Philosophy of X?


When philosophers do their thinking within a field called “the philosophy of X”, there is always a natural question that arises: how will philosophical reflection about X be helpful or constructive for the practitioners of X? For example, how might the philosophy of science be helpful for working scientists? How can the philosophy of biology or economics be helpful to biologists or economists? And, for that matter — why isn’t there a philosophy of plumbing or long-distance bus driving?

As for the last question, there seem to be two separate reasons for this gap in the spectrum — a dearth of difficult conceptual problems and a lack of potentially useful consequences. First, philosophy finds traction when it deals with subject matter that raises difficult conceptual or inferential issues. Philosophers are particularly good at untangling unclear concepts; they are experienced at the task of formulating problems clearly and logically; they are ready to unmask the hidden presuppositions underlying a particular formulation. This is the kind of work Wittgenstein describes as “letting the fly out of the fly bottle”; it is what J. L. Austin does so well in “Three ways of spilling ink” (link). Drawing distinctions and formulating ideas clearly — these are core intellectual tools, and they lie at the root of philosophy.

Another core intellectual tool is the commitment to providing justification for the things we believe, and raising reflective questions about the nature of rational justification. What is the evidence that supports a given belief? What degree of warrant does this evidence create? Why do statements like these make it more likely that P is true? Questions like these too are foundational for philosophy — from Plato to Locke to Quine. And philosophers have a developed and nuanced set of frameworks and vocabularies in terms of which to interrogate them.

Both these types of intellectual work are doubly cognitive. They represent cognitive effort directed at examples of cognitive effort — efforts to explain the workings of nature, the behavior of other people, or the workings of social institutions. Putting the point very simply — philosophers are good at helping us think clearly about thinking. And, at their best, they can help us think more clearly and coherently.

So now we have part of an answer for why there is no philosophy of plumbing: plumbing is a routine activity with few conceptual puzzles and a secure base of practical knowledge. There just isn’t any room for philosophical analysis in this realm. And, second, there is the pragmatic point: it is very hard to see how the plumbers might benefit from philosophical analysis. If Deleuze were to write a treatise on plumbing, how could that possibly enhance the practical discipline of plumbing? The plumbers’ effective ability to control the water and waste systems of our buildings would not be enhanced by conceptual or epistemic analysis. Their conceptual and theoretical problems are well-mapped; all that remains is to discover the source of the leak. And this does not require philosophy.

Why, then, do we need other philosophies of X’s? What is it about economics, evolution, or the mind that makes it intellectually and practically valuable to have a philosophy of economics, biology, or psychology? The answer proceeds along the lines sketched here. All these disciplines confront huge problems of concept formation, theory construction, and inference and justification. The most basic questions remain unsettled: does capitalism exist? How are theories and models related to the empirical world? Is there such a thing as group selection? How do emotions intersect with reasoning? What is consciousness? And in all these fields, there is the problem of inference and method — again, unresolved. So there is ample room for philosophical thinking in these fields.

But more importantly, philosophy can help to improve the intellectual practices of the cognitive-empirical disciplines. By working productively in tandem with creative scientific researchers, with a focus on the conceptual and methodological problems that matter the most, philosophers can help contribute to real progress in the disciplines. This requires the philosopher to engage with the discipline in depth. But the fruits of this sort of synergy can be highly productive. It is sometimes complained that philosophy brings only “logic chopping” and dry conceptual analysis. But this is a caricature; the conceptual issues faced by the special sciences are deeply challenging, and sustained dialogue with philosophers can potentially lead to meaningful progress in the science. And reciprocally, quite a few traditional concerns of philosophy –in ontology, epistemology, and the theory of the mind, for example — can be significantly deepened through close engagement with current scientific work. There need not be a sharp line of demarcation between philosophy and empirical research.

What is social scientific knowledge?

The social and behavioral sciences endeavor to describe, explain, and interpret the range of the social and behavioral facts that surround us. To refer to this body of findings as “science” is to claim a set of epistemic values about the nature of the methods of inquiry and evaluation that are used to arrive at and assess the conclusions offered about this domain. The label “science” brings with it a set of presuppositions about rigor, evidence, generalizability, logical analysis, objectivity, cumulativeness, and the likelihood that the assertions that are made are true.

Consider a few assumptions that are often made about scientific knowledge—some valid and some not. Science is based on a set of rationally justified methods of inquiry and testing. Scientific knowledge progresses, in scope, in detail of understanding, and in reliability. Science is performed by specialists, working within equally exacting communities of peers and competitors and subject to a demanding set of standards of evaluation—peer-reviewed journals, university review processes, national laboratories, and international associations and conferences. The result of these processes of testing and evaluation, we expect, is an expanding body of hypotheses, experimental findings, observations, theories, and explanations that have substantial credibility—and substantially higher credibility than the writings of casual observers of a given range of phenomena. We come to know the nature of the world better through the institutions and methods of science.

In addition to these reasonably valid assumptions about scientific knowledge, there is another group of more questionable ideas that derive from assumptions drawn from the natural sciences. Science permits generalizations; it permits us to systematize otherwise apparently separate domains of phenomena (planetary motion, the tides; rational choice theory, behavior of the family) and to demonstrate that apparently heterogeneous sets of phenomena are in fact governed by the same general laws. Science permits predictions; if the fundamentals are thus-and-so, then the compounds will behave thusly. Science aims at unification: the discovery of unitary systems of forces and entities whose aggregate properties represent the whole of nature.

Notice that these latter expectations are derived from the successes and specific characteristics of certain of the natural sciences. And this marks the first of many opportunities for error in the philosophy of social science. There is no reason to expect that the social domain possesses the underlying nature and orderliness that would make it possible to achieve some of these characteristics (in particular, uniformity, generalizability, unification, simplicity). Consider some other areas of possible empirical research—for example, animal behavior. We should not expect there to be comprehensive theories of animal behavior. Instead, we should expect many threads of research, corresponding to many dimensions of animal behavior: cognition, memory, instinct, social behaviors, migratory behavior. And these many strands of research would reach out to different kinds of causal backgrounds: evolutionary biology, neurophysiology, intra-group learning. Likewise with the domain of social behavior. There is no single unified “theory of human motivation”—whether rational choice theory, social psychology, or any other unified theory. And this is so, because there is no unified reality of motivation and action; rather, there is a heterogeneous range of motives, errors, impulses, commitments, and habits that together constitute “dispositions to behave.”

What underwrites the claim of truthfulness for scientific knowledge? What gives us a rational basis for believing that the results of the socially constructed activities of science lead to true hypotheses about the nature and workings of the phenomena that scientific inquiry considers? There is, first, the basic argument of empiricism: we can observe some features of the world and establish certain statements as being probable. And we can use a collection of tools of inference to establish credibility of other non-observational statements (deductive and inductive logic, statistics, the experimental method, causal modeling).

This simple empiricist epistemology underwrites the strongest claims for veridicality and justification for the social sciences. The discovery of empirical facts about the social world is possible but challenging; this is what much of social science methodology attempts to under-gird. And hypotheses about the causal relationships that exist among social entities and processes can be tested using a variety of methods of inference that themselves possess strong epistemic justification. We have learned from the writings of philosophers of science since the 1960s to emphasize corrigibility and anti-foundationalism in our interpretation of scientific knowledge; but a coherentist epistemology and a perspective of causal realism provides a philosophically powerful grounding for social science knowledge. (See articles in the Stanford Encyclopedia of Philosophy on coherence epistemology and scientific realism by Kvanvig and Boyd).

In addition, in some areas of the natural sciences, there is the fact that cumulative scientific research leads to the invention of technologies that work as they were designed to do: new materials are invented in the electronics industry, new designs are created for large structures (buildings, aircraft, electron microscopes), and these materials and artifacts perform as expected on the basis of the underlying theories. So scientific theories of materials, structures, and natural systems are supported by the effectiveness of the technologies that they give rise to. If the theories and hypotheses were fundamentally untrue about the parts of the natural world that they describe, then we would expect the technologies to fail; the technologies do not fail; so we have some additional reason to believe the scientific theories that underlay these technologies. (This is a version of Richard Boyd’s argument of methodological realism.)

Is there anything analogous to the relationship between the natural sciences and technology, for the social and behavioral sciences? On the whole, there is not. Social predictions are notoriously unreliable; public policies based on social-science theory commonly give rise to unanticipated consequences; and the twists and turns of deliberate social processes (war, alliance, efforts to address global warming) continue to surprise us. This unpredictability in the social world derives from the nature of social action. Human behavior and social processes are plainly subject to an open-ended range of causes, motives, and influences. The construction of various areas of science with the goal of understanding and explaining this multiplicity is therefore a profoundly challenging task.

Here, then, is a very elliptical description of a plausible interpretation of social science epistemology: There are empirical foundations for knowledge in the form of social observation (empiricism); there are social causes that influence social behavior, processes, and outcomes (causal realism); there is no a priori reason to expect strong generalizations across social phenomena, “regulating” the social world; and there is no reason to expect unified master theories of social phenomena, suggesting instead a preference for theories of the middle range.

Tributaries of the philosophy of the social sciences


The philosophy of the social sciences is largely focused on questions about the nature of our knowledge, representation, and explanation of social phenomena. There is an ontological side to some of the questions in this field — for example, what is the nature of social phenomena? But many of the questions are epistemological, having to do with the conditions of knowledge and representation that obtain when it comes to social facts. I think it is useful to sketch out a map that indicates the topography of some of the fundamental questions and approaches that have contributed to a better understanding of social science. And this effort will demonstrate that there is no single, coherent field that is the “philosophy of social science”; instead, there are overlapping and intertwined efforts by several traditions to arrive at better and more justified representations of social knowledge.

The fruitful ideas in this field derive from several separate tributaries, it seems to me. One important source is the group of “founders” of the social sciences who themselves thought very hard about the question of the conditions of establishing a social “science”. Max Weber, Emile Durkheim, Georg Simmel, William Thomas, and George Herbert Mead all had original and insightful ideas about what a scientific study of social reality might consist in. And, in most instances, these ideas were driven by their acquaintance with the richness of social life rather than by philosophical presuppositions. So these founders forged a philosophy of social research along the way as they constructed their models of what theory and research ought to look like in the study of the social world.

Another important source for current philosophy of social science is the tradition of empiricism that led to twentieth-century analytic philosophy of science. Here we can highlight John Stuart Mill, Moritz Schlick, Carl Hempel, and Ernest Nagel as philosophers who brought the machinery of positivist epistemology to a conception of what the social sciences ought to look like. As suggested in an earlier posting, there are profound problems with some of these ideas; but there is no doubt that they have been influential. And this influence shows up very explicitly in social science writings concerned with the logic of quantitative social research.

There is another source for contemporary philosophy of social science that has something in common with both these but is nonetheless distinct. This is the impulse that comes from rational choice theory and the idea that social patterns are the expression of individual rational choices. Mill’s writings suggest this idea, and it is a very strong component of the classics defining microeconomic theory as well (Walras, Pareto, the Austrian school). The effort to bring decision theory and game theory into play in explaining concrete social developments is a manifestation of this approach — for example, Samuel Popkin’s work on peasant rebellions (The Rational Peasant: The Political Economy of Rural Society in Vietnam). What makes this framework philosophical is the implicit idea of reductionism that it offers as a strategy of explanation: high-level social facts need to be decomposed into logical compounds of lower-level facts at the level of individuals. (This is the doctrine of methodological individualism.)

The intellectual framework of “scientific realism” is also an important tributary to contemporary philosophy of social science. Against the instrumentalism associated with positivism, this approach maintains that the social or natural worlds possess an objective set of characteristics, and it is possible to know the approximate outlines of these characteristics. When brought into contact with the social sciences, realism leads us to expect that there are real social structures, conditions, and causes, and that it is one of the functions of social science to describe those real circumstances and their relationships with each other. The recent emphasis on “social-causal mechanisms” is a version of scientific realism in application to the social world — for example, Social Mechanisms: An Analytical Approach to Social Theory.

There are two other tributaries that are important contributions but that have been less influential for analytic philosophers of social science, one deriving from Marx and the other from thinkers like Dilthey and Gadamer. The first is materialism and an emphasis on social structures, and the other is the hermeneutic tradition. The materialist tradition attempts to organize social reality around a set of structures with causal properties (modes of production, property relations, forms of technology). The hermeneutic tradition takes “social action” as the fundamental social fact, and looks at the challenge of interpreting social action as the fundamental problem in social research. Yvonne Sherratt’s Continental Philosophy of Social Science is a very useful study of the influence of these traditions, and I will return to her discussion in a later posting.

Quasi-experimental data?

Stan Lieberson is one of a group of sociologists for whom I have great respect when it comes to intelligent thinking about social science methodology. His 1985 book, Making It Count: The Improvement of Social Research and Theory, is a good example of some of this thinking about the foundations of social science knowledge, and I also admire A Matter of Taste: How Names, Fashions, and Culture Change in the way it offers a genuinely novel topic and method of approach.

Lieberson urges us to consider “a different way of thinking about the rigorous study of society implied by the phrase ‘science of society'” instead of simply assuming that social science should resemble natural science (3-4). His particular object of criticism in this book is the tendency of quantitative social scientists to use the logic of experiments to characterize the data they study.

An experiment is an attempt to measure the causal effects of one factor X on another factor Z by isolating a domain of phenomena — holding constant all other causal factors — and systematically varying one causal factor to observe the effect this factor has on an outcome of interest. The basic assumption is that an outcome is the joint effect of a set of (as yet unknown) causal conditions:

C1 & C2 & … & Cn cause Z,

where we do not yet know the contents of the list Ci. We consider the hypothesis that Cm is one of the causes of Z. We design an experimental environment in which we are able to hold constant all the potentially relevant causal conditions we can think of (thereby holding fixed Ci), and we systematically vary the presence or absence of Cm and observe the state of the outcome Z. If Z varies appropriately with the presence or absence of Cm, we tentatively conclude that Cm is one of the causes of Z.

In cases where individual differences among samples or subjects may affect the outcome, or where the causal processes in question are probabilistic rather than deterministic, experimentation requires treating populations rather than individuals and assuring randomization of subjects across “treatment” and “no-treatment” groups. This involves selecting a number of subjects, randomly assigning them to controlled conditions in which all other potential causal factors are held constant, exposing one set of subjects to the treatment X while withholding the treatment from the other group, and measuring the outcome variable in the two groups. If there is a significant difference in the mean value of the outcome variable between the treatment group and the control group, then we can tentatively conclude that X causes Z and perhaps estimate the magnitude of the effect. Take tomato yields per square meter (Z) as affected by fertilizer X: plants in the control group are subjected to a standard set of growing conditions, while the treatment group receives these conditions plus the measured dose of X. We then measure the quantity produced by the two plots and estimate the effect of X. The key ideas here are causal powers, random assignment, control, and single-factor treatment.

However, Lieberson insists that most social data are not collected under experimental conditions. It is normally not possible to randomly assign individuals to groups and then observe the effects of interventions. Likewise, it is not possible to systematically control the factors that are present or absent for different groups of subjects. If we want to know whether “presence of hate speech on radio broadcasts” causes “situations of ethnic conflict” to progress to “situations of ethnic violence” — we don’t have the option of identifying a treatment group and a control group of current situations of ethnic conflict, and then examine whether the treatment with “hate speech on radio broadcasts” increases the incidence of ethnic violence in the treatment group relative to the control group. And it is fallacious to reason about non-experimental data using the assumptions developed for analysis of experiments. This fallacy involves making “assumptions that appear to be matters of convenience but in reality generate analyses that are completely off the mark” (6).

Suppose we want to investigate whether being a student athlete affects academic performance in college. In order to treat this topic experimentally we would need to select a random group of newly admitted students; randomly assign one group of individuals to athletic programs and the other group to a non-athletic regime; and measure the academic performance of each individual after a period of time. Let’s say that GPA is the performance measure and that we find that the athlete group has a mean GPA of 3.1 while the non-athlete group has an average of 2.8. This would be an experimental confirmation of the hypothesis that “participation in athletics improves academic performance.”

However, this thought experiment demonstrates the common problem about social data: it is not possible to perform this experiment. Rather, students decide for themselves whether they want to compete in athletics, and their individual characteristics will determine whether they will succeed. Instead, we have to work with the social realities that exist; and this means identifying a group of students who have chosen to participate in athletics; comparing them with a “comparable” group of students who have chosen not to participate in athletics; and measuring the academic performance of the two groups. But here we have to confront two crucial problems: selectivity and the logic of “controlling” for extraneous factors.

Selectivity comes in when we consider that the same factors that lead a college student to participate in athletics may also influence his/her academic performance; so measuring the difference between the two groups may only measure the effects of this selective difference between membership in the groups — not the effect of the experience of participating in athletics on academic performance. In order to correct for selectivity, the researcher may attempt to control for potentially influential differences between the two groups; so he/she may attempt to control for family factors, socio-economic status, performance in secondary school, and a set of psycho-social variables. “Controlling” in this context means selecting sub-groups within the two populations that are statistically similar with respect to the variables to be controlled for. Group A and Group B have approximately the same distribution of family characteristics, parental income, and high school GPA; the individuals in the two groups are “substantially similar”. We have “controlled” for these potentially relevant causal factors — so any observed differences between academic performance across the two groups can be attributed to the treatment, “participation in athletics.”

But Lieberson makes a critical point about this approach: there is commonly unmeasured selectivity within the control variables themselves — crudely, students with the same family characteristics, parental income, and high school GPA who have selected athletics may nonetheless be different from those who have not selected athletics, in ways that influence academic performance. As Lieberson puts the point, “quasi-experimental research almost inevitably runs into a profound selectivity issue” (41).

There is lots more careful, rigorous analysis of social-science reasoning in the book. Lieberson crosses over between statistical methodology and philosophy of social science in a very useful way, and what is most fundamental is his insistence that we need to substantially rethink the assumptions we make in assigning causal influence on the basis of social variation.


Components of positivism

Many of us agree that “positivist” social science isn’t a good idea. But what is encompassed by “positivism” in this setting?

First, the favorable part of the story: positivism puts forward two ideas about conceptual clarity and empirical rigor that surely need to be a part of any intellectually sound effort to understand society, or to contribute to social science. Our concepts need to make sense (by some criterion of sense-making), and our assertions need to be supportable by some combination of empirical evidence and logical inference. These amount simply to the requirement that science should be rationally articulated and rationally justified. These are aspects of the epistemology of science advanced by the progenitors of positivism — for example, Mill, Comte, the Vienna Circle, Schlick, Carnap, Hempel — that I, for one, do accept. And if this were the full extent of positivism, then it would be hard to be anti-positivist.

But positivist social science makes several additional assumptions about social knowledge that are untenable, in my view.

First is naturalism — the idea that the social and behavioral sciences should have the same structure and logical characteristics as the natural sciences. Chemistry and physics — especially the classical versions of these sciences — have a unified hypothetico-deductive structure; they discover laws of nature; and they derive the observable features of the domains of phenomena they encompass. Naturalism postulates, therefore, that sociology, economics, or psychology should have the same logical structure, because that is what “science” requires. John Stuart Mill clearly presupposed this assumption in his discussion of the “moral sciences.”

Second, relatedly, is the unity of science — the idea that ultimately all scientific theories should be subsumable under one “most fundamental” master theory. This assumption brings with it the idea of reductionism; higher-level sciences (psychology) should be reducible to lower-level sciences (neurophysiology). And “reducible” means “derivable from given suitable bridge definitions and laws”. (This topic was central for the Vienna Circle logical positivists.)

Third is an assumption about methodology, to the effect that measurement and quantification are essential aspects of scientific knowledge. So quantitative statements and theories are preferable to qualitative or descriptive statements; and the goal of a social science should be to discover a set of variables within the domain of investigation that can be observed, measured, and counted. This is a different aspect of the unity-of-science doctrine: the idea that there should be one privileged method of discovery and presentation for the social sciences. Where does this assumption come from? In part, it seems to derive from the physics-envy associated with naturalism; but perhaps there is also a Platonic dimension as well — a preference for mathematics over descriptive or interpretive language.

Fourth is an assumption about explanation, regularities, and laws. The assumption here is that explanation requires the discovery of law-like generalizations about the domain of phenomena encompassed by the scientific field. This assumption has two components: the idea that a well-defined domain of investigation must somehow embody a set of regularities, perhaps disguised by the noise; and second, that explanations within the domain of individual events or patterns of events must take the form of a derivation of the explanandum from the general laws mentioned in the explanans. Carl Hempel and J.S. Mill agree about this premise.

Fifth is an assumption about causation — that causation is a feature of statistical relationships among variables rather than a feature involving causal necessity or causal mechanisms. This is a Humean approach to causation, and it leads positivist social scientists to restrict their attention to causal regularities rather than looking for real causal mechanisms.

Finally, there is a sixth premise that has also created debate but seems less intrusive to the practice of innovative social science — the insistence on the fact-value distinction. “Positive” science has to do with the discovery of facts, whereas ethics or policy stidies have to do with values.

Do these assumptions necessarily travel together? Not necessarily, though there are some internal logical connections among them that make it more difficult to imagine them standing completely independently. But it appears to be a characteristic of the observed sociology of science for an important stream of twentieth-century social science research, that these features are clustered together. And many critics argue that these assumptions have created blinders for social-science researchers, limiting their originality in theories, concepts, and explanations of the social world.

Critics of positivist social science ask us to consider a broader space of possibilities for research and theory formation in the social sciences. Taking the premise of scientific rationality as a given, what would a philosophy of social science look like that questioned the other premises on this list? What is a “post-positivist realism” for the social sciences?

  • It is realist about causation; it affirms the scientific validity of seeking for real social mechanisms.
  • It advocates for a conception of scientific explanation that hinges on the discovery of real causal connections among features of the social world.
  • It is pluralistic about method; it acknowledges that there are multiple rationally supportable methods of inquiry in the social sciences, and multiple forms that social-science knowledge can take.
  • It is even-handed among quantitative, qualitative, comparative, and narrative approaches to social inquiry and social explanation.
  • It is anti-reductionist and anti-naturalistic: it does not presuppose that various areas of the social sciences should be reducible to some other, more fundamental scientific theory; and it does not presuppose that the social sciences should resemble the natural sciences.
  • And, finally, it is fully committed to the positive features of rationality that were mentioned above: the scientific virtues of conceptual clarity and empirical-rational justification for scientific beliefs.

This set of alternatives opens up the space of the social sciences quite dramatically; it permits a wide and pluralistic range of inquiries to proceed, without the requirement of theoretical or methodological unity. And this frees researchers to arrive at accounts of their domains of research that are well suited to the particulars of these domains.

In a later posting I will come back to an important contribution to this debate, George Steinmetz’s The Politics of Method in the Human Sciences: Positivism and Its Epistemological Others.

Piecemeal empirical assessment of social theories

The philosophy of science devotes a large fraction of its wattage to this question: what is the logic of empirical confirmation for scientific beliefs? (A good short introduction is Samir Okasha, Philosophy of Science: A Very Short Introduction.) In the natural sciences this question became entangled with the parochial fact about the natural sciences, that scientific theories postulated unobservable entities and processes and that the individual statements or axioms of a theory could not be separately confirmed or tested. So a logic of confirmation was developed according to which theories are empirically evaluated as wholes; we need to draw out a set of deductive or probabilistic consequences of the theory; observe the truth or falsity of these consequences based on experiment or observation; and then assign a degree of empirical credibility to the theory based on the success of the observational consequences. This could be put as a slogan: “No piecemeal confirmation of scientific beliefs!”

This is the familiar hypothetico-deductive model of confirmation (H-D), articulated most rigorously by Carl Hempel and criticized and amended by philosophers such as Karl Popper, Nelson Goodman, Norwood Hanson, and Imre Lakatos. These debates constituted most of the content of the evolution of positivist philosophy of science into post-positivist philosophy of science throughout the 1960s and 1970s.

I don’t want to dive into this set of debates, because I am interested in knowledge in the social sciences; and I don’t think that the theory-holism that this train of thought depends upon actually has much relevance for the social sciences. The H-D model of confirmation is approximately well suited — but only to a certain range of scientific areas of knowledge (mathematical physics, mostly). But the social sciences are not theoretical in the relevant sense. Social science “theories” are mid-level formulations about social mechanisms and structures; they are “theories of the middle range” (Robert Merton, On Theoretical Sociology). They often depend on formulations of ideal types of social entities or organizations of interest — and then concrete empirical investigation of specific organizations to determine the degree to which they conform or diverge from the ideal-typical features specified by the theory. And these mid-level theories and hypotheses can usually be empirically investigated fairly directly through chains of observations and inferences.

This is not a trivial task, of course, and there are all sorts of challenging methodological and conceptual issues that must be addressed as the researcher undertakes to consider whether the world actually conforms to the statements he/she makes about it. But it is logically very different from the holistic empirical evaluation that is required of the special theory of relativity or the string theory of fundamental physics. The language of hypothesis-testing is not quite right for most of the social sciences. Instead, the slogan for social science epistemology ought to be, “Hurrah, piecemeal empirical evaluation!”

I want to argue, further, that this epistemological feature of social knowledge is a derivative of some basic facts about social ontology: social processes, entities, and structures lack the rigidity and law-governedness that is characteristic of natural processes, entities, and structures. So general, universal theories of social entities that cover all instances are unlikely. But second, it is a feature of the accessibility of social things: we interact with social entities in a fairly direct manner, and these interactions permit us to engage in scientific observation of these entities in a way that permits the piecemeal empirical investigation that is highlighted here. And we can construct chains of observations and inferences from primary observations (entries in an archival source) to empirical estimates of a more abstract fact (the level of crop productivity in the Lower Yangzi in 1800).

Let’s say that we were considering a theory that social unrest was gradually rising in a region of China in the nineteenth century because of a gradual shift in the sex ratios found in rural society. The connection between sex ratios and social unrest isn’t directly visible; but we can observe features of both ends of the equation. So we can gather population and family data from registries and family histories; we can gather information about social unrest from gazettes and other local sources; and we can formulate subsidiary theories about the social mechanisms that might connect a rising male-female ratio to the incidence of social unrest. In other words — we can directly investigate each aspect of the hypothesis (cause, effect, mechanism), and we can put forward an empirical argument in favor of the hypothesis (or critical of the hypothesis).

This is an example of what I mean by “piecemeal empirical investigation”. And the specific methodologies of the various social and historical sciences are largely devoted to the concrete tasks of formulating and gathering empirical data in the particular domain. Every discipline is concerned to develop methods of empirical inquiry and evaluation; but, I hold, the basic logic of inquiry and evaluation is similar across all disciplines. The common logic is piecemeal inquiry and evaluation.

(I find Tom Kelly’s article on “Evidence” in the Stanford Encyclopedia of Philosophy to be a better approach to justification in the social sciences than does the hypothetico-deductive model of confirmation, and one that is consistent with this piecemeal approach to justification. Kelly also reviews the essentials of H-D confirmation theory.)

Realism for the social sciences?


Scientific realism is the idea that scientific theories provide descriptions of the world that are approximately true. This view implies a correspondence theory of truth — the idea that the world is separate from the concepts that we use to describe it. And it implies some sort of theory of scientific rationality — a theory of the grounds that we have for believing or accepting the findings of a given area of science. (See a brief article on the basics of scientific realism including some useful references here.) Realism, objectivity, and facts go together. We can interpret a theory realistically just in case we believe that there is a fact of the matter concerning the assertions contained in the theory. (See earlier postings relevant to this topic, Concepts and the World and Social Construction.)

Realism raises all kinds of interesting questions when we consider applying it to the social sciences. For one thing, it requires a useable distinction between the world and the knower. This raises the question: is there an objective social world independent from the perceptions and concepts of observers? And this also is a complicated question, because the persons who make up social processes at the micro-level are themselves “knowers” of the social world. So there is a question about the objectivity of the social world and a corresponding question about social construction of social reality. If all social phenomena are socially constructed, then how can it be the case that some statements about social phenomena are objective and independent from the conceptual schemes of the observer?

Scientific realism got its impetus from the fact that physical theories invoke theoretical concepts that are not themselves directly observational — muon, gravity wave, gene (at an early stage of biology). So the question arose, what is the status of the reference and truth of scientific sentences that include non-observational concepts — for example, “muons have a negative electric charge and a spin of -1/2”? Since we can’t directly inspect muons and measure their charge and spin, sentences like this depend for their empirical confirmation on their logical relationships to larger bits of physical theory — and ultimately upon a measure of the overall degree to which this physical theory issues true experimental and observational predictions. And the empirical confirmation of the theory as a whole, the story goes, provides a rational basis for assigning a reference and truth value to its constituent sentences. So the fact that “muon” is embedded within a mathematical theory of subatomic reality and the theory is well confirmed by experimental means, gives us reason to believe that muons exist and possess approximately the characteristics attributed to them by muon theory.

But all of this has to do with esoteric physical theory. Is there any relevant application of realism in the social sciences? Here’s one important difference: the social sciences are barely “theoretical” at all in the sense associated with the natural sciences. The concepts that play central roles in social theories — charisma, bureaucratic state, class, power — aren’t exactly “theoretical” in the sense of being non-observational. And social concepts aren’t defined implicitly, in terms of the role that they play in an extended formal theoretical structure. Rather, we can give a pretty good definition of social concepts in terms of behavior and common-sense attributes of social entities. In the social sciences we don’t find the conceptual holism that Duhem and Quine attributed to the natural sciences (Pierre Duhem, The Aim and Structure of Physical Theory; W. V. O. Quine, Word and Object). Instead, both meaning and confirmation can proceed piecemeal. So if realism were primarily a doctrine about the interpretation of theoretical terms, there wouldn’t be much need for it in the social sciences.

But here are several specific ways in which scientific realism is useful in the social sciences, I think. And they all have to do with the kinds of statements in the social sciences that we think can be interpreted as expressing facts about the world, independent of our theories and concepts.

Causal realism. We can be realist about the meaning of assertions about causation and causal mechanisms. We can take the position that there is a fact of the matter as to whether X caused Y in the circumstances, and we can assert the objective reality of social causal mechanisms. On the realist interpretation, social causal mechanisms exist in the social world — they are not simply constructs of the observer’s conceptual scheme. And the statement that “Q is the process through which X causes Y” makes a purportedly objective and observer-independent claim about Q; it is an objective social process, and it conveys causation from X to Y. Q is the causal mechanism underlying the causal relationship between X and Y.

Structure realism. We can be realist about the existence of extended social entities and structures — for example, “the working class,” “the American Congress,” “the movement for racial equality.” These social entities and structures have some curious ontological characteristics — it is difficult to draw boundaries between members of the working class and the artisan class, so the distinctness of the respective classes is at risk; institutions like the Congress change over time; a social movement may be characterized in multiple and sometimes incompatible ways; and social entities don’t fall into “kinds” that are uniform across settings. But surely it is compelling to judge that the Civil Rights movement was an objective fact in the 1960s or that the Congress exists and is a partisan environment. And this is a version of social realism.

Social-relations realism. If we say that “Pierre is actively involved in a network of retired French military officers”, we refer to a set of social relations encapsulated under the concept of a social network and composed of many pair-wise social relations. Here too we can take the perspective of social realism. It seems unproblematic to postulate the objective reality of both the pair-wise social relations and the aggregate network that these constitute. Each level of social relationship can be investigated empirically (we can discover that Pierre has regular interactions with Jean but not with Claude), and it seems unproblematic to judge that there is a fact of the matter about the existence and properties of the network — independent of the assumptions and concepts of the observer.

Meaning realism. Now, how about the hardest case: meanings and the objectivity of interpretation. Can we say that there is ever a fact of the matter about the interpretation of an action or thought? When Thaksin offends Charat by exposing the bottoms of his feet to him — can we say that “Charat’s angry reaction is the result of the meaning of this insulting gesture in Thai culture”? Even here, it is credible to me that there is a basis for saying that this judgment expresses an objective fact (even if it is a fact about subjective experience); and therefore, we can interpret this sentence along realist lines: “Thaksin’s gesture was objectively offensive to Charat in the setting of Thai culture.” It is evident that many of our interpretations of behavior and action are substantially underdetermined by context and evidence; so it may be that much interpretation of meaning does not constitute a “fact of the matter.” But this seems to be a fact about particular judgments rather than a universal feature of the interpretation of meanings.

So it seems that it is feasible and useful to take a social realist perspective on many of the assertions and theories of the social sciences; and what this says, is that we can interpret social science statements as being approximately true of a domain of social phenomena that have objective properties (i.e. properties that are independent from our conceptualization of them).

Concepts and the world

What is the relation between concepts and the world? And how do we arrive at a conceptual scheme that provides a perspicuous way of representing reality?

This way of putting the question invokes one of the central polarities that has defined modern philosophy, including the traditions of Locke, Descartes, and Kant. It is the contrast of representation and reality; the polarity between the structures of mentation through which we conceptualize and represent the world, and the world itself, the given, the objective features of reality independent from our schemes of representation. Richard Rorty’s title, Philosophy and the Mirror of Nature, expresses his skepticism about this polarity and about the conception of the relation between knower and the known that it corresponds to. But the intellectual conundrums these questions raise are unavoidable.

One of the views that we can take on the relation between mind and world is “realism”: the idea that, for a given range of stuff, the stuff is independent from the ways in which we represent it. So we can be realist about fish or electrons, for example; and what this means is that we think there are real fish and electrons, and that their properties are objective and independent of the ways in which we conceive or represent them. But there is a huge problem with this view — even as much as I want to defend a form of realism. It is the problem that “properties” are not objective features of the world, but are rather attributes singled out by concepts. And concepts are part of our mental schemes — not inherent or objective features of the world that we are representing. So properties are not objective features of reality. “Facts” are similarly representation-dependent: we can’t express a fact about the world except on the basis of a set of concepts. So we can’t say that properties and facts are independent of the scheme of representation. And this seems to lead to the conclusion that naive realism is untenable.

The alternatives to realism include idealism and conceptualism — the view that the properties of “stuff” are constituted by the mental schemes that we bring to the study of “stuff”. But idealism is intellectually unpalatable, in its Berkeleian version anyway: in the idea that there is no objective, material world, but only a set of subjective mental representations conforming to an orderly succession in the mind. A particular version of conceptualism is the Whorf hypothesis, according to which different cultures inhabit different worlds because of the fundamental and incommensurable differences that exist between their conceptual schemes (Benjamin Whorf, Language, Thought, and Reality). And there is the Kantian version of these ideas — the view that empirical reality cannot be recognized except through an organizing set of concepts or categories; so naive realism is ultimately incoherent. There is nothing we can say about a “noumenal” world — a world as it really is beyond the categories of empirical experience.

So we might cautiously admit to the correctness of “conceptualism” — that anything we say about the objective or real features of the world in inevitably couched within a set of concepts or categories, and there is no uniquely best set of concepts on the basis of which to analyze experience. This doesn’t take us to idealism, however. It doesn’t say that the world is subjective or constituted by consciousness; it doesn’t say that the world lacks an independent status from the mind. What it says is that knowledge and representation of the world are inherently conceptual — and this is an act of mentation rather than simply a reflection of the objective features of the world.

Within this conceptualism, we might say that the best we can do is to acknowledge that the world of stuff interacts with the knower; the knower brings a set of concepts to his/her interactions with stuff; and knowledge and the world are the joint product of this interaction. This view isn’t necessarily idealist in the Berkeley sense — according to which reality is simply an ensemble of mental representations. But it is non-objectivist when it comes to the facts about the world: the facts are dependent upon a scheme of concepts within the context of which we characterize states of affairs. And, at the same time, it permits us to assert that there are objective facts of the matter once we have settled on a conceptual scheme.

This set of issues has special relevance to the philosophy of social science; I’ll have to return to the topic in another posting.

Politics and science

In the idealized version of science, the enterprise of scientific knowledge discovery follows its own logic without extraneous non-cognitive or non-rational influences. But this is unrealistic. Science is a social activity, conditioned by institutions, governments, and other social forces. So it is worth considering how politics influences the course of science and how these influences affect the rationality or veridicality of the enterprise. Does the fact of political influence on science make science either less rational or less true?

There are at least two ways in which science can be affected by social or political factors: in terms of the formulation of research questions and priorities, and in terms of the content of the findings of scientific research. Pernicious examples of the second kind of influence are easy to find in the history of science. For example, Stalin’s insistence on the correctness of Lysenko’s adaptationist theory of species led Soviet biology into a biological science that was profoundly untrue: organisms do not evolve according to the processes or mechanisms attributed to them by Lysenkoism. So the political imperative from Stalin to the adoption of a particular scientific hypothesis led to erroneous science. It was also irrational science (because it depended on criteria of acceptance that were political rather than empirical).

This form of influence of politics on science is clearly anti-scientific and anti-rational. And, regrettably, we appear to have clear instances of this kind of substitution of political expediency for rational scientific judgment in the behavior of the current US administration, in the form of its efforts to control the content of scientific judgments about climate change (article).

So one form of political influence on science is clearly anti-scientific. When politicians substitute their wishes for the judgment of capable scientific researchers, it is inevitable that the result will be bad science. And, of course, bad science is a bad basis for future problem-solving.

But consider the other form of influence mentioned here: the setting of priorities for scientific research, especially through funding strategies. Is this kind of influence inherently inconsistent with the empirical and rational claims of science?

It is obvious that science is subject to this kind of influence. When the National Institutes of Health decides to give higher priority to one kind of cancer rather than another, or to diabetes research over Alzheimer’s research, this national institution is setting the agenda for university researchers throughout the country. When the US government gave priority to space exploration research over atmospheric or oceanic research in the 1960s, it likewise gave encouragement to certain scientific disciplines and inhibition to others. And, predictably, there was more progress in the scope and depth of scientific knowledge in some disciplines than others, following the spending priorities. Science requires resources, and one of the duties of a democratic government is to decide about priorities in the expenditure of public moneys.

What this kind of influence does not do, is to dictate the content of the findings. Once the resources are committed, it is essential that the normal processes of science — empirical study, peer review, experimentation, theory development — should take place without interference from political or social pressure.

So we might say that the “priority-setting” influence of politics on science is benign from the point of view of scientific rationality. The political decision-makers decide what scientific problems are most important from the public’s point of view; and the scientists, following the funding sources, then do their best to understand and solve those problems. But this means that one of our crucial political goals ought to be to create and defend institutions that assure the political independence of scientists, so their research findings are the result of empirical investigation and theory formation rather than pressured conformance to the state’s expectations.

Now let’s bring these ideas back to the social sciences. Social science research has been subject to both kinds of political influence in American history. There have sometimes been intense pressures on social scientists and historians about the content of their research — for example, the field of China studies during the period of McCarthyism. When social scientists arrive at unpalatable truths, they are sometimes subjected to shameful political pressures. But second, the social sciences have certainly been shaped in the past forty years by the spending priorities of federal and non-profit funding agencies. This influence isn’t necessarily bad, or anti-scientific. In fact, it is unavoidable. But, in the social sciences especially, it may have the insidious effect of pushing social science research away from some difficult or controversial topics; and this may be so, even when those topics turn out to be particularly important for arriving at a better understanding of where our society is going.

Social description as science

Descriptive research and writing in the social sciences is generally looked at with a degree of condescension. The complaint is that science should be explanatory, and descriptive work is both shallow and trivial. We can almost hear the doctoral supervisor responding to the candidate who has spent a year in primary research in the field and in tax offices in Indonesia, producing a finely detailed descriptive study of how the fiscal institutions actually work across levels and regions: “That’s well and good, but what do you make of your findings? What patterns have you discovered? Why do the variations you’ve documented occur as they do? Where’s your theory?”

The “shallow and trivial” criticism is unfounded and unjust. Our talented field researcher will have found an enormous and surprising range of variation among the institutions and practices he has studied. And these variations cannot be inferred from some general theory of fiscal institutions. They must be discovered and documented on the ground. Further, we can’t come up with any useful theory of institutions in the absence of some rigorous, concrete, and particular descriptions of a variety of institutions. We need the complexity and texture of good, rigorous description to help produce genuinely explanatory theories. (Robert Klitgaard’s treatment of corruption fits this description nicely; Controlling Corruption.)

So detailed descriptive research is important — because the social world is unruly and varied, and there is no single rule or law that generates this diversity; and it is difficult, in that it requires extensive and disciplined efforts at observation and discovery. Moreover, descriptive research is theoretical in one important respect: deciding upon the features of the phenomena that are worth recording is itself the result of preliminary hunches about what is salient or significant. (Of course there is no such thing as pure description.)

At the same time, the critic is right in one important respect: having documented variation in practices and local implementation of the basic fiscal institutions, it is quite reasonable to expect the researcher to try to find some explanations of this variation. Our graduate student now needs to reconsider the manuscript and try to determine whether any of the variation and particularity makes sense from the point of view of known social mechanisms. Why did the Indonesian fiscal system evolve into the variegated structure it now consists of? And this is where social theory is most useful — not as a grand explanatory scheme, but as many small bits of theory capturing some relevant features of behavior and institution-building in these particular circumstances. So, for example, our graduate student may notice that principal-agent problems are endemic in fiscal institutions. Given that taxes are being assessed and paid, all the participants have some motivation to subvert the process. So it may be that some observed variants can be explained as strategic efforts to solve principal-agent problems. Or as another example — limited and unreliable forms of communication may exist in some parts of the country under study, and features of the fiscal system in these under-served regions may have been selected because they are less reliant on swift, accurate communication.

Maybe this gives a basis for assessing the role of descriptive inquiry in the social sciences.

  • Because social phenomena are heterogeneous and plastic, there is an important and enduring role for careful descriptive inquiry. The task of discovering and documenting the variety and diversity of social phenomena is both important and intellectually challenging.
  • Because social phenomena emerge from purposive human agency, there are an open-ended number of social mechanisms that are potentially relevant to the diversity that is discovered.
  • And because we are ultimately interested in explaining as much variation as we can, it is desirable to bring those theoretical widgets to bear on various elements of the diversity that is discovered in the descriptive research.

And, finally, it is unrealistic to imagine that either description or theorizing can be conducted solely independently. Instead, description requires theorizing and conceptualizing, and theorizing requires some accurate descriptions of the world to work with. As Kant wrote in a different context, “Concepts without percepts are empty, percepts without concepts are blind.”

(As I imagine the hypothetical example above I think of Alfred Russel Wallace’s fine and detailed descriptions of the flora and fauna of the Malay archipelago, and the role that this detailed natural history played in the formation of his own and Darwin’s theories of natural selection. The purpose of the theory was to find some degree of order in the intricate diversity of the biological domain. But the biology of species had a major advantage over the sociology of institutions and practices: there was in fact only one governing mechanism, random variation and selection, so it was possible to encompass the full range of observed diversity under a single ecological theory. The case is different in the social world because there are multiple independent causes leading to social differentiation.)

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