Monetary Rewards and Decision Cost in Experimental Economics

By Smith, Vernon L.; Walker, James M. | Economic Inquiry, April 1993 | Go to article overview

Monetary Rewards and Decision Cost in Experimental Economics


Smith, Vernon L., Walker, James M., Economic Inquiry


This paper provides a theoretical framework and some evidence about how the size of payoffs affects outcomes in laboratory experiments. We examine theoretical issues related to the question of how payoffs can matter, and what the trade-offs with nonmonetary arguments might be in individual utility functions. Essentially, we accomplish this with an effort theory of subject behavior.

Experimentalists frequently argue that experimental subjects may have other motivations besides monetary gain that impinge upon the subject's decision making and that experimental results should be interpred with this caveat in mind.(1) The literature on adaptive and behavioral economic modelling often cites decision-making cost as part of the implicit justification for such models.(2) Conlisk |1988~ provides some examples of how "optimizing cost" (we will use the term "decision cost") can be explicitly integrated into modelling problems and suggests a generalization for the class of quadratic loss functions. Our approach is to formulate the decision cost problem as one of balancing the benefit against the effort cost of reducing "error," the latter defined as the difference between the optimal decision in the absence of decision cost and the agent's actual decision. This normalization has the advantage that the implications of the model can be directly tested from data on the error properties of a wide range of reported experiments. Our approach also attempts to encompass and formalize the argument that decision makers may fail to optimize because of the problem of flat maxima, as in von Winterfeldt and Edwards |1986~, or because of low opportunity cost for deviations from the optimal as in Harrison |1989~. Since standard theory predicts that decision makers will make optimal decisions however gently rounded is the payoff function, the theory is misspecified and needs modification. When the theory is properly specified, there should be nothing left to say about opportunity cost or flat maxima; i.e., when the benefit margin is weighed against decision cost, then there should be nothing left to forego. This is consistent with the arguments of Harrison and von Winterfeldt and Edwards.

The theoretical approach we examine is based on a perspective originally suggested by Simon |1956~, and operationalized by Siegel |1959~: rational choice theory is a correct first approximation to the analysis of decision behavior, but it is incomplete, and making it more complete requires the guidance of data from experimental designs motivated by this objective. Simon's original thesis was that "To predict how economic man will behave we need to know not only that he is rational, but also how he perceives the world--what alternatives he sees and what consequences he attaches to them" |1956, 271~. Thus there is no denial of human rationality; the issue is in what sense are individuals rational and how far can we go with abstract objective principles of how "rational" people "should" act.

But if a study of payoff effects is in need of a theoretical foundation, it also requires evidence. If traditional economic models assume that only monetary reward matters, psychologists tend to assume that such rewards do not matter.(3) The facts reviewed below support the more common sense view that rewards matter, and that neither of the polar views--only reward matters, or reward does not matter--are sustainable across the range of experimental economics. There will always be discrepancy between precise theory and observation, and thus room for theory improvement. Since rational theory postulates motivated decision makers, it follows that varying reward levels is one of the many important tools needed to explore this discrepancy. Our fundamental view is that the experimentalist has as much to learn from experimental subjects about subjective rationality, as human decision makers have to learn from the models that we call "rational."

I. MOTIVATION THEORY IN THE PRESENCE OF DECISION COST

In this section we develop a simple theoretical framework to help: (i) improve understanding of the circumstances that might yield predicted optimal decisions or deviations therefrom; and (ii) provide guidance in experimental design and in interpreting observations. …

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