Factors Affecting Probability Matching Behavior

Abstract

In life, people commonly face repeated decisions under risk or uncertainty. While normative economic models assume that people tend to make choices that maximize their expected utility, suboptimal behavior - in particular, probability matching - is frequently observed in research on repeated decisions. Probability matching is the tendency to match prediction probabilities of each outcome with the observed outcome probabilities in a random binary prediction task. For example, when people are faced with making with a sequence of predictions, such as repeatedly predicting the outcome of rolling a die with four sides colored green and two sides colored red, most people allocate about two-thirds of their predictions to green, and one-third to red. The optimal strategy, referred to as maximizing, would be to choose the outcome with the higher probability in every trial in the prediction task. Various causes for probability matching have been proposed during the past several decades. Here it is proposed that implicit adoption of a perfect prediction goal by decision makers might tend to elicit probability matching behavior. Thus, one factor that might affect the prevalence of probability matching behavior (investigated in Studies 1 and 2) is the type of performance goal. The manipulation in Study 1 contrasted single-trial prediction with prediction of four-trial sequences, which it is hypothesized might create an implicit perfect prediction goal for the sequence. In Study 2, three levels of goal were explicitly manipulated for each sequence: a perfect prediction goal, an 80% correct goal, and a 60% correct goal. In both studies it was predicted that more matching behavior would be observed for those who have a goal of perfect prediction than those who have a more reasonable (lower) goal. The results of both studies, conducted in an online worker marketplace, supported the goal-level hypothesis. The second factor proposed to affect the prevalence of probability matching is the type of conceptual schema describing the events to be predicted: independent events or complementary events. Study 3 investigated the effects of schema type and abstraction level of context on matching or maximizing behavior. Three abstraction levels of stories were included: abstract, concrete random devices, and real-world stories. The main hypothesis was that when the two options to be predicted are independent events, less matching and more maximizing behavior should be observed. Data from Study 3 supported the hypothesis that independent events tend to elicit more maximizing behavior. No effects of abstraction level were observed.

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