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arxiv: 2606.06971 · v3 · pith:VGHUDRL5new · submitted 2026-06-05 · 💻 cs.MA · cs.SI

Modeling U.S. Attitudes Toward China via an Event-Steered Multi-Agent Simulator

Chenxu Zhu , Hantao Yao , Wu Liu , Junbo Guo , Yongdong Zhang This is my paper

Pith reviewed 2026-06-27 20:40 UTC · model grok-4.3

classification 💻 cs.MA cs.SI
keywords multi-agent simulationopinion dynamicsevent-driven modelingU.S.-China relationsCURE datasetLLM agentsgeopolitical attitudesnews-driven interaction
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The pith

An event-steered multi-agent simulator reproduces real U.S. attitude shifts toward China more closely than static-rule models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing LLM-based multi-agent simulators rely on fixed rules and static datasets, which prevents them from reflecting how real-world events and news actually move public opinion. The paper introduces ES-MAS, built on a new CURE dataset of 258 events and 14,000 news items across 20 quarters, together with a Dual-Stream Data Integration Engine and a News-Driven Dynamic Interaction module that groups agents by shared news exposure. Experiments show the simulator matches observed historical trends on the CURE dataset substantially better than prior approaches. A reader would care because improved modeling of opinion dynamics supplies a concrete tool for assessing geopolitical risks that arise from shifting attitudes.

Core claim

The paper claims that steering multi-agent simulations with continuous macro-level events and daily news, aligned through the Dual-Stream Data Integration Engine and localized via the News-Driven Dynamic Interaction module, produces bottom-up opinion evolution that reproduces real historical trends on the CURE dataset more accurately than simulators using static rules.

What carries the argument

The Event-Steered Multi-Agent Simulator (ES-MAS) together with its Dual-Stream Data Integration Engine and News-Driven Dynamic Interaction module, which continuously feed events and personalized news into agent interactions.

If this is right

  • The simulator supplies a scalable framework that can ingest new event streams to model ongoing opinion dynamics.
  • Adaptive grouping of agents by shared news interests reduces the formation of isolated information cocoons while still allowing localized consensus.
  • Dual-stream alignment of macro events with micro-level news exposure produces opinion trajectories that track real historical records more closely than fixed-rule baselines.
  • The approach can be applied to other topics by swapping the underlying event and news corpus while keeping the same interaction structure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same event-steering structure could be tested on attitude data from additional countries or issue domains to check whether the performance gain generalizes.
  • Running the simulator forward on upcoming scheduled events would generate testable forecasts that can be compared against future polls.
  • Adding real-time social-media signals as extra contextual inputs might further refine the personalization of news exposure without breaking the bottom-up interaction design.
  • The CURE dataset itself provides a public benchmark that other modeling groups can use to measure progress on event-driven opinion simulation.

Load-bearing premise

Significant events and daily news items can be aligned with individual agent profiles to generate realistic opinion changes without the simulation requiring post-hoc tuning or collapsing into isolated information cocoons.

What would settle it

Running ES-MAS on a held-out quarter or post-2025 events from the CURE dataset and finding that the resulting opinion distributions on U.S. attitudes toward China diverge from independent public polls or surveys.

Figures

Figures reproduced from arXiv: 2606.06971 by Chenxu Zhu, Hantao Yao, Junbo Guo, Wu Liu, Yongdong Zhang.

Figure 1
Figure 1. Figure 1: The trajectory of U.S. attitudes toward China, illustrating how Real [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The content and structure of the CURE dataset about the Significant Events (SE) and Daily News (DN) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The architecture of ES-MAS framework. Simulating U.S. Attitude Changes Toward China by Dual-Stream Data Integration Engine and News-Driven [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of simulated and real-world U.S. public attitude dynamics [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Sensitivity Analysis on different initial attitudes. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The attitude trajectories of two representative agents (Mike and Ella) toward China from 2021 to 2025, illustrates how individual opinions dynamically [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

Understanding the dynamic evolution of opinions, such as U.S. public attitudes toward China, is essential for assessing geopolitical risks. However, existing LLM-based multiagent simulators predominantly rely on static rules and fixed datasets, limiting their ability to capture the dynamic, event-driven nature of macro-level opinion shifts in real-world settings. To address this limitation, we propose an Event-Steered Multi-Agent Simulator (ES-MAS), in which significant events and daily news continuously drive opinion evolution through dynamic interactions among agents. We first construct the China-U.S. Relation Evolution (CURE) dataset, covering 20 quarters from 2021 to 2025, including 258 major events and over 14,000 daily news articles, and providing a comprehensive temporal foundation for modeling opinion dynamics. Building upon the CURE dataset, we propose a Dual-Stream Data Integration Engine (DSDIE) that aligns simulations with historical timelines via macro-level events while enabling personalized information exposure based on individual agent profiles and contextual signals. Furthermore, we design a News-Driven Dynamic Interaction (NDDI) module, which adaptively groups agents with shared news interests into localized interaction contexts, facilitating bottom-up consensus formation while mitigating the risk of isolated information cocoons. Experimental results on the CURE dataset demonstrate that ES-MAS substantially outperforms existing simulators in reproducing real-world historical trends, offering a scalable and effective framework for modeling dynamic opinion evolution.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper proposes an Event-Steered Multi-Agent Simulator (ES-MAS) to model dynamic opinion evolution, specifically U.S. attitudes toward China. It constructs the China-U.S. Relation Evolution (CURE) dataset spanning 20 quarters (2021-2025) with 258 major events and over 14,000 daily news articles. It introduces a Dual-Stream Data Integration Engine (DSDIE) for aligning macro events with personalized news exposure and a News-Driven Dynamic Interaction (NDDI) module for grouping agents by shared interests to enable bottom-up consensus. The central empirical claim is that ES-MAS substantially outperforms existing simulators in reproducing real-world historical trends on the CURE dataset.

Significance. If the outperformance claim is substantiated with quantitative metrics and validation protocols, the work would contribute a scalable event-driven framework for multi-agent opinion modeling and supply the CURE dataset as a reusable temporal resource for geopolitical and computational social science research. The emphasis on mitigating information cocoons via adaptive grouping is a constructive direction if empirically validated.

major comments (1)
  1. [Abstract / Experimental Results] Abstract and Experimental Results: The assertion that 'ES-MAS substantially outperforms existing simulators in reproducing real-world historical trends' supplies no metrics (e.g., MAE, correlation, or accuracy scores), baseline definitions, data splits, error bars, or exclusion rules. Without these, the central claim cannot be evaluated and is load-bearing for the paper's contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for your constructive review of our manuscript on ES-MAS. We appreciate the identification of the need for explicit quantitative support for the central outperformance claim, which is indeed load-bearing. We will revise the abstract and experimental results accordingly to include the requested metrics and protocol details.

read point-by-point responses

  1. Referee: [Abstract / Experimental Results] Abstract and Experimental Results: The assertion that 'ES-MAS substantially outperforms existing simulators in reproducing real-world historical trends' supplies no metrics (e.g., MAE, correlation, or accuracy scores), baseline definitions, data splits, error bars, or exclusion rules. Without these, the central claim cannot be evaluated and is load-bearing for the paper's contribution.

    Authors: We agree that the current abstract and experimental results section do not report specific quantitative metrics such as MAE, correlation coefficients, or accuracy scores, nor do they explicitly define baselines, data splits, error bars, or exclusion rules. This omission prevents full evaluation of the claim. In the revised manuscript, we will update the abstract to incorporate key results (e.g., specific MAE and correlation values showing outperformance) and expand the Experimental Results section to specify: baseline simulators used for comparison, the temporal data split (e.g., first 15 quarters for calibration and last 5 for validation), error bars computed over multiple runs, and any event exclusion criteria. These changes will directly address the concern and allow readers to assess the empirical contribution. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper introduces ES-MAS, DSDIE, and NDDI as architectural components for an event-driven simulator, constructs the CURE dataset from external events and news, and reports comparative performance on historical trend reproduction. No equations, parameter-fitting procedures, or self-referential definitions are described in the provided abstract or architecture summary. The central claim of outperformance rests on empirical comparison against prior simulators rather than any reduction of outputs to inputs by construction, self-citation chains, or renamed ansatzes. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 4 invented entities

The abstract rests on the domain assumption that macro-level opinion shifts are primarily event- and news-driven and that localized agent interactions can produce consensus without external validation of that mechanism.

axioms (1)
  • domain assumption Opinion evolution is continuously driven by significant events and daily news through dynamic agent interactions
    Stated as the core limitation of prior work and the motivation for ES-MAS in the abstract.
invented entities (4)
  • ES-MAS simulator no independent evidence
    purpose: Event-steered multi-agent opinion model
    Newly proposed framework
  • CURE dataset no independent evidence
    purpose: Temporal foundation covering 20 quarters of events and news
    Newly constructed dataset
  • DSDIE module no independent evidence
    purpose: Aligns simulations with historical timelines via macro events and personalized exposure
    Newly designed component
  • NDDI module no independent evidence
    purpose: Groups agents by shared news interests for localized consensus formation
    Newly designed component

pith-pipeline@v0.9.1-grok · 5792 in / 1547 out tokens · 19865 ms · 2026-06-27T20:40:51.503675+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

43 extracted references · 11 canonical work pages · 4 internal anchors

  1. [1]

    Destined for war?

    G. T. Allison, “Destined for war?”The National Interest, no. 149, pp. 9–21, 2017

    2017

  2. [2]

    Media consumption patterns and public hostility toward china in the united states,

    X. Li and Y . Y . Ang, “Media consumption patterns and public hostility toward china in the united states,”Communication and the Public, p. 20570473251401708, 2025

    2025

  3. [3]

    The china reckoning: how beijing defied american expectations,

    K. M. Campbell and E. Ratner, “The china reckoning: how beijing defied american expectations,”Foreign Aff., vol. 97, p. 60, 2018

    2018

  4. [4]

    Silver, C

    L. Silver, C. Huang, L. Clancy, and M. Fagan,Americans Are Critical of China’s Global Role-as Well as Its Relationship With Russia. JSTOR, 2023, vol. 12

    2023

  5. [5]

    Artificial intelligence techniques for political risk man- agement: An nlp analysis of the 2019 us-china trade war,

    M. D. Wang, “Artificial intelligence techniques for political risk man- agement: An nlp analysis of the 2019 us-china trade war,” inMachine Learning and Data Mining Annual Volume 2023. IntechOpen, 2023

    2019

  6. [6]

    Mixing beliefs among interacting agents,

    G. Deffuant, D. Neau, F. Amblard, and G. Weisbuch, “Mixing beliefs among interacting agents,”Advances in Complex Systems, vol. 3, no. 01n04, pp. 87–98, 2000

    2000

  7. [7]

    How can extremism prevail? a study based on the relative agreement interaction model,

    G. Deffuant, F. Amblard, G. Weisbuch, and T. Faure, “How can extremism prevail? a study based on the relative agreement interaction model,”Journal of artificial societies and social simulation, vol. 5, no. 4, 2002

    2002

  8. [8]

    Computa- tional social science,

    D. Lazer, A. Pentland, L. Adamic, S. Aral, A.-L. Barab ´asi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmannet al., “Computa- tional social science,”Science, vol. 323, no. 5915, pp. 721–723, 2009

    2009

  9. [9]

    War and peace (waragent): Large language model-based multi-agent simulation of world wars,

    W. Hua, L. Fan, L. Li, K. Mei, J. Ji, Y . Ge, L. Hemphill, and Y . Zhang, “War and peace (waragent): Large language model-based multi-agent simulation of world wars,”arXiv preprint arXiv:2311.17227, 2023

    work page arXiv 2023

  10. [10]

    Richelieu: Self-evolving llm-based agents for ai diplomacy,

    Z. Guan, X. Kong, F. Zhong, and Y . Wang, “Richelieu: Self-evolving llm-based agents for ai diplomacy,”Advances in Neural Information Processing Systems, vol. 37, pp. 123 471–123 497, 2024

    2024

  11. [11]

    Unveiling the truth and facilitating change: Towards agent-based large-scale social movement simulation,

    X. Mou, Z. Wei, and X.-J. Huang, “Unveiling the truth and facilitating change: Towards agent-based large-scale social movement simulation,” inFindings of the Association for Computational Linguistics: ACL 2024, 2024, pp. 4789–4809

    2024

  12. [12]

    S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents

    C. Gao, X. Lan, Z. Lu, J. Mao, J. Piao, H. Wang, D. Jin, and Y . Li, “S3: Social-network simulation system with large language model- empowered agents,”arXiv preprint arXiv:2307.14984, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  13. [13]

    Debiasing international attitudes: Llm agents for simulating us-china perception changes,

    N. Sukiennik, Y . Xu, Y . Kan, J. Piao, Y . Yan, C. Gao, and Y . Li, “Debiasing international attitudes: Llm agents for simulating us-china perception changes,”arXiv preprint arXiv:2508.08837, 2025

    work page arXiv 2025

  14. [14]

    T. C. Schelling,Micromotives and macrobehavior. WW Norton & Company, 2006

    2006

  15. [15]

    Reaching a consensus,

    M. H. DeGroot, “Reaching a consensus,”Journal of the American Statistical association, vol. 69, no. 345, pp. 118–121, 1974

    1974

  16. [16]

    Birds of a feather: Homophily in social networks,

    M. McPherson, L. Smith-Lovin, and J. M. Cook, “Birds of a feather: Homophily in social networks,”Annual review of sociology, vol. 27, no. 1, pp. 415–444, 2001

    2001

  17. [17]

    Collective dynamics of ‘small- world’networks,

    D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small- world’networks,”nature, vol. 393, no. 6684, pp. 440–442, 1998

    1998

  18. [18]

    Recent research on selective exposure to information,

    D. Frey, “Recent research on selective exposure to information,”Ad- vances in experimental social psychology, vol. 19, pp. 41–80, 1986

    1986

  19. [19]

    The social identity theory of intergroup behavior,

    H. Tajfel and J. C. Turner, “The social identity theory of intergroup behavior,” inPolitical psychology. Psychology Press, 2004, pp. 276– 293

    2004

  20. [20]

    Individual attitude change and societal dynamics: Computational experiments with psychological theories

    J. Lorenz, M. Neumann, and T. Schr ¨oder, “Individual attitude change and societal dynamics: Computational experiments with psychological theories.”Psychological Review, vol. 128, no. 4, p. 623, 2021

    2021

  21. [21]

    Adaptive agents, intelligence, and emergent human organization: Capturing complexity through agent- based modeling,

    B. J. Berry, L. D. Kiel, and E. Elliott, “Adaptive agents, intelligence, and emergent human organization: Capturing complexity through agent- based modeling,”Proceedings of the National Academy of Sciences, vol. 99, no. suppl 3, pp. 7187–7188, 2002

    2002

  22. [22]

    Simulating social media using large language models to evaluate alternative news feed algorithms,

    P. T ¨ornberg, D. Valeeva, J. Uitermark, and C. Bail, “Simulating social media using large language models to evaluate alternative news feed algorithms,”arXiv preprint arXiv:2310.05984, 2023

    work page arXiv 2023

  23. [23]

    Out of one, many: Using language models to simulate human samples,

    L. P. Argyle, E. C. Busby, N. Fulda, J. R. Gubler, C. Rytting, and D. Wingate, “Out of one, many: Using language models to simulate human samples,”Political Analysis, vol. 31, no. 3, pp. 337–351, 2023

    2023

  24. [24]

    Using large language models to simulate multiple humans and replicate human subject studies,

    G. V . Aher, R. I. Arriaga, and A. T. Kalai, “Using large language models to simulate multiple humans and replicate human subject studies,” in International conference on machine learning. PMLR, 2023, pp. 337– 371

    2023

  25. [25]

    Zaller,The nature and origins of mass opinion

    J. Zaller,The nature and origins of mass opinion. Cambridge university press, 1992. 10

    1992

  26. [26]

    The rise and potential of large language model based agents: A survey,

    Z. Xi, W. Chen, X. Guo, W. He, Y . Ding, B. Hong, M. Zhang, J. Wang, S. Jin, E. Zhouet al., “The rise and potential of large language model based agents: A survey,”Science China Information Sciences, vol. 68, no. 2, p. 121101, 2025

    2025

  27. [27]

    A survey on large language model based autonomous agents,

    L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Zhang, Z. Chen, J. Tang, X. Chen, Y . Linet al., “A survey on large language model based autonomous agents,”Frontiers of Computer Science, vol. 18, no. 6, p. 186345, 2024

    2024

  28. [28]

    CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict

    Y . Sun, W. Liu, H. Ming, X. Wang, H. Yao, and Y . Zhang, “Cyberjurors: A multi-agent simulation task for e-commerce disputes verdict,”arXiv preprint arXiv:2605.28369, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  29. [29]

    Generative agents: Interactive simulacra of human behavior,

    J. S. Park, J. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, “Generative agents: Interactive simulacra of human behavior,” inProceedings of the 36th annual acm symposium on user interface software and technology, 2023, pp. 1–22

    2023

  30. [30]

    Oasis: Open agent social interaction simulations with one million agents,

    Z. Yang, Z. Zhang, Z. Zheng, Y . Jiang, Z. Gan, Z. Wang, Z. Ling, J. Chen, M. Ma, B. Donget al., “Oasis: Open agent social interaction simulations with one million agents,”arXiv preprint arXiv:2411.11581, 2024

    work page arXiv 2024

  31. [31]

    AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society

    J. Piao, Y . Yan, J. Zhang, N. Li, J. Yan, X. Lan, Z. Lu, Z. Zheng, J. Y . Wang, D. Zhouet al., “Agentsociety: Large-scale simulation of llm- driven generative agents advances understanding of human behaviors and society,”arXiv preprint arXiv:2502.08691, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  32. [32]

    From skepticism to acceptance: Simulating the attitude dynamics toward fake news,

    Y . Liu, X. Chen, X. Zhang, X. Gao, J. Zhang, and R. Yan, “From skepticism to acceptance: Simulating the attitude dynamics toward fake news,”arXiv preprint arXiv:2403.09498, 2024

    work page arXiv 2024

  33. [33]

    GASim: A Graph-Accelerated Hybrid Framework for Social Simulation

    X. Zhou, Y . Sun, H. Yao, A. He, Y . Zhang, and W. Liu, “Gasim: A graph-accelerated hybrid framework for social simulation,”arXiv preprint arXiv:2605.07692, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  34. [34]

    Dy- namix: Large-scale dynamic social network simulator,

    Y . Sun, W. Liu, W. Wang, H. Yao, J. Luo, and Y . Zhang, “Dy- namix: Large-scale dynamic social network simulator,”arXiv preprint arXiv:2507.19929, 2025

    work page arXiv 2025

  35. [35]

    Simulating opinion dynamics with networks of llm-based agents,

    Y .-S. Chuang, A. Goyal, N. Harlalka, S. Suresh, R. Hawkins, S. Yang, D. Shah, J. Hu, and T. Rogers, “Simulating opinion dynamics with networks of llm-based agents,” inFindings of the association for computational linguistics: NAACL 2024, 2024, pp. 3326–3346

    2024

  36. [36]

    Social simulacra: Creating populated prototypes for social computing systems,

    J. S. Park, L. Popowski, C. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, “Social simulacra: Creating populated prototypes for social computing systems,” inProceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, 2022, pp. 1–18

    2022

  37. [37]

    arXiv preprint (2024) arXiv:2410.20746

    X. Zhang, J. Lin, L. Sun, W. Qi, Y . Yang, Y . Chen, H. Lyu, X. Mou, S. Chen, J. Luo, X. Huang, S. Tang, and Z. Wei, “Electionsim: Massive population election simulation powered by large language model driven agents,”CoRR, vol. abs/2410.20746, 2024. [Online]. Available: https://doi.org/10.48550/arXiv.2410.20746

    work page doi:10.48550/arxiv.2410.20746 2024

  38. [38]

    Gensim: A general social simulation platform with large language model based agents,

    J. Tang, H. Gao, X. Pan, L. Wang, H. Tan, D. Gao, Y . Chen, X. Chen, Y . Lin, Y . Liet al., “Gensim: A general social simulation platform with large language model based agents,” inProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations),...

    2025

  39. [39]

    M ¨uller,Information retrieval for music and motion

    M. M ¨uller,Information retrieval for music and motion. Springer, 2007

    2007

  40. [40]

    Computing discrete fr ´echet distance,

    T. Eiter, H. Mannilaet al., “Computing discrete fr ´echet distance,” 1994

    1994

  41. [41]

    Link recommendation algorithms and dynamics of polarization in online social networks,

    F. P. Santos, Y . Lelkes, and S. A. Levin, “Link recommendation algorithms and dynamics of polarization in online social networks,” Proceedings of the National Academy of Sciences, vol. 118, no. 50, p. e2102141118, 2021

    2021

  42. [42]

    Opinion dynamics and bounded confidence: models, analysis and simulation,

    R. Hegselmann, “Opinion dynamics and bounded confidence: models, analysis and simulation,”The Journal of Artificial Societies and Social Simulation, 2015

    2015

  43. [43]

    Uniformity, bipolarization and pluriformity captured as generic stylized behavior with an agent-based simulation model of attitude change,

    W. Jager and F. Amblard, “Uniformity, bipolarization and pluriformity captured as generic stylized behavior with an agent-based simulation model of attitude change,”Computational & Mathematical Organization Theory, vol. 10, no. 4, pp. 295–303, 2005

    2005