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Artificial Intelligence, Computer Simulation and Theory Construction in the Social Science∗ Rainer Schnell 1991 Abstract The current use of Artificial Intelligence methods as tools for theory construction in sociology is reviewed. The main use seems to be the modelling of cognitive models processes of individual actors. Other approaches to simulation in sociology uses very simple models of individual actors, especially cellular automata. The theoretical importance of this kind of model for sociology is neglectable. The most promising application of simulation methods are explications of rational choice based theories. keywords: Artificial Intelligence, Computer Simulation, Theory Construction in the Social Science 1 Artificial Intelligence Approaches to Theory Construction In the social sciences with very few exceptions (for example: Schrodt 1985) most articles concerning the uses of artificial intelligence methods1 are only ∗ This is a reprint of a lecture given 1991 and published in 1992 in the conference reader (Frank Faulbaum (ed.) (1992): SoftStat ’91. Advances in Statistical Software 3, Stuttgart, p.335-342). Only a few typos have been corrected. The lecture itself was an extension of an earlier review of computer simulations in sociology (Schnell 1990). 1 A critical account of the achievements of artificial intelligence, which in the dominant reception mostly and wrongly boils down to expert systems, can be found a recent collection of articles by Stephen Graubart (1988). 1 demonstrations of an extended vocabulary of the author. An example of this kind of work is Garson’s article (1989). The main current real use of artificial intelligence methods in social science for theoretical work is the development of explicit theoretical models of cognitive processes2 . For Cognitive Sciences, theoretical based simulations are essential (H.A. Simon and C.A. Caplan 1989, Z.W. Pylyshyn 1989). The astonishing progress of Cognitive Science (in sharp contrast to the stagnation of sociological theory) seems to be impossible without computer simulations. Computational cognitive science is directly relevant for sociological work: For example as a theory basis of data collection (Computational Memory Models, see D.L. Hintzman 1990), for the theory of personal identity (M. Minsky 1987, Hill and Schnell 1990), emotions (P. Johnson-Laird 1988) and for the theory of interaction structures (Doran 1985, Banerjee 1986). Data structures like frames and scripts can be directly imported and included as part of the working model of the actors of a sociological simulation. If the explanandum requires an extended microtheory of an actor, elements of it can be found in cognitive science. Therefore, the possible applications of artificial intelligence for theory construction in the social sciences may be more in the domain of using data structures of AI-simulations for the organization of every day knowledge required by individual actors3 . The frame problem in artificial intelligence (see Pylyshyn 1987) has a direct correspondence to the aims of ethnomethodology: What kind of knowledge is required for every day actions? Because of the refusal of most ethnomethodologists to use any formal method, the striking similarities of both problems has not been noticed in the literature. There is only one major exception: The work of Kathleen Carley (1989). She uses a detailed model of cognitive processes of individual actors for the explanation of the generation of every day social knowledge. Her theory (named "constructuralism") is used for the explanation of social processes like diffusion of innovation and the genesis of invisible colleges. Unfortunately, it seems to be, that she doesn’t use a computer model. Her work shows, that the accumulated knowledge of sociologists is not sufficient for the explanation of 2 Other uses are the of logical consistency of a theory with expert systems. Such is demonstrated by Edward E. Brent (1986) under the strange label "qualitativ formalism". An earlier similar application is David Sylvan’s und Barry Glassner’s (1985) formalization of a theory by Simmel as LOGLISP-program. These applications are no simulations (the generating mechanism is not specified) and are therefore not considered further. 3 Social scientists are just beginning to realize the theoretical power of these concepts for their own work. See for example, Esser (1990). 2 individual knowledge structures. Of course the results of ethnomethodology are of no use for the solution of practical modeling problems. If you want to simulate actors on this level of detail, you have to do your own fieldwork. But very often, this is not necessary. The currently most promising explanation methodology in the social science seems to be the rational choice approach, specially of the kind of Lindenberg’s approach: His "method of decreasing abstraction" ("As simple as possible and as complex as necessary", Lindenberg 1989a:11) uses detailed models of individual actors only if no simple model is sufficient. This approach will be illustrated below. 2 Models of individual actors and the rational choice approach The most simple version of models with individual "actors" are variations of the so called "Checkerboard Model of Social Interaction" (Sakoda 1971), or if you prefer, simple cellular automata. Most of these simulations are extensions of the segregation model of Schelling (1971). There are no theoretical advances since 1971 in the application of this kind of models in the social sciences4 . Due to the easy programming of stunning graphical effects with these models, a lot of students is busy with this kind of model, but they are only reinventing the wheel. There is a very interesting, partly even formalized, literature concerning cellular automata (Burks 1966, 1970, Kauffman 1984, Toffoli and Margolus 1987 and expecially fascinating: Poundstone 1987) but I was unable to locate any theoretically relevant idea for the development of social science theory. As always, there is at least one exception: Phillip Schrodt (1981) has shown by such a simulation, that three simple assumptions concerning the behavior of individual actors are sufficient for explanation of territorial units. An example of non-sociological work on computer simulations of individual actors for sociological problems consists of two papers of the Second Conference on Artificial Life, which was held in 1990. MacLennan (1990a, 1990b) has simulated a population of simple machines, which reacted to their environment and were able to modify it. Due to a selection pressure in favor of cooperating behavior in the simulated environment, it was shown, that the possibility of communication among the machines increased average fitness 4 For an example, see the simple model of Nowak, Szamrej and Latane (1990). 3 by 84%. Furthermore, a significant association between symbols and situation was observed. Therefore, the simulation has shown a possible mechanism of the development of communication and language. A beautiful example for a theoretical based simulation of individual actors is a waiting queue model of Norman P. Hummon (1990)5 . Without any assumptions concerning exchanges, rewards or sanctions, his model explains the development of division of labor and organization on in a population of individual "bcrats". It is remarkable, that a sociologist, who doesn’t share the rational choice perspective, has reached very similar conclusions concerning the structure of a simulation of social actors6 . Bo Anderson (1989:214) has described the elements of such a simulation. He does not mention rational choice theory at all, but his description of simulation models comes sufficiently close to an economic explanation: His simulated actors have interests, they try to maximize, they live in an environment with economic constraints. Furthermore, his actors have world-views representing the input received from other actors and the environment. I would prefer to call this "subjective expected utilities". At last, he writes: "For each actor, there are also production rules that connect the interpretations of the input from other actors and the events in the environment with the available action options. The actors cognitive structures, in combination with the inputs, produce beliefs about causes, about what other actors did, and what the actor himself can do." If we use the means of such production rules as simulation tool for the formalization of the production functions of the rational choice theories7 , we have the basis for a complete explanation. For the explanation of preferences Lindenberg (1989a, 1989b) uses social production functions, which vary according to social positions. Production functions are the knowledge of the technical means to reach the ultimate goals of human beeings: physical well being and social approval (Lindenberg 1989a:14, concerning norms and social production 5 This is the only theoretical based waiting queue model I’m aware of. See also the multi-actor system of the TEAMWORK project described by Jim Doran (1985:163 ): "(...) a multi-actor system requires several actors independently interacting with a common task environment. Within a computer program this is achieved by setting up ’clones’ of the basic actor program each with its own knowledge repertoire and temporary plan structures. The simulated task environment is a separate part of the program capable of being inspected or manipulated by any of the actors." 7 Other, and more simple tools than production rules are available, if we use a subjective expected utility framework for decisions. 6 4 functions, see Lindenberg 1989b:190-194). Although the application of explicitly rational choice based simulations of individual actors for the explanation of macro-phenomena is a new field, a few simulations have been completed recently in Germany. Frank Kalter (1990) has done a computer simulation of the so called theory of the "Spiral of Silence." He showed the ill-definition of the theory and the extremely small range of possible applications due to the very specific initial conditions implicitly required by the theory. Christof Wolf (1991) is currently working on the simulation of various theories of friendship choices. He has tried to study the implications of various constraints on friendship choices and compares his results to empirical data of the GSS. Wolfgang Sodeur (University of Essen) has studied by computer simulation the impact of demographic constraints such as the number of persons within an acceptable spatial range and an acceptable age range on friendship choices of children. Karl-Dieter Opp (1991) has used a BASIC-program with simulated simple individual actors for exploration of a rational choice theory of collective action. 3 Final remarks Finally, I would like to add three technical remarks. First, currently, theoretical relevant simulations in the social sciences do not need expensive machines or special hardware. For psychology, Broadbent (1987:171) summarizes: "You should be able to replicate the results by borrowing from your nearest teenager the machine usually used for playing space invaders." Second, because simulations should be used as a tool for theory construction, which only is possible by a public discussion of proposed theories, it is essential to use a language, which is widely available and as much self-documenting as possible. I believe, only PASCAL without any extensions fulfill all these points. Furthermore, it should by now be clear, that a simulation without easily public available source code is scientifically worthless. That is much more a psychological problem than a technical: Many (european) authors don’t like to publish programs, in which they had invested a large amount of work. But for a public discussion, this attitude has to be changed8 . 8 Technically it may be necessary to establish a fileserver in a research network, which can hold such files. Examples for such servers are the BITNET TRICKLEs, see Schnell (1989). 5 4 References Anderson, B. (1989): On artificial intelligence and theory construction in sociology; in: Journal of mathematical sociology, 14, 2-3, p.209-216. Banerjee, S. (1986): Reproduction of Social Structures: An Artificial Intelligence Model, in: Journal of Conflict Resolution, 30, 2, p.221-252. Brent, E.E. (1986): Knowledge-Based Systems: A Qualitative Formalism, in: Qualitative Sociology, 9, 1986, 3, p.256-282. Broadbent,D. (1987): Simple models for experimental situations; in: Morris,P. (ed.): Modelling Cognition, Chichester, p.169-185. Burks, A.W. (ed.) (1966): Theory of Self-Reproducing Automata, Urbana. Burks, A.W. (ed.) (1970): Essays of Cellular Automata, Champaign. Carley, K. (1989): The Value of Cognitive Foundations for Dynamic Social Theory; in: Journal of Mathematical Sociology, 14, 3, 1989, p.171-208. Doran, J. (1985): The computational approach to knowledge, communication and structure in multi-actor systems; in: Gilbert,G.N./Heath,C. (eds.): Social Action and Artificial Intelligence, Aldershot, p.160-171. Esser, H. (1990): Habits, Frames und Rational Choice; in: Zeitschrift für Soziologie, 19, 4, p.231-247. Garson, G. D. (1989): Computer Simulation, Artificial Intelligence, and Political Science; in: Garson,G.D./Nagel,S.S. (eds.): Advances in Social Science and Computers, Vol. 1, Greenwich, p.25-45. Graubart, S. R. (ed.) (1988): The Artificial Intelligence Debate: False Starts, Real Foundations, Cambridge/Mass. Hill, P.B./Schnell, R. (1990): Was ist "‘Identität"’ ?; in: Esser,H./Friedrichs,J. (Hrsg.): Generation und Identität, Opladen, p.25-42. Hintzman, D.L. (1990): Human Learning and Memory: Connections and Dissociations; in: Annual Review of Psychology, p.109-139. Hummon, N.P. (1990): Organizational Structures and Network Processes; in: Journal of Mathematical Sociology, 15, 2, p.149-161. Johnson-Laird,P. (1988): The Computer and the Mind, Cambridge, Mass. Kalter, F. (1990): Dynamische Prozesse der öffentlichen Meinung, unpublished manuscript, Institut für angewandte Sozialforschung, Universität zu Köln, 1990. Kauffman,S.A. (1984): Emergent Properties in Random Complex Automata; in: Physica 10D, p.145-156. Lindenberg, S. (1989a): Toward the Construction of Interdisciplinary Theoretical Models to Explain Demographic Behavior; A Comment, Unpublished 6 Manuscript, Groningen. Lindenberg, S. (1989b): Choice and Culture: The Behavioral Basis of Cultural Impact on transactions; in: Haferkamp,H. (ed.): Social Structure and Culture, Berlin/New York, p.175-200. MacLennan, B. (1990a): Evolution of Communications in a Population of Simple Machines, Technical Report, Computer Science Department, University of Tennessee, Knoxville. MacLennan, B. (1990b): Synthetic Ethology: An Approach to the Study of Communication, Technical Report, Computer Science Department, University of Tennessee, Knoxville. Minsky, M. (1987): The Society of Mind, London. Nowak,A., Szamrej,J., Latane,B. (1990): From Private attitude to public opinion: A Dynamic Theory of Social Impact; in: Psychological Review, 97, 1990, 3, p.362-376. Opp, K.-D. (1991): Processes of Collective Political Action. A Dynamic Model And The Results Of A Computer Simulation; in: Rationality And Society, 3, 2, p.215-251. Poundstone, W. (1987): The Recursive Universe. Cosmic Complexity and the Limits of Scientific Knowledge, Oxford. Pylyshyn, Z.W. (eds.) (1987): The Robot’s Dilemma: The Frame Problem in Artificial Intelligence, Norwood. Pylyshyn, Zenon W. (1989): Computing in Cognitive Science; in: Posner, M.I. (ed.): Foundations of Cognitive Science, Cambridge (Mass.), 1989, p.5191. Sakoda,J.M. (1971): The Checkerboard Model of Social Interaction; in: Journal of Mathematical Sociology, 1, 1971, p.119-132. Schelling, T.C. (1971): Dynamic Models of Segregation, in: Journal of Mathematical Sociology, 1, p.143-186 Schnell, R. (1990): Computersimulation und Theoriebildung in den Sozialwissenschaften; in: Kölner Zeitschrift für Soziologie und Sozialpsychologie, 42, 1, p.109-128. Schnell, R. (1989): Möglichkeiten der Nutzung von BITNET in den Sozialwissenschaften; in: ZA-Information 24, 1989, p.101-115. Schrodt, P.A. (1981): Conflict as a determinant of territory; in: Behavioral Science, 26, 1981, p.37-50. Schrodt, P.A. (1985): Adaptive Precedent-Based Logic and Rational Choice: A Comparison of Two Approaches to the Modeling of International Behavior, in: U.Luterbacher und M. D. Ward (eds.): Dynamic Models of International 7 Conflict, Boulder/Colorado, p.373-400. Simon, H.A., Kaplan, C.A. (1989): Foundations of Cognitive Science; in: Posner, M.I. (ed.): Foundations of Cognitive Science, Cambridge (Mass.), p.1-47. Sylvan, D.J., Glassner,B. (1985): A Rationalist Methodology for the Social Sciences, Oxford. Toffoli,T., Margolus,N. (1987): Cellular Automata Machines, London. Wolf, C. (1991): Unpublished dissertation manuscript, University of Cologne. [now published as: Gleich und gleich gesellt sich: individuelle und strukturelle Einflüsse auf die Entstehung von Freundschaften, Hamburg, Kovac, 1996] 8