a host of cue types to which we respond by training our stereotype classifier system. For example, we respond to movement of large objects, to differences in colour and shade, and so on, in our optical system. Quine (1953) referred to this as our “quality spaces” – these are fields of discriminata, to which we (in Quine’s view, behaviouralistically) react. They are adaptations to the exigencies of survival for organisms of the kind that we are. The problem is that so long as our survival and reproductive success is ensured, evolution cannot guarantee us access to the way things “really” are. At best it gives us a good balance between false positives and false negatives. It is good enough, as it were, for government work (Godfrey-Smith 1991). But is it good enough for science and metaphysics?

One of the standard accounts of the success of science is that it increasingly approaches the truth. This is called the Ultimate Argument for Scientific Realism by van Fraassen (1980) and the Miracle Argument by Putnam (1975, that unless science does converge on reality, science would be a miracle). It is quite clear that the received dispositions evolution has bequeathed our cognitive capacities is not enough. While one might reject the Plantingan argument against all naturalism based on this insufficiency of our evolved cognitive powers (Plantinga 2002), there is a problem. How do we come to identify aspects of the world reliably and properly?

Science proceeds by refining its categories of what exists in the world based on two main sources. These are evidence, and explanatory force. In the case of a domain of investigation for which there is as yet no explanation, all we have is evidence, but apart from our evolved dispositions to respond to certain stimuli, how do we identify the salient aspects of evidence? There is an almost infinite amount of possible information we might make use of, and so we must glean the right sources of information. One source is economic necessity. Over time, farmers and hunters will tend to respond to the features of the things they are engaged in acquiring and using that are more or less important for success, because those features which are not salient will impose a cost of time and effort that tends to reduce success. This is a process very like natural selection, and has been the basis for what came to be known as evolutionary epistemology, in which a parallel process to biological evolution occurs in the domain of knowledge. Cognitive traditions become better at acquiring reliable knowledge because ideas and approaches that do not aid this goal are costly and are abandoned.

However, we have a superfluity of cognitive and conceptual resources. We can retain ideas and practices that are not really relevant for social reasons, such as rituals and “explanations” that have no counterpart with the reality being dealt with. So the fact that a particular culture is successful at farming by relying upon a ritual calendar (as in pre-Indonesian Bali) doesn’t warrant belief in Hindu gods. The functional aspects of the rituals acts to transmit the information even if nobody in the culture (or in Western agribusiness) fully understands why those rituals make farming successful (Lansing 2007).

So when a classifier recognises patterns in economic circumstances, what counts is not the conceptual superstructure, the theories and ideologies, but the categories of what matters – in this case of water, soil, and landscapes. How might this explain the success of science?

Taxonomists are classifiers in a particular economic situation: professional science. When a taxonomist encounters organisms in the wild, they are in the same situation as when a hunter hunts in that ecology. To succeed at taxonomy, as to succeed at hunting, the agent must know the right things about the target objects. A hunter that doesn’t know what different species of bird look like and how they behave and where they live is in exactly the same economic conditions as a taxonomist who also lacks knowledge. Neither will end up with dinner on the plate (qua hunter or taxonomist). In the case of the taxonomist, the gap between failure and hunger is somewhat more distal than for the hunter (but hunters typically get most of their food from foraging rather than hunting anyway, courtesy of the non hunters, mostly women, in their village), but ultimately economic success depends directly upon correct pattern recognition.

Ernst Mayr was fond of telling the story of how when he visited Papua in the 1930s, he and the local hunters identified the same species of bird, with an exception where western ornithologists also disagreed, and he used this as justification for the reality of those (and all) species. He made the inference that science was able to discover kinds of things that were real in the world, and he may have been right (many biologists and philosophers believe species are not real), but it was not, I think, because of the pattern recognition abilities of humans per se to see species. When Ed Wilson tried the same experiment about ants, a subject he knows intimately, instead of the locals counting the same species he did (several dozens) he got something like “the black ones, the bitey ones and the red ones”. Why did Mayr’s informants know their birds while WIlson’s did not know their ants? The answer is that birds, but not ants, were of economic importance to the locals, while ants were of economic importance to Wilson and entomologists only.

By “economic” I do not mean fiscal, but the acquisition of resources, success at which gives the person investigating a living. What distinguishes scientific success is a unique socioeconomic system of professionalism, credit in society, and access to funds and resources like labs, students, and equipment. The motivations of the individuals concerned are several, often (but not always) based on personal curiosity, but curiosity is not enough if you don’t get the resources to do the work.

So we are very good at turning our perceptual pattern recognition systems to scientific work. What evolution provides, science refines. It happens that pattern recognition and the subsequent classificatory activities can deliver reliable knowledge of the world when it matters. But being as it is parasitic upon those evolved capacities, and being as scientists are social organisms, this is not without its failures. Social influences, particularly the inherited traditions of ritual and conception that history bequeaths, can skew and bias our categories about the world. This is where theory and experiment come in.

Science, by way of its historical accidents, also seeks to explain things in ways that can be tested. Here the ordinary philosophical issues come into play – we inductively generalise based on the patterns we have recognised, and form hypotheses, and from those hypotheses we derive deductive consequences, which we can test in ways that are not circular, which do not rely upon our original observations. As T. H. Huxley once said, nature whispers yes, but shouts NO! And so we can eliminate hypotheses that are not fit to the facts, more or less. This is what the evolutionary epistemologists, and philosophers like Popper, built their views upon. What evolutionary epistemology never explained, nor Popper, was how we came up with those hypotheses in the first place. Pattern recognition does.

For a half century or more we have had the view that observation is theory laden. As I have argued before (and which is part of our forthcoming book), observation need not be laden with theory of the domain under investigation. And what evolution has bequeathed need not be in the slightest theoretical, nor even reliable (as the massive literature on illusions shows us). We can naively observe things that we know little about, but we never start knowing, or at least being disposed to know, nothing.

So can we say that science is adequate to tell us the true nature of the world? Putnam’s miracle argument indicates a reason for thinking the world is knowable. If we could not know the world, there would be no reason to think that success indicated anything. And while success is not a guarantee of truth, it is as good as fallible knowers can ever achieve. In the end, I think that truth is, as the pragmatists said, what works. More than that is restricted to gods, demons and mathematicians.

References

Bishop, Christopher M. 1995. Neural networks for pattern recognition. Oxford, New York: Clarendon Press; Oxford University Press. [Sorry, I forgot to put this in]

Van Fraassen, Bas C. 1980. The scientific image. Oxford: Clarendon Press.

Godfrey-Smith, Peter. 1991. Signal, decision, action. Journal of Philosophy 88:709-722.

Lansing, J. Stephen. 2007. Priests and programmers: technologies of power in the engineered landscape of Bali. Princeton NJ: Princeton University Press.

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Filed under Epistemology, Natural Classification, Philosophy, Science, Systematics

by | January 26, 2013

More on reductionism

I am presently teaching in a history subject dealing with ideas of nature, and I notice that the historians we are using often refer to a distinction between reductionism and holism. The former is the Bad Old Science (“we murder to dissect”) and the latter is the New Improved Science. This is something often stated as if the issue were obviously resolvable. And it is, in my view, a complete myth.

In biological science, people often suggest that reductionism is more than a mistake: it is in fact a morally dangerous position. Genetic reductionism is supposed to be the explanation of everything including behaviour in terms of genes. Many people have attacked it, including critics of sociobiology. And it must be said that genes function as a magic molecule in many people’s minds. But the problem is not that we give reductionist accounts of traits, but that we do so in terms of single genes. The error here is single cause explanations, not that we look for the properties of causal parts.

To reduce a domain to another, in this case behaviour to biology, is a virtue in science. Nearly all progress in biology has been made by identifying what causes observed properties of organisms and ecologies in terms of the parts of the systems being investigated. Cell theory in the 19th century, genetics in the 20th, and biochemistry throughout have shown us why organisms develop, react and adapt the way they do. The problem is not that we look for explanations in the parts of organisms, but that we do it hamfistedly.

Reductionism is supposed to look only at the parts, according to proponents of “holism”, when according to them we should look at the entire system and how it interacts. But I am hard pressed to find any reductionist who ever denied this. Instead we get methodological decisions to break systems into parts for the purposes of tractability. You simply cannot identify all the variables in a complex system, so scientists in general will attempt to model systems in ways that deal with a few aspects of the system, in order to see how much can be explained that way.

Reductionists believe several things: one of the more important is that the behaviour of a whole is explicable in terms of the properties of the parts that comprise it. Without this we would not have physics, chemistry, cell biology, or any other general science. But focusing on the properties of the parts has never made sense unless we consider how the parts interact. If you know that a cell produces a protein, to understand how it functions in the organism and its environment, you need to consider how that protein is distributed, and how other cells take it up and process it.

Likewise, in general, a reductionist account has to consider the interactions of the parts with each other. The properties of a single electron, for example, can be stated on their own, but how electrons interact with other subatomic particles depends on the properties of those particles, and so a “holistic” approach is implicit in the modelling of the electron itself.

So I get very tired of the general charge that reductionism is unable to understand the system-level properties of the parts. This is simply a rhetorical trick.

I gave my view of “pizza reductionism” before.

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Filed under Biology, Epistemology, Evolution, Philosophy, Rant, Science