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We study the tradeoff between fundamental risk and time. A time-constrained agent has to solve a problem. She dynamically allocates effort between implementing a risky initial idea and exploring alternatives. Discovering an alternative implies progress that has to be converted to a solution. As time runs out, the chances of converting it in time shrink. We show that the agent may return to the initial idea after having left it in the past to explore alternatives. Our model helps explain so-called false starts. To finish fast, the agent delays exploring alternatives reducing the overall success probability.

生成讲解博客的中文版本

生成AI智能校验的中文版本