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Operational decisions can be complicated by the presence of uncertainty. In many cases, there exist means to reduce uncertainty, though these may come at a cost. Decision makers then face the dilemma of acting based on current, incomplete information versus investing in trying to minimize uncertainty. Understanding the impact of this trade-off on decisions and performance is the central topic of this thesis. When attempting to construct probabilistic models based on data, operational decisions often affect the amount and quality of data that is collected.
The entertainment industry is a highly competitive and risky business with only few successes. The ways in which we experience music, movies, games, books, and television in our lives have changed significantly in the past few decades, depending more on people's experiences. As these mainstream forms of entertainment are experience goods, it is hard to measure the value and fit of the product before trial.
When are decision makers able to learn from others? I argue that actors occupying network positions that enable social learning gain a competitive advantage. I show that the accuracy of security analysts' earnings forecasts improves when the coverage network readily conveys information about competitors' decision-making context. The benefits of social learning are most pronounced in unstable environments, measured by firms' forecast dispersion.