Science as a Market Process



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Science as a Market Process
Allan Walstad

Department of Physics

University of Pittsburgh at Johnstown

Johnstown, PA 15904


Abstract: Scientific inquiry is amenable to economic interpretation and analysis because scientists, like other people, pursue their individual goals through purposeful action. They make choices on the basis of costs, benefits, and risks as they perceive them. Their interaction with other scientists involves both cooperation and competition in a market that bears many similarities to the traditional economic market. The Austrian school of economics offers a particularly apt basis for an economic perspective on scientific inquiry. Economic concepts remain highly underutilized and ripe for exploitation.
This article originally appeared in The Independent Review: A Journal of Political Economy (Summer 2002 Volume VII, no. 1). © Copyright 2002, The Independent Institute, 100 Swan Way, Oakland, CA 94621-1428 USA; www.independent.org
Science as a Market Process: Table of Contents

Introduction 3
The Economic Point of View 6
The Scope of Economics 6

The Market 10


Economic Modeling 12
Assumptions and Scenarios 12

Mathematical Models Versus Idealized Scenarios 14


Similar Features of the Traditional and Scientific Markets 20
Specialization 20

Exchange 22

Investment and the Structure of Production 23

Entrepreneurship 27

Organization 31

Self-Regulation 33

Market Failure 35

Differences: A Market Without Money 38


Economics as a Critical Perspective 42
On Methodology 42

On the Darwinian Analogy 47

On Mertonian Norms 48
The Economic Structure of Scientific Revolutions 54
Kuhn’s Political Metaphor 54

Normal Science Versus Scientific Revolutions 57

Insights from the Modeling Approach 58
The Rationality of Science as Economic Rationality 63
Summary 71
Footnotes 74
References 76

Introduction
To allocate resources in the pursuit of chosen ends is an economic matter: a matter of costs and benefits, of investments, risks, and payoffs--above all, a matter of choices and trade-offs. Surely, the allocation of cognitive resources in the pursuit of knowledge must be a case in point. In science, we may devote all our efforts to making a few extremely precise measurements, or we may achieve a greater number of measurements at the cost of poorer precision. We may spend years attempting to solve a particularly significant theoretical problem--at the risk of complete failure--or we may choose safer, less significant problems. There are trade-offs associated with collaboration versus independent work, with the strictness of one's standards for accepting experimental results and other researchers' findings, and with the choice between adopting a new theory or continuing to work within the old: thus, collaboration brings the benefit of others' expertise, but the coordination of multiple efforts takes time and imposes limits on individual initiative; strict epistemic standards carry the benefit of minimizing error at the risk of rejecting truth; adopting a new theory involves an investment in learning to use it, the risk that it will prove fruitless, and the opportunity cost of results that might have been achieved with the old; but, there is the potential payoff of achieving revolutionary advances with the new one.

This paper is intended as a manifesto for an economic theory of scientific inquiry. My focus is not on traditional economic concerns about how societal resources are allocated to the funding of science and how scientific research contributes to technological advances and economic growth. Rather, my attention centers on using economic concepts to illuminate the conduct of scientific inquiry itself.

This work was originally conceived in the mid-1980s independently of the few then-existing efforts along roughly the same lines, and before I was well acquainted with the Austrian paradigm that now informs it. In the years since, a number of works have appeared which recognize the relevance of economic concepts and apply them to issues in scientific research. Nevertheless, a distinct contribution is offered through several major features of the present essay:

>The theory is grounded in the outlook of a particular school of economics, the Austrian school, which I claim lends itself especially well to extensions of economic thinking beyond its traditional sphere.

>The relevance of an economic point of view is demonstrated through numerous parallels between science and traditional economic activity.

>A proposal is made to extend the concept of the “market” to encompass a broader range of transactions than fall traditionally within its scope, thereby opening the door to a conception of science as a market process. In the scientific market (or “marketplace of ideas”) there is a process of exchange in which citation is the payment for use of another’s published work; nevertheless, the right to receive citation is not usefully characterized as a property right.

>Economics is applied as a critical perspective on several classic approaches to understanding the process of scientific inquiry: logical methodology, evolutionary epistemology, Mertonian norms, and Kuhnian revolutions.

>Together with insights adopted from the modeling approach to philosophy of science, economic thinking is used to shed light on the nature of scientific change and scientific rationality.

Among those who have argued for the relevance of economic concepts to an understanding of scientific inquiry, Radnitzky (1987a; 1987b) and Rescher (1989) have emphasized a cost-benefit approach. Diamond (1988), Goldman and Shaked (1991), and Wible (1998) have offered mathematical models of, respectively, theory-choice, truth acquisition, and misconduct in science, based on the principle of utility maximization by individual scientists. Numerous authors have drawn attention to one economic concept or another, such as exchange (Storer 1966), competition (Hagstrom 1965), and division of labor (Kitcher 1990), in discussing scientific inquiry. Polanyi (1951; 1962; 1967), Ghiselin (1989), Railton (1984), Bartley (1990), and Lavoie (1985) are among others who have developed extensive economic parallels. Economists who in recent years have been taking seriously a comprehensive economic approach to science include Dasgupta and David (1987; 1994), Stephan (1996; also Stephan and Levin 1992), Leonard (1998), and Wible (1998).

In a recent book, Wible (1998) seeks to establish an economics of science, examining various aspects of scientific inquiry on the assumption that the scientist is a rational economic agent. Our approaches differ entirely in that Wible adopts a mainstream economic perspective rather than Austrian, does not consider scientific inquiry to be a market process, and addresses a rather different mix of issues. Among Wible’s main concerns are a) scientific misconduct and deficiencies in the institutional “self-correctiveness” of science, with implications for its ability to serve society, b) how scientists choose research problems and programs, and c) the self-referential nature of an economic perspective on economics itself, taken to be a science.

The work of Leonard (1998) appears close in spirit to my own. Leonard advocates "using economics to study science and its product, scientific knowledge". He sees science as an "invisible hand" process in which competition among self-interested agents--whose interests are not purely epistemic--leads very successfully to the production of reliable knowledge. He carefully contrasts the economic perspective with traditional philosophical as well as post-modern views.

Thus, the work presented below finds its place within a growing body of literature devoted to, or touching on, scientific inquiry as an economic process.



The Economic Point of View
The Scope of Economics: On the view I am adopting, the scope of economics is not limited to such traditional concerns as the creation of wealth, or transactions involving money, or even the allocation of scarce resources among competing purposes. Human beings pursue their individually chosen goals through purposeful action; economics is the intellectual discipline which traces the consequences of that fact. This conception is close to that which Israel Kirzner (1976), in reviewing the history of attempts to define the nature of economics, identifies as originating with the Austrian school, of which Ludwig von Mises, F. A. Hayek, and Kirzner himself have been prominent members. Mises' magnum opus Human Action ([1949] 1996) provides a comprehensive exposition of economic theory according to the Austrian school and will serve here as my standard economics reference.

Mises defines human action as purposeful behavior, as aiming at ends and goals (p. 11). He uses the term praxeology to refer to the general study of human action so defined (p. 3, 12), reserving the term catallactics for the subset of problems which fall within the traditional scope of economics. Significantly, he emphasizes that no strict boundary can be drawn to demarcate catallactics from the rest of praxeology (p. 3, 10, 232-4).

Mises uses the word "economics" flexibly. In some passages, such as the following one from page 3 of Human Action, he clearly means thereby traditional economics, or catallactics:
Out of the political economy of the classical school emerges the general theory of human action, praxeology. The economic or catallactic problems are embedded in a more general science, and can no longer be severed from this connection. No treatment of economic problems proper can avoid starting from acts of choice; economics becomes a part, although the hitherto best elaborated part, of a more universal science, praxeology.
Elsewhere, as on page 266, he speaks of economics in a broad sense, as equivalent to praxeology itself:
Economics is, of course, not a branch of history or of any other historical science. It is the theory of all human action, the general science of the immutable categories of action and of their operation under all thinkable special conditions under which man acts.
Economics in this broad sense is to be distinguished from "the field of catallactics or of economics in the narrower sense" (p. 234).

In this paper, "economics" will be understood in its broad sense. The limited sphere of traditional economic applications will be referred to as "traditional economics". Just as traditional economic activity (which I may refer to as "business", for short) is to be regarded as only one imprecisely delimited province of the larger realm of human action amenable to economic analysis, science is another such province. Sometimes I will refer to "scientific inquiry" in place of "science" in order to emphasize the activities, choices, and interactions of scientists more than subject matter, data, and theories.

Clearly, economic insight is not a substitute for specialized knowledge and experience, in science or elsewhere. Economics can no more tell a scientist whether a theory is correct, or how to apply it, or how to devise an experiment, than it can instruct an automotive engineer how to design a reliable motor. Note, however, that the engineer's knowledge is not by itself sufficient to determine the parameters of the motor that will actually be manufactured. Different sizes and designs will offer different levels of power, durability, and fuel economy, will require more or less expensive materials and more or less time to develop and build, and will ultimately prove more or less profitable. Thus, there remains the problem of choice and trade-offs among alternatives. This problem exists in science (where the many trade-offs include those identified in the opening paragraph of this paper) as well as in business and all other realms of human endeavor. It is the problem addressed by economics.

In recent decades a number of overt extensions of economic analysis beyond its traditional scope have been put forward. Becker (1976; see also Tommasi and Ierulli, eds. 1995) has applied economic reasoning to subjects typically associated with such fields as sociology, political science, law, and even psychology. Radnitzky (ed.) (1992) and Radnitzky and Bernholz (eds.) (1987) promote an economic approach to a variety of fields. The interaction of politicians, bureaucrats, and special interests in the political arena has been examined from an economic perspective by the Public Choice school (see Gwartney and Wagner 1988). Sowell's Knowledge and Decisions (1980) offers a non-technical economic analysis of social, legal, and political institutions. McKenzie and Tullock (1989) built an introductory text around diverse non-traditional applications of economic thinking. Thus, economic interpretations of scientific inquiry fit into an existing body of extended economic scholarship. Such extensions are not universally welcome, and it is perhaps ironic that the Austrians, from whose perspective they so naturally flow, did not take the lead in developing them.


The Market: Nevertheless, from the Austrian point of view, to develop an economic interpretation of scientific inquiry is simply to apply praxeological analysis to an area of human action. What I am proposing, however, goes a bit further: not just an economic perspective, but a view of science as a market process. For this proposal to succeed, it is necessary that the concept of the market be broadened beyond its traditional meaning in a way that parallels the broadened understanding of economics.

The market is ordinarily defined in terms of, or associated with, buying, selling, and prices, and that is how Mises clearly portrays it in many passages of Human Action. Thus, on pages 232-4 he refers to "market phenomena" as "the determination of the market exchange ratios of the goods and services negotiated on markets, their origin in human action and their effects upon later action"; he says, "The subject matter of catallactics is all market phenomena with all their roots, ramifications, and consequences"; and, "Market exchange and monetary calculation are inseparably linked together". But Human Action, like other treatises on economics, is really about traditional economics, even though Mises devotes considerable space to grounding the subject in the larger field of praxeology. As we move the focus of our attention beyond a limited subject area, surely it is reasonable to entertain a broader application of terminology which had been defined for use primarily within that limited area.

A passage on page 258 leaves the door at least slightly ajar:
The market process is the adjustment of the individual actions of the various members of the market society to the requirements of mutual cooperation. The market prices tell the producers what to produce, how to produce, and in what quantity. The market is the focal point to which the activities of the individuals converge. It is the center from which the activities of the individuals radiate.
Within traditional economics, prices do inform the process of "adjustment of the individual actions...to the requirements of mutual cooperation". But cooperation also occurs outside the realm of traditional economics; such cooperation must involve a process of adjustment of individual actions, and that process must be informed in some way. To the extent that the process involves exchange, it deserves to be called a market process.

Let the concept of the marketplace, or simply market, encompass the entire array of institutions and customary modes of interaction through which people engage in exchange in pursuit of their individually chosen goals. Must exchange involve buying, selling, and prices? No. I argue, in partial agreement with a number of authors, that cooperation in science is mediated by a process of exchange which does not possess such features--namely, the practice of citation. It will follow that scientific inquiry is characterized by a market (the "scientific market") that is distinct from the market of traditional economics (the "traditional market"). This scientific market is indeed the focal point of the activities of the community of scientists, where they offer the results of their own research and acquire access to the research of others, where they give and receive proper credit.



Economic Modeling
Assumptions and Scenarios: In economics, idealized scenarios or models are employed to gain insight into complex systems of interaction. (Mises [1949] 1996, 236-7, uses the term "imaginary constructions".) These scenarios embody assumptions concerning a) the goals and preferences of the people acting and b) the constraints and influences under which they act. A highly idealized scenario may serve as a basis for devising more realistic ones by incorporating additional assumptions. To investigate the benefits of exchange, we may consider the plight of an isolated individual attempting to supply his or her basic needs self-sufficiently. We may imagine two farmers seeking to maximize their cash incomes through cultivating adjacent plots of land independently and compare that situation with one in which they pool their efforts. We may foresee complications which will arise when additional farmers and plots of land are brought into the cooperative effort, and we may anticipate institutions which might evolve to handle those complications. An economic scenario might be constructed on the premise that individuals seek to maximize their cash incomes in a free market with no governmental constraints other than enforcement of contracts and punishment of aggression. This scenario could then be modified by allowing for a wider range of individual goals (status, security, altruism, etc.) and imposed constraints (taxes, quotas, regulations, prohibitions, etc.).

Quite similarly, by developing idealized scenarios of scientific inquiry based on simplifying assumptions about scientists' motives and the constraints and societal influences under which they act, we may better understand the observed features of the research process and anticipate how the institutions and progress of science might vary with different circumstances.

I propose to adopt, as a first approximation, the assumption that scientists are motivated by a desire for recognition from their professional peers. Now of course, scientists have other motives as well, which vary in relative importance from one individual to the next. Someone might indeed pursue scientific research purely out of curiosity, with no thought of recognition, just as many people engage in hobbies and charitable work with no expectation of financial reward. Such purposeful behavior would still fall within the scope of economics as construed here. Nevertheless, it is clear that most scientists seek professional recognition, either for its own sake or as a key to other rewards such as tenure and financial gain. Priority disputes (Merton 1957) and near-universal anxiety over having research results anticipated (Hagstrom 1965, ch. II) indicate what a powerful incentive recognition is.

Professional recognition is not to be confused with public acclaim. What we are taking as the prime motivating factor is recognition for contributing to the advance of science, as judged by experts in the field. (If professional recognition is sought as a means to public acclaim, then our assumption is still good. To the extent that scientists seek public acclaim that is not grounded in professional recognition, our assumption is inadequate and perhaps misleading.) Even a perfectly selfless seeker of truth might well consider recognition to be a useful form of guidance from the scientific community, an indicator regarding the effectiveness of his or her research efforts; those efforts could be, in effect, directed toward the pursuit of recognition.

As for constraints and influences which arise from outside the scientific community, our first approximation might be simply to ignore them. Let us imagine that scientists communicate only among themselves, that they have independent sources of income to support themselves and their research, and that they are not subject to external forces such as censorship. The resulting picture of scientific inquiry as a self-contained competition for collegial recognition will be taken for granted in much of this paper, with additional assumptions about how science is funded, about scientists' motivations other than recognition, etc. brought in where salient.
Mathematical Models Versus Idealized Scenarios: Scientists employ idealized models of physical systems. An example from physics, which is used to gain insight into the electronic structure of solids, describes a single electron that is free to move in only one dimension, subject to a simplified potential energy function (the "periodic square-well potential"). A real solid is a three-dimensional array of atomic nuclei and electrons, perhaps dozens of electrons per atom. Nevertheless, the extremely idealized model elucidates major differences in the electrical and optical properties of metals, semiconductors, and insulators. Experience gained with this model facilitates development of progressively more sophisticated ones incorporating realistic potential functions, lattice vibrations, impurities, defects, three dimensions, etc. Through such models, one gains insight into the properties of known materials and predicts the properties of others that might be fabricated, e.g., variously doped semiconductors.

Clearly, some parallels might be drawn between the use of idealized models of physical systems and idealized scenarios of human action. How deep does the similarity run? In particular, given that analytically powerful models of physical systems tend to be formulated or articulated in terms of mathematics, should we expect corresponding mathematical models of human action (here, models of scientific inquiry) to be similarly fruitful? In Diamond (1988), Goldman and Shaked (1991), Kitcher (1990), and Wible (1998), cited earlier, the focus is indeed on mathematical models in which functions with adjustable parameters are said to characterize the various options, propensities, and outcomes. As is typical of work in mainstream traditional economics, these authors even invoke a maximization of expected utility (or "optimality analysis") in much the same way that a physicist might employ, say, maximization of entropy or minimization of energy.

But the systems studied by physicists differ radically from human action in ways that cast doubt on the suitability of a mathematical approach to modeling the latter. Silicon atoms are identical. Electrons are identical. Relevant properties of silicon atoms and electrons are measurable and characterizable mathematically, once and for all, in terms of a few parameters. There is little doubt that the interaction of silicon atoms and electrons in a semiconductor crystal is correctly described by the known equations of quantum mechanics. We may need to use approximations and idealizations, but we obtain quantitative results by solving mathematical equations. We can perform repeated experiments on the same sample of silicon or on different samples identically prepared, with repeatable quantitative results. Testing quantitative theoretical predictions with precise, repeatable measurements is the key to refining our models.

Unlike atoms, human beings are unique individuals. Each makes frequent choices on the basis of individual goals, preferences, and capabilities which are subject to continual change and are not fully articulable. In striking contrast with the realm of physics, there are no fundamental or enduring numerical constants characterizing human action (Mises [1949] 1996, 55-56, 118). Nor is human history subject to controlled, repeatable experiments. Under these circumstances, how meaningful is it to represent interacting humans via mathematical functions? When the equations are solved, do the results have any significance? Such concerns have provoked trenchant Austrian-school criticisms of econometrics and mathematical modeling in traditional economics. (See for example Mises [1949] 1996, 350-7; Yeager 1991, 150-63.) These same concerns must cast doubt on mathematical modeling in an economic perspective on scientific inquiry.

Exploring idealized scenarios through verbal reasoning stands as the alternative to mathematical modeling. A great deal of knowledge regarding human action is available to us through introspection and common experience, but this knowledge is qualitative, not quantitative. We are aware of the diversity of human motivations, aptitudes, and circumstances. We know that humans pursue goals, that to pursue goals requires pursuing the means to those goals, that action involves choices among alternatives, that people commonly prefer to receive benefits sooner rather than later, that they communicate, cooperate, and compete. In exploring idealized scenarios we can draw on all our knowledge without articulating it fully in advance (which would be impossible anyway). Simplifying assumptions expressed verbally carry with them a large component of tacit understanding. As the consequences of our assumptions are explored, the assumptions themselves become refined and clarified.

The scenario therefore accesses a wide range of qualitative and even unarticulated knowledge in a process that involves active reasoning throughout, in contrast to a mathematical model, which only generates numerical output in response to numerical values of a few input parameters. Scenarios can provide only qualitative results, but the quantitative output of mathematical models represents little or no advantage; for, given the degree of uncertainty and idealization involved, little significance can be attached to the precise values of numerical inputs and outputs.

To trust mathematical models of human action as something more than suggestive or illustrative Tinkertoys could be profoundly misleading. Mathematical models in the physical sciences routinely provide a basis for the design and control of physical systems to serve useful purposes: a basis for engineering. That major, enduring, spontaneously evolved human institutions, possessed of ecological complexity and interdependence, might be redesigned, replaced, or substantially improved through analogous social engineering is highly questionable. The danger of mathematical models is precisely that they may lend false plausibility to misguided utopian schemes.

Consider Philip Kitcher’s 1990 paper, “The Division of Cognitive Labor.” Given the existence of competing experimental methods for solving a particular problem in science, Kitcher assumes that the probability of success of each method can be expressed as a mathematical function of the number of scientists utilizing it. Knowing these functions, it is a simple matter to calculate the distribution of scientists among the competing methods such that the total probability of success is maximized. (A similar analysis is offered with regard to competing theories.)

Unfortunately, neither the functions nor the experimental methods are simply given to us, and their discovery itself becomes a difficult problem requiring the allocation of intellectual resources--how? Scientists are not mere interchangeable parts that can be counted out by the dozen. And who decides in the first place which problems are most worthy of attack? Never mind. Kitcher, a distinguished philosopher, sees the way clear to redesigning the very institutions of science on the basis such optimality analyses! Here are his conclusions:
...[W]e can ask how, given all the aims that we have for ourselves and our fellows, we should allocate resources to the pursuit of our community epistemic goals. Given the solution to this optimization problem, we know the size of the work force that the sciences can command. We can then ask for the optimal division of labor among scientific fields, and, finally, proceed to the question that has been addressed in a preliminary way in this essay: what is the optimal division of labor within a scientific field, and in what ways do personal epistemic and nonepistemic interests lead us toward or away from it? That question ultimately finds its place in a nested

set of optimization problems.

...[I]t would be highly surprising if the existing social structures of science, which have evolved from the proposals of people who had quite different aims for the enterprise and who practiced it in a very different social milieu, were to be vindicated by optimality analysis. How do we best design social institutions for the advancement of learning? The philosophers have ignored the social structure of science. The point, however, is to change it.
I find it impossible to take this passage seriously. What it amounts to is nothing other than a proposal for central economic planning, an idea that received devastating theoretical criticism from Mises and Hayek in the 1920s and 1930s and failed in practice everywhere it was tried. As John Ziman (1994, 118) points out, if central planning will not work in the traditional economic realm, it certainly will not work in science:
Everybody now appreciates the practical impossibility of planning in advance, from a single centre, the routine manufacture of all manner of standard products to meet the foreseeable needs of a nation: it is scarcely credible that this approach could succeed [in science] where every item is novel, where the means of production are uncertain, and where the needs to be met are not even clearly conceived.
I have pursued the contrast between idealized scenarios and mathematical models at length in this section in order to make clear that my use of the former and avoidance of the latter reflects a deliberate, principled choice. In the traditional economic realm, the existence of numerical data such as prices, total expenditures, and the unemployment rate lends at least superficial plausibility to mathematical modeling through which one might hope to relate, reproduce, or even predict trends in those data. In philosophical and related studies of scientific inquiry, the deployment of mathematical and logical formalism appears to offer little benefit by way of insight or application, while the display of technical virtuosity may lend undue credence to insubstantial claims and misguided policy recommendations.

An idealized scenario is of course a kind of model. Devising and exploring a scenario is a kind of model building. Because "model" is the term in common use, and because modeling in general is an important theme later in this paper, henceforth I will feel free to refer to an idealized scenario as just an economic model.


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