Collective decision-making under changing social environments among agents adapted to sparse connectivity

TL;DR

This paper analyzes how rational agents make collective decisions in sparse social networks, revealing adaptive strategies based on limited social information and showing that decision efficacy depends on current connectivity.

Contribution

It introduces a model of social response in sparse networks, highlighting how limited social information influences collective decision-making and outcomes.

Findings

Decision efficacy depends on current social connectivity.

Agents use simplified social cues based on observed choice differences.

Sparse connectivity influences collective outcomes significantly.

Abstract

Humans and other animals often follow the decisions made by others because these are indicative of the quality of possible choices, resulting in `social response rules': observed relationships between the probability that an agent will make a specific choice and the decisions other individuals have made. The form of social responses can be understood by considering the behaviour of rational agents that seek to maximise their expected utility using both social and private information. Previous derivations of social responses assume that agents observe all others within a group, but real interaction networks are often characterised by sparse connectivity. Here I analyse the observable behaviour of rational agents that attend to the decisions made by a subset of others in the group. This reveals an adaptive strategy in sparsely-connected networks based on highly-simplified social information: the difference in the observed number of agents choosing each option. Where agents employ this strategy, collective outcomes and decision-making efficacy are controlled by the social connectivity at the time of the decision, rather than that to which the agents are accustomed, providing an important caveat for sociality observed in the laboratory and suggesting a basis for the social dynamics of highly-connected online communities.