This paper presents an agent-based simulation that frames trust as a collective resource and attention as a scarce private budget: when aggregate attention shifts toward low credibility content, the trust environment degrades, making credible information harder to process and correct.
Abstract
Misinformation often harms society not just by spreading a single false belief, but by breaking down the shared trust people rely on to evaluate what is true. This paper presents an agent-based simulation that frames trust as a collective resource and attention as a scarce private budget: when aggregate attention shifts toward low credibility content, the trust environment degrades, making credible information harder to process and correct. Across experiments, the model produces four recurring modes: credible stability, misinformation dominance, polarization, and a mixed baseline, with distinct signatures in trust trajectories and network structure. The results separate two control problems that matter for simulation-based policy exploration: the balance of trust repair versus harm largely determines whether the system recovers or collapses, while homophily and rewiring determine whether disagreement remains integrated or separates into persistent clusters. This foundation provides a transparent testbed for comparative experiments on interventions that must address both trust restoration and structural conditions for cross-cutting exposure.
Social interaction can improve collective learning but also amplify early mistakes. We study this tension when the credibility of social information is generated by the sender's own decision process rather than fixed ex ante. Reinforcement-learning agents make binary choices through a drift--diffusion process that jointly determines choice, decision time, and confidence; decision confidence then becomes social credibility by weighting anticipatory influence and retrospective social learning. Under balanced community exposure, the anticipatory field admits an exact quotient representation. Its local Jacobian is a scalar decision-sensitivity term multiplying the community-coupling matrix, which yields a common-mode amplification threshold and an analytical role for cross-community permeability in damping relative community differences. Monte Carlo experiments show the corresponding non-monotone performance pattern: moderate transmission accelerates correction, whereas strong transmission can lock populations into wrong consensus; low permeability instead sustains disagreement. Ablations reveal a dual role for confidence: credibility-sensitive transmission amplifies social error, while confidence-dependent private learning stabilises it. The model yields testable predictions linking sender confidence to receiver behaviour conditional on accuracy.
Advanced digital communication has revolutionized how people create and consume information, making information diffusion an important topic of research for domains from public health to national security. Real-world scenarios of information diffusion often involve competing narratives - true and false - spreading simultaneously. We propose a novel agent-based co-diffusion model, grounded in"complex-contagion"and"spiral of silence"theories, to capture how network dynamics exploit cognitive biases to shape such interactions. Our findings reveal that manipulative narratives dominate when early spreaders hold them. These network dynamics further exploit inherent cognitive biases to amplify information diffusion regardless of veracity. Further, while favourable previous experience strengthen collective optimism, unfavourable experiences attenuate optimism only modestly. However, we found that early seeding of agents with lower self-censorship not only constrains the spread of manipulation but can also lead to dominance of well-informed populance. This has implications for policies that aim to facilitate healthier discourse, strengthen social cohesion, and ensure equitable access to reliable information.
Vaibhav Krishna, H. Shirado, Feng Fu et al.· 0 citations
ForesightSafety-TIDE, a controlled evaluation framework that strictly pairs all-honest collaboration with controlled deception by a key evidence holder and analyzes the aggregation process through multi-stage voting, testimony adoption, and evidence-root lineage propagation, reveals both the fragility of distributed fact recovery and its underlying mechanism.
Chen Yan, Zeyang Yue, Feifei Zhao et al.· 0 citations
Can a population of people not individually inclined to harm others nonetheless produce harmful collective outcomes, purely because of the institutional structure they inhabit? Social scientists have long argued yes, but existing accounts are largely qualitative and provide no precise condition distinguishing safe institutions from unsafe ones. We develop a threshold cascade model in which agents have positive activation thresholds, harmful behavior is irreversible, and the institution exerts both standing pressure and peer influence along a weighted network. We give a necessary and sufficient condition, checkable from the institution's structure and its members'thresholds, for resistance to any shock up to a given size. The criterion extends to signed influence, in which some peer effects counteract harm, and yields a convex optimization formulation for least-cost repair. It also reveals a sharp frontier between functionality and safety. An institution can coordinate its members and remain safe if and only if the exposure that coordination creates stays below the weakest member's net threshold. A further tension arises when coordination requires responsiveness to peer influence, which can make it impossible to prevent the most exposed group from cascading. We then analyze a mean-field model of two groups differing in how easily their members are pushed into harm. When one group is unstable in isolation but the system is stable under full mixing, disproportionate within-group influence creates a sharp homophily threshold beyond which the harm-free state becomes unstable. In the model, identical treatment of both groups does not generally equalize their cascade robustness.
LLMs make it increasingly easy to generate deceptive content at scale, creating a need for scalable misinformation risk evaluation based on whether readers find such content credible and are willing to share it. A natural approach is to ask an LLM these questions directly and treat the returned scores as predictions of the corresponding human ratings. Implicit in this practice is the assumption that asking about a reader response produces the score that best predicts it. We test this assumption using matched credibility and willingness-to-share ratings for 290 deceptive articles from 317 participants and eight LLM evaluators. Unexpectedly, the assumption holds for credibility but fails for sharing. For every evaluator, credibility scores track human sharing at least as closely as sharing scores, while sharing scores offer no detectable benefit beyond credibility when predicting responses to unseen scenarios. This pattern persists when the questions are asked separately or in reversed order. Our results suggest that directly asking for the target response may not always yield the most effective score for predicting it. Comparing direct scores with indirect paths through related judgments may reveal a more effective predictive route. Deciding what to ask may be as important as refining how to ask it.
Zonghuan Xu, Xiang Zheng, Yutao Wu et al.· 0 citations
Cooperative capabilities in language models are dual-use. The same social reasoning that supports civic deliberation can also enable strategic omission, false consensus, and manipulative framing. We argue that Cooperative AI evaluations should separate what models can do under benign instructions from what they tend to do under realistic civic pressure. We introduce DiffCoop-Civic, a 10-scenario pilot evaluation suite spanning preference understanding, evidence and persuasion, commitment design, asymmetric information, and dissent preservation. Across seven models from four model families, subtle omission pressure produces a near-uniform shift: manipulative enablement rises by 1.17 points and dissent preservation falls by 1.67 points on a 5-point scale. Overt false-consensus pressure behaves differently: it triggers refusal or redirection in some aligned API models, but direct compliance in several open-weight models. A lightweight Pareto-Trace prompting intervention improves pressure robustness without simply relying on hard refusal. An anonymous reproducibility package is available at https://anonymous.4open.science/r/diffcoop-civil-771C.