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政治行为

Are Voters Myopic or Looking for Improvement?

APSR — pp. 1-18 2026-09-01 Original Are Voters Myopic or Looking for Improvement? MARKUS PRIOR, TALBOT M. ANDREWS Abstract Abstract Timing of valence outcomes plays an important role in key theories of electoral choice, and different models place different emphases on past and future outcomes. Whereas sanctioning models consider only retrospective evaluations as an accountability mechanism, selection models hold that voters use past and proposed future conditions to infer the incumbent’s ability to deliver benefits. Drawing on four surveys run on six different samples, we demonstrate that voters prefer improving conditions in both retrospection and prospection. In retrospection, this pattern is difficult to distinguish from overweighting of temporally proximate conditions, which gave rise to the notion of “the myopic voter.” Distinguishing the two processes, we find that people do not ignore early datapoints and reward improving conditions (in both retrospection and prospection). This preference for improvement does not simply reflect an uncritical heuristic for upward trends. People are not universally myopic and many genuinely value improving aggregate conditions retrospectively and prospectively.

Subversive Amplification: Online Dissent and the Limits of Information Control in China

CQ — pp. 1-18 2026-08-28 Original Subversive Amplification: Online Dissent and the Limits of Information Control in China Christian Göbel Abstract Abstract Intensified information control severely constrains digital contention in China, yet online mass events occasionally occur. Using a dataset of 274,721 Weibo messages, this study examines mobilization, coordination and grievance articulation during a two-day episode of online dissent in the aftermath of the 2022 Urumqi residential building fire, which preceded the White Paper protests. It argues that users were activated by public communication failures that transformed manifold grievances into outrage, and that the associated hashtags provided anchors to which criticism could be attached. In a process this study terms “subversive amplification,” concentrated engagement amplified the hashtags’ visibility, and with it the critical content attached to them. However, absent the unifying function of opinion leaders, grievance articulation remained fragmented and did not converge on unified demands. The case illustrates the inherent vulnerabilities of information control but also the conditions and limits of their exploitation.

Personality pairing improves human–AI collaboration

PNAS 123/35 pp. e2530627123-e2530627123 2026-08-28 Original Personality pairing improves human–AI collaboration Harang Ju, Sinan Aral Abstract Here we examine how AI agent "personalities" interact with human personalities to shape human-AI collaboration and performance. In a large-scale, preregistered randomized experiment, we paired 1,258 participants with AI agents prompted to exhibit varying levels of the Big Five personality traits. These human-AI teams produced 7,266 display ads for a real think tank, which we evaluated using 1,168 independent human raters, and a field experiment on X that generated nearly 5 million impressions. We found that human and AI personalities individually shaped ad quality and teamwork and that human-AI personality pairings directly influenced ad quality. For example, extraverted humans paired with conscientious AI produced the lowest quality ads, followed by conscientious humans paired with agreeable AI and neurotic humans paired with conscientious AI. In the field experiment, ad quality significantly influenced ad performance, measured by click-through rates and cost-per-click. Together, these results demonstrate that personality pairing can improve human-AI collaboration and performance. They also motivate future research on the complex implications of AI personalization for human-AI collaboration, teamwork, and performance.

Indirect reciprocity with dual private assessment

PNAS 123/35 pp. e2624656123-e2624656123 2026-08-27 Original Indirect reciprocity with dual private assessment Yukari Jessica Tham, Christian Hilbe, Yohsuke Murase Abstract People often cooperate out of concern for their reputation. The corresponding theory of indirect reciprocity predicts that such cooperation can only evolve if people's opinions of each other are sufficiently correlated. This correlation, however, can be difficult to achieve when individuals form their opinions independently from one another, as in private assessment models. In that case, errors can generate disagreements that propagate throughout a population. Here, we identify a simple mechanism that mitigates this problem. Most prior work assumes that observers revise only the reputations of donors-the individuals who choose whether to cooperate or defect. We instead allow observers to also revise the reputations of recipients-the individuals affected by the donors' choices. Using analytical calculations and simulations, we show that such dual reputation updates help synchronize opinions and facilitate cooperation. To demonstrate this approach, we focus on a particularly simple rule, which we call Recipient Image Scoring (RIS). Under RIS, recipients are assessed as good whenever donors choose to cooperate with them. We show that this rule for judging recipients effectively complements the classical leading-eight rules for judging donors. Our results establish dual assessment as a simple mechanism for cooperation that relies only on directly observable information.

Forgiving the Government: The Waning Effect of Citizen Grievances in China

JCC — pp. 1-17 2026-08-27 Original Forgiving the Government: The Waning Effect of Citizen Grievances in China Jinfeng Wu, Yongshun Cai Abstract Public grievances do not automatically translate into collective or even individual resistance, but pent-up grievances threaten governments because they may trigger regime-challenging actions when opportunities arise. This study examines to what extent public grievances against state authorities may become pent up. Using a longitudinal survey in China, this article finds that public grievances against local state authorities reduce citizens’ trust in the political system, but citizen grievances may not become pent up. Instead, these grievances tend to be individualized in the sense that they neither last for a long time nor diffuse in society. Individualized grievances reduce political pressure because they do not constitute threats to social stability. However, whether these grievances will last if citizens re-encounter similar conflicts warrants further research.

Partisan bias in a sorted party system: A problem of epistemic inequality

AJPS — 2026-08-26 Original Partisan bias in a sorted party system: A problem of epistemic inequality Justin Pottle Abstract Abstract Normative and empirical scholars typically treat partisans’ distrust of their political rivals as a product of individual cognitive biases. But even if bias is inevitable, it is parties’ coalition‐building and messaging strategies that determine who bias is directed toward. This paper argues that the social sorting of party conflict exacerbates the epistemic and democratic harm of partisan distrust by generating distinctive patterns of epistemic inequality. I show that when voters associate parties with particular social groups, members of those groups suffer greater risk of testimonial injustice and related credibility deficits owing to their perceived partisanship. These dynamics are most damaging for members of marginalized groups, as party–group overlap can reinforce the prejudices they already face. Addressing these harms, I argue, requires looking beyond individual partisans to the background features of party systems that shape how elites mobilize group identities and animus in the struggle for office.

Value misalignment in X’s feed algorithm is a reflection of value tensions in engagement

PNAS 123/34 pp. e2610388123-e2610388123 2026-08-17 Original Value misalignment in X’s feed algorithm is a reflection of value tensions in engagement Ziv Epstein, Farnaz Jahanbakhsh, Tiziano Piccardi, Axel Peytavin, Isabel Gallegos, Shardul Sapkota, Dora Zhao, Johan Ugander, Michael S. Bernstein