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We empirically study the interplay between exploration and competition.Systems that learn from interactions with users often engage in exploration:making potentially suboptimal decisions in order to acquire new information forfuture decisions. However, when multiple systems are competing for the samemarket of users, exploration may hurt a system's reputation in the near term,with adverse competitive effects. In particular, a system may enter a "deathspiral", when the short-term reputation cost decreases the number of users forthe system to learn from, which degrades its performance relative tocompetition and further decreases its market share.  We ask whether better exploration algorithms are incentivized undercompetition. We run extensive numerical experiments in a stylized duopoly modelin which two firms deploy multi-armed bandit algorithms and compete for myopicusers. We find that duopoly and monopoly tend to favor a primitive "greedyalgorithm" that does not explore and leads to low consumer welfare, whereas atemporary monopoly (a duopoly with an early entrant) may incentivize betterbandit algorithms and lead to higher consumer welfare. Our findings shed lighton the first-mover advantage in the digital economy by exploring the role thatdata can play as a barrier to entry in online markets.

The Perils of Exploration under Competition: A Computational Modeling Approach