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α: calibrated so average coauthorship-adjusted count equals average raw count
We provide a revealed preference characterization of expected utility maximization in binary lotteries with prize-probability trade-offs. We start by characterizing optimizing behavior when the empirical analyst exactly knows the utility function or the probability function of winning. Next, we consider the situation with both the probability function and the utility function unknown. In this case utility maximization has empirical content when imposing the mild shape restriction that at least one of these functions is log-concave.