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α: calibrated so average coauthorship-adjusted count equals average raw count
Abstract Pricing decisions are increasingly in the “hands” of artificial algorithms. Scholars and competition authorities have voiced concerns that those algorithms are capable of sustaining collusive outcomes more effectively than can human decision makers. If this is so, then our traditional policy tools for fighting collusion may have to be reconsidered. We discuss these issues by critically surveying the relevant law, economics, and computer science literature.