Micro-bias and macro-performance

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.

11 pages, 5 figures

References in corpus (8)

• Community structure in social and biological networks
• Finding and evaluating community structure in networks
• Self-similar community structure in organisations
• Voter Model on Heterogeneous Graphs
• Does a Single Zealot Affect an Infinite Group of Voters ?
• On the Role of Zealotry in the Voter Model
• Voter model dynamics in complex networks: Role of dimensionality, disorder and degree distribution
• Incomplete ordering of the voter model on small-world networks

Cited by in corpus (5)

• Cross-Issue Solidarity and Truth Convergence in Opinion Dynamics
• Impact of network randomness on multiple opinion dynamics
• Sensitivity to noise and ergodicity of an assembly line of cellular automata that classifies density
• Knowledge Acquisition by Networks of Interacting Agents in the Presence of Observation Errors
• Density classification performance and ergodicity of the Gacs-Kurdyumov-Levin cellular automaton model IV