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Persistent link: https://www.econbiz.de/10012542205
A novel debate within competition policy and regulation circles is whether autonomous machine learning algorithms may learn to collude on prices. We show that when fims face short-run price commitments, independent Q-learning (a simple but well-established self-learning algorithm) learns to...
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Increasingly, retailers have access to better pricing technology, especially in online markets. Firms employ automated pricing algorithms that allow for high-frequency price changes. What are the implications for price competition? We develop a model of price competition where firms can differ...
Persistent link: https://www.econbiz.de/10012175360
We develop new quasi-experimental tools to understand algorithmic discrimination and build non-discriminatory algorithms when the outcome of interest is only selectively observed. These tools are applied in the context of pretrial bail decisions, where conventional algorithmic predictions are...
Persistent link: https://www.econbiz.de/10014544682
Consumer choices are increasingly mediated by algorithms, which use data on those past choices to infer consumer preferences and then curate future choice sets. Behavioral economics suggests one reason these algorithms so often fail: choices can systematically deviate from preferences. For...
Persistent link: https://www.econbiz.de/10014226178
We let "Algorithmic Market-Makers" (AMMs), using Q-learning algorithms, choose prices for a risky asset when their clients are privately informed about the asset payoff. We find that AMMs learn to cope with adverse selection and to update their prices after observing trades, as predicted by...
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This paper investigates pricing in laboratory markets when human players interact with an algorithm. We compare the degree of competition when exclusively humans interact to the case of one firm delegating its decisions to an algorithm, an n-player generalization of tit-for-tat. We further vary...
Persistent link: https://www.econbiz.de/10013414764
Recent experimental simulations have shown that autonomous pricing algorithms are able to learn collusive behavior and thus charge supra-competitive prices without being explicitly programmed to do so. These simulations assume, however, that both firms employ the identical price-setting...
Persistent link: https://www.econbiz.de/10013534374