A Multi-Agent Simulation Framework for Studying Algorithmic Influence on Social Behavior

Abstract

Algorithmic systems are becoming universal social mediators, shaping the information individuals receive, the decisions they make, and the collective behaviors they exhibit. However, due to the complex interactions, sheer scale and the ethical considerations, systematically studying the impact of these systems in the real world is difficult. Multi-agent simulations provide a controllable setting, but are rarely designed to specifically focus on the effect of algorithmic systems. This research proposes to develop and validate a multi-agent simulation framework to simulate how algorithmic systems (such as recommender systems or ranking algorithms) influence the behaviors of individuals and groups. The hypothesis is that algorithmic intervention can substantially affect the emergent social phenomena such as opinion formation, polarization and cooperation. An approach utilizing computational, agent-based modeling is adopted where heterogeneous agents populate a simulated social network. Agents possess a set of behavioral rules (e.g., learning, adaptation, social influence) that govern their interaction while a set of algorithms selectively filter and prioritizes information flow. An experimental simulation of scenarios with diverse algorithms and their parameters are employed to generate and measure the emergent societal patterns (e.g., network structures, opinion diversity, speed of convergence). This research expects that the algorithmic systems can amplify some societal behaviors, like echo chambers or sudden consensus formations depending on the parameters, and that there are non-linear effects, where slight modifications to algorithmic system lead to profound changes in social phenomena. Comparisons with control scenario will emphasize how sensitive social systems are to algorithmic interventions. This work is expected to deliver a scalable and extensible research framework, suitable for analyzing the societal consequences of existing and emerging algorithmic systems and contribute to the design of responsible and trustworthy algorithms.