A Test of the Satisfaction-Balance Decision Model Using Direct Numeric Estimation

Previous studies of decision making using mathematical models have focused on repetitive choices in simple situations. As a result of such work, very precise models have been developed but at the expense of breadth and applicability to nonrepetitive-choice situations. In this study we apply a formal theory of decision making, the satisfactionbalance model, to cases involving nonrepetitive choices with uncertain outcomes. We also examine a survey approach to determining subjects' subjective perceptions of probabilities and magnitudes of values and costs. We develop a measurement model to account for this survey method and examine the ability of the adjusted model to predict single choices. Our results support the applicability of the satisfaction-balance model in nonrepetitive-choice situations, using direct numeric estimation. Sociology and Rational Choice Models In a recent book on theoretical sociology, Fararo (1989) argues that, at its best, rational choice modeling serves as an excellent foundation for building dynamic sociological theory. His approach is simple: "Start with a dynamic action model and then couple models of actors to derive social systemic outcomes" (253-54). Other writers (Burt 1982; Coleman 1990) have made similar claims. Their claims are particularly poignant because their avowed goals are to develop a theory of action within social structure. Thus, they see in sociological theory a need for a theory of the individual. Why spend precious research time and energy on rational choice models? The answer is given in eloquent fashion by Tallman and Gray (1990): "The @ The University of North Carolina Press Social Forces, December 1991, 70(2):475-494 * An earlier version of this article was presented at the annual meetings of the Pacific Sociological Association, Spokane, Washington, 5-8 April 1990. We would like to thank Louis N. Gray, David Ward, and Eugene Rosa of Washington State Universityfor their comments on the earlier draft. We would also like to acknowledge the special contribution of Scott Gerard Allard, also of Washington State University, and the helpful comments of anonymous reviewers. Direct correspondence to D.H. Judson, Department of Sociology, Washington State University, Pullman, WA 99164-4020. This content downloaded from 207.46.13.114 on Thu, 26 May 2016 06:45:36 UTC All use subject to http://about.jstor.org/terms 476 / Social Forces 70:2, December 1991 moment of choice may be conceived of as a microcosm within which all the forces that affect human and social behavior combine to influence the course of collective or individual action. It is therefore desirable to see if these forces can be distilled into the essential elements needed to explain human choice" (406). Individual choice, which is believed to be partially a function of individual psychology, never takes place apart from situational constraints. But what is a situational constraint? That which to the individual is a situational constraint is in the aggregate a social fact. That is, we believe that social structure sets the context for individual decision making. Individual decisions reflect this context and create further contexts. The goal of rational choice models in sociology is to explicate the process by which individuals integrate information about their situations. A strength of rational choice modeling is theoretical precision. Because choice theories are typically cast in mathematical form, their predictions are more easily testable, and hence falsifiable. The result has been a good deal of formal development. However, formal development has progressed at the cost of empirical testing. At times the aesthetic beauty of decision theories has overshadowed the real heuristics and biases (Kahneman, Slovic & Tversky 1982) known to infect human decision making. In addition, with only a few exceptions (Rachlin et al. 1986), development of rational choice theories has proceeded without attention to the strongly related line of research in animal behavior and choice. Finally, decision models have had only limited use in sociology. Such limited use may be due to the difficulty of linking a mathematical model with appropriate measurement techniques. On the other hand, it may simply be a result of lack of exposure. Our goal in this article is to incorporate the two advantages of rational choice models, theoretical centrality and precision, while overcoming the weaknesses of lack of empirical reality and applicability. We do this by taking an empirically based, mathematical decision model and developing a measurement tool that makes the model useable by sociologists working outside the laboratory. We believe that the insights and developments of the theory of individual decision making will prove foundational to a scientific sociology in the future. The Satisfaction-Balance Decision Model A growing body of research on the satisfaction-balance model and related decision models has indicated that a person's decision making is a lawful, though not necessarily maximizing, process. In a recent comparison of decisionmaking models, Gray, Stafford, and Tallman (1989) demonstrated excellent fits, with appropriate parameter estimates, of these models to data generated in an experimental situation. The situation was simple: subjects were to attempt to guess a computer-generated number, 1 or 2, in a series of (typically 100) trials, where a correct guess resulted in a win of a given amount of points, and an incorrect guess resulted in a loss of a given amount of points. Such an experimental situation is simultaneously the strength and the weakness of this research tradition. While eliminating extraneous factors, Gray This content downloaded from 207.46.13.114 on Thu, 26 May 2016 06:45:36 UTC All use subject to http://about.jstor.org/terms Satisfaction-Balance Decision Model / 477 and Tallman (1984, 1986, 1987) have been able to directly control probabilities of success or failure and magnitudes of reward or cost given success or failure. In the course of their research, they have developed a precise mathematical model of decision making known as the satisfaction-balance model. The precision of their model, however, has come at the expense of wide applicability. While they have developed extensions of their model involving observational learning (Gray et al. 1985), notions of deterrence (Stafford et al. 1986, 1989), and group-level decision making (Gray & Stafford 1988), the methodology for examining the model has typically consisted of a computer game similar to that described above, in a repetitive-choice situation, where data on choices are aggregated over blocks of time. Unfortunately, many decisions of interest to social scientists rarely take place in such a situation. Decisions to migrate audson 1990), to leave an abusive relationship (Greenberg 1989), and to make single choices (Kahneman & Tversky 1984) are a few examples of one-shot decisions that necessarily involve subjective estimation of probabilities, values, and costs. Thus, the question to pose of the satisfaction-balance model is not whether it works, as has been amply demonstrated, but whether it works as well in nonexperimental, nonrepetitive-choice situations with nonexperimental methodologies.