Stabilising Generative Models of Attitude Change

2026-04-02

AI Summary

Classic theories of attitude change have proven difficult to stably replicate in computational models due to their lack of operational detail. This work proposes a “model stabilization” methodology that translates three foundational theories—cognitive dissonance, self-consistency, and self-perception—into agent decision-making logic grounded in natural language: agents generate behavioral suffixes conditioned on attitudinal prefixes. Implemented within the Concordia simulation environment, this approach iteratively clarifies the implicit situational and representational assumptions underlying each theory, thereby exposing their unstated operational requirements and socio-ecological dependencies. The experiments successfully reproduce canonical psychological behavioral patterns and systematically identify the critical constraints necessary for stable computational instantiation of these theoretical frameworks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

Attitude change - the process by which individuals revise their evaluative stances - has been explained by a set of influential but competing verbal theories. These accounts often function as mechanism sketches: rich in conceptual detail, yet lacking the technical specifications and operational constraints required to run as executable systems. We present a generative actor-based modelling workflow for"rendering"these sketches as runnable actor - environment simulations using the Concordia simulation library. In Concordia, actors operate by predictive pattern completion: an operation on natural language strings that generates a suffix which describes the actor's intended action from a prefix containing memories of their past and observations of the present. We render the theories of cognitive dissonance (Festinger 1957), self-consistency (Aronson 1969), and self-perception (Bem 1972) as distinct decision logics that populate and process the prefix through theory-specific sequences of reasoning steps. We evaluate these implementations across classic psychological experiments. Our implementations generate behavioural patterns consistent with known results from the original empirical literature. However, we find that achieving stable reproduction requires resolving the inherent underdetermination of the verbal accounts and the conflicts between modern linguistic priors and historical experimental assumptions. And, we document how this manual process of iterative model"stabilisation"surfaces specific operational and socio-ecological dependencies that were largely undocumented in the original verbal accounts. Ultimately, we argue that the manual stabilisation process itself should be regarded as a core part of the methodology functioning to clarify situational and representational commitments needed to generate characteristic effects.

Problem

• attitude change
• generative models
• verbal theories
• model stabilisation
• cognitive dissonance

Innovation

• generative actor-based modelling
• predictive pattern completion
• theory formalization
• model stabilisation
• attitude change simulation