Rationality in the Analysis of Behavioral Simulation Models

Abstract

An important task in the analysis of a behavioral simulation model is to explain clearly how the model’s organizational assumptions lead to its simulated behavior. All too often, model-based arguments involve an uncomfortable “leap of logic” between equations and consequences. This paper proposes two methods of analysis, premise description and partial model testing, which provide stepping stones between model equations and their simulated consequences. Premise description examines the bounded rationality of policies or decision functions in the model, pointing out the process and cognitive limitations assumed in decisionmaking. Partial model tests expose the intended rationality of smalt combinations of policies, showing that policies produce “sensible” actions with respect to their premises. The application of those methods is illustrated with a simulation model of a sales organization in which sales-force productivity is prone to decline. The behavior of productivity is traced to dysfunctional interactions between objectives, overtime, and salesforce motivation.

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