"Conservatives Overfit, Liberals Underfit": The Social-Psychological Control of Affect and Uncertainty

TL;DR

This paper introduces an advanced computational model, BayesAct, grounded in social-psychological theory, enabling artificial agents to reason about affect and identity, thereby improving social interactions and understanding cognitive biases.

Contribution

It develops a revised BayesAct model that integrates social-psychological concepts more deeply and demonstrates its ability to explain biases and unify exploration strategies in reinforcement learning.

Findings

BayesAct accounts for cognitive biases like fairness, dissonance, and conformity.

The model unifies different exploration strategies in reinforcement learning.

Enhanced social-psychological grounding improves agent affective reasoning.

Abstract

The presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio-technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well-established social-psychological Bayesian Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high-level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social-psychological theory. We introduce a revised BayesAct model that more deeply integrates social-psychological theorising, and we demonstrate a component of the model as being sufficient to account for cognitive biases about fairness, dissonance and conformity. We show how the model can unify different exploration strategies in reinforcement learning.