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Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty.

Intraclass collusion poses a significant challenge for automated detection systems within homogeneous datasets from e-learning platforms, where legitimate contextual overlap often leads to high false-positive rates. This “context similarity problem” questions the utility of advanced semantic models. This study confronts this issue through a quantitative comparison of a state-of-the-art Sentence-BERT model against traditional lexical methods (Levenshtein, Jaccard) on a real-world dataset of 854 student answers. Our findings reveal a compelling, counter-intuitive result: lexical methods are demonstrably more effective. Levenshtein Similarity achieved a superior F1-score (0.74) and F2-score (0.75), underpinned by a strong recall of 0.76. Conversely, the semantic model was confounded by the dataset’s homogeneity, yielding a modest F1-score of 0.57 and requiring an impractically high similarity threshold of 0.98 for optimal performance. This research provides a critical contribution by empirically demonstrating the limitations of purely semantic approaches in this specific context. We conclude that well-established lexical methods are not obsolete but remain a more reliable and practical tool for the initial screening of academic collusion, suggesting a need for hybrid strategies in modern academic integrity systems.

Blockchain technology provides decentralized consensus and potentially enlarges the contracting space through smart contracts. Meanwhile, generating decentralized consensus entails distributing information that necessarily alters the informational environment. We analyze how decentralization relates to consensus quality and how the quintessential features of blockchain remold the landscape of competition. Smart contracts can mitigate informational asymmetry and improve welfare and consumer surplus through enhanced entry and competition, yet distributing information during consensus generation may encourage greater collusion. In general, blockchains sustain market equilibria with a wider range of economic outcomes. We further discuss the implications for antitrust policies targeted at blockchain applications.

Shopping has always been a game. And now it’s being rigged against you.

Prices are increasingly set by algorithms. One concern is that intelligent algorithms may learn to collude on higher prices even in the absence of the kind of coordination necessary to establish an antitrust infringement. However, exactly how this may happen is an open question. I show how in simulated sequential competition, competing reinforcement learning algorithms can indeed learn to converge to collusive equilibria when the set of discrete prices is limited. When this set increases, the algorithm considered increasingly converges to supra-competitive asymmetric cycles. I show that results are robust to various extensions and discuss practical limitations and policy implications.