Results 81 to 90 of about 5,328 (177)
Tacit collusion through pricing algorithms is feasible and increasingly likely – and its effects may be as harmful as those of anticompetitive agreements. By virtue of a new approach set out in this study, Adrian Doerr shows how the law can and should respond within the existing legal framework.
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Algorithmic collusion, genuine and spurious
We clarify the difference between the asynchronous pricing algorithms analyzed by Asker, Fershtman and Pakes (2021) and those considered in the previous literature. The difference lies in the way the algorithms explore: randomly or mechanically.
CALVANO, Emilio +3 more
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Attribute-Based Verifiable Conditional Proxy Re-Encryption Scheme. [PDF]
Tang Y, Jin M, Meng H, Yang L, Zheng C.
europepmc +1 more source
Collusive Outcomes via Pricing Algorithms
Hansen, Karsten T. +2 more
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Algorithmic collusion under competitive design
We study a simple model of algorithmic collusion in which Q-learning algorithms are designed in a strategic fashion. We let players (\textit{designers}) choose their exploration policy simultaneously prior to letting their algorithms repeatedly play a prisoner's dilemma.
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Blockchain-enhanced incentive-compatible mechanisms for multi-agent reinforcement learning systems. [PDF]
Tian K.
europepmc +1 more source
IMFLKD: an incentive mechanism for decentralized federated learning based on knowledge distillation. [PDF]
Ying X, Yan K, Gao X, Huang J.
europepmc +1 more source
Fine-Grained Personalized Data Aggregation Scheme with High Quality and Privacy Protection. [PDF]
Xia Z, Murugesan RK.
europepmc +1 more source
Dignity, properly used, could be a useful construct in AI ethics. [PDF]
Lamberton C +3 more
europepmc +1 more source
Revisiting big data optimism: risks of data-driven black box algorithms for society. [PDF]
Mahajan S, Helbing D.
europepmc +1 more source

