Model of human collective decision-making in complex environments
This addresses collective decision-making in human groups, offering insights for organizational behavior and social systems, but it is incremental as it builds on existing models of opinion dynamics.
The study tackled how groups make decisions in complex environments by modeling a continuous-time Markov process with self-interest and social interactions, finding that optimal group performance occurs at a critical social interaction threshold where consensus emerges, and moderate knowledge levels suffice for high performance.
A continuous-time Markov process is proposed to analyze how a group of humans solves a complex task, consisting in the search of the optimal set of decisions on a fitness landscape. Individuals change their opinions driven by two different forces: (i) the self-interest, which pushes them to increase their own fitness values, and (ii) the social interactions, which push individuals to reduce the diversity of their opinions in order to reach consensus. Results show that the performance of the group is strongly affected by the strength of social interactions and by the level of knowledge of the individuals. Increasing the strength of social interactions improves the performance of the team. However, too strong social interactions slow down the search of the optimal solution and worsen the performance of the group. In particular, we find that the threshold value of the social interaction strength, which leads to the emergence of a superior intelligence of the group, is just the critical threshold at which the consensus among the members sets in. We also prove that a moderate level of knowledge is already enough to guarantee high performance of the group in making decisions.