The Evolutionary Dynamics of AI, Politicization, Contestation, and Trust in Science Funding

2026-07-27

AI Summary

This study investigates how political interference in scientific funding affects the operational capacity and institutional legitimacy of peer review systems. By developing the first evolutionary game-theoretic model incorporating artificial intelligence, the authors simulate strategic interactions among scientists, funding agencies, and the public regarding resistance, AI-assisted review, and acceptance dynamics. Integrating numerical simulations with finite-population analysis, the research demonstrates that operational capacity and institutional legitimacy can fail independently and identifies five key mechanisms: their decoupling, bistability in legitimacy, low-cost resistance triggering cascading collapse, the conditional efficacy of salary increases under budget constraints, and the dominant role of initial resistance levels and politicization in determining system outcomes. A central contribution is the elucidation of how public acceptance of AI shapes the evolutionary trajectory of institutional legitimacy.

Abstract

Economic stability and progress in modern technological societies depend on vigorous and independent public funding of science and engineering research. When peer review or funding decisions are perceived as politically directed, scientists, funding agencies, and the public react in coupled and conflicting ways. We an evolutionary game-theoretic model to analyze how perceived political interference in science funding affects the interrelated behaviors of scientists, funding agencies, and the public. The model simulates scientists choosing to refuse peer reviews and retaliate, agencies responding by adopting AI-assisted review and altering reviewer pay, and the public accepting or rejecting these AI systems. Through numerical simulations, five principal findings are identified: (1) Operational capacity and institutional legitimacy are governed by separate conditions and can fail independently. (2) Legitimacy of the process is bistable, meaning final states are determined by the public's acceptance of AI. (3) Since the career cost for researchers refusing to review is generally low, resistance/retaliation cascades can readily ignite, leading identical institutions to entirely opposite fates. (4) Increasing reviewer pay only stabilizes participation within a strict budget-solvency frontier, and emergency pay can paradoxically erode the legitimacy it aims to protect. (5) Finally, finite-population simulations reveal that baseline scenarios partition into either legitimacy recovery without capacity or joint failure, confirming that the fundamental separation of capacity and legitimacy outcomes is a dominant structural feature driven primarily by initial scientific resistance and politicization levels. This theoretical work quantifies issues for future work in science policy.

Problem

• politicization
• trust in science
• science funding
• institutional legitimacy
• peer review

Innovation

• evolutionary game theory
• AI-assisted peer review
• science funding politicization
• institutional legitimacy
• scientific resistance