论文标题
通过强盗环境中的行动进行社会学习
Social learning via actions in bandit environments
论文作者
论文摘要
我在贝叶斯强盗环境中研究了一款具有私人回报和公共行为的战略探索游戏。特别是，我看一下级联平衡，在这种平衡中，只有在足够悲观的时刻，代理人会随着时间的推移从风险的行动转变为无风险的行动。我表明这些均衡存在在某些条件下并建立其显着特性。这些均衡中的个人探索可能比单一代理水平高于单位级别，具体取决于代理是否以共同的先验开头，但最乐观的代理总是毫无疑问。我还表明，允许代理商撰写可强制执行的前Ante合同将导致最前进的代理商购买所有收益流，从而对更既定的公司的较小初创企业的收购提供了解释。
I study a game of strategic exploration with private payoffs and public actions in a Bayesian bandit setting. In particular, I look at cascade equilibria, in which agents switch over time from the risky action to the riskless action only when they become sufficiently pessimistic. I show that these equilibria exist under some conditions and establish their salient properties. Individual exploration in these equilibria can be more or less than the single-agent level depending on whether the agents start out with a common prior or not, but the most optimistic agent always underexplores. I also show that allowing the agents to write enforceable ex-ante contracts will lead to the most ex-ante optimistic agent to buy all payoff streams, providing an explanation to the buying out of smaller start-ups by more established firms.
