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In the modern economy, algorithms influence many aspects of our lives, from how much we pay for groceries and what adverts we see, to the decisions taken by health professionals. As is true with all new technologies, algorithms bring new economic opportunities and make our lives easier, but they also bring new challenges. Indeed, many competition authorities have voiced their concerns that under certain circumstances algorithms may harm consumers, lead to exclusion of some competitors and may even enable firms (knowingly or otherwise) to avoid competitive pressure and collude. In this article, we explain how algorithms work and what potential benefits and harms they bring to competition.

We investigate if, and why, an initial success can trigger a string of successes. Using random variations in success in a real‐effort laboratory experiment, we cleanly identify the causal effect of an early success in a competition. We confirm that an early success indeed leads to increased chances of a later success. By alternatively eliminating strategic features of the competition, we turn on and off possible mechanisms driving the effect of an early success. Standard models of dynamic contest predict a strategic effect due to asymmetric incentives between initial winners and losers. Surprisingly, we find no evidence that they can explain the positive effect of winning. Instead, we find that the effect of winning seems driven by an information revelation effect, whereby players update their beliefs about their relative strength after experiencing an initial success.

No abstract

We investigate how people make choices when they are unsure about the value of the options they face and have to decide whether to choose now or wait and acquire more information first. In an experiment, we find that participants deviate from optimal information acquisition in a systematic manner. They acquire too much information (when they should only collect little) or not enough (when they should collect a lot). We show that this pattern can be explained as naturally emerging from Fechner cognitive errors. Over time participants tend to learn to approximate the optimal strategy when information is relatively costly.