The Grand Bazaar of Wisdom (1)
The term ‘economics of scientific knowledge’ (ESK) was coined as a reaction to the field known from the seventies as ‘sociology of scientific knowledge’. The latter had been defined by the members of the so called ‘Strong Programme’1 in contraposition to the classical notion of a ‘sociology of science’, having in mind a distinction between the sociological explanation of the institutional, political and cultural aspects of science, on the one hand, and a sociological explanation of the cognitive aspectsof science, on the other hand. ‘Sociology of science’ would be devoted to the ‘external’ (non epistemic) aspects of science, whereas ‘sociology of scientific knowledge’ would study the ‘internal’ content of science, i.e., why certain theories, facts or paradigms are accepted or rejected. Sociologists in the ‘Strong Programme’ derived some radically relativist conclusions from this starting point, in opposition to most traditional views about scientific knowledge. An open question was, hence, whether the application of analytical instruments drawn from the economist’s toolkit to the understanding of the process of knowledge generation, i.e., the view of scientists as agents within an economic model, would support the relativist claims of radical sociologists or, on the contrary, would serve to ‘save’ the intuitive character of scientific knowledge as a paradigm of ‘objectivity’. As we shall see, most contributions to ESK fall under the second of these options.
Obviously, in retrospect we can realize that many antecedent works can be identified under the ESK label, though the main production of papers and books on the topic has been made in the last two decades. One possible way of classifying all these works is according to the type of economic models or metaphors they attempt to apply to the study of the creation of scientific knowledge. From this point of view, we can distinguish, first, formal (or ‘mathematical’) from non-formal (or ‘institutional’) approaches. One of the most important contributions in the first group is Philip Kitcher’s paper entitled “The Division of Cognitive Labour”2, in which he develops a set of models based on the assumption of interacting rational self-interested scientists. According to Kitcher, the aim of his models is “to identify the properties of epistemically well-designed social systems, that is, to specify the conditions under which a group of individuals, operating according to various rules for modifying their individual practices, succeed, through their interactions, in generating a progressive sequence of consensus practices”. Other interesting mathematical models of scientific activity that have been developed during the last decades refer to the ‘game’ between researchers and journal editors, or the way in which researchers try to change the subjective probabilities of their colleagues), or about the decision whether to replicate another resarcher’s experiments, or on the decision of accepting a more ‘popular’ theory or defending a more ‘heterodox’ one, on the basis of the different information about both theories each individual scientist has. The last two cases show the possible existence of more than one equilibrium in the ‘cognitive state’ of the scientific community, what can lead to phenomena of path-dependence, inefficiency, and sudden ‘revolutions’. Some more recent contributions have analysed the properties of the priority rule, the choice of methodological rules, and the negotiation about the interpretation of empirical findings. In the last times, due surely to the availability of more powerful software, it has become relatively common the use of simulation models to study Kitcherian ‘division of epistemic labour’ problems, especially in cases where complexity is. Many of these papers are grounded on the simulation models of Reiner Hegselmann and Urich Krause3 , which in turn are inspired on the work by Keith Lehrer and Carl Wagner4 on ‘rational’ belief aggregation.
Before finishing this brief review of the formal approach to ESK, we have to mention Samir Okasha’s paper5 on “Theory choice and social choice”. Here an analogy is suggested between the aggregation of individual preference functions to which the famous Arrow impossibility theorem was originally applied (showing that there is no way of constructing a ‘social’ preference function that respects certain minimal and reasonable requirements), and the combination of different ‘scientific values’ that would result in something like an objective ‘epistemic preference function’. Okasha argues that this analogy justifies in a way Thomas Kuhn’s thesis that there is no algorithm allowing to determine in an objective sense when is a theory epistemically better than another; the difference would be that, whereas Kuhn’s original intuition, and almost all of the subsequent interpretations, have been that the problem is that there is an indefinite number of possible ways of aggregating the different scientific values, and none of them justifiably better than the others, Arrow’s theorem would imply that the problem is that there is simply no ‘rational’ way of performing such an aggregation. It can be argued, however, that even if it is true several scientific values cannot be algorithmically combined, scientists might agree in employing a common epistemic scale which is non optimal for each one, but which is an equilibrium in a negotiation (or ‘social contract’) about how to evaluate research.
All these works, as well as others we will refer to in next entries, support the conclusion that, in spite of being motivated by ‘personal’ goals like recognition, power, or ‘credit’ (to use the concept coined by Pierre Bourdieu 6, a sociological work which has been decisively influential and inspiring in the development of ESK), we can reasonably expect that, under certain circumstances and mechanisms of interaction, a group of scientists can attain results that will score high on an epistemic scale.
In the next entry of this series we will approach to some of the non-formal approaches to the Economics of Scientific Knowledge.
- Bloor, D., 1976, Knowledge and Social Imagery, London, Routledge. ↩
- Kitcher, Ph., 1993, The Advancement of Science: Science without Legend, Objectivity without Illusions, Oxford, Oxford University Press. ↩
- Hegselmann, R., and U. Krause, 2004, “Opinion Dynamics Driven by Various Ways of Averaging”. Computational Economics, 25:381–405. ↩
- Lehrer, K., and C. G. Wagner, 1981, Rational Consensus in Science and Society. Dordrecht. D. Reidel. ↩
- Okasha, S., 2011, “Theory Choice and Social Choice: Kuhn Versus Arrow”. Mind, 120(477):83-115. ↩
- Bourdieu, P., 1975, “The Specificity of the Scientific Field and the Social Conditions of the Progress of Reason”, Social Science Information, 14.6:19-47. ↩