Gradual learning from incremental actions
研究一群人在不确定性下选择不可逆行动时机的问题，公共反馈逐渐到来，对比社会最优与分散均衡，发现均衡可简化为一系列一维问题。
We introduce a collective experimentation problem where a continuum of agents choose the timing of irreversible actions under uncertainty and where public feedback from the actions arrives gradually over time. The leading application is the adoption of new technologies. The socially optimal expansion path entails an informational trade‐off where acting today speeds up learning but postponing capitalizes on the option value of waiting. We contrast the social optimum to the decentralized equilibrium where agents ignore the social value of information they generate. We show that the equilibrium can be obtained by assuming that agents ignore the future actions of other agents, which lets us recast the complicated two‐dimensional problem as a series of one‐dimensional problems.