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Algorithmic Pricing: What Implications for Competition Policy?

https://doi.org/10.2139/ssrn.3209781
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1 of 20 checkable references need attention · checked 2026-08-28

At the dated check, the references listed below either did not resolve in Crossref or DataCite, or carried a retraction notice. Each one is shown with the registry record that put it there.

11 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

References needing attention

does not resolve to a known work10.1038/nature14236PMID
The 19 checked references that resolve
resolves10.1613/jair.4818
Evolutionary Dynamics of Multi-Agent Learning: A Survey
resolves10.1109/tsmcc.2007.913919
A Comprehensive Survey of Multiagent Reinforcement Learning
resolves10.1145/2872427.2883089
An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace
resolves10.1257/mic.6.2.247
Communication, Renegotiation, and the Scope for Collusion
resolves10.1038/s41467-017-02597-8
Cooperating with machines
resolves10.1111/exsy.12054
A reinforcement learning approach to competitive ordering and pricing problem
resolves10.1016/S1573-448X(06)03030-5
Chapter 30 A Framework for Applied Dynamic Analysis in IO
resolves10.3386/w24197
Match Quality, Search, and the Internet Market for Used Books
resolves10.2139/ssrn.2591874
Artificial Intelligence & Collusion: When Computers Inhibit Competition
resolves10.1257/jep.30.4.151
Whither Game Theory? Towards a Theory of Learning in Games
resolves10.1093/joclec/nhy016
DEVELOPING COMPETITION LAW FOR COLLUSION BY AUTONOMOUS ARTIFICIAL AGENTS†
resolves10.1016/s0899-8256(05)80020-x
Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term
resolves10.1016/0303-2647(95)01551-5
Multiagent reinforcement learning in the Iterated Prisoner's Dilemma
resolves10.1257/aer.97.5.1794
Impossibility of Collusion under Imperfect Monitoring with Flexible Production
resolves10.1006/game.1999.0783
Predicting How People Play Games: A Simple Dynamic Model of Choice
resolves10.1371/journal.pone.0172395
Multiagent cooperation and competition with deep reinforcement learning
resolves10.1023/A:1015504423309
Pricing in Agent Economies Using Multi-Agent Q-Learning
resolves10.1016/j.jedc.2008.01.003
-learning agents in a Cournot oligopoly model
resolves10.1007/s11518-006-0178-7
Studies on horizontal competition among homogenous retailers through agent-based simulation
The 11 references without a DOI — listed, not checked
no DOI — not checkedSelection Criteria in Coordination Games: Some Experimental Results
no DOI — not checkedPredicting how people play games: reinforcement learning in experimental games with unique, mixed strategy equilibria
no DOI — not checkedNash Q-learning for general-sum stochastic games
no DOI — not checkedMulti-agent Reinforcement Learning in Sequential Social Dilemmas
no DOI — not checkedref16
no DOI — not checkedAntitrust and the Robo-Seller: Competition in the Time of Algorithms
no DOI — not checkedref20
no DOI — not checkedref24
no DOI — not checkedref25
no DOI — not checked\Q-learning
no DOI — not checkedClasses of multiagent q-learning dynamics with epsilon-greedy exploration
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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