Category Archives: Science

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 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.

Plantinga, Alvin. 2002. The Evolutionary Argument against Naturalism. In Naturalism Defeated? Essays on Plantinga’s Evolutionary Argument against Naturalism, edited by J. K. Beilby. Ithaca, NY: Cornell University Press:1-13.

Putnam, Hilary. 1975. Mind, language, and reality, His Philosophical papers v. 2. Cambridge Eng. ; New York: Cambridge University Press.

Quine, Willard Van Orman. 1953. From a logical point of view: 9 logico-philosophical essays. Cambridge MA: Harvard University Press.

Rosenberg, Alexander. 2005. Philosophy of science: a contemporary introduction. 2nd ed, Routledge contemporary introductions to philosophy. New York; London: Routledge.

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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

by | January 6, 2013

Ronin Institute for masterless scholars

Ronin

As readers may know, I, along with a great many other researchers, have no permanent position, making do with casual work to get by. This is an increasing problem around the world as educational institutions transition from being a public good to a service provider to government and economic goals. Jon Wilkins (no close relation), latterly of the Santa Fe Institute, has started the Ronin Institute, and is seeking funding for us to help us do our research. Here is a letter he is sending around:

Happy New Year,

I wanted to take just a moment to introduce you to my new venture, the Ronin Institute for Independent Scholarship.

I founded the Ronin Institute in 2012 in order to create a new model for doing scholarly research outside of the traditional academic system. While the traditonal system has a number of strengths, it also comes with serious limitations. These limitations include artifical barriers to interdisciplinary research and collaboration arising from departmental boundaries, large bureaucratic and teaching loads placed on faculty, and the financial demands involved in supporting the infrastructure of the university.

The fact is, in many fields, the independent scholar with access to library resources can pursue research at the highest levels, often at a fraction of the cost of a university researcher. Furthermore, in the United States alone, there are tens of thousands of underemployed PhDs, representing a vast, untapped resource. We are identifying the most highly motivated independent scholars and working to ensure that they are able to make productive use of their expertise.

At the moment, there are about twenty five Ronin Institute Research Scholars, representing fields from Physics to Biology to History to Philosophy. A number of us are engaged in full-time research. Others are pursuing a model of “fractional scholarship,” engaging part time in academic research while working at another career, fulfilling family obligations, etc. Our goal is to create new career paths and funding opportunities to support a diversity of ways of engaging in scholarship.

This fall, we recieved approval of our 501c3 nonprofit status from the IRS, meaning that we are now ready to move forward with raising funds to support individual projects, help send independent scholars to conferences, and providing small pilot grants to help to restart research programs for people who have taken time off (e.g., to have kids).

I am hoping that you might be able to help us out, if not now, then at some point in the future. This could mean a financial donation, of course, and if you’re inclined to donate, you can do so online, or visit the Ronin Institute Donation page. We are strongly dedicated to following donor intent. If you would like to discuss directing your donation towards a specific project or program, feel free to contact us at development@ronininstitute.org

Alternatively, maybe you know someone who is a highly motivated independent scholar, and you would like to point them in our direction. Or maybe you are looking for a collaborator on an upcoming project, in which case you might have a look through the list of our Research Scholars, some of whom are actively seeking out collaborations, and all of whom are open to collaborating on the right project.

To find out more about the Ronin Institute more generally, you can check us out on the web, on Facebook and on Google+:
http://ronininstitute.org
Facebook
Google+

If you’re more of a listener, you can check out this radio interview that I did with WBUR in Boston over the summer.

You can also contact me with any questions you might have at jon.f.wilkins@ronininstitute.org

Wishing all the best for you in 2013,
Jon Wilkins

[The image above is my own, not Jon’s, but I’m trying to convince him to make it the RI logo…]

8 Comments

Filed under Administrative, Education, Philosophy, Science

by | January 2, 2013

Evopsychopathy 5: Conclusion

The criticisms of evolutionary psychology and its predecessors sociobiologies 1 through 3 focus on three major points:

1. It is adaptively-biased;

2. It is gene-centric (or biological determinist, which amounts to the same thing);

3. It is culturally biased in favour of the privileged classes of the people making the claims.

I hope I have dealt with, or guarded against, each of these, but I would like to note something that any evolutionary thinking person must accept: our biological foundations for psychological and cognitive dispositions did evolve. Something like SB must be true. So what we must do is to limit the excesses (which exist in every kind of social and psychological science anyway, and must be limited in every approach), and seek to uncover what the bases of our minds are. This has to be acceptable to any naturalistic evolutionary theorist. If it is not, then one has to suspect that there is what Dennett once called “white picket fence” mentality in play: humans are more important, qualitatively different, or somehow dualistically distinct from all other living things. And to hold this view is to run contrary to all the available science. One might understand why Plantinga wants to defend this kind of qualitative dualism (for him, humans are different to all other living things; he is not a naturalist), but why Fodor? Why Gould? What is happening here?

This falls out of a larger project of what philosophers refer to as the naturalisation project. It is the view that everything can be given a natural account, at least if we were able to gather the right data and understand the natures involved. Most naturalists are physicalists, but naturalism is not necessarily about ontology; it is about explanations. So far as explanations rest on ontologies, naturalists are physicalists, but it doesn’t do to equate the two.

Those who, like Fodor, wish to privilege human (and possibly others species’) intellection and semantic reference as being irreducible to computation or to physical processes (usually relying upon a failure of denotation of terms, which is, in my view, a matter of confusing the signs for the signification par excellence; but leave that to one side for now), treat these mental events as non-physical (although they must of course exist on a physical substrate in most accounts). So EP and SB fail because they presume that the irreducibility is a failure of language not of principle, and that we are making some kind of mistake.

Others have consequentialist objections, like the apocryphal bishop’s wife who said that if evolution from monkeys is true, let us hope it does not become widely known. If we have our prized characteristics by evolution and selection, then we are lessened thereby. We might find out that we are inclined to racism, sexism, and oppression. If these things were true in virtue of an evolutionary account (rather than being what we all understand from experience anyway), perhaps we might justify them thereby. But we all know (at least if we have read our Moore) that the mere fact that something evolved doesn’t serve as justification any more than the success of the Romans (or the Americans) justified the Caesars’ (or the Kennedys’) pre-eminence.

If we did evolve with a predisposition towards rape (and I do not think this has even been shown to have a non-cultural component yet, so bear with me), surely to know this is not to justify it, but to forewarn and forearm? If males tend to rape, change the culture to guard against rape. If they do not, then you will find that there are other factors that explain, for example, the high rates of rape in India or other societies, and be able to look for these factors and modulate them. To know ourselves is a virtue not something to be feared.

As was once said by of all people a seventeenth century preacher, things are as they are, and their consequences will be what they will be. Why, then, should we seek to be deceived? Humans must be what they are via natural processes if you take the science seriously. Knowing what we are can only aid us in building a better society.

I have tried to suggest that adaptationism is not the evil demon it is sometimes painted to be, but this needs more qualification. Individual alleles or variant traits may indeed go to fixation in a population by random processes (although something like an SNP – single nucleotide polymorphism – is way below anything that would count as a psychological trait unless it happens to be akin to a single base pair defect in a psychological process, like Williams’ Syndrome†). However, I regard the overall absolute fitness of modern organisms to be very high indeed. In the light of the rigid stick and rubber band metaphor I used above, we might expect that multiple-gene traits will be maintained at a high fitness. So it resolves to a question of what the explananda are. In short, how do we atomise the biology here?

We do it the way we approach any problem domain that is not already clearly atomised. We observe, try different things out and when we find a promising and productive line of research, we follow it. When we have several such lines of research we run them in parallel and wait and see. Sociobiology is one (several, perhaps) of those lines of research, and it should be followed to the degree it is both promising and productive. And it seems to be productive, whatever the promise its proponents see in it. Massive modularity is a dead issue, in my view, but we still can identify, quite clearly, heritable traits, and seek to find out if they are heritable because they are adaptive or because they are side effects of something that is adaptive. Ruling the sociobiological approach out of hand tout court is simply dogmatism. It is the opposite of scientific reasoning.

So I have nailed my colours to the mast. I am a born again sociobiologist. I don’t like some of the tenets of other sociobiologists (such as massive modularity or group selectionism) but they aren’t definitive of the approach; merely the contingent hypotheses and methodologies of some sociobiologists. If this be heresy, then you mistakenly think science is a religion or ideology.

This series:

† Clem Stanyon, who worked on Williams Syndrome and is the source of all I know about it, corrects me here: Williams’ Syndrome consists of around 30 deletions. However, the point stands.

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Filed under Biology, Cognition, Epistemology, Ethics and Moral Philosophy, Evolution, Logic and philosophy, Philosophy, Politics, Science, Social evolution

by | December 19, 2012

Classification and the periodic table

It might be thought that classification in the special and historical sciences is occasionally atheoretical, but that in the general sciences, physics and chemistry, it is derived from Theory. But in fact one of the most exemplary cases of empirical classification that led to Theory is in these sciences: the periodic table.

According to a study by Eric Scerri (2007), the standard textbook story of the periodic table is wrong, unsurprisingly since most such textbook narratives are. The idea of a table of elements did not derive from Dalton’s revival of atomism, but from increasingly refined laboratory techniques for ascertaining the atomic weight of elements. The notion of atoms, of course, did derive from Dalton but these developments did not seemingly rely much upon it. Because samples of element include what we now understand are isotopes, atomic weights are not perfectly correlated with atomic numbers. Consequently while it was apparent from the experimental data as the atomic weights of elements were refined that there was some sort of pattern, e.g., by Stanislao Cannizarro and Alfred Naquet who both arranged these weights in a table, early attempts tended instead to rely on a kind of platonist numerology, Johann Döbereiner’s “theory of triads” in which patterns of elemental properties fell into threefold relations. These were not unlike the affinities of systematics, and indeed the term “affinity” was used by chemists. However, there were too many exceptions for triads to be the basis for a table of elements (although something like it later returned briefly).

Mendeleev's first published table, 1869

Until Dmitri Mendeleev’s table was published in 1869, the best previous version was that of Julius Lothar Meyer,[1] who, like Mendeleev had arranged elements in a series based on their atomic weights. What Mendeleev did that was better than Lothar Meyer and other precursors was to employ his vaster knowledge of the empirical properties of chemical elements, and to make an arrangement based on weights and properties, in a kind of family resemblance (Scerri 2007: 125, 150). He refined it over many years, again based on the experimental results. The term “experimental” here is a little bit misleading, in our terms, because while there was intervention to refine and purify the samples used, this was not intervention to experimentally modify the samples along some independent variable to test a hypothesis. In our terms, it was simple empirical research.[2]

Mendeleev even expressly noted that he was taking a Lockean or even operationalist approach:

. .  . by  investigating  and  describing  what  is visible  and  open  to  direct  observation  by  the organs of the senses, we may hope to arrive, first at hypotheses,  and afterwards at theories, of what has now to be taken as the basis of our investigations. (quoted in Kultgen 1958: 180)

Subsequent to the adoption of the table by chemists, there arose a program to improve and explain the “periodic law”. As Scerri says, once scientists have a classification, they seek an underlying cause of the regularities (as Darwin did).

Following on from the adoption of atomic theory and the discovery of electrons by Thompson in 1897, and the nuclear structure of the atom proposed by Rutherford, Bohr’s introduction of the quantum to the structure of the atom led to an explanation of valency, which was originally discovered by Edward Frankland and Friedrich Kerkule in the 1860s. Eventually, Rutherford and Antonius van den Broek proposed around 1911 that each atom had an integer number that gave it its weight. About the same time, the discovery of isotopes by Frederick Soddy explained why atomic weights varied: different samples had varying admixtures of the isotopes, whose weights varied.

In short, the periodic table is a mixed case of empirical research guiding theory development. It is not a simple example, because there was prior Theory, and also because much of atomic theory derived from other sources and cases, especially in the study of radioactivity, but nevertheless classification plays a key role in this most central of physical scientific domains.

1. Scerri gives notice of at least four later “precursors”, Emile Beguyer De Chancourtois, Gustavus Hinrichs, John Newlands and William Odling, who proposed some of the features later credited to Mendeleev.
2. Scerri 2012 has argued that Weisberg 2007 is wrong to think Mendeleev was producing, inadvertently or otherwise, a model. He proposes that classification is a kind of “sideways explanations”. This need not affect Weisberg’s view of the importance of models (since he allowed that Mendeleev was not actually making models, but gave a hypothetical interpretation in which he was), but it is indicative of the lack of philosophical attention generally given to what he calls mere classifications.
 

References

Kultgen, J. H. 1958. Philosophic Conceptions in Mendeleev’s Principles of Chemistry. Philosophy of Science 25 (3):177-183.

Scerri, Eric R. 2007. The Periodic Table: Its Story and Its Significance. New York: Oxford University Press.

Scerri, Eric R. 2012. A critique of Weisberg’s view on the periodic table and some speculations on the nature of classifications. Foundations of Chemistry.

Weisberg, Michael. 2007. Who is a Modeler? British Journal for the History of Philosophy 58:207–233.

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