Abstract This paper critiques Kenkel and Ramsay’s (in J Politics. https://doi.org/10.1086/732953 , 2024) “The Effective Power of Military Coalitions,” arguing that the study falls short of structural modeling standards despite its formal presentation. While adopting the aesthetics of game-theoretic analysis, their model simplifies coalitions to groups of independent actors and overlooks the interconnected nature of coalition warfare. This simplification commits what Tsebelis (in Am Polit Sci Rev 83(1):77–91, 1989) called the “Robinson Crusoe Fallacy”: assuming strategic independence when mutual responsiveness is essential. Their simulations do not re-optimize behavior as conditions change but instead reuse fixed parameters, resulting in calibrated, comparison-based results that are mistaken for structural inference. The model’s empirical fragility and theoretical rigidity weaken its explanatory power, especially when compared to historical examples of coordinated mobilization during World War I and World War II. As an alternative, I propose a theory-driven simulation framework that explicitly includes strategic interdependence. This approach preserves analytical clarity while recognizing the contingent and collective dynamics that shape military coalitions. Graphical Abstract

Abstract Why do states impose export controls that harm their own economies? We reinterpret such measures as costly signals of technological autonomy rather than coercive sanctions. A Bayesian signaling model with dynamic cost structures and bounded monotonicity shows that credible signaling functions only within limited regimes. Once perceived threat crosses a critical threshold, information revelation triggers defensive counter-investment and welfare loss—a tragedy of signaling in which credibility becomes self-defeating. Methodologically, the framework infers information from regime identification rather than parameter estimation: observed escalation locates the system within a bounded equilibrium region, while theory predicts when qualitative regime shifts occur. Empirically, China’s 2023–2025 export controls on gallium, graphite, and rare earths exhibit perfect rank-ordering, consistent with operation inside the credible-signaling regime even as Western counter-investments approach its boundary. The model offers testable predictions about regime transitions, illustrating how formal theory can anticipate structural breaks in technologically interdependent rivalries.

Abstract Digital platforms now act as the primary environments for public discourse, where recommender systems shape visibility, emotion, and interpretation. This study introduces the Recommender Systems LLMs Playground (RecSysLLMsP), a simulation framework designed to examine how algorithmic personalization interacts with language generation to influence engagement and polarization. The research provides a reproducible and transparent environment for testing algorithmic effects on collective reasoning, which is an issue central to democratic communication. The study employs a one‑hundred‑agent simulation grounded in psychometric and demographic data from Serbian social media users. Agents interact through five stages of progressively personalized content feeds mediated by LLM‑generated posts. Quantitative metrics such as engagement intensity, network modularity, sentiment variance and qualitative linguistic validation are used to assess behavioral and structural change. Results reveal that moderate personalization maximizes engagement, while full personalization reduces diversity and amplifies both structural and affective polarization (Q = 0.22 → 0.68). LLM‑based agents successfully reproduce realistic patterns of emotional contagion and ideological clustering. The implications extend to computational social science and policy. Simulation‑based experimentation can inform ethical recommender design and algorithmic governance. Limitations concern the absence of genuine human cognition. Thus, findings indicate systemic tendencies rather than behavioral prediction. Future research should integrate real‑world datasets, multilingual testing, and policy‑driven intervention modeling to further calibrate this digital “laboratory” for exploring AI‑mediated communication.

Abstract This study examines how China employs high-level people-to-people dialogues (PPDs) with European counterparts as institutionalised mechanisms of societal diplomacy. It addresses the question of how China uses these dialogues to advance its foreign policy objectives within the liberal international order (LIO). Whilst often regarded as peripheral cultural exchanges, PPDs function as structured platforms through which China embeds cooperation, manages international identity, and projects normative narratives. The analysis covers 19 PPD rounds with the European Union, United Kingdom, France, and Germany between 2012 and 2025. Using grounded theory coding and abductive reasoning, it identifies outcomes across four dimensions: tangible (projects and exchanges), formalised (institutional procedures and continuity), symbolic (rituals and representational practises), and normative (discursive reframing of values). Findings show that the PPDs advance China’s dual posture as a strategist-reformist actor. As a strategist, China stabilises cooperation and consolidates legitimacy through institutional embedding. As a reformist, it incrementally recalibrates liberal vocabularies, layering emphases on civilisation, pluralism, and development within shared principles of peace, inclusivity, and sustainability. The study contributes to scholarship on rising powers and global order by conceptualising institutionalised societal diplomacy as a model through which China pursues continuity with recalibration, reforming elements of the LIO from within through structured, low-politics diplomacy.

Abstract Conventional international political economy treats U.S. hegemony as a provider of global public goods, with preferential trade and security commitments fostering stability through open access. This view, we argue, violates the definition of a public good and obscures the club-like nature of the American-led order. This paper develops a Schmittian club-goods framework in which tariffs, dues, and exclusion are instruments of sovereign pricing, and stability emerges endogenously from the elimination of free-riding. In this model, members pay for access through market concessions, defense contributions, and strategic alignment, while non-members face higher tariffs or exclusion. We demonstrate that the Trump administration’s trade policy—characterized by tariffs on allies, higher duties on rivals, and the frequent invocation of sovereign exceptions—is internally coherent within this logic. We formalize the hegemon’s objective as a dynamic pricing problem to balance rent extraction, expense control, and rule-setting advantage. Calibrations for U.S. relations with the European Union, India, and China illustrate how tariff bands, dues thresholds, and repricing events sustain a hierarchical order. This analysis challenges the public-goods myth and reframes stability as a byproduct of enforced hierarchy rather than systemic openness.