Team Decision Making with Social Learning: Human Subject Experiments - Rhim, Joong Bum; Goyal, Vivek K (2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018)
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.