The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis | Paper Citation Record

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Unresolved cited work

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis A model of online misinformation

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The multiplicative weights updatemethod: ameta-algorithmandapplications

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Deep reinforcement learning: A brief survey.IEEE Signal Processing Magazine, 34(6):26–38, 2017

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis An efficient dynamic mechanism.Economet- rica, 81(6):2463–2485, 2013

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This paper cites Using confidence bounds for exploitation-exploration trade- offs.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Using confidence bounds for exploitation-exploration trade- offs

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This paper cites The nonstochastic multiarmed bandit problem.SIAM Journal on Com- puting, 32(1):48–77, 2002.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The nonstochastic multiarmed bandit problem.SIAM Journal on Com- puting, 32(1):48–77, 2002

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This paper cites Correlated equilibrium as an expression of Bayesian rationality.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Correlated equilibrium as an expression of Bayesian rationality

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The emergence of cooperation among egoists.American Political Science Review, 75(2):306–318, 1981

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This paper cites The Formula: The Universal Laws of Success.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The Formula: The Universal Laws of Success

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This paper cites Dynamic incentives for congestion control.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Dynamic incentives for congestion control

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis A neural probabilistic language model.J

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This paper cites Taming the Matthew effect in online markets with social influence.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Taming the Matthew effect in online markets with social influence

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This paper cites The dynamic pivot mechanism.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The dynamic pivot mechanism

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This paper cites Dynamic mechanism design: An introduction.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Dynamic mechanism design: An introduction

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This paper cites From external to internal regret.Jour- nal of Machine Learning Research, 8(6), 2007.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis From external to internal regret.Jour- nal of Machine Learning Research, 8(6), 2007

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis An Introduction to the Theory of Mechanism Design

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This paper cites Superintelligence: Paths, Dangers, Strategies.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Superintelligence: Paths, Dangers, Strategies

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This paper cites Ethical issues in advanced artificial intelligence.Machine Ethics and Robot Ethics, pages 69–75, 2020.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Ethical issues in advanced artificial intelligence.Machine Ethics and Robot Ethics, pages 69–75, 2020

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This paper cites Learn- ing to mitigate AI collusion on economic platforms.Advances in Neural Information Processing Systems, 35:37892–37904, 2022.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Learn- ing to mitigate AI collusion on economic platforms.Advances in Neural Information Processing Systems, 35:37892–37904, 2022

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This paper cites A survey of monte carlo tree search methods.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis A survey of monte carlo tree search methods

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This paper cites A comprehensive survey of graph embedding: Problems, techniques, and applications.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis A comprehensive survey of graph embedding: Problems, techniques, and applications

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This paper cites Artificial intelligence, algorithmic pricing, and collusion.American Economic Review, 110(10):3267–3297, 2020.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Artificial intelligence, algorithmic pricing, and collusion.American Economic Review, 110(10):3267–3297, 2020

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This paper cites Self-predictive universal AI.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Self-predictive universal AI

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This paper cites Optimal coordi- nated planning amongst self-interested agents with private state.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Optimal coordi- nated planning amongst self-interested agents with private state

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Cambridge University Press, 2006

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Evolu- tionary dynamics of biological auctions.Theoretical Population Biology, 81(1):69–80, 2012

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Dynamic pricing in a labor market: Surge pricing and flexible work on the Uber platform.Ec, 16:455, 2016

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Hedging in games: Faster convergence of external and swap regrets

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Prediction with expert evaluators’ advice

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Unresolved cited work

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis A formulation of the simple theory of types.Journal of Symbolic Logic, 5:56–68, 1940

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The problem of social cost.Journal of Law and Economics, 3(1):1–44, 1960

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The Firm, The Market, and The Law

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Law for the platform economy.UCDL Rev., 51:133, 2017

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Cambridge University Press, 1996

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis A collusion-proof dynamic mechanism.SSRN, 2024

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis From external to swap regret 2.0: An efficient reduction for large action spaces

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Logical and relational learning

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The algorithmic foundations of differ- ential privacy.Found

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Relational reinforce- ment learning

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Matchmakers: the new economics of multisided platforms

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Reward tampering problems and solutions in reinforcement learning: A causal influence diagram perspective.Synthese, 198(Suppl 27):6435–6467, 2021

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Re- inforcement learning with a corrupted reward channel

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis AGI safety literature review

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Reflective or- acles: A foundation for game theory in artificial intelligence

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Unresolved cited work

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Economicsofoilrefining

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Calibrated learning and correlated equilibrium

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis A decision-theoretic generalization of on-line learning and an application to boosting.Journal of Computer and System Sciences, 55(1):119–139, 1997

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Schapire, Yoram Singer, and Manfred K

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The rationality of quali- fied lotteries

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis MIT press, 1998

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Artificial intelligence, values, and alignment.Minds and Machines, 30(3):411–437, 2020

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Bayesian reinforcement learning: A survey

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Graph embedding techniques, applica- tions, and performance: A survey.Knowledge-Based Systems, 151:78–94, 2018

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Inconsistency of Bayesian in- ference for misspecified linear models, and a proposal for repairing it

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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The off-switch game

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Observation 4ab5d993-fd02-4ff2-bef8-71e847ea15de · outbound

This paper cites Cooperative inverse reinforcement learning.Advances in Neural Informa- tion Processing Systems, 29, 2016.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Cooperative inverse reinforcement learning.Advances in Neural Informa- tion Processing Systems, 29, 2016

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Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.572056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3de8e233-2243-4902-9b2b-afabaf3aae6b · outbound

This paper cites The tragedy of the commons.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The tragedy of the commons

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.576001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0089685-5f65-4ff5-ac89-3132df8e7545 · outbound

This paper cites Completeness in the theory of types.Journal of Symbolic Logic, 15(2):81–91, 1950.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Completeness in the theory of types.Journal of Symbolic Logic, 15(2):81–91, 1950

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.579850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 241c5f4b-ba30-4405-8efa-f41aa3b0a07f · outbound

This paper cites Tracking the best expert.Ma- chine learning, 32(2):151–178, 1998.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Tracking the best expert.Ma- chine learning, 32(2):151–178, 1998

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.583839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c3e2999-b6e2-4642-be3a-2296dc69c0cc · outbound

This paper cites The many faces of exponentialweightsinonlinelearning.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The many faces of exponentialweightsinonlinelearning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.587304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae6fca03-0fa4-45bf-8a8b-86ed41623d39 · outbound

This paper cites The exponential mechanism for social welfare: Private, truthful, and nearly optimal.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The exponential mechanism for social welfare: Private, truthful, and nearly optimal

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.591019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e41b946f-d2a9-4f18-aec3-8b8ba6e923ca · outbound

This paper cites Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:56:01.211238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d1af85ec-7077-43b8-a0eb-af19d41d7921 · outbound

This paper cites Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.598968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2f60829-63bc-40e4-b756-836d44a65cc0 · outbound

This paper cites Feature reinforcement learning: Part I.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Feature reinforcement learning: Part I

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.602602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2a59516-a8d6-4b10-bfa8-a10c41b4b133 · outbound

This paper cites CRC Press, 2024.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis CRC Press, 2024

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.179729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 593c4a24-bc2d-4fd1-b98c-b2ebc05da740 · outbound

This paper cites Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.609953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd15f004-c4c4-4f94-85d8-6503d780457d · outbound

This paper cites Reward-free exploration for reinforcement learning.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Reward-free exploration for reinforcement learning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.167275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 542e3856-66cc-4f4d-82e6-50489218de5c · outbound

This paper cites Provably efficient reinforcement learning with linear function approximation.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Provably efficient reinforcement learning with linear function approximation

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.618166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e1afb36-a4b4-488e-a518-f999dd481eaa · outbound

This paper cites Rational learning leads to Nash equilib- rium.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Rational learning leads to Nash equilib- rium

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.147664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3ced9075-2192-4b19-8c01-e9b52668bcca · outbound

This paper cites Gonzalez, Michael I.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Gonzalez, Michael I

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.136678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a88ebf50-8c57-418b-8b0a-3a91b5bc9d8b · outbound

This paper cites A survey of reinforcement learning from human feedback.arXiv:2312.14925, 10, 2023.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis A survey of reinforcement learning from human feedback.arXiv:2312.14925, 10, 2023

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.629715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0429409-8cec-4ad5-9f34-5312f0368b9f · outbound

This paper cites Mech- anism design in large games: Incentives and privacy.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Mech- anism design in large games: Incentives and privacy

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.125025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6057d7ed-cf49-4855-9040-50dfabe934bf · outbound

This paper cites The rise of the platform economy.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis The rise of the platform economy

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.112429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 40ec5578-7771-41e2-bb94-0d228b314f3d · outbound

This paper cites Bellman goes relational.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Bellman goes relational

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.099661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f534922c-bb39-4664-9de3-d49fe285958d · outbound

This paper cites Bandit based monte-carlo planning.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Bandit based monte-carlo planning

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.086990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 26c6102a-c9ae-43d8-8244-5ddc4813b053 · outbound

This paper cites Paying to do better: Games with payments between learning agents.arXiv:2405.20880, 2024.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Paying to do better: Games with payments between learning agents.arXiv:2405.20880, 2024

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.648645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e276eed5-bc61-4161-b383-cbbf2093d810 · outbound

This paper cites Universal codes from switching strategies.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Universal codes from switching strategies

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.073826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 456e641f-cee3-4c73-99f1-159d7fbdbec3 · outbound

This paper cites Krichevsky and V.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Krichevsky and V

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.061464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cb36b20f-8d48-4205-bf24-ad770218ee0a · outbound

This paper cites A unified game-theoretic approach to multiagent reinforcement learning.Advances in Neural Information Processing Systems, 30, 2017.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis A unified game-theoretic approach to multiagent reinforcement learning.Advances in Neural Information Processing Systems, 30, 2017

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.048431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c8c05919-8918-4a19-a339-a7a62b1035c1 · outbound

This paper cites Bandit Algorithms.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Bandit Algorithms

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-11T23:56:00.663954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25a13960-66f4-4ec6-bcb6-736074a9099c · outbound

This paper cites Thomp- son sampling is asymptotically optimal in general environments.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Thomp- son sampling is asymptotically optimal in general environments

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.024580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8345c7a4-cec9-4623-9c03-e25ed384c392 · outbound

This paper cites A formal solution to the grain of truth problem.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis A formal solution to the grain of truth problem

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:02.011678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e428798b-4a9b-4b95-87b0-bfaf050d9f62 · outbound

This paper cites Walsh, and Michael L.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Walsh, and Michael L

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.999348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1df2304d-fb51-411e-86a1-775d5095c3ed · outbound

This paper cites An Introduction to Kolmogorov Complexity and Its Applications.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis An Introduction to Kolmogorov Complexity and Its Applications

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.986452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e20590f9-ba97-4875-880b-7294ab2241d2 · outbound

This paper cites Markov games as a framework for multi-agent rein- forcement learning.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Markov games as a framework for multi-agent rein- forcement learning

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.975202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 181ee530-d9e1-4ce0-990a-21317afb815d · outbound

This paper cites an unresolved cited work.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Unresolved cited work

Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:56:01.963992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a8a56564-3a59-4409-9d80-1c74351f735b · outbound

This paper cites Lloyd and Kee Siong Ng.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Lloyd and Kee Siong Ng

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.952311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 13d86ac0-2ee9-4b2b-9153-1e27c224aa40 · outbound

This paper cites Pes- simism meets VCG: Learning dynamic mechanism design via offline rein- forcement learning.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Pes- simism meets VCG: Learning dynamic mechanism design via offline rein- forcement learning

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.940697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2eb78945-7ed2-4e78-b17c-88f2a9763c59 · outbound

This paper cites Mechanism design via differential privacy.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Mechanism design via differential privacy

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.928234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b4fe5859-b4c2-4c27-a59f-862ef65d6c35 · outbound

This paper cites Kenneth Arrow’s last theorem.The Journal of Mechanism and Institution Design, 9(1):7–11, 2024.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Kenneth Arrow’s last theorem.The Journal of Mechanism and Institution Design, 9(1):7–11, 2024

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.916339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8b8cd9ba-76a8-411e-92a0-51f5eb0b23bd · outbound

This paper cites Human-level control through deep re- inforcement learning.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Human-level control through deep re- inforcement learning

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.904186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 60fd1852-a849-474e-9128-7c669a8c1986 · outbound

This paper cites Efficient tracking of a growing number of experts.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Efficient tracking of a growing number of experts

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.891161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 534956a3-9058-4f25-8a44-63ab8767111a · outbound

This paper cites Exploratory engineering in artificial intelligence.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Exploratory engineering in artificial intelligence

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.877411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4c834a32-4dce-456e-8f78-af4041217695 · outbound

This paper cites Lloyd, and William Uther.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Lloyd, and William Uther

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.863052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1dfc2b30-6ea0-42a7-8fcc-37bfe1605a43 · outbound

This paper cites Feature re- inforcement learning in practice.

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Feature re- inforcement learning in practice

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.849385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 477810e9-3361-4bc2-a97e-0daadef4a59d · outbound

This paper cites Introduction to mechanism design (for computer scientist).

The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis Introduction to mechanism design (for computer scientist)

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:56:01.836606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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