When more of the same is better

TL;DR

This study uses an agent-based model to explore how diversity among agents affects cooperative problem-solving performance, revealing that homogeneity is optimal for easy tasks, while diversity benefits difficult tasks, though homogeneous systems often outperform heterogeneous ones when optimized.

Contribution

It introduces an agent-based model analyzing the impact of agent diversity on cooperative problem solving, highlighting conditions where sameness or diversity is advantageous.

Findings

Homogeneous systems excel at easy tasks with high copy propensities.

Diversity helps prevent sub-optimal solutions in difficult tasks.

Optimized homogeneous systems outperform heterogeneous ones in overall performance.

Abstract

Problem solving (e.g., drug design, traffic engineering, software development) by task forces represents a substantial portion of the economy of developed countries. Here we use an agent-based model of cooperative problem solving systems to study the influence of diversity on the performance of a task force. We assume that agents cooperate by exchanging information on their partial success and use that information to imitate the more successful agent in the system -- the model. The agents differ only in their propensities to copy the model. We find that, for easy tasks, the optimal organization is a homogeneous system composed of agents with the highest possible copy propensities. For difficult tasks, we find that diversity can prevent the system from being trapped in sub-optimal solutions. However, when the system size is adjusted to maximize performance the homogeneous systems outperform the heterogeneous systems, i.e., for optimal performance, sameness should be preferred to diversity.