Team Decision Making with Social Learning: Human Subject Experiments

We demonstrate that human decision-making agents do social learning whether it is beneficial or not. Specifically, we consider binary Bayesian hypothesis testing with multiple agents voting sequentially for a team decision, where each one observes earlier-acting agents' votes as well as a condi...

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Bibliographic Details
Published in2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 6971 - 6975
Main Authors Rhim, Joong Bum, Goyal, Vivek K
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
Bayes methods
Bayesian hypothesis testing
Cognition
Decision making
distributed detection and fusion
Error probability
human behavior
Indexes
Measurement
sequential decision making
social learning
Testing
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Summary:We demonstrate that human decision-making agents do social learning whether it is beneficial or not. Specifically, we consider binary Bayesian hypothesis testing with multiple agents voting sequentially for a team decision, where each one observes earlier-acting agents' votes as well as a conditionally independent and identically distributed private signal. While the best strategy (for the team objective) is to ignore the votes of earlier-acting agents, human agents instead tend to be affected by others' decisions. Furthermore, they are almost equally affected in the team setting as when they are incentivized only for individual correctness. These results suggest that votes of earlier-acting agents should be withheld (not shared as public signals) to improve team decision-making performance; humans are insufficiently rational to innately apply the optimal decision rules that would ignore the public signals.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8461633