An Economist’s Perspective on Probability Matching

Vulkan, Nir

doi:10.1111/1467-6419.00106

Cited by 329 publications

(325 citation statements)

References 57 publications

(50 reference statements)

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“…Given that the probability of receiving "win" feedback in this condition, if a correct choice is made, is 0.61 (see Eq. (1)), this observation is in line with probability matching behavior, as shown in a number of previous studies (see e.g., Vulkan, 2000). In sum, these results further support the conclusion that the shift in cue usage was a strategic choice of participants to restrict their search space and therefore to integrate less cues to arrive at good-enough decisions under severe time pressure.…”

Section: Decision Strategy Model Selectionsupporting

confidence: 91%

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

et al. 2017

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A B S T R A C TReal-life decision-making often involves combining multiple probabilistic sources of information under finite time and cognitive resources. To mitigate these pressures, people "satisfice", foregoing a full evaluation of all available evidence to focus on a subset of cues that allow for fast and "good-enough" decisions. Although this form of decision-making likely mediates many of our everyday choices, very little is known about the way in which the neural encoding of cue information changes when we satisfice under time pressure. Here, we combined human functional magnetic resonance imaging (fMRI) with a probabilistic classification task to characterize neural substrates of multi-cue decision-making under low (1500 ms) and high (500 ms) time pressure. Using variational Bayesian inference, we analyzed participants' choices to track and quantify cue usage under each experimental condition, which was then applied to model the fMRI data. Under low time pressure, participants performed nearoptimally, appropriately integrating all available cues to guide choices. Both cortical (prefrontal and parietal cortex) and subcortical (hippocampal and striatal) regions encoded individual cue weights, and activity linearly tracked trial-by-trial variations in the amount of evidence and decision uncertainty. Under increased time pressure, participants adaptively shifted to using a satisficing strategy by discounting the least informative cue in their decision process. This strategic change in decision-making was associated with an increased involvement of the dopaminergic midbrain, striatum, thalamus, and cerebellum in representing and integrating cue values. We conclude that satisficing the probabilistic inference process under time pressure leads to a cortical-to-subcortical shift in the neural drivers of decisions.

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Section: Decision Strategy Model Selectionsupporting

confidence: 91%

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

et al. 2017

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“…This result is consistent with an extensive literature of suboptimal human performance on manual free choice tasks in which probabilities are assigned independently between trials (reviewed by Vulkan 2000), and probability classification tasks (Gluck and Bower 1998;Knowlton et al 1994;Gluck et al 2002). One possible reason subjects did not reach optimal performance is that they may have used a poor model to guide their saccade selections.…”

Section: Discussionsupporting

confidence: 88%

Saccade selection when reward probability is dynamically manipulated using Markov chains

2008

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Markov chains (stochastic processes where probabilities are assigned based on the previous outcome) are commonly used to examine the transitions between behavioral states, such as those that occur during foraging or social interactions. However, relatively little is known about how well primates can incorporate knowledge about Markov chains into their behavior. Saccadic eye movements are an example of a simple behavior influenced by information about probability, and thus are good candidates for testing whether subjects can learn Markov chains. In addition, when investigating the influence of probability on saccade target selection, the use of Markov chains could provide an alternative method that avoids confounds present in other task designs. To investigate these possibilities, we evaluated human behavior on a task in which stimulus reward probabilities were assigned using a Markov chain. On each trial, the subject selected one of four identical stimuli by saccade; after selection, feedback indicated the rewarded stimulus. Each session consisted of 200-600 trials, and on some sessions, the reward magnitude varied. On sessions with a uniform reward, subjects (n = 6) learned to select stimuli at a frequency close to reward probability, which is similar to human behavior on matching or probability classification tasks. When informed that a Markov chain assigned reward probabilities, subjects (n = 3) learned to select the greatest reward probability more often, bringing them close to behavior that maximizes reward. On sessions where reward magnitude varied across stimuli, subjects (n = 6) demonstrated preferences for both greater reward probability and greater reward magnitude, resulting in a preference for greater expected value (the product of reward probability and magnitude). These results demonstrate that Markov chains can be used to dynamically assign probabilities that are rapidly exploited by human subjects during saccade target selection.

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“…However, the typical outcome is that subjects' average response probability is close to the payoff probability; see Vulkan (2000) for an overview. 5 Similarly, Rubinstein (2002) observes that individuals diversify their choices in experiments with multiple decisions even though optimal behavior implies not to diversify.…”

Section: Literaturementioning

confidence: 99%

Locus of control and consistent investment choices

Pinger

Schäfer

Schumacher

2018

Journal of Behavioral and Experimental Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Locus of Control and Consistent Investment Choices MAY 2018Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the world's largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.

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An Economist’s Perspective on Probability Matching

Cited by 329 publications

References 57 publications

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

Saccade selection when reward probability is dynamically manipulated using Markov chains

Locus of control and consistent investment choices

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