人工智能、政治化、竞争与科学资助信任的演化动力学
The Evolutionary Dynamics of AI, Politicization, Contestation, and Trust in Science Funding
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中文总结 AI 辅助
该研究构建演化博弈论模型，模拟AI辅助评审、公众接受度等因素，发现科学资助中能力与合法性可独立失效等五个结论，为科学政策研究量化了相关问题。
中文摘要 AI 辅助
现代技术社会的经济稳定与进步依赖于对科学与工程研究的积极且独立的公共资助。当同行评审或资助决策被认为带有政治导向时，科学家、资助机构与公众会产生相互关联且相互冲突的反应。我们采用演化博弈论模型，分析科学资助中感知到的政治干预如何影响科学家、资助机构与公众的相互关联行为。该模型模拟了科学家选择拒绝同行评审并进行报复、机构采用人工智能辅助评审并调整评审人员薪酬、公众接受或拒绝这些人工智能系统的过程。通过数值模拟，确定了五个主要发现：(1) 运作能力与制度合法性受不同条件支配，可独立失效；(2) 过程合法性具有双稳态，即最终状态由公众对人工智能的接受程度决定；(3) 由于研究人员拒绝评审的职业成本通常较低，抵抗/报复级联可轻易引发，导致相同机构走向完全相反的命运；(4) 提高评审人员薪酬仅能在严格的预算偿付前沿内稳定参与，而应急薪酬可能适得其反地侵蚀其旨在保护的合法性；(5) 有限种群模拟显示，基线情景要么分化为无能力的合法性恢复，要么共同失效，证实能力与合法性结果的根本分离是主要结构特征，主要由初始科学抵抗与政治化水平驱动。这项理论工作为未来科学政策研究量化了相关问题。
英文摘要
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.
