Academic journal article Memory & Cognition

Probability Matching in Risky Choice: The Interplay of Feedback and Strategy Availability

Academic journal article Memory & Cognition

Probability Matching in Risky Choice: The Interplay of Feedback and Strategy Availability

Article excerpt

Abstract Probability matching in sequential decision making is a striking violation of rational choice that has been observed in hundreds of experiments. Recent studies have demonstrated that matching persists even in described tasks in which all the information required for identifying a superior alternative strategy-maximizing-is present before the first choice is made. These studies have also indicated that maximizing increases when (1)the asymmetry in the availability of matching and maximizing strategies is reduced and (2)normatively irrelevant outcome feedback is provided. In the two experiments reported here, we examined the joint influences of these factors, revealing that strategy availability and outcome feedback operate on different time courses. Both behavioral and modeling results showed that while availability of the maximizing strategy increases the choice of maximizing early during the task, feedback appears to act more slowly to erode misconceptions about the task and to reinforce optimal responding. The results illuminate the interplay between "topdown" identification of choice strategies and "bottom-up" discovery of those strategies via feedback.

Keywords Probability matching . Maximizing . Decision making . Heuristics . Rational choice theory

(ProQuest: ... denotes formulae omitted.)

When faced with a choice between two options, one of which offers a higher probability of receiving a fixed payoffthan does the other, a rational agent should always choose the option with the higher payoffprobability. For example, if one option delivers one dollar 70 % of the time (and nothing the rest of the time), while the other pays one dollar only 30% of the time (and otherwise nothing), a rational agent should choose the 70 % option. This is true whether the choice is faced once or repeatedly: The option offering the higher probability of receiving the payoffshould always be chosen.

Despite the clear superiority of this strategy (referred to as maximizing), participants faced with a series of such choices often show responding closer to probability matching-allocating responses to the two options in proportion to their relative probabilities of occurrence. In other words, people bet on the 30 % option 30 % of the time, and the 70 % option 70 % of the time (James & Koehler, 2011).

This striking violation of rational choice has been studied extensively over the last 60 years (Vulkan, 2000). Most probability-matching experiments have used paradigms in which participants had to learn, over successive trials, the contingencies associated with each option (e.g., Shanks, Tunney, & McCarthy, 2002).1 However, some recent studies have investigated probability matching in tasks in which the outcomes and their probabilities of occurrence are fully described to participants (e.g., Gal & Baron, 1996; James & Koehler, 2011; Koehler & James, 2009, 2010; Newell & Rakow, 2007; West & Stanovich, 2003). The finding that probability matching is common even in these situations is remarkable, given that the described problems provide all of the information necessary for rational responding (i.e., identification of the maximizing strategy as optimal), even before a single choice is made.

Consider, for example, predicting the outcome of rolls of a ten-sided die with seven green sides and three red sides, with a fixed payment for each correct prediction. Here there is no ambiguity about the optimal strategy (always predict green), no need for a period of experimentation or exploration of the environment to determine which option is best, and no need to consider the possibility that outcome probabilities will change across successive trials. Thus, from a normative perspective (i.e., a rational economic analysis of the problem), the decision maker simply needs to identify the optimal strategy (predict green) and execute it on every trial.

Newell and Rakow (2007) examined a problem like the one described above, in which participants predicted the outcomes of 300 rolls of a simulated die. …

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