Phenomenological theory of collective decision-making

TL;DR

This paper introduces a quantitative formalism for optimizing the distribution of competences in groups tackling complex, multidimensional problems, emphasizing the importance of specialists with broader insights for effective collective decision-making.

Contribution

It presents a novel formal framework for determining optimal group compositions in complex decision tasks, supported by empirical citation data.

Findings

Optimal groups have at least one specialist per sub-problem

Specialists benefit from broader insight into related sub-problems

Empirical data aligns with the theoretical predictions

Abstract

An essential task of groups is to provide efficient solutions for the complex problems they face. Indeed, considerable efforts have been devoted to the question of collective decision-making related to problems involving a single dominant feature. Here we introduce a quantitative formalism for finding the optimal distribution of the group members' competences in the more typical case when the underlying problem is complex, i.e., multidimensional. Thus, we consider teams that are aiming at obtaining the best possible answer to a problem having a number of independent sub-problems. Our approach is based on a generic scheme for the process of evaluating the proposed solutions (i.e., negotiation). We demonstrate that the best performing groups have at least one specialist for each sub-problem -- but a far less intuitive result is that finding the optimal solution by the interacting group members requires that the specialists also have some insight into the sub-problems beyond their unique field(s). We present empirical results obtained by using a large-scale database of citations being in good agreement with the above theory. The framework we have developed can easily be adapted to a variety of realistic situations since taking into account the weights of the sub-problems, the opinions or the relations of the group is straightforward. Consequently, our method can be used in several contexts, especially when the optimal composition of a group of decision-makers is designed.