Paper Citation Record · LEDGER

DECAF: Learning to be Fair in Multi-agent Resource Allocation

As of 9 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 2 inbound Pith citation observations for arXiv:2502.04281.

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

pith.paper-citation-record.v1
2502.04281 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:01:03.038516Z

measured 26 of 26 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T01:10:43.655095Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy20
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 34eab174-fd15-4710-b564-caf1997580df · outbound

This paper cites write newline.

DECAF: Learning to be Fair in Multi-agent Resource Allocation write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T23:01:02.959133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4839ba59-1630-4840-bc21-2ff9223993d4 · outbound

This paper cites On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment.

DECAF: Learning to be Fair in Multi-agent Resource Allocation On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.284153Z

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.

Observation baff29a1-0939-498a-a2e8-e11423044478 · outbound

This paper cites Constrained multiagent Markov decision processes: A taxonomy of problems and algorithms.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Constrained multiagent Markov decision processes: A taxonomy of problems and algorithms

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.274510Z

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.

Observation 5aa1fe05-61aa-4ea8-97cd-25e19aef4c04 · outbound

This paper cites Fairness through awareness.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Fairness through awareness

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.264549Z

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.

Observation ab01be4e-b8ec-4344-8d1f-37a38f27830c · outbound

This paper cites predict, then optimize.

DECAF: Learning to be Fair in Multi-agent Resource Allocation predict, then optimize

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.254934Z

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.

Observation 023d2b39-243d-41fa-be70-f067772cfd0e · outbound

This paper cites Survey on Fair Reinforcement Learning: Theory and Practice.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Survey on Fair Reinforcement Learning: Theory and Practice

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T23:01:02.977333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fc93097-fe80-4f14-a247-cda727870246 · outbound

This paper cites Alleviating matthew effect of offline reinforcement learning in interactive recommendation.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Alleviating matthew effect of offline reinforcement learning in interactive recommendation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.245379Z

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.

Observation 56eee756-bd70-4ca4-a445-76e0142a302f · outbound

This paper cites Equality of opportunity in supervised learning.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Equality of opportunity in supervised learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.235728Z

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.

Observation 3974272c-e6a6-44a5-bb42-897cfe2e8556 · outbound

This paper cites v., Guez, A., and Silver, D.

DECAF: Learning to be Fair in Multi-agent Resource Allocation v., Guez, A., and Silver, D

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.225934Z

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.

Observation 0248c422-8931-4803-b041-b2edf4593d89 · outbound

This paper cites and Lu, Z.

DECAF: Learning to be Fair in Multi-agent Resource Allocation and Lu, Z

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.216390Z

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.

Observation 3bbc867a-7233-4a6e-ab30-5c95272dd088 · outbound

This paper cites R., Das, S., and Fowler, P.

DECAF: Learning to be Fair in Multi-agent Resource Allocation R., Das, S., and Fowler, P

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.206402Z

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.

Observation a0e04025-2da8-4db7-b841-b6496ac85b6e · outbound

This paper cites R., Das, S., and Fowler, P.

DECAF: Learning to be Fair in Multi-agent Resource Allocation R., Das, S., and Fowler, P

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.196040Z

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.

Observation ca3d4f73-f4cd-44dc-8290-c0b7e9985d6f · outbound

This paper cites Using simple incentives to improve two-sided fairness in ridesharing systems.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Using simple incentives to improve two-sided fairness in ridesharing systems

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.186004Z

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.

Observation 7531ea81-1b49-462e-8480-a6c2287524c2 · outbound

This paper cites A survey on bias and fairness in machine learning.

DECAF: Learning to be Fair in Multi-agent Resource Allocation A survey on bias and fairness in machine learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.175895Z

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.

Observation dc186d4e-1937-4774-bb1f-2cfdb53df591 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Playing Atari with Deep Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T23:01:03.007443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07796150-3cb5-4e91-9bf5-70d0d523174b · outbound

This paper cites T., Zhu, H., and Ye, J.

DECAF: Learning to be Fair in Multi-agent Resource Allocation T., Zhu, H., and Ye, J

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.165924Z

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.

Observation 4514a41c-d9a5-421c-a756-3fe84d16ff05 · outbound

This paper cites Mitigating bias in algorithmic hiring: Evaluating claims and practices.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Mitigating bias in algorithmic hiring: Evaluating claims and practices

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.155993Z

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.

Observation 5555ee8f-2e6a-4a8f-8b9e-5bf1370104bb · outbound

This paper cites S., Farquhar, G., Foerster, J., and Whiteson, S.

DECAF: Learning to be Fair in Multi-agent Resource Allocation S., Farquhar, G., Foerster, J., and Whiteson, S

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T23:01:03.018251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d567c75c-6c22-462d-a5f7-6c88f4d1ca82 · outbound

This paper cites The Matthew Effect: How Advantage Begets Further Advantage.

DECAF: Learning to be Fair in Multi-agent Resource Allocation The Matthew Effect: How Advantage Begets Further Advantage

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.139909Z

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.

Observation 4413bada-c0b4-456f-9002-58aabd354589 · outbound

This paper cites Neural approximate dynamic programming for on-demand ride-pooling.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Neural approximate dynamic programming for on-demand ride-pooling

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.129010Z

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.

Observation c658335e-ba55-4161-a453-d295c025a1c2 · outbound

This paper cites Learning fair policies in multi-objective (deep) reinforcement learning with average and discounted rewards.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Learning fair policies in multi-objective (deep) reinforcement learning with average and discounted rewards

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.118544Z

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.

Observation 3fe660db-e308-4ec9-acea-1ee160bd2d0e · outbound

This paper cites M., Zambaldi, V., Jaderberg, M., Lanctot, M., Sonnerat, N., Leibo, J.

DECAF: Learning to be Fair in Multi-agent Resource Allocation M., Zambaldi, V., Jaderberg, M., Lanctot, M., Sonnerat, N., Leibo, J

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.108067Z

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.

Observation db0535b3-ce5b-4617-b401-1ef52079cd51 · outbound

This paper cites Learning MDP s from features: Predict-then-optimize for sequential decision making by reinforcement learning.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Learning MDP s from features: Predict-then-optimize for sequential decision making by reinforcement learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.098134Z

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.

Observation 4eb31064-72b5-4ef2-a675-4b25e4bbb589 · outbound

This paper cites Learning fair policies in decentralized cooperative multi-agent reinforcement learning.

DECAF: Learning to be Fair in Multi-agent Resource Allocation Learning fair policies in decentralized cooperative multi-agent reinforcement learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:01:03.088043Z

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.

Pith citing papers

Observation a3c252f8-e92d-4464-9b19-efbb5cfa5a04 · inbound

The Optimization Trilemma: Efficiency, Comfort and Fairness in Decentralized Multi-agent Coordination cites this paper.

The Optimization Trilemma: Efficiency, Comfort and Fairness in Decentralized Multi-agent Coordination DECAF: Learning to be Fair in Multi-agent Resource Allocation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T18:27:45.014865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a8d9dd9-0dde-461a-b086-01a4893b3e8c · inbound

Inference-Time Policy Alignment for Fair Reinforcement Learning cites this paper.

Inference-Time Policy Alignment for Fair Reinforcement Learning DECAF: Learning to be Fair in Multi-agent Resource Allocation

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-04T01:10:43.655095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.