Micro-bias and macro-performance

TL;DR

This paper uses agent-based modeling to explore how conservatism and partisanship affect large populations' ability to solve complex consensus tasks, revealing that conservatism can improve efficiency while partisanship can cause deadlock.

Contribution

It demonstrates through modeling that conservatism can enhance collective problem-solving, whereas partisanship can hinder consensus, providing insights into social dynamics.

Findings

Conservatism improves efficiency in solving the density classification task.

Partisanship leads to deadlock or incorrect consensus even at low levels.

Low levels of partisanship can cause significant social costs.

Abstract

We use agent-based modeling to investigate the effect of conservatism and partisanship on the efficiency with which large populations solve the density classification task--a paradigmatic problem for information aggregation and consensus building. We find that conservative agents enhance the populations' ability to efficiently solve the density classification task despite large levels of noise in the system. In contrast, we find that the presence of even a small fraction of partisans holding the minority position will result in deadlock or a consensus on an incorrect answer. Our results provide a possible explanation for the emergence of conservatism and suggest that even low levels of partisanship can lead to significant social costs.