Of matchers and maximizers: How competition shapes choice under risk and uncertainty

•Probability matching or maximizing was reinforced in competitive choice tasks.•We observed accurate asymptotic adoption of both strategies.•Learning models based on competitor avoidance describe choice behavior best.•We demonstrated that probability matching can be optimal under competition. In a w...

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Published inCognitive psychology Vol. 78; pp. 78 - 98
Main Authors Schulze, Christin, van Ravenzwaaij, Don, Newell, Ben R.
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier Inc 01.05.2015
Academic Press
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Abstract •Probability matching or maximizing was reinforced in competitive choice tasks.•We observed accurate asymptotic adoption of both strategies.•Learning models based on competitor avoidance describe choice behavior best.•We demonstrated that probability matching can be optimal under competition. In a world of limited resources, scarcity and rivalry are central challenges for decision makers—animals foraging for food, corporations seeking maximal profits, and athletes training to win, all strive against others competing for the same goals. In this article, we establish the role of competitive pressures for the facilitation of optimal decision making in simple sequential binary choice tasks. In two experiments, competition was introduced with a computerized opponent whose choice behavior reinforced one of two strategies: If the opponent probabilistically imitated participant choices, probability matching was optimal; if the opponent was indifferent, probability maximizing was optimal. We observed accurate asymptotic strategy use in both conditions irrespective of the provision of outcome probabilities, suggesting that participants were sensitive to the differences in opponent behavior. An analysis of reinforcement learning models established that computational conceptualizations of opponent behavior are critical to account for the observed divergence in strategy adoption. Our results provide a novel appraisal of probability matching and show how this individually ‘irrational’ choice phenomenon can be socially adaptive under competition.
AbstractList In a world of limited resources, scarcity and rivalry are central challenges for decision makers-animals foraging for food, corporations seeking maximal profits, and athletes training to win, all strive against others competing for the same goals. In this article, we establish the role of competitive pressures for the facilitation of optimal decision making in simple sequential binary choice tasks. In two experiments, competition was introduced with a computerized opponent whose choice behavior reinforced one of two strategies: If the opponent probabilistically imitated participant choices, probability matching was optimal; if the opponent was indifferent, probability maximizing was optimal. We observed accurate asymptotic strategy use in both conditions irrespective of the provision of outcome probabilities, suggesting that participants were sensitive to the differences in opponent behavior. An analysis of reinforcement learning models established that computational conceptualizations of opponent behavior are critical to account for the observed divergence in strategy adoption. Our results provide a novel appraisal of probability matching and show how this individually 'irrational' choice phenomenon can be socially adaptive under competition.
•Probability matching or maximizing was reinforced in competitive choice tasks.•We observed accurate asymptotic adoption of both strategies.•Learning models based on competitor avoidance describe choice behavior best.•We demonstrated that probability matching can be optimal under competition. In a world of limited resources, scarcity and rivalry are central challenges for decision makers—animals foraging for food, corporations seeking maximal profits, and athletes training to win, all strive against others competing for the same goals. In this article, we establish the role of competitive pressures for the facilitation of optimal decision making in simple sequential binary choice tasks. In two experiments, competition was introduced with a computerized opponent whose choice behavior reinforced one of two strategies: If the opponent probabilistically imitated participant choices, probability matching was optimal; if the opponent was indifferent, probability maximizing was optimal. We observed accurate asymptotic strategy use in both conditions irrespective of the provision of outcome probabilities, suggesting that participants were sensitive to the differences in opponent behavior. An analysis of reinforcement learning models established that computational conceptualizations of opponent behavior are critical to account for the observed divergence in strategy adoption. Our results provide a novel appraisal of probability matching and show how this individually ‘irrational’ choice phenomenon can be socially adaptive under competition.
Author Schulze, Christin
Newell, Ben R.
van Ravenzwaaij, Don
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Keywords Competition
Probability matching
Cognitive models
Decision making
Reinforcement learning
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Snippet •Probability matching or maximizing was reinforced in competitive choice tasks.•We observed accurate asymptotic adoption of both strategies.•Learning models...
In a world of limited resources, scarcity and rivalry are central challenges for decision makers-animals foraging for food, corporations seeking maximal...
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StartPage 78
SubjectTerms Adolescent
Asymptotic methods
Choice Behavior
Cognitive models
Cognitive psychology
Competition
Competitive Behavior
Decision Making
Decision making models
Female
Humans
Male
Probability
Probability matching
Reinforcement (Psychology)
Reinforcement learning
Risk
Uncertainty
Young Adult
Title Of matchers and maximizers: How competition shapes choice under risk and uncertainty
URI https://dx.doi.org/10.1016/j.cogpsych.2015.03.002
https://www.ncbi.nlm.nih.gov/pubmed/25868112
https://www.proquest.com/docview/1679892459
https://search.proquest.com/docview/1677881146
Volume 78
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