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Saturday, January 20, 2018

Exploiting Risk–Reward Structures in Decision Making under Uncertainty

Christina Leuker Thorsten Pachur Ralph Hertwig Timothy Pleskac
PsyArXiv Preprints
Posted December 21, 2017


People often have to make decisions under uncertainty — that is, in situations where the probabilities of obtaining a reward are unknown or at least difficult to ascertain. Because outside the laboratory payoffs and probabilities are often correlated, one solution to this problem might be to infer the probability from the magnitude of the potential reward. Here, we investigated how the mind may implement such a solution: (1) Do people learn about risk–reward relationships from the environment—and if so, how? (2) How do learned risk–reward relationships impact preferences in decision-making under uncertainty? Across three studies (N = 352), we found that participants learned risk–reward relationships after being exposed to choice environments with a negative, positive, or uncorrelated risk–reward relationship. They learned the associations both from gambles with explicitly stated payoffs and probabilities (Experiments 1 & 2) and from gambles about epistemic
events (Experiment 3). In subsequent decisions under uncertainty, participants exploited the learned association by inferring probabilities from the magnitudes of the payoffs. This inference systematically influenced their preferences under uncertainty: Participants who learned a negative risk–reward relationship preferred the uncertain option over a smaller sure option for low payoffs, but not for high payoffs. This pattern reversed in the positive condition and disappeared in the uncorrelated condition. This adaptive change in preferences is consistent with the use of the risk–reward heuristic.

From the Discussion Section:

Risks and rewards are the pillars of preference. This makes decision making under uncertainty a vexing problem as one of those pillars—the risks, or probabilities—is missing (Knight, 1921; Luce & Raiffa, 1957). People are commonly thought to deal with this problem by intuiting subjective probabilities from their knowledge and memory (Fox & Tversky, 1998; Tversky & Fox, 1995) or by estimating statistical probabilities from samples of information (Hertwig & Erev, 2009). Our results support another ecologically grounded solution, namely, that people estimate the missing probabilities from their immediate choice environments via their learned risk–reward relationships.

The research is here.