Classification of human decision behavior: finding modular decision rules with genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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In the field of decision-making a fundamental problem is how to uncover people's choice behavior. While choices them- selves are often observable, our underlying decision strategies determining these choices are not entirely understood. Previous research defined a number of decision strategies and conjectured that people do not apply only one strategy but switch strategies during the decision process. To the best of our knowledge, empirical evidence for the latter conjecture is missing. This is why we monitored the purchase decisions 624 consumers shopping online. We study how many of the observed choices can be explained by the existing strategies in their pure form, how many decisions can be explained if we account for switching behavior, and investigate switching behavior in detail. Since accounting for switching leads to a large search space of possible mixed decision strategies, we apply a genetic algorithm to find the set of mixed decision strategies which best explains the observed behavior. The results show that mixed strategies are used more often than pure ones and that a set of four mixed strategies is able to explain 93.9% of choices in a scenario with 4 alternatives and 75.4% of choices in a scenario with 7 alternatives.