Paper Citation Record · LEDGER

Socially Fair Reinforcement Learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2208.12584.

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

pith.paper-citation-record.v1
2208.12584 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:48:52.367061Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T12:29:52.055883Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9cae0340-d051-47f5-a34d-f4eeeb3f4ca1 · inbound

p-Mean Regret for Stochastic Bandits cites this paper.

p-Mean Regret for Stochastic Bandits Socially Fair Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T15:48:52.367061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cca406cb-314b-4baf-b432-62cb5f4153e4 · inbound

Fairness in Reinforcement Learning with Bisimulation Metrics cites this paper.

Fairness in Reinforcement Learning with Bisimulation Metrics Socially Fair Reinforcement Learning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T05:52:34.548025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a870e05-a123-40e1-ad4d-9e1d458f7817 · inbound

Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare cites this paper.

Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare Socially Fair Reinforcement Learning

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:11:06.360159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8ba50916-e4d7-4757-9530-79e66ab7eb87 · inbound

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning cites this paper.

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning Socially Fair Reinforcement Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:58:58.452809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 385d0b49-f910-4ce8-ac99-6a870df8a9dd · inbound

Welfarist Control Design -- How to fulfill the societal mandate in multi-agent control? cites this paper.

Welfarist Control Design -- How to fulfill the societal mandate in multi-agent control? Socially Fair Reinforcement Learning

Reference 72

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T12:29:52.060586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.