Discovering the nucleus

In the past year or so I've been reading a handful of fascinating biographies and histories involving the evolution of early twentieth-century physics, paying attention to the individuals, the institutions, and the ideas that contributed to the making of post-classical physics. The primary focus is on the theory of the atom and the nucleus, and the emergence of the theory of quantum mechanics. The major figures who have come into this complex narrative include Dirac, Bohr, Heisenberg, von Neumann, Fermi, Rutherford, Blackett, Bethe, and Feynman, along with dozens of other mathematicians and physicists. Institutions and cities played a key role in this story -- Manchester, Copenhagen, Cambridge, Göttingen, Budapest, Princeton, Berkeley, Ithaca, Chicago. And of course written throughout this story is the rise of Nazism, World War II, and the race for the atomic bomb. This is a crucially important period in the history of science, and the physics that was created between 1900 and 1960 has fundamentally changed our view of the natural world.



One level of interest for me in doing this reading is the math and physics themselves. As a high school student I was fascinated with physics. I learned some of the basics of the story of modern physics before I went to college -- the ideas of special relativity theory, the hydrogen spectrum lines, the twin-slit experiments, the puzzles of radiation and the atom leading to the formulation of the quantum theory of electromagnetic radiation, the discoveries of superconductivity and lasers. In college I became a physics and mathematics major at the University of Illinois, though I stayed with physics only through the end of the first two years of course work (electricity and magnetism, theoretical and applied mechanics, several chemistry courses, real analysis, advanced differential equations). (Significantly for the recent reading I've been doing, I switched from physics to philosophy while I was taking the junior level quantum mechanics course.) I completed a mathematics major, along with a philosophy degree, and did a PhD in philosophy because I felt philosophy offered a broader intellectual platform on questions that mattered.

So I've always felt I had a decent layman's understanding of the questions and issues driving modern physics. One interesting result of reading all this historical material about the period of 1910-1935, however, is that I've realized what large holes there are in my mental map of the topics, both in the physics and the math. And it is genuinely interesting to realize that there are deeply fascinating questions in this terrain which I haven't really got an inkling about. It is energizing to know that it is entirely possible to open up new areas of knowledge and inquiry for oneself. 

Of enduring interest in this story is the impression that emerges of amazingly rapid progress in physics in these few decades, with major discoveries and new mathematical methods emerging in weeks and months rather than decades and centuries. The intellectual pace in places like Copenhagen, Princeton, and Göttingen was staggering, and scientists like Bohr, von Neumann, and Heisenberg genuinely astonish the reader with the fertility of their scientific abilities. Moreover, the theories and mathematical formulations that emerged had amazingly precise and unexpected predictive consequences. Physical theory and experimentation reached a fantastic degree of synergy together. 

The institutions of research that developed through this period are fascinating as well. The Cavendish lab at Cambridge, the Institute for Advanced Studies at Princeton, the Niels Bohr Institute in Copenhagen, the math and physics centers at Göttingen, and the many conferences and journals of the period facilitated rapid progress of atomic and nuclear physics. The USSR doesn't come into the story as fully as one would like, and it is intriguing to speculate about the degree to which Stalinist dogmatism interfered with the development of Soviet physics. 

I also find fascinating in retrospect the relations that seem to exist between physics and the philosophy of science in the twentieth century. In philosophy we tend to think that the discipline of the philosophy of science in its twentieth-century development was too dependent on physics. That is probably true. But it seems that the physics in question was more often classical physics and thermodynamics, not modern mathematical physics. Carnap, for example, gives no serious attention to developments in the theory of quantum mechanics in his lectures, Philosophical Foundations of Physics. The philosophy of the Vienna Circle could have reflected relativity theory and quantum mechanics, but it didn't to any significant degree. Instead, the achievements of nineteenth-century physics seem to have dominated the thinking of Carnap, Schlick, and Popper. Logical positivism doesn't seem to be much influenced by modern physics, including relativity theory, quantum theory, and mathematical physics.  Post-positivist philosophers Kuhn, Hanson, and Feyerabend refer to some of the discoveries of twentieth-century physics, but their works don't add up to a new foundation for the philosophy of science. Since the 1960s there has been a robust field of philosophy of physics, and the focus of this field has been on quantum mechanics; but the field has had only limited impact on the philosophy of science more broadly. (Here is a guide to the philosophy of physics provided to philosophy graduate students at Princeton; link.)

On the other hand, quantum mechanics itself seems to have been excessively influenced by a hyper version of positivism and verificationism. Heisenberg in particular seems to have favored a purely instrumentalist and verificationist interpretation of quantum mechanics -- the idea that the mathematics of quantum mechanics serve solely to summarize the results of experiment and observation, not to allow for true statements about unobservables. It is anti-realist and verificationist.

I suppose that there are two rather different ways of reading the history of twentieth-century physics. One is that quantum mechanics and relativity theory demonstrate that the physical world is incomprehensibly different from our ordinary Euclidean and Kantian ideas about ordinary-sized objects -- with the implication that we can't really understand the most fundamental level of the physical world. Ordinary experience and relativistic quantum-mechanical reality are just fundamentally incommensurable. But the other way of reading this history of physics is to marvel at the amount of new insight and clarity that physics has brought to our understanding of the subatomic world, in spite of the puzzles and anomalies that seem to remain. Mathematical physical theory made possible observation, measurement, and technological use of the microstructure of the world in ways that the ancients could not have imagined. I am inclined towards the latter view.

It is also sobering for a philosopher of social science to realize that there is nothing comparable to this history in the history of the social sciences. There is no comparable period where fundamental and enduring new insights into the underlying nature of the social world became possible to a degree comparable to this development of our understanding of the physical world. In my view as a philosopher of social science, that is perfectly understandable; the social world is not like the physical world. Social knowledge depends on fairly humdrum discoveries about actors, motives, and constraints. But the comparison ought to make us humble even as we explore new theoretical ideas in sociology and political science.

If I were asked to recommend only one out of all these books for a first read, it would be David Cassidy's Heisenberg volume, Beyond Uncertainty. Cassidy makes sense of the physics in a serious but not fully technical way, and he raises important questions about Heisenberg the man, including his role in the German search for the atomic bomb. Also valuable is Richard Rhodes' book, The Making of the Atomic Bomb: 25th Anniversary Edition.

Menon and Callender on the physics of phase transitions

In an earlier post I considered the topic of phase transitions as a possible source of emergent phenomena (link). I argued there that phase transitions are indeed interesting, but don't raise a serious problem of strong emergence. Tarun Menon considers this issue in substantial detail in the chapter he co-authored with Craig Callender in The Oxford Handbook of Philosophy of Physics, "Turn and face the strange ... ch-ch-changes: Philosophical questions raised by phase transitions" (link). Menon and Callender provide a very careful and logical account of three ways of approaching the physics of phase transitions within physics and three versions of emergence (conceptual, explanatory, ontological). The piece is technical but very interesting, with a somewhat deflating conclusion (if you are a fan of emergence):

We have found that when one clarifies concepts and digs into the details, with respect to standard textbook statistical mechanics, phase transitions are best thought of as conceptually novel, but not ontologically or explanatorily irreducible. 

Menon and Callendar review three approaches to the phenomenon of phase transition offered by physics: classical thermodynamics, statistical mechanics, and renormalization group theory. Thermodynamics describes the behavior of materials (gases, liquids, and solids) at the macro level; and statistical mechanics and renormalization group theory are theories of the micro states of materials intended to allow derivation of the macro behavior of the materials from statistical properties of the micro states. They describe this relationship in these terms:

Statistical mechanics is the theory that applies probability theory to the microscopic degrees of freedom of a system in order to explain its macroscopic behavior. The tools of statistical mechanics have been extremely successful in explaining a number of thermodynamic phenomena, but it turned out to be particularly difficult to apply the theory to the study of phase transitions. (193)

Here is the mathematical definition of phase transition that they provide:

Mathematically, phase transitions are represented by nonanalyticities or singularities in a thermodynamic potential. A singularity is a point at which the potential is not infinitely differentiable, so at a phase transition some derivative of the thermo­dynamic potential changes discontinuously. (191)

And they offer this definition:

(Def 1) An equilibrium phase transition is a nonanalyticity in the free energy. (194)

Here is their description of how the renormalization group theory works:

To explain the method, we return to our stalwart Ising model. Suppose we coarse­grain a 2­D Ising model by replacing 3 × 3 blocks of spins with a single spin pointing in the same direction as the majority in the original block. This gives us a new Ising system with a longer distance between lattice sites, and possibly a different coupling strength. You could look at this coarse­graining procedure as a transformation in the Hamiltonian describing the system. Since the Hamiltonian is characterized by the coupling strength, we can also describe the coarse­graining as a transformation in the coupling parameter. Let K be the coupling strength of the original system and R be the relevant transformation. The new coupling strength is K′ = RK. This coarse­graining procedure could be iterated, producing a sequence of coupling parameters, each related to the previous one by the transformation R. The transformation defines a flow on parameter space. (195)

Renormalization group theory, then, is essentially the mathematical basis of coarse-graining analysis (link).

The key difficulty that has been used to ground arguments about strong emergence of phase transitions is now apparent: there seems to be a logical disjunction between the resources of statistical mechanics and the findings of thermodynamics. In theory physicists would like to hold that statistical mechanics provides the micro-level representation of the phenomena described by thermodynamics; or in other words, that thermodynamic facts can be reduced to derivations from statistical mechanics. However, the definition of a phase transition above specifies that the phenomena display "nonanalyticities" -- instantaneous and discontinuous changes of state. It is easily demonstrated that the equations used in statistical mechanics do not display nonanalyticities; change may be abrupt, but it is not discontinuous, and the equations are infinitely differentiable. So if phase transitions are points of nonanalyticity, and statistical mechanics does not admit of nonanalytic equations, then it would appear that thermodynamics is not derivable from statistical mechanics. Similar reasoning applies to renormalization group theory.

This problem was solved within statistical mechanics by admitting of infinitely many bodies within the system that is represented (or alternatively, admitting of infinitely compressed volumes of bodies); but neither of these assumptions of infinity is realistic of the material world.

So are phase transitions "emergent" phenomena in either a weak sense or a strong sense, relative to the micro-states of the material in question? The strongest sense of emergence is what Menon and Callender call ontological irreducibility.

Ontological irreducibility involves a very strong failure of reduction, and if any phenomenon deserves to be called emergent, it is one whose description is ontologically irreducible to any theory of its parts. Batterman argues that phase transitions are emergent in this sense (Batterman 2005). It is not just that we do not know of an adequate statistical mechanical account of them, we cannot construct such an account. Phase transitions, according to this view, are cases of genuine physical discontinuities. (215)

The possibility that phase transitions are ontologically emergent at the level of thermodynamics is raised by the point about the mathematical characteristics of the equations that constitute the statistical mechanics description of the micro level -- the infinite differentiability of those equations. But Menon and Callender give a compelling reason for thinking this is misleading. They believe that phase transitions constitute a conceptual novelty with respect to the resources of statistical mechanics -- phase transitions do not correspond to natural kinds at the level of the micro-constitution of the material. But they argue that this does not establish that the phenomena cannot be explained or derived from a micro-level description. So phase transitions are not emergent according to the explanatory or ontological understandings of that idea.

The nub of the issue comes down to how we construe the idealization of statistical mechanics that assumes that a material consists of an infinite number of elements. This is plainly untrue of any real system (gas, liquid, or solid). The fact that there are boundaries implies that important thermodynamic properties are not "extensive" with volume: twice the volume leads to twice the entropy. But the way in which the finitude of a volume of material affects its behavior is through the effects of novel behaviors at the edges of the volume. And in many instances these effects are small relative to the behavior of the whole, if the volume is large enough.

Does this fact imply that there is a great mystery about extensivity, that extensivity is truly emergent, that thermodynamics does not reduce to finite N statistical mechanics? We suggest that on any reasonably uncontentious way of defining these terms, the answer is no. We know exactly what is happening here. Just as the second law of thermodynamics is no longer strict when we go to the microlevel, neither is the concept of extensivity. (201-202)

There is an important idealization on the thermodynamic description as well -- the notion that several specific kinds of changes are instantaneous or discontinuous. But this assumption can also be seen as an idealization, corresponding to a physical system that is undergoing changes at different rates under different environmental conditions. What thermodynamics describes as an instantaneous change from liquid to gas may be better understood as a rapid process of change at the molar level which can be traced through in a continuous way.

(The fact that some systems are coarse-grained has an interesting implication for this set of issues (link). The interesting implication is that while it is generally true that the micro states in such a system entail the macro states, the reverse is not true: we cannot infer from a given macro state to the exact underlying micro state. Rather, many possible micro states correspond to a given macro state.)

The conclusion they reach is worth quoting:

Phase transitions are an important instance of putatively emergent behavior. Unlike many things claimed emergent by philosophers (e.g., tables and chairs), the alleged emergence of phase transitions stems from both philosophical and scientific arguments. Here we have focused on the case for emergence built from physics. We have found that when one clarifies concepts and digs into the details, with respect to standard textbook statistical mechanics, phase transitions are best thought of as conceptually novel, but not ontologically or explanatorily irreducible. And if one goes past textbook statistical mechanics, then an argument can be made that they are not even conceptually novel. In the case of renormalization group theory, consideration of infinite systems and their singular behavior provides a central theoretical tool, but this is compatible with an explanatory reduction. Phase transitions may be “emergent” in some sense of this protean term, but not in a sense that is incompatible with the reductionist project broadly construed. (222)

Or in other words, Menon and Callender refute one of the most technically compelling interpretations of ontological emergence in physical systems. They show that the phenomena of phase transitions as described by classical thermodynamics are compatible with being reduced to the dynamics of individual elements at the micro-level, so phase transitions are not ontologically emergent.

Are these arguments relevant in any way to debates about emergence in social system dynamics? The direct relevance is limited, since these arguments depend entirely on the mathematical properties of the ways in which the micro-level of physical systems are characterized (statistical mechanics). But the more general lesson does in fact seem relevant: rather than simply postulating that certain social characteristics are ontologically emergent relative to the actors that make them up, we would be better advised to look for the local-level processes that act to bring about surprising transitions at critical points (for example, the shift in a flock of birds from random flight to a swarm in a few seconds).

Coarse-graining of complex systems

The question of the relationship between micro-level and macro-level is just as important in physics as it is in sociology. Is it possible to derive the macro-states of a system from information about the micro-states of the system? It turns out that there are some surprising aspects of the relationship between micro and macro that physical systems display. The mathematical technique of "coarse-graining" represents an interesting wrinkle on this question. So what is coarse-graining? Fundamentally it is the idea that we can replace micro-level specifics with local-level averages, without reducing our ability to calculate macro-level dynamics of behavior of a system.

A 2004 article by Israeli and Goldenfeld, "Coarse-graining of cellular automata, emergence, and the predictability of complex systems" (link) provides a brief description of the method of coarse-graining. (Here is a Wolfram demonstration of the way that coarse graining works in the field of cellular automata; link.) Israeli and Goldenfeld also provide physical examples of phenomena with what they refer to as emergent characteristics. Let's see what this approach adds to the topic of emergence and reduction. Here is the abstract of their paper:

We study the predictability of emergent phenomena in complex systems. Using nearest neighbor, one-dimensional Cellular Automata (CA) as an example, we show how to construct local coarse-grained descriptions of CA in all classes of Wolfram's classification. The resulting coarse-grained CA that we construct are capable of emulating the large-scale behavior of the original systems without accounting for small-scale details. Several CA that can be coarse-grained by this construction are known to be universal Turing machines; they can emulate any CA or other computing devices and are therefore undecidable. We thus show that because in practice one only seeks coarse-grained information, complex physical systems can be predictable and even decidable at some level of description. The renormalization group flows that we construct induce a hierarchy of CA rules. This hierarchy agrees well apparent rule complexity and is therefore a good candidate for a complexity measure and a classification method. Finally we argue that the large scale dynamics of CA can be very simple, at least when measured by the Kolmogorov complexity of the large scale update rule, and moreover exhibits a novel scaling law. We show that because of this large-scale simplicity, the probability of finding a coarse-grained description of CA approaches unity as one goes to increasingly coarser scales. We interpret this large scale simplicity as a pattern formation mechanism in which large scale patterns are forced upon the system by the simplicity of the rules that govern the large scale dynamics.

This paragraph involves several interesting ideas. One is that the micro-level details do not matter to the macro outcome (italics above). Another related idea is that macro-level patterns are (sometimes) forced by the "rules that govern the large scale dynamics" -- rather than by the micro-level states.

Coarse-graining methodology is a family of computational techniques that permits "averaging" of values (intensities) from the micro-level to a higher level of organization. The computational models developed here were primarily applied to the properties of heterogeneous materials, large molecules, and other physical systems. For example, consider a two-dimensional array of iron atoms as a grid with randomly distributed magnetic orientations (up, down). A coarse-grained description of this system would be constructed by taking each 3x3 square of the grid and assigning it the up-down value corresponding to the majority of atoms in the grid. Now the information about nine atoms has been reduced to a single piece of information for the 3x3 grid. Analogously, we might consider a city of Democrats and Republicans. Suppose we know the affiliation of each household on every street. We might "coarse-grain" this information by replacing the household-level data with the majority representation of 3x3 grids of households. We might take another step of aggregation by considering 3x3 grids of grids, and representing the larger composite by the majority value of the component grids.

How does the methodology of coarse-graining interact with other inter-level questions we have considered elsewhere in Understanding Society (emergence, generativity, supervenience)? Israeli and Goldenfeld connect their work to the idea of emergence in complex systems. Here is how they describe emergence:

Emergent properties are those which arise spontaneously from the collective dynamics of a large assemblage of interacting parts. A basic question one asks in this context is how to derive and predict the emergent properties from the behavior of the individual parts. In other words, the central issue is how to extract large-scale, global properties from the underlying or microscopic degrees of freedom. (1)

Note that this is the weak form of emergence (link); Israeli and Goldenfeld explicitly postulate that the higher-level properties can be derived ("extracted") from the micro level properties of the system. So the calculations associated with coarse-graining do not imply that there are system-level properties that are non-derivable from the micro-level of the system; or in other words, the success of coarse-graining methods does not support the idea that physical systems possess strongly emergent properties.

Does the success of coarse-graining for some systems have implications for supervenience? If the states of S can be derived from a coarse-grained description C of M (the underlying micro-level), does this imply that S does not supervene upon M? It does not. A coarse-grained description corresponds to multiple distinct micro-states, so there is a many-one relationship between M and C. But this is consistent with the fundamental requirement of supervenience: no difference at the higher level without some difference at the micro level. So supervenience is consistent with the facts of successful coarse-graining of complex systems.

What coarse-graining is inconsistent with is the idea that we need exact information about M in order to explain or predict S. Instead, we can eliminate a lot of information about M by replacing M with C, and still do a perfectly satisfactory job of explaining and predicting S.

There is an intellectual wrinkle in the Israeli and Goldenfeld article that I haven't yet addressed here. This is their connection between complex physical systems and cellular automata. A cellular automaton is a simulation governed by simple algorithms governing the behavior of each cell within the simulation. The game of Life is an example of a cellular automaton (link). Here is what they say about the connection between physical systems and their simulations as a system of algorithms:

The problem of predicting emergent properties is most severe in systems which are modelled or described by undecidable mathematical algorithms[1, 2]. For such systems there exists no computationally efficient way of predicting their long time evolution. In order to know the system’s state after (e.g.) one million time steps one must evolve the system a million time steps or perform a computation of equivalent complexity. Wolfram has termed such systems computationally irreducible and suggested that their existence in nature is at the root of our apparent inability to model and understand complex systems [1, 3, 4, 5]. (1)

Suppose we are interested in simulating the physical process through which a pot of boiling water undergoes sudden turbulence shortly before 100 degrees C (the transition point between water and steam). There seem to be two large alternatives raised by Israeli and Goldenfeld: there may be a set of thermodynamic processes that permit derivation of the turbulence directly from the physical parameters present during the short interval of time; or it may be that the only way of deriving the turbulence phenomenon is to provide a molecule-level simulation based on the fundamental laws (algorithms) that govern the molecules. If the latter is the case, then simulating the process will prove computationally impossible.

Here is an extension of this approach in an article by Krzysztof Magiera and Witold Dzwinel, "Novel Algorithm for Coarse-Graining of Cellular Automata" (link). They describe "coarse-graining" in their abstract in these terms:

The coarse-graining is an approximation procedure widely used for simplification of mathematical and numerical models of multiscale systems. It reduces superfluous – microscopic – degrees of freedom. Israeli and Goldenfeld demonstrated in [1,2] that the coarse-graining can be employed for elementary cellular automata (CA), producing interesting interdependences between them. However, extending their investigation on more complex CA rules appeared to be impossible due to the high computational complexity of the coarse-graining algorithm. We demonstrate here that this complexity can be substantially decreased. It allows for scrutinizing much broader class of cellular automata in terms of their coarse graining. By using our algorithm we found out that the ratio of the numbers of elementary CAs having coarse grained representation to “degenerate” – irreducible – cellular automata, strongly increases with increasing the “grain” size of the approximation procedure. This rises principal questions about the formal limits in modeling of realistic multiscale systems.

Here K&D seem to be expressing the view that the approach to coarse-graining as a technique for simplifying the expected behavior of a complex system offered by Israeli and Goldenfeld will fail in the case of more extensive and complex systems (perhaps including the pre-boil turbulence example mentioned above).

I am not sure whether these debates have relevance for the modeling of social phenomena. Recall my earlier discussion of the modeling of rebellion using agent-based modeling simulations (link, link, link). These models work from the unit level -- the level of the individuals who interact with each other. A coarse-graining approach would perhaps replace the individual-level description with a set of groups with homogeneous properties, and then attempt to model the likelihood of an outbreak of rebellion based on the coarse-grained level of description. Would this be feasible?

Inductive reasoning and the philosophy of science

I've just finished reading Sharon Bertsch McGrayne's book on Bayesian statistics, The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy. McGrayne presents a very interesting story of the advancement of a scientific idea over a very long period (1740s through the 1950s). As she demonstrates at length, the idea that "subjective prior beliefs" could enhance our knowledge about causation and the future was regarded as paradoxical and irrational by mathematicians and statisticians for well over a century.

McGrayne's book does a very good job of highlighting the scientific controversies that have arisen with respect to Bayesian methods, and the book also makes a powerful case for the value of the methods in many important contemporary problems. But it isn't very detailed about the logic and mathematics of the field. She gives a single example of applied Bayesian reasoning in appendix b, using the example of breast cancer and mammograms. This is worth reading carefully, since it makes clear how the conditional probabilities of a Bayesian calculation work.

As McGrayne demonstrates with many examples, Bayesian reasoning permits a very substantial ability to draw novel conclusions based on piecemeal observations and some provisional assumptions about mechanisms in the messy world of complex causation. Examples can be found in epidemiology (the cause of lung cancer), climate science, and ecology. And she documents how Bayesian ideas have been used to enhance search processes for missing things -- for example, lost hydrogen bombs and nuclear submarines. Here is an important example of the power of Bayesian reasoning to identify causal linkages to lung cancer, including especially cigarette smoking.

In 1951 Cornfield used Bayes’ rule to help answer the puzzle. As his prior hypothesis he used the incidence of lung cancer in the general population. Then he combined that with NIH’s latest information on the prevalence of smoking among patients with and without lung cancer. Bayes’ rule provided a firm theoretical link, a bridge, if you will, between the risk of disease in the population at large and the risk of disease in a subgroup, in this case smokers. Cornfield was using Bayes as a philosophy-free mathematical statement, as a step in calculations that would yield useful results. He had not yet embraced Bayes as an all-encompassing philosophy. Cornfield’s paper stunned research epidemiologists. 

More than anything else, it helped advance the hypothesis that cigarette smoking was a cause of lung cancer. Out of necessity, but without any theoretical justification, epidemiologists had been using case studies of patients to point to possible causes of problems. Cornfield’s paper showed clearly that under certain conditions (that is, when subjects in a study were carefully matched with controls) patients’ histories could indeed help measure the strength of the link between a disease and its possible cause. Epidemiologists could estimate disease risk rates by analyzing nonexperimental clinical data gleaned from patient histories. By validating research findings arising from case-control studies, Cornfield made much of modern epidemiology possible. In 1961, for example, case-control studies would help identify the antinausea drug thalidomide as the cause of serious birth defects. (110-111)

One fairly specific thing that strikes me after reading the book concerns the blindspots that existed in the neo-positivist tradition in the philosophy of science that set the terms for the field in the 1960s and 1970s (link). This tradition is largely focused on theories and theoretical explanation, to the relative exclusion of inductive methods. It reveals an underlying predilection for the idea that scientific knowledge takes the form of hypothetico-deductive systems describing unobservables. The hypothetico-deductive model of explanation and confirmation makes a lot of sense in the context of this perspective. But after reading McGrayne I'm retrospectively surprised at the relatively low priority given within standard philosophy of science curriculum to probabilistic reasoning -- either frequentist or Bayesian. Many philosophers of science have absorbed a degree of disregard for "inductive logic", or the idea that we can discover important features of the world through careful observation and statistical analysis. The basic assumption seems to have been that statistical reasoning is boring and Humean -- not really capable of discovering new things about nature or society. But in hindsight, this disregard for inductive reasoning is an odd distortion of the domain of scientific knowledge, and, in particular, of the project of sorting out causes.

Some philosophers of science have indeed given substantial attention to Bayesian reasoning. (Here is a good article on Bayesian epistemology by Bill Talbott in the Stanford Encyclopedia of Philosophy; link.) Ian Hacking's textbook An Introduction to Probability and Inductive Logic provides a very accessible introduction to the basics of inductive logic and Bayesian reasoning, and his The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference provides an excellent treatment of the history of the subject from a philosophy of science point of view. Another philosopher of science who has treated Bayesian reasoning in detail is Michael Strevens. Here Strevens provides a good brief treatment of the subject from the point of view of the philosophy of science (link). And here is a first-rate unpublished manuscript by Strevens on the use of Bayesian ideas as a theory of confirmation (link). Strevens' recent Tychomancy: Inferring Probability from Causal Structure is also relevant. And the research program on causal reasoning of Judea Pearl has led to a flourishing of Bayesian reasoning in the theory of causality (link).

What is the potential relevance of Bayesian reasoning in sociology and other areas of the social sciences? Can Bayesian reasoning lead to new insights in assessing social causation? Several features of the social world seem particularly distinctive in the context of a Bayesian approach. Bayesianism conforms very naturally to a scenario-based way of approaching the outcomes of a system or a complicated process; and it provides an elegant and rigorous way of incorporating "best guesses" (subjective probability estimates) into the analysis of a given process. Both features are well suited to the social world. One reason for this is the relatively narrow limits of frequency-based estimates of probabilities of social events. The social sciences are often concerned with single-instance events -- the French Revolution, the Great Depression, the rise of ISIS. In cases like these frequency-based probabilities are not available. Second, there is the problem of causal heterogeneity in many social causal relations. If we are interested in the phenomenon of infant mortality, we are led immediately to the realization that there are multiple social factors and conditions that influence this population characteristic; so the overall infant mortality rate of Bangladesh or France is the composite effect of numerous social and demographic causes. This means that there is no single underlying causal property X, where X can be said to create differences in infant mortality rates in various countries. And this in turn implies that it is dubious to assume that there are durable objective probabilities underlying the creation of a given rate of infant mortality. This is in contrast to the situation of earthquakes or hurricanes, where a small number of physical factors are causally relevant to the occurrence of the outcome.

Both these factors suggest that subjective probabilities based on expert-based assessment of the likelihood of various scenarios represent a more plausible foundation for assigning probabilities to a given social outcome. This is the logic underlying Philip Tetlock's approach to reliable forecasting in Superforecasting: The Art and Science of Prediction and Expert Political Judgment: How Good Is It? How Can We Know? (link). Both points suggest that Bayesian reasoning may have even more applicability in the social world than in the natural sciences.

The joining of Monte Carlo methods with Bayesian reasoning that McGrayne describes in the case of the search for the missing nuclear submarine Thresher (199 ff.) is particularly relevant to social inquiry, it would seem. This is true because of the conjunctural nature of social causation and the complexity of typical causal intersections in the social domain. Consider a forecasting problem similar to those considered by Tetlock -- for example, the likelihood that Russia will attempt to occupy Latvia in the next five years. One way of analyzing this problem is to identify a handful of political scenarios moving forward from the present that lead to consideration of this policy choice by Russian leadership; assign prior probabilities to the component steps of each scenario; and calculate a large number of Monte Carlo "runs" of the scenarios, based on random assignment of values to the component steps of each of the various scenarios according to the prior probabilities assigned by the experts. Outcomes can then be classified as "Russia attempts to occupy Latvia" and "Russia does not attempt to occupy Latvia". The number of outcomes in the first cell allows an estimate of the overall likelihood of this outcome. The logic of this exercise is exactly parallel to the calculation that McGrayne describes for assigning probabilities to geographic cells of ocean floor for the final resting spot of the submarine, given the direction and speed scenarios considered. And the Bayesian contribution of updating of priors is illuminating in this analysis as well: as experts' judgments of the probabilities of the component steps change given new information, the overall probability of the outcome changes as well.

Here is a very simple illustration of a scenario analysis. The four stages of the scenario are:

A: NATO signals unity
B: LATVIA accepts anti-missile defense
C: US signals lack of interest
D: KREMLIN in turmoil

Here is a diagram of the scenarios, along with hypothetical "expert judgments" about the likelihoods of outcomes of the branch points:

This analysis leads to a forecast of a 7.8% likelihood of occupation (O1, O10, O13). And an important policy recommendation can be derived from this analysis as well: most of the risk of occupation falls on the lower half of the tree, stemming from a NATO signal of disunity. This risk can be avoided by NATO giving the signal of unity instead; then the risk of occupation falls to less than 1%.

Philosophy of social science and the graduate student

Is there any reason to think that a course in philosophy of social science can be helpful for a graduate student in sociology or political science (or education, public health, or public policy)? Is this part of philosophy a useful contribution to a PhD education in the social sciences?

I think there are several reasons to support this idea. I believe that a good course in this area can help the aspiring researcher extend his or her imagination and modes of inquiry in ways that can make the first years of research particularly fruitful. In what ways is this so? There are several, in my view.

First, though, I must confess that it wasn't always so. The courses I took in philosophy of science and philosophy of social science as an undergraduate were in fact stultifying and discouraging rather than eye-opening and expanding. There was the idea that the nature of "science" had been settled by the Vienna Circle, that there was an all-encompassing model for explanation and justification (the hypothetico-deductive method), and that the significant problems facing young social scientists had to do with forming adequate concepts and finding ways of operationalizing these concepts to test them against the world of observable data. Essentially, then, the work of the social scientist was simply to fill in the blanks in a schema that had already been prepared. (I'm thinking in particular of two textbooks, Hempel (Philosophy of Natural Science) and Rudner (Philosophy of Social Science).)

And the anti-positivist reaction to this kind of philosophy of science wasn't much more helpful. Kuhn, Lakatos, Feyerabend, and Hanson pointed out the shortcomings of the theory of science contained in logical positivism. But they didn't have much to say that was very specific or helpful when it came to the task of formulating theories and hypotheses that would serve to explain social outcomes. So I wouldn't have said that the typical course in philosophy of science in the 1980s was particularly valuable for the young researcher either.

But the situation in this field changed in the 1990s and after. Most important, many philosophers who took up the philosophy of social science embraced the idea that the field needed to be developed in tandem with the real problems of research and explanation that sociologists and political scientists grappled with. PSS could not remain an apriori discipline; instead, the philosopher needed to gain expert understanding of the disputes and problems that were under debate in the disciplines of the social sciences. (This switch occurred even a little earlier in the philosophy of biology and philosophy of psychology.) Philosophers needed to work as peers and colleagues with social scientists.

And once philosophers began to step away from the dogmas of received formulations of philosophy of science, they began to ask new questions. What is a good explanation? How does social causation work? What is a causal mechanism in the social world? What kind of thing is a social structure? How do structures maintain their causal properties over time? How do individual actors contribute to social causation? These are all questions that are intertwined with the ordinary reasoning that talented sociologists and political scientists are led to through their own efforts at theory formation. And philosophers found helpful mid-level locations from which to address questions like these in ways that made substantive contributions to the concrete work of social research and inquiry.

Here are some concrete results. Philosophers have worked productively to help arrive at better ways of treating social causation. They have clarified the nature of social causal mechanisms. They have brought new clarity to questions about the relationships among levels of social and individual activity. They have highlighted the centrality of the idea of microfoundations. They have helped to dissolve the apparent contradiction between structural causation and actor-centered social processes. They have problematized the assumptions we sometimes make about social kinds and social generalizations. They have directed new attention to the ways that we characterize the actor in the social world. And it seems to me that each of these kinds of insights makes a difference to the researcher in training.

So, indeed, it makes good sense to offer a challenging course in the philosophy of social science to PhD students in the social sciences. This isn't because there is a new set of verities that these young researchers need to master. Rather, it is because the nature of the current discussions in the philosophy of social science parallels very nicely the process of theory formation and development that we would like to see take place in sociology and political science. We would like to see the exercise of intelligent imagination by social researchers, unconstrained by the dogmas of methodology or ontology that a discipline is all too ready to provide. The social world is strikingly and permanently surprising, with novel conjunctions of processes and causes leading to unexpected outcomes. we need new ways of thinking about the social world and the social sciences, and philosophy of social science can help stimulate some of that thinking.

Here are the books I'll be discussing in my graduate course in philosophy of social science this semester:

Quine's indeterminacies

W.V.O. Quine's writings were key to the development of American philosophy in the 1950s, 1960s, and 1970s. His landmark works ("Two Dogmas of Empiricism," "Ontological Relativity," and Word and Object, for example) provided a very appealing combination of plain speaking, seriousness, and import. Quine's voice certainly stands out among all American philosophers of his period.

Quine's insistence on naturalism as a view of philosophy's place in the world is one of his key contributions. Philosophy is not a separate kind of theorizing and reasoning about the world, according to Quine; it is continuous with the empirical sciences through which we study the natural world (of which humanity and the social world are part). Also fundamental is his coherence theory of the justification of beliefs, both theoretical and philosophical. This theory was the source of John Rawls's method of reasoning for a theory of justice based the idea of "reflective equilibrium." This approach depended on careful weighing of our "considered judgments" and the adjustments of ethical beliefs needed to create the most coherent overall system of ethical beliefs.

There is another feature of Quine's work that is particularly appealing: the fundamental desire that Quine had to make sense of obscure issues and to work through to plausible solutions. There is sometimes a premium on obscurity and elliptical thinking in some corners of the intellectual world. Quine was a strong antidote to this tendency. (John Searle makes similar points about the value of clarity in philosophical argument in his comments on Foucault here.)

Take "Ontological Relativity" (OR), the first of the Dewey Lectures in 1968 (link). The essay articulates some of Quine's core themes -- the behaviorist perspective on language and meaning, the crucial status of naturalism, and the indeterminacy of meaning and reference. But the essay also demonstrates a sensitive and careful reading of Dewey. Quine shows himself to be a philosopher who was able to give a respectful and insightful account of the ideas of other great philosophers.

Philosophically I am bound to Dewey by the naturalism that dominated his last three decades. With Dewey I hold that knowledge, mind, and meaning are part of the same world that they have to do with, and that they are to be studied in the same empirical spirit that animates natural science. There is no place for a prior philosophy. (185).

In OR Quine refers to a key metaphor in his own understanding of language and meaning, the "museum myth" theory of meaning. "Uncritical semantics is the myth of a museum in which the exhibits are meanings and the words are labels. To switch languages is to change the labels" (186). Against the museum myth, Quine argues here (as he does in Word and Object as well) for the indeterminacy of "meaning" and translation. The basic idea of indeterminacy of translation, as expressed in WO, comes down to this: there are generally alternative translation manuals that are possible between two languages (or within one's own) which are equally compatible with all observed verbal behavior, and yet which map expressions onto significantly different alternative sentences. Sentence A can be mapped onto B1 or B2; B1 and B2 are apparently not equivalent; and therefore Sentence A does not have a fixed and determinate meaning either in the language or in the heads of the speakers. As Quine observes in his commentary on his example from Japanese concerning the translation of "five oxen", "between the two accounts of Japanese classifiers there is no question of right and wrong" (193).

For naturalism the question whether two expressions are alike or unlike in meaning has no determinate answer, known or unknown, except insofar as the answer is settled in principle by people's speech dispositions, known or unknown. If by these standards there are indeterminate cases, so much the worse for the terminology of meaning and likeness of meaning. (187)

Returning to the extended example he develops of indeterminacy of translation around the word "gavagai" that he introduced in Word and Object, Quine notes that the practical linguist will equate gavagai with "rabbit", not "undetached rabbit part". But he insists that there is no objective basis for this choice.

The implicit maxim guiding his choice of 'rabbit', and similar choices for other native words, is that an enduring and relatively homogeneous object, moving as a whole against a constrasting background, is a likely reference for a short expression. If he were to become conscious of this maxim, he might celebrate it as one of the linguistic universals, or traits of all languages, and he would have no trouble pointing out its psychological plausibility. But he would be wrong; the maxim is his own imposition, toward settling what is objectively indeterminate. It is a very sensible imposition, and I would recommend no other. But I am making philosophical point. (191)

In "Ontological Relativity" Quine takes the argument of the indeterminacy of meaning an important step forward, to argue for the "inscrutability of reference." That is: there is no behavioral basis for concluding that a given language system involves reference to this set of fundamental entities rather than that set of fundamental entities. So not only can we not say that there are unique meanings associated with linguistic expressions; we cannot even say that expressions refer uniquely to a set of non-linguistic entities. This is what the title implies: there is no fixed ontology for a language or a scientific or mathematical theory.

These are radical and counter-intuitive conclusions -- in some ways as radical as the "incommensurability of paradigms" notion associated with Thomas Kuhn and the critique of objectivity associated with Richard Rorty. What is most striking, though, is the fact that Quine comes to these conclusions through reasoning that rests upon very simple and clear assumptions. Fundamentally, it is his view that the only kinds of evidence and the only constraints that are available to users and listeners of language are the evidences of observable behavior; and the full body of this system of observations is insufficient to uniquely identify a single semantic map and a single ontology.

(Peter Hylton's article in the Stanford Encyclopedia of Philosophy does a good job of capturing the logic of Quine's philosophy; link.)

Hacking on Kuhn

The fourth edition of Thomas Kuhn's The Structure of Scientific Revolutions appeared in 2012, fifty years after its original appearance in 1962. This edition contains a very good introduction by Ian Hacking, himself a distinguished philosopher and philosopher of science. So it is very interesting to reread Kuhn's classic book with the commentary and intellectual frame that Hacking provides. (Here is an earlier post on Kuhn; link.)

Hacking's reading is somewhat deflationary, compared to the relativist and anti-rationalist interpretations that are sometimes offered of Kuhn's theories. Hacking sees a great deal of continuity between the Vienna Circle traditions of philosophy of science and Kuhn's own intellectual commitments about scientific rationality. (Hacking pursues this analogy even down to noting a parallel between Carnap's title Logical Syntax of Language and a similar description of Kuhn's later work, Logical Syntax of Scientific Language.)

According to Hacking, the scope of the concept of "paradigm" has been exaggerated by subsequent interpreters. Paradigms are not systems of thought or conceptual systems; they are not even discipline-specific sets of shared assumptions that don't get questioned in the ordinary pursuit of scientific knowledge. Instead, Hacking argues that Kuhn's intended meaning sticks fairly close to the classical meaning of the term, as an exemplar of something or other. He quotes Kuhn:

"The paradigm as shared example is the central element of what I now take to be the most novel and least understood aspect of this book."  (from the Postscript, kl 213)

So a good example of a paradigm in science is something like the Millikan oil drop experiment; it constituted a clear and admirable example of experimental design and implementation which helped to guide later experimentalists in the design of their own experiments.

Hacking also notes that Kuhn later allows for a local use and a global use of the concept, but he suggests that Kuhn did not wholly endorse the global use. Here is how Hacking paraphrases the global use, in the context of the things that hold a scientific research community together:

That's the global sense of the word, and it is constituted by various kinds of commitment and practices, among which he emphasizes symbolic generalizations, models, and exemplars. (Kl 318)

Hacking gently suggests that Kuhn under-values "normal science," because he shares a bias towards theory with many other philosophers of science of the preceding generation. But Hacking argues that later philosophers and historians of science, such as Peter Galison, have given more weight to the innovations associated with experimentation and instrumentation (kl 199), and the process of normal science is precisely the context in which innovations in these aspects of science are most likely to occur.

Hacking makes an interesting point about the scientific context in which Kuhn's ideas took shape. Physics, both classical and modern, set the standard for what was most exciting within the scientific enterprise in the 1950s and 1960s. But Hacking asks an interesting question: what if the examples of biology and the life sciences had been the backdrop against which Kuhn had formulated his theories? Molecular biology and the chemistry of DNA constituted a revolution in biology at roughly the time of the original publication of SSR. How valid are Kuhn's observations about scientific research and progress against that backdrop? Would the results have perhaps been quite different if he had concentrated on these examples?

Thus The Structure of Scientific Revolutions may be -- I do not say is -- more relevant to a past epoch in the history of science than it is to the sciences as they are practiced today. (Kl 98)

Hacking gives a very succinct summary of Kuhn's main theory of the course of science:

Here is the sequence. (1) normal science ...; (2) puzzle solving ...; (3) paradigm, a word which, when he used it, was uncommon, but which after Kuhn has become banal ... ; (4) anomaly; (5) crisis; and (6) revolution, establishing a new paradigm.  (Kl 114)

Hacking thoroughly rejects the most subjectivist aspects of many readings of Kuhn: the idea that scientists inhabiting different paradigms also literally inhabit different worlds. Hacking doesn't believe that Kuhn actually believes this, or even unambiguously asserts it.

There are many passages in Kuhn's original text that are worth pulling out again. Here is one, on the gap between observation and scientific beliefs:

Observation and experience can and must drastically restrict the range of admissible scientific belief, else there would be no science. But they cannot alone determine a particular body of such belief. An apparently arbitrary element, compounded of personal and historical accident, is always a formative ingredient of the beliefs espoused by a given scientific community at a given time. (4)

Effective research scarcely begins before a scientific community thinks it has acquired firm answers to questions like the following: What are the fundamental entities of which the universe is composed? How do these interact with each other and with the senses? What questions may legitimately be asked about such entities and what techniques employed in seeking solutions? (4)

These passages make it clear that Kuhn does in fact think that a scientific community possesses a set of unifying but contestable  beliefs -- what many of us now mean by a paradigm. And this seems more pervasive and comprehensive than Hacking's analysis would seem to allow.

I first read Kuhn as an undergraduate in 1969 or 1970, and I confess that my own understanding of his meaning concerning scientific knowledge and reasoning gravitated towards the more anti-objectivist reading that Hacking rejects. I understood paradigms as sets of semi-articulated assumptions about science, the world, and the instruments that hung together as a community-dependent worldview; a worldview that could not be directly empirically evaluated. And I understood incommensurablity to mean that scientists within these mental frameworks arrived at empirical judgments and theories that could not be strictly compared across communities, because their underlying conceptual structures were systematically different. I had read Quine on the indeterminacy of translation at roughly the same time, and I understood incommensurablity in analogy with indeterminacy across language communities. (Kuhn's preface to the book makes it clear that he too had read Quine, though in the 1950s and therefore prior to the publication of Word and Object (1960); kl 550.)

I also understood Kuhn to hold that standards of scientific reasoning were likewise dependent on the mental frameworks of the research communities -- with the result that some disagreements among physicists or biologists could not be resolved on the basis of standards of scientific reasoning or method. There was no "paradigm-independent" scientific method, no community-neutral standard of rational preferability.

It may be that Hacking is right, and that Kuhn never intended to support these radical claims about the limits of scientific rationality. But whether he did or not, the position is an intelligible one, and thinkers as diverse as Althusser and Feyerabend have advocated it. Frederick Jameson's title, The Prison-House of Language: A Critical Account of Structuralism and Russian Formalism, picturesquely captures the core idea.

Structure of Scientific Revolutions richly rewards a rereading fifty years after its original publication. And as is true of so many deeply original works, we are likely to find different things most striking today than we did on first reading decades ago.

Technical knowledge

There is a kind of knowledge in an advanced mechanical society that doesn't get much attention from philosophers of science and sociologists of science, but it is critical for keeping the whole thing running. I'm thinking here of the knowledge possessed by skilled technicians and fixers -- the people who show up when a complicated piece of equipment starts behaving badly. You can think of elevator technicians, millwrights, aircraft maintenance specialists, network technicians, and locksmiths.

I had an interesting conversation along these lines on the hotel shuttle at the Beijing airport recently. Tim was traveling from Milwaukee to someplace he described as being on the Russian-Mongolian border where there was a mine with a malfunctioning piece of heavy equipment provided by his US company. He expects to be on site for two months, and knows that whatever problems he encounters, they won't be in the users' manual.

This trip is routine for Tim. His company's equipment is used in mines all over the world, from Sweden to India to Brazil. And he is routinely dispatched with his 80-pound duffel, his hard hat, and a few essentials to try to correct the problem.

I said to him, you probably run into problems that don't have a ready solution in the handbook. He said in some amazement, "none of the problems I deal with have textbook solutions. You have to make do with what you find on the ground and nothing is routine." I also asked about the engineering staff back in Wisconsin. "Nice guys, but they've never spent any time in the field and they don't take any feedback from us about how the equipment is failing." He referred to the redesign of a heavy machine part a few years ago. The redesign changed the geometry and the moment arm, and it's caused problems ever since. "I tell them what's happening, and they say it works fine on paper. Ha! The blueprints have to be changed, but nothing ever happens."

I would trust Tim to fix the machinery in my gold mine, if I had one. And it seems that he, and thousands of others like him, have a detailed and practical kind of knowledge about the machine and its functioning in a real environment that doesn't get captured in an engineering curriculum. It is practical knowledge: "If you run into this kind of malfunction, try replacing the thingamajig and rebalance the whatnot." It's also a creative problem-solving kind of knowledge: "Given lots of experience with this kind of machine and these kinds of failures, maybe we could try X." And it appears that it is a cryptic, non-formalized kind of knowledge. The company and the mine owners depend crucially on knowledge in Tim's head and hands that can only be reproduced by another skilled fixer being trained by Tim.

In philosophy we have a few distinctions that seem to capture some aspects of this kind of knowledge: "knowing that" versus "knowing how", epistime versus techne, formal knowledge versus tacit knowledge. Michael Polanyi incorporated some of these distinctions into his theory of science in Personal Knowledge: Towards a Post-Critical Philosophy sixty years ago, but I'm not aware of developments since then.

In sociology and anthropology there has also been some beginning of work on the role that this kind of tacit or non-formalized knowledge plays in the modern technological system. In the early 1980s Xerox Parc commissioned an effort at business anthropology that studied the work practices of Xerox copier repairmen (link). This was part of a knowledge process called Eureka. The repairmen drive around with vans full of manuals on various models of copier. But it turns out that the bulk of their work depends on shared practical knowledge within the group of repairers at the time. Phone calls are made, interventions are tried, copy machines come back into service. But the manuals are never part of the process.

One of Tim's points seems entirely valid: it is a serious mistake for a company to create a system where engineers design things without ever dealing with their machines in the field. This feedback loop seems critical. The engineers lack access to the tacit technical knowledge that would be gained by practical immersion.

Chuck Sabel's research on machinists in Italy falls in this category of investigation. Interestingly, he too found some of the dissonance Tim reported between the university-educated engineers and the working fixers who actually interfaced with the machines (Work and Politics: The Division of Labour in Industry). Another scholar who takes this kind of concrete knowledge seriously is historian of technology Phil Scranton in Endless Novelty and also in his research on jet engines.

It seems that there is an opportunity for a very interesting kind of micro research in the sociology of knowledge here: identify a specific technical repair community and interview them in detail to discover what they know, how they know, and how it all works. This knowledge system is difficult to categorize but seems crucial for an advanced technological society.

European philosophy of social science

There is an active and extended group of scholars in Europe with a very focused concentration on the philosophy of the social sciences. A good cross-section of this community gathered in Rotterdam Monday and Tuesday this week for a small conference on social mechanisms at the Erasmus Institute for Philosophy and Economics (link). These are for the main part younger scholars up to 15 years out from the PhD. And they are a genuinely impressive group of researchers.

There is a common set of topics and references that bind this extended research community together from Finland to Belgium to the Netherlands to Italy. They share a focus on social explanation. They are intellectually committed to the construct of "causal mechanisms" rather than causal laws. They have affinities and connections to the Analytical Sociology network, though few would explicitly identify themselves as analytical sociologists. Jim Woodward's theory of causation, the classic paper by Machamer, Darden, and Craver ("Thinking about Mechanisms", 2000), and Carl Craver's theorizing about mechanisms and levels in the neurosciences represent a few common intellectual landmarks, and almost all of these young scholars are current with the latest twists and turns of Peter Hedstrom's theories.

Finally, there is a decided absence of classic European voices about the social sciences -- conversations never invoke Bourdieu, Habermas, or Gadamer. This is a community organized around the intellectual values of analytic philosophy -- clarity, logical rigor, analysis, and causality.

There is also a high degree of interaction among members of this group. Mini conferences and workshops are able to bring many of them together, with the European rail system making it feasible to travel from London, Amsterdam, and Paris for a short conference in Ghent. In my observation this easy interaction stimulates a great deal of intellectual progress year after year, both individually and collectively.

As one small example, Bert Leuridan's effort this week to specify a rigorous relationship between meso and macro levels of a social system will certainly be fruitful for a number of us as we think further about this issue. And likewise, I will be interested to reflect further on Petri Ylikoski's call for a "flat" understanding of social causes. These examples illustrate the micro-steps of the advancement of a body of thought.

Sometimes it is difficult to see whether an intellectual field is progressing or just retracing old ground. Lakatos's ideas of "progressive" and "degenerative" research communities seem useful here. European philosophy of social science seems to be making real progress in this generation.

There is one academic reality that is worrisome. Many of these young scholars are making their way on the basis of research appointments and post-doc positions. These opportunities are pretty well funded by European universities and governments -- substantially more so than in the US. But many of these positions have a maximum term of six years. And the prospects of a regular faculty position in Europe seem bleak. A department chair estimated to me that only 20% of PhDs eventually find regular academic positions. This suggests an academic ecology that may prove stifling for the kinds of innovation we now see.

I think there could be a useful piece of research within the new sociology of ideas advocated by Neil Gross and and Charles Camic (link) based on this example. It wouldn't be difficult to draw out a network map of relationships among these philosophers, the major institutions that facilitate their work, and the movement of various new ideas through their conference papers and published work. Looking backward, it would be feasible as well to reconstruct the generation of writers and scholars who helped shape this generation's intellectual agenda. And I think the results would be very interesting.

It would also be interesting to begin developing a shared map of centers and locations of work on the philosophy of social science in Europe. Here is a start -- it's set for public access, so please feel free to add other points of interest, including centers, conferences, and concentrations of researchers.


View European Philosophy of Social Science in a larger map

Paradigms, research communities, and the rationality of science

An earlier post on scientific explanation provoked some interesting comments from readers who wanted to know why Thomas Kuhn was not mentioned.  My brief answer is that Kuhn's contribution doesn't really offer a theory of scientific explanation at all, but instead an account of the cognitive and practical processes involved in formulating scientific knowledge.  Here I'll dig into this question a bit deeper.

In The Structure of Scientific Revolutions (1962) Kuhn asks us to recenter our thinking about scientific knowledge in several important ways.  He de-emphasizes questions about the logic of theory and explanation.  He argues that we should not think of science as an accumulation of formal, logical and mathematical expressions that permit codification of observable phenomena.  He doubts the availability of a general, abstract "scientific method" that serves to guide the formation of scientific knowledge.

Against the abstract ideas about the logic of science associated with positivism, Kuhn advocated for a more practical and historical study of science as a concrete human activity.  He arrives at several ideas that have turned out to have a great deal of influence -- the idea of a scientific paradigm, the idea of incommensurability across paradigms, and the idea that science doesn't just consist in the formal theories that a research tradition advances.  But the most fundamental insight that he developed throughout his career, in my judgment, is the idea that we can learn a great deal about method and scientific rationality by considering the history of science in close detail.

This approach has important implications for the philosophy of science at numerous levels.  First, it casts doubt on the hope that we might reconstruct an ideal "scientific method" that should govern all scientific research.  This was a goal of the logical positivists, and it doesn't survive close scrutiny of the ways in which the sciences have developed.  Second, it leaves room for the idea of scientific rationality, but here again, it suggests that the standards of scientific reasoning need to be specified in each research tradition and epoch, and there is no single "logic" of scientific reasoning that could be specified once and for all.  The injunction, "Subject your theories to empirical tests!", sounds like a universal prescription for scientific rationality; but in fact, the methods and processes through which theories are related to observations are widely different throughout the history of science.  (And, of course, the post-positivist philosophers of science demonstrated that we can't draw a sharp distinction between observation and theory.) Methods of experimentation and instrumentation have varied widely across time and across disciplines.  So empirical evaluation takes many different forms in different areas of the sciences.

There is an implicit tension between a sociological and historical understanding of the sciences, on the one hand, and a realist understanding of the sciences, on the other.  When we look at the formation of scientific beliefs and theories as the output of a specific research tradition and set of research institutions, we will be struck by the scope of contingency that seems to exist in the development of science.   When we look at science as a set of theories about the world, we would like to imagine that they are sometimes true and that they represent reality in approximately the way that it really works.  And we would like to suppose that the cognitive and social values that surround scientific research -- attentiveness to data, openness to criticism, willingness to revise one's beliefs -- will gradually lead to systems of scientific belief that are more and more faithful to the ways the world is.  From this perspective, the contingency and social dependency of the research process seems at odds with the hope that the results will be univocal.

Following Kuhn's historical turn, efforts to place scientific knowledge within a social context gained support within sociology rather than philosophy.  A field of thought emerged, called the sociology of scientific knowledge (SSK), which took very seriously the idea that social conditions and institutions very deeply influenced or created systems of scientific belief.  Here we can think of David Bloor (Knowledge and Social Imagery), Barry Barnes et al (Scientific Knowledge: A Sociological Analysis), and Bruno Latour (Laboratory Life: The Construction of Scientific Facts), who took the discipline in the direction of a more relativist understanding of the nature of systems of scientific belief.  According to the relativist position on scientific knowledge, belief systems are internally consistent but incomparable from one to another, and there is no absolute standard that allows us to conclude that X is more rationally justified than Y as an explanation of the world.

Here is David Bloor towards the beginning of Knowledge and Social Imagery:

The sociologist is concerned with knowledge, including scientific knowledge, purely as a natural phenomenon. The appropriate definition of knowledge will therefore be rather different from that of either the layman or the philosopher.  Instead of defining it as true belief -- or perhaps, justified true belief -- knowledge for the sociologist is whatever people take to be knowledge.  It consists of those beliefs which people confidently hold to and live by. In particular the sociologist will be concerned with beliefs which are taken for granted or institutionalised, or invested with authority by groups of people. (5)

He goes on to assert that the sociology of science needs to be -- causal, impartial with respect to truth and falsity, symmetrical in explanation, and reflexive (its principles should apply to sociology itself) (7).

From a philosopher's point of view, it would be desirable to find a position that reconciles the social groundedness of scientific belief formation -- and the self-evident ways in which the research process is sometimes pushed by non-cognitive, non-rational forces -- with the ideas of scientific truth and reference.  Essentially, I think that most philosophers would like to acknowledge that human rationality is socially constituted, but is still a form of rationality, and is capable of discovering approximate truths about the world.  It would be desirable to arrive at a philosophy of science that is both sociologically informed and realist.  Imre Lakatos took something like this perspective in the 1970s in his writings about scientific research programmes (The Methodology of Scientific Research Programmes, Criticism and the Growth of Knowledge). But in general, it is my impression that the discipline of the philosophy of science hasn't taken much heed of the challenges presented by SSK and the more sociological-historical approach. That is unfortunate, because the premises of the sociological-historical approach, and of the SSK approach in particular, are pretty compelling.