这篇论文用实验证明AI竞赛中不安全开发不是单纯的风险偏好问题，而是竞争压力让你不得不跟——别人都冲了你敢不冲吗？读起来挺有意思。
研究通过一个理想化AI竞赛的行为实验，探讨竞争压力对不安全开发的影响。参与者成对重复选择安全或不安全开发，不安全开发提供更快进度但积累个人风险，最大风险分别设为10%、60%、90%。数据发现，不安全行为更多受战略状态影响而非风险偏好：参与者更可能在对手选择不安全后跟进，落后时增加不安全选择。研究引入四种策略的进化模型，再现了实验效应，说明竞争动态可能偏好条件性不安全行为。
Falling Behind Drives Unsafe Development in an Idealised AI Race Experiment
Technological races create tension between speed and safety: actors may gain by moving faster than competitors, even when risky development is harmful. This is prominent in debates about artificial intelligence (AI), where competitive pressure is often argued to incentivise riskier, less safety-conscious development. We study this using a framed behavioural experiment based on an idealised AI race, in which paired participants repeatedly chose between Safe and Unsafe development under an uncertain time horizon. Unsafe development gave faster progress and higher immediate payoffs but accumulated private risk up to a treatment-specific maximum of 10\%, 60\%, or 90\%; the race's competitive structure was held constant, and only this maximum risk varied. Neither the pre-registered comparison between risk levels nor the role of elicited risk preferences was supported by the data. Instead, exploratory analyses motivated by the task's repeated structure show that Unsafe behaviour is shaped less by risk preferences than by the evolving strategic state of the race: participants are more likely to choose Unsafe after their opponent does so, being ahead reduces Unsafe play while falling behind increases it, and first-round choices predict later behaviour. To interpret these effects we introduce a reduced evolutionary model with four strategies -- Always Safe, Always Unsafe, Conditionally Safe, and Conditionally Antisocial Safe -- which reproduces the treatment effect and shows how conditional Unsafe behaviour can be favoured by competitive race dynamics. Together, the experiment and model show that unsafe development can emerge from early behavioural momentum, opponent behaviour, and fear of falling behind, rather than from risk preferences alone, suggesting policy should focus on reducing competitive pressure and promoting cooperation in AI development rather than only individual risk.