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Computational Agent-based Models in Opinion Dynamics: A Survey on Social Simulations and Empirical Studies

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arxiv 2306.03446 v1 pith:NNOWT47O submitted 2023-06-06 cs.SI cs.CY

Yun-Shiuan Chuang , Timothy T. Rogers This is my paper · ORCID
classification cs.SIcs.CY
keywords abmssocialagent-basedattitudedeductiveinductiveinfluencesopinion
verification ladder T0 review T1 audit T2 compute T3 formal
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Understanding how an individual changes its attitude, belief, and opinion due to other people's social influences is vital because of its wide implications. A core methodology that is used to study the change of attitude under social influences is agent-based model (ABM). The goal of this review paper is to compare and contrast existing ABMs, which I classify into two families, the deductive ABMs and the inductive ABMs. The former subsumes social simulation studies, and the latter involves human experiments. To facilitate the comparison between ABMs of different formulations, I propose a general unified formulation, in which all ABMs can be viewed as special cases. In addition, I show the connections between deductive ABMs and inductive ABMs, and point out their strengths and limitations. At the end of the paper, I identify underexplored areas and suggest future research directions.

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Cited by 3 Pith papers

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  1. IO Factory: Simulating AI-Enabled Influence Campaigns at Scale

    cs.AI 2026-08 conditional novelty 6.0 of 10

    A framework that simulates AI influence campaigns end to end, recording exposure and belief shifts in a controlled platform with matched baselines.

  2. A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application

    cs.CL 2024-12 conditional novelty 4.0 of 10

    This survey organizes recent LLM-based multi-agent research into task-solving, simulation, and agent-evaluation applications, and identifies efficiency and evaluation gaps as key open problems.

  3. From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents

    cs.CL 2024-12 conditional novelty 4.0 of 10

    A structured survey that categorizes LLM-based social simulation into individual, scenario, and society simulation, with associated methods, benchmarks, and observed trends.

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