PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulation

• URL: http://arxiv.org/abs/2512.19933v1
• Date: Mon, 22 Dec 2025 23:31:49 GMT
• Title: PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulation
• Authors: Zhixiang Lu, Xueyuan Deng, Yiran Liu, Yulong Li, Qiang Yan, Imran Razzak, Jionglong Su,
• Abstract summary: We introduce the Personality-Refracted Intelligent Simulation Model (PRISM) for continuous emotional evolution with a personalityconditional partially observable Markov decision process.<n>PRISM achieves superior consistency with human ground truth, outperforming standard homogeneous and Big Five benchmarks.<n>This framework effectively replicates emergent phenomena such as rational suppression and affective resonance, offering a robust tool for analyzing complex social media ecosystems.
• Score: 24.36529339525981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional agent-based models (ABMs) of opinion dynamics often fail to capture the psychological heterogeneity driving online polarization due to simplistic homogeneity assumptions. This limitation obscures the critical interplay between individual cognitive biases and information propagation, thereby hindering a mechanistic understanding of how ideological divides are amplified. To address this challenge, we introduce the Personality-Refracted Intelligent Simulation Model (PRISM), a hybrid framework coupling stochastic differential equations (SDE) for continuous emotional evolution with a personality-conditional partially observable Markov decision process (PC-POMDP) for discrete decision-making. In contrast to continuous trait approaches, PRISM assigns distinct Myers-Briggs Type Indicator (MBTI) based cognitive policies to multimodal large language model (MLLM) agents, initialized via data-driven priors from large-scale social media datasets. PRISM achieves superior personality consistency aligned with human ground truth, significantly outperforming standard homogeneous and Big Five benchmarks. This framework effectively replicates emergent phenomena such as rational suppression and affective resonance, offering a robust tool for analyzing complex social media ecosystems.

Related papers

• Interpretable Debiasing of Vision-Language Models for Social Fairness [55.85977929985967]
  We introduce an interpretable, model-agnostic bias mitigation framework, DeBiasLens, that localizes social attribute neurons in Vision-Language models.<n>We train SAEs on facial image or caption datasets without corresponding social attribute labels to uncover neurons highly responsive to specific demographics.<n>Our research lays the groundwork for future auditing tools, prioritizing social fairness in emerging real-world AI systems.
  arXiv  Detail & Related papers  (2026-02-27T13:37:11Z)
• TCEval: Using Thermal Comfort to Assess Cognitive and Perceptual Abilities of AI [0.5366500153474746]
  Thermal comfort serves as an ideal paradigm for evaluating real-world cognitive capabilities of AI systems.<n>We propose TCEval, the first evaluation framework that assesses three core cognitive capacities of AI.
  arXiv  Detail & Related papers  (2025-12-29T05:41:25Z)
• Social World Model-Augmented Mechanism Design Policy Learning [58.739456918502704]
  We introduce SWM-AP (Social World Model-Augmented Mechanism Design Policy Learning), which learns a social world model hierarchically to enhance mechanism design.<n>We show that SWM-AP outperforms established model-based and model-free RL baselines in cumulative rewards and sample efficiency.
  arXiv  Detail & Related papers  (2025-10-22T06:01:21Z)
• The Social Laboratory: A Psychometric Framework for Multi-Agent LLM Evaluation [0.16921396880325779]
  We introduce a novel evaluation framework that uses multi-agent debate as a controlled "social laboratory"<n>We show that assigned personas induce stable, measurable psychometric profiles, particularly in cognitive effort.<n>This work provides a blueprint for a new class of dynamic, psychometrically grounded evaluation protocols.
  arXiv  Detail & Related papers  (2025-10-01T07:10:28Z)
• Population-Aligned Persona Generation for LLM-based Social Simulation [58.84363795421489]
  We propose a systematic framework for synthesizing high-quality, population-aligned persona sets for social simulation.<n>Our approach begins by leveraging large language models to generate narrative personas from long-term social media data.<n>To address the needs of specific simulation contexts, we introduce a task-specific module that adapts the globally aligned persona set to targeted subpopulations.
  arXiv  Detail & Related papers  (2025-09-12T10:43:47Z)
• DynamiX: Large-Scale Dynamic Social Network Simulator [101.65679342680542]
  DynamiX is a novel large-scale social network simulator dedicated to dynamic social network modeling.<n>For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances.<n>For ordinary users, we construct an inequality-oriented behavior decision-making module.
  arXiv  Detail & Related papers  (2025-07-26T12:13:30Z)
• Large Population Models [5.935007288459162]
  Large Population Models simulate entire populations with realistic behaviors and interactions at unprecedented scale.<n>This allows researchers to observe how agent behavior aggregates into system-level outcomes and test interventions before real-world implementation.<n>LPMs offer a complementary path in AI research illuminating collective intelligence and providing testing grounds for policies and social innovations before real-world deployment.
  arXiv  Detail & Related papers  (2025-07-14T04:11:54Z)
• GGBond: Growing Graph-Based AI-Agent Society for Socially-Aware Recommender Simulation [2.7083394633019973]
  We propose a high-fidelity social simulation platform to realistically simulate user behavior evolution under recommendation interventions.<n>The system comprises a population of Sim-User Agents equipped with a five-layer cognitive architecture that encapsulates key psychological mechanisms.<n>In particular, we introduce the Intimacy--Curiosity--Reciprocity--Risk (ICR2) motivational engine grounded in psychological and sociological theories.
  arXiv  Detail & Related papers  (2025-05-27T13:09:21Z)
• MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework [53.82097200295448]
  Mean-Field LLM (MF-LLM) is first to incorporate mean field theory into social simulation.<n>MF-LLM models bidirectional interactions between individuals and the population through an iterative process.<n> IB-Tune is a novel fine-tuning method inspired by the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-04-30T12:41:51Z)
• Adaptive Preference Aggregation [1.6317061277457001]
  Social choice theory provides a framework to aggregate preferences, but was not developed for the multidimensional applications typical of AI.<n>This work introduces a preference aggregation strategy that adapts to the user's context and that inherits the good properties of the maximal lottery, a Condorcet-consistent solution concept.
  arXiv  Detail & Related papers  (2025-03-13T09:57:41Z)
• Emergence of human-like polarization among large language model agents [79.96817421756668]
  We simulate a networked system involving thousands of large language model agents, discovering their social interactions, result in human-like polarization.<n>Similarities between humans and LLM agents raise concerns about their capacity to amplify societal polarization, but also hold the potential to serve as a valuable testbed for identifying plausible strategies to mitigate polarization and its consequences.
  arXiv  Detail & Related papers  (2025-01-09T11:45:05Z)
• Synthetic Social Media Influence Experimentation via an Agentic Reinforcement Learning Large Language Model Bot [7.242974711907219]
  This study provides a novel simulated environment that combines agentic intelligence with Large Language Models (LLMs) to test topic-specific influence mechanisms.<n>Our framework contains agents that generate posts, form opinions on specific topics, and socially follow/unfollow each other based on the outcome of discussions.
  arXiv  Detail & Related papers  (2024-11-29T11:37:12Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, a framework for better data construction and model tuning.<n>For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction.<n>For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models' personalities.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.