Balancing utility and cognitive cost in social representation

TL;DR

This paper explores how agents can balance the usefulness of social representations with the cognitive costs of maintaining them, proposing approaches to optimize this trade-off in resource-limited scenarios.

Contribution

It introduces the problem of balancing utility and cognitive cost in social representations and demonstrates two approaches to resource-constrained social learning.

Findings

Highlighting the importance of trade-offs in social representation

Proposing two methods for resource-efficient social learning

Illustrating the impact of representation choices on task performance

Abstract

To successfully navigate its environment, an agent must construct and maintain representations of the other agents that it encounters. Such representations are useful for many tasks, but they are not without cost. As a result, agents must make decisions regarding how much information they choose to store about the agents in their environment. Using selective social learning as an example task, we motivate the problem of finding agent representations that optimally trade off between downstream utility and information cost, and illustrate two example approaches to resource-constrained social representation.