Examining learning coherence in group decision-making: triads vs. tetrads

Abstract

Abstract This study examined whether three heads are better than four in terms of performance and learning properties in group decision-making. It was predicted that learning incoherence took place in tetrads because the majority rule could not be applied when two subgroups emerged. As a result, tetrads underperformed triads. To examine this hypothesis, we adopted a reinforcement learning framework using simple Q-learning and estimated learning parameters. Overall, the results were consistent with the hypothesis. Further, this study is one of a few attempts to apply a computational approach to learning behavior in small groups. This approach enables the identification of underlying learning parameters in group decision-making.