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Paper Citation Record · LEDGER

Learning Recommender Mechanisms for Bayesian Stochastic Games

As of 10 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2505.22979.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.22979 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:07:23.032365Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8ca446d6-801f-4b54-9130-aec71a16b413 · outbound

This paper cites Pure nash equilibria and best-response dynamics in random games.

Learning Recommender Mechanisms for Bayesian Stochastic Games Pure nash equilibria and best-response dynamics in random games

Reference 1

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Observation 4ecb6eef-91bd-407b-ba52-3cff4b77692d · outbound

This paper cites Dynamic mechanism design: An introduction.

Learning Recommender Mechanisms for Bayesian Stochastic Games Dynamic mechanism design: An introduction

Reference 2

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Observation 92c17410-2ec2-49c4-98be-69f3e3134c87 · outbound

This paper cites Evolutionary dynamics of multi-agent learning: A survey.

Learning Recommender Mechanisms for Bayesian Stochastic Games Evolutionary dynamics of multi-agent learning: A survey

Reference 3

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Observation 7a916475-7220-47f2-b346-2dbea441bb4b · outbound

This paper cites Convex optimization.

Learning Recommender Mechanisms for Bayesian Stochastic Games Convex optimization

Reference 4

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Source-reported events for the cited work

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Observation cacbcd73-50a0-4811-ae5e-49d9e9ecb89e · outbound

This paper cites On equilibrium in pure strategies in games with many players.

Learning Recommender Mechanisms for Bayesian Stochastic Games On equilibrium in pure strategies in games with many players

Reference 5

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Source-reported events for the cited work

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Observation b3338809-ae26-49ef-bd12-9a1a515c104f · outbound

This paper cites Multiagent learning in the presence of memory-bounded agents.

Learning Recommender Mechanisms for Bayesian Stochastic Games Multiagent learning in the presence of memory-bounded agents

Reference 6

Resolution
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Observation 97dbbbfc-99e9-4ef7-afcf-ba773210bec0 · outbound

This paper cites Mechanism Design for Facility Location Problems: A Survey.

Learning Recommender Mechanisms for Bayesian Stochastic Games Mechanism Design for Facility Location Problems: A Survey

Reference 7

Resolution
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Source-reported events for the cited work

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Observation 7314d85a-ea88-46d0-8948-077dc33410b3 · outbound

This paper cites Automated mechanism design for a self-interested designer.

Learning Recommender Mechanisms for Bayesian Stochastic Games Automated mechanism design for a self-interested designer

Reference 8

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9280ad74-8920-4092-aa70-425157abdf88 · outbound

This paper cites Awesome: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents.

Learning Recommender Mechanisms for Bayesian Stochastic Games Awesome: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents

Reference 9

Resolution
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Source-reported events for the cited work

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Observation 6bebd234-0a3b-4f6a-9408-eff17e655975 · outbound

This paper cites Privacy and truthful equilibrium selection for aggregative games.

Learning Recommender Mechanisms for Bayesian Stochastic Games Privacy and truthful equilibrium selection for aggregative games

Reference 10

Resolution
verified fuzzy
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Observation ebff0efb-d7a1-42a7-810e-c959fd5ef6fd · outbound

This paper cites Differentiable economics for randomized affine maximizer auctions.

Learning Recommender Mechanisms for Bayesian Stochastic Games Differentiable economics for randomized affine maximizer auctions

Reference 11

Resolution
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Source-reported events for the cited work

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Observation 2c37e558-faa0-4942-a1e1-70923c30e5cb · outbound

This paper cites Optimal auctions through deep learning.

Learning Recommender Mechanisms for Bayesian Stochastic Games Optimal auctions through deep learning

Reference 12

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Source-reported events for the cited work

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Observation 6d87e41a-9024-4b5a-a8bc-27767b5e6ea9 · outbound

This paper cites Game theory.

Learning Recommender Mechanisms for Bayesian Stochastic Games Game theory

Reference 13

Resolution
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Source-reported events for the cited work

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Observation 09e97297-61e3-4a96-b2a7-826e6a3d9114 · outbound

This paper cites Addressing function approximation error in actor-critic methods, 2018.

Learning Recommender Mechanisms for Bayesian Stochastic Games Addressing function approximation error in actor-critic methods, 2018

Reference 14

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Observation 83389e03-1618-4b1d-a717-764ef68058de · outbound

This paper cites Online mechanism design for electric vehicle charging.

Learning Recommender Mechanisms for Bayesian Stochastic Games Online mechanism design for electric vehicle charging

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 548f31f4-f6a5-46e3-b80b-b30f0523315a · outbound

This paper cites Deep learning for multi-facility location mechanism design.

Learning Recommender Mechanisms for Bayesian Stochastic Games Deep learning for multi-facility location mechanism design

Reference 16

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 43a501f5-0203-48c9-90cb-1832a38a53ec · outbound

This paper cites Generative adversarial networks.

Learning Recommender Mechanisms for Bayesian Stochastic Games Generative adversarial networks

Reference 17

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5c64643d-b0a0-4d1b-936a-b3aca53dacb2 · outbound

This paper cites Automated online mechanism design and prophet inequalities.

Learning Recommender Mechanisms for Bayesian Stochastic Games Automated online mechanism design and prophet inequalities

Reference 18

Resolution
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Source-reported events for the cited work

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Observation 9d62580e-acba-4eff-b08a-6a257bf1e7fe · outbound

This paper cites Multiagent reinforcement learning: theoretical framework and an algorithm.

Learning Recommender Mechanisms for Bayesian Stochastic Games Multiagent reinforcement learning: theoretical framework and an algorithm

Reference 19

Resolution
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Source-reported events for the cited work

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Observation cae71230-54f6-4969-b573-d5958b0a0f33 · outbound

This paper cites A simple, fast, and safe mediator for congestion management.

Learning Recommender Mechanisms for Bayesian Stochastic Games A simple, fast, and safe mediator for congestion management

Reference 20

Resolution
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Source-reported events for the cited work

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Observation b6e833dd-c2b8-4478-a16c-f63779a37331 · outbound

This paper cites Mediated multi-agent reinforcement learning.

Learning Recommender Mechanisms for Bayesian Stochastic Games Mediated multi-agent reinforcement learning

Reference 21

Resolution
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Source-reported events for the cited work

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Observation ee29bab5-8956-447e-bb37-e4eb3c695fe9 · outbound

This paper cites Categorical reparameterization with gumbel-softmax, 2017.

Learning Recommender Mechanisms for Bayesian Stochastic Games Categorical reparameterization with gumbel-softmax, 2017

Reference 22

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 1c1acbd6-c523-401b-bdb1-dde9c939baa1 · outbound

This paper cites Mechanism design in large games: incentives and privacy.

Learning Recommender Mechanisms for Bayesian Stochastic Games Mechanism design in large games: incentives and privacy

Reference 23

Resolution
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Source-reported events for the cited work

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Observation 84ee941e-fd04-425c-b5de-614cc4fcddc4 · outbound

This paper cites Robust Mediators in Large Games.

Learning Recommender Mechanisms for Bayesian Stochastic Games Robust Mediators in Large Games

Reference 24

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Source-reported events for the cited work

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Observation fb61b852-fc0e-4f70-aa5d-15249fc8fefb · outbound

This paper cites Zero tolerance for bias.

Learning Recommender Mechanisms for Bayesian Stochastic Games Zero tolerance for bias

Reference 25

Resolution
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Source-reported events for the cited work

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Observation a8495b01-4eca-41fc-bd4f-b8a80b1a8aa1 · outbound

This paper cites JAXRL: Implementations of Reinforcement Learning algorithms in JAX , 10 2021.

Learning Recommender Mechanisms for Bayesian Stochastic Games JAXRL: Implementations of Reinforcement Learning algorithms in JAX , 10 2021

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f3219fad-1a4e-4e3c-9d1f-daaaa36d4b5d · outbound

This paper cites End-to-end training of deep visuomotor policies.

Learning Recommender Mechanisms for Bayesian Stochastic Games End-to-end training of deep visuomotor policies

Reference 27

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f7a0f7b2-8ebd-463b-8dc8-212d17ad8621 · outbound

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Learning Recommender Mechanisms for Bayesian Stochastic Games Continuous control with deep reinforcement learning

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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This paper cites Lillicrap, Jonathan J.

Learning Recommender Mechanisms for Bayesian Stochastic Games Lillicrap, Jonathan J

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0e3b3c07-75c5-432d-aee6-2383da7ba4a6 · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive environments.

Learning Recommender Mechanisms for Bayesian Stochastic Games Multi-agent actor-critic for mixed cooperative-competitive environments

Reference 30

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 6001c100-2149-4f19-ae27-e0ed1757848c · outbound

This paper cites Independent reinforcement learners in cooperative markov games: A survey regarding coordination problems.

Learning Recommender Mechanisms for Bayesian Stochastic Games Independent reinforcement learners in cooperative markov games: A survey regarding coordination problems

Reference 31

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Observation 9dbfc1a3-4e30-45a9-a373-0421f8af2bc7 · outbound

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Learning Recommender Mechanisms for Bayesian Stochastic Games Congestion games with player-specific payoff functions

Reference 32

Resolution
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Source-reported events for the cited work

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Observation 103cc238-3e36-4697-9684-68dca1f41924 · outbound

This paper cites Playing atari with deep reinforcement learning, 2013.

Learning Recommender Mechanisms for Bayesian Stochastic Games Playing atari with deep reinforcement learning, 2013

Reference 33

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 823def57-9fb8-4398-9982-f106c88efe64 · outbound

This paper cites Strong mediated equilibrium.

Learning Recommender Mechanisms for Bayesian Stochastic Games Strong mediated equilibrium

Reference 34

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation f05efa7c-424c-4d45-8567-db29dded7f9a · outbound

This paper cites Optimal coordination mechanisms in generalized principal--agent problems.

Learning Recommender Mechanisms for Bayesian Stochastic Games Optimal coordination mechanisms in generalized principal--agent problems

Reference 35

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 0f3c1de3-06ab-4778-a175-260e64b524d9 · outbound

This paper cites A Course in Game Theory.

Learning Recommender Mechanisms for Bayesian Stochastic Games A Course in Game Theory

Reference 36

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:21.559257Z digest=sha256:f3896faf6c826ecc0b2b53fdd66e4d87f0f9b8074a082d1666f30c8774dc4ba1

Observation 5c75edac-177c-4c6c-91a3-f644a2c27bb3 · outbound

This paper cites An mdp-based approach to online mechanism design.

Learning Recommender Mechanisms for Bayesian Stochastic Games An mdp-based approach to online mechanism design

Reference 37

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:21.622580Z digest=sha256:de76be8545b3cbc1309f54ead130cf47398320bf90d319bfb599a0544c4ce39d

Observation bdd70126-a1a6-40e4-af35-0f1fac532143 · outbound

This paper cites New criteria and a new algorithm for learning in multi-agent systems.

Learning Recommender Mechanisms for Bayesian Stochastic Games New criteria and a new algorithm for learning in multi-agent systems

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:26.021548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:21.672690Z digest=sha256:fa899cf80ddda157eea7378025dc62856bb151cd51977c1f241ac0565ddc7178

Observation 0796c567-03f0-4251-a988-ff3036ebfd8d · outbound

This paper cites Approximate mechanism design without money.

Learning Recommender Mechanisms for Bayesian Stochastic Games Approximate mechanism design without money

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:25.928486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:21.754387Z digest=sha256:d657923081188387c54f3a4cbb10d0d6f5d8d9d7b220470305f8ed1145e712df

Observation ae70d220-8d88-467c-83e8-79119f026e6c · outbound

This paper cites Marketplaces, markets, and market design.

Learning Recommender Mechanisms for Bayesian Stochastic Games Marketplaces, markets, and market design

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:25.825176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:21.795442Z digest=sha256:6eece59b07cb10932efd065f0fc7813ae4053030ea701b1e8af208ccea5c853b

Observation af7768d7-5049-462c-8cfa-162b00d784d1 · outbound

This paper cites Chapter 18: Routing games.

Learning Recommender Mechanisms for Bayesian Stochastic Games Chapter 18: Routing games

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:25.661621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:21.882295Z digest=sha256:90283309e5ab5ed633dcefd9e5e6b9d75a248b53d24857e19c29458cec6284cc

Observation 89f3e9c5-7eab-4049-9ea1-8d23dc1c547f · outbound

This paper cites Perspectives on multiagent learning.

Learning Recommender Mechanisms for Bayesian Stochastic Games Perspectives on multiagent learning

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:25.423736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:21.956595Z digest=sha256:8be28a5ff56e6271c8731db7efd38bd08c9988c4c2d6d165cab66f08a1c41281

Observation 8c505629-a10c-4bca-95e1-c97dfb985da7 · outbound

This paper cites Automated Mechanism Design via Neural Networks.

Learning Recommender Mechanisms for Bayesian Stochastic Games Automated Mechanism Design via Neural Networks

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T13:07:22.016623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:07:22.016623Z digest=sha256:36af3e1329e4d4844f1fe83fbe987f49e7e5f75e760a5fd02b127f47d5e76bb3

Observation 025902e0-b733-47d6-aab8-16298a1bd90d · outbound

This paper cites If multi-agent learning is the answer, what is the question? Artificial intelligence, 171 0 (7): 0 365--377, 2007.

Learning Recommender Mechanisms for Bayesian Stochastic Games If multi-agent learning is the answer, what is the question? Artificial intelligence, 171 0 (7): 0 365--377, 2007

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T13:07:22.073204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:07:22.073204Z digest=sha256:f37d703d706ff113b106d7b73b61a971f046a69b562c495072876328837d54b8

Observation 8843bd46-7ba8-402e-b477-39a8fd8507e5 · outbound

This paper cites Leibo, Karl Tuyls, and Thore Graepel.

Learning Recommender Mechanisms for Bayesian Stochastic Games Leibo, Karl Tuyls, and Thore Graepel

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:25.182203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:22.125183Z digest=sha256:1db3c78b8a8669ee83322485b10a8f35a876ff07535b7d405f8668bc9a643c42

Observation a04e1fa4-1cf6-4e2b-ac41-ad355082369f · outbound

This paper cites Multiagent cooperation and competition with deep reinforcement learning, 2015.

Learning Recommender Mechanisms for Bayesian Stochastic Games Multiagent cooperation and competition with deep reinforcement learning, 2015

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:25.009194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:22.178000Z digest=sha256:ea58b40d6f33393922671aeee99d97990e54666cab6459ae5e739e5fdbea9d0c

Observation 4d060c08-384e-4bbb-9596-fd0c7f2db94c · outbound

This paper cites Multi-agent reinforcement learning: Independent versus cooperative agents.

Learning Recommender Mechanisms for Bayesian Stochastic Games Multi-agent reinforcement learning: Independent versus cooperative agents

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:24.853614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:22.220903Z digest=sha256:0e4b7436b6cdbae525ed7d75b5f14a5326e3050258eff18654b948ce98fd20ea

Observation eb923dcf-66d1-45ef-8a26-323fbfb767a8 · outbound

This paper cites Of mechanism design and multiagent planning.

Learning Recommender Mechanisms for Bayesian Stochastic Games Of mechanism design and multiagent planning

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:24.748195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:22.273096Z digest=sha256:9e03c71cadff8acfc837471cccca5d122da38b383c81fc799f595566debaa1f4

Observation e867939d-2bc8-4507-8e2b-b57d5b86a925 · outbound

This paper cites Empirical mechanism design: Methods, with application to a supply-chain scenario.

Learning Recommender Mechanisms for Bayesian Stochastic Games Empirical mechanism design: Methods, with application to a supply-chain scenario

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:24.589918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:22.339889Z digest=sha256:f460e7be6b9d1e4c624b306075bd86a6d1d72c8a7afed48af4c6349f77e0aff2

Observation 0249d090-2349-4596-a487-2c0bc525bac7 · outbound

This paper cites Constrained automated mechanism design for infinite games of incomplete information.

Learning Recommender Mechanisms for Bayesian Stochastic Games Constrained automated mechanism design for infinite games of incomplete information

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:24.466558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:22.371266Z digest=sha256:26e3cdb6658b435e8df7ec035ee2770ffc3be5ce91d4279f31584905b46f23ec

Observation 4018268c-2166-4889-a85f-d0bb4b238f00 · outbound

This paper cites The handbook of market design.

Learning Recommender Mechanisms for Bayesian Stochastic Games The handbook of market design

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:24.319815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:22.555695Z digest=sha256:d0c5a911a6c0c674cdfefce1e28ce2b08285e91185a333bcf027f8e2e56ab517

Observation 7bbc123a-7acd-43b1-99ba-8073e8277243 · outbound

This paper cites Deep contract design via discontinuous networks.

Learning Recommender Mechanisms for Bayesian Stochastic Games Deep contract design via discontinuous networks

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:24.158796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:22.685037Z digest=sha256:0010e89dc80d31665c97182af0350b5be96c1e46891c5ce90e427d2f65615d45

Observation 337ee67f-947c-476d-9bc7-90f8c6b6a273 · outbound

This paper cites Polynomial-time optimal equilibria with a mediator in extensive-form games.

Learning Recommender Mechanisms for Bayesian Stochastic Games Polynomial-time optimal equilibria with a mediator in extensive-form games

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:24.025853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:22.795153Z digest=sha256:5811de685491cd213f9024434442ca3ad4e87c90a9a1de718f074c53dac77c78

Observation 2fc20942-97e9-4bc4-9a0f-a1dacbe8a13e · outbound

This paper cites Computing optimal equilibria and mechanisms via learning in zero-sum extensive-form games.

Learning Recommender Mechanisms for Bayesian Stochastic Games Computing optimal equilibria and mechanisms via learning in zero-sum extensive-form games

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:23.903771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:22.896833Z digest=sha256:436369854454df5bf680a01065bcf3ac6f304768bcc9c0e444e16ed51f94b9ed

Observation 425546ad-6e82-4831-ac5d-0e00d93e1054 · outbound

This paper cites Automated dynamic mechanism design.

Learning Recommender Mechanisms for Bayesian Stochastic Games Automated dynamic mechanism design

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:07:23.687505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:07:23.032365Z digest=sha256:55d4ea82cab7ce3165a688db6259c6d6b362ebdc786b412215f1ebe819b75b65

Pith citing papers

No inbound Pith citation observations are available.