Paper Citation Record · LEDGER

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach

As of 18 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2507.17433.

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

pith.paper-citation-record.v1
2507.17433 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:54:15.357592Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

29 of 29 outbound references displayed

verified exact1
verified fuzzy27
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f4ae8f50-2028-41ba-84ca-c1f63b8e7779 · outbound

This paper cites Yang, Fatemeh B.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Yang, Fatemeh B

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:20.810408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e3687972-63d3-47fe-ba54-5b9707a4793a · outbound

This paper cites Iyengar and Mark R.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Iyengar and Mark R

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:20.615482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cb117726-8e54-4e16-b569-55fa49cf4dd8 · outbound

This paper cites Expect the worst! expectations and social interactive decision making.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Expect the worst! expectations and social interactive decision making

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:20.469144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cf8ff580-a596-4818-84b4-2057d8b142a3 · outbound

This paper cites Hybrid intelligence.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Hybrid intelligence

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:20.219785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b5bf354a-cc53-4223-a126-a13b2137f991 · outbound

This paper cites The impossibility of automating ambiguity.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach The impossibility of automating ambiguity

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:20.041616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 421a2167-26e1-47b4-bab6-b6c585c6987e · outbound

This paper cites Compu- tational modelling of public policy: Reflections on practice.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Compu- tational modelling of public policy: Reflections on practice

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:19.831061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b92ac29f-5849-4da4-a5ed-5f017099cb0c · outbound

This paper cites Sutton and A.G.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Sutton and A.G

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:19.623374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e751a412-2378-43b8-9d3f-8e6488dff3fd · outbound

This paper cites Issues, principles or ideology? how young voters decide.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Issues, principles or ideology? how young voters decide

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:19.423202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6f6d825d-009d-44c4-bbfd-2b9a9dc4ca05 · outbound

This paper cites Multi-agents reinforcement learning in iterative voting, 2019.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Multi-agents reinforcement learning in iterative voting, 2019

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:19.250172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 746cdbf1-d734-44ee-baa4-43c7567e373c · outbound

This paper cites Learning agents for iterative voting.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Learning agents for iterative voting

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:19.016556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 523640a4-69db-42d6-bd40-a56930241386 · outbound

This paper cites Fair voting outcomes with impact and novelty compromises? unravelling biases in electing participatory budgeting winners.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Fair voting outcomes with impact and novelty compromises? unravelling biases in electing participatory budgeting winners

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:18.810282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9ff7ddb8-47dd-439f-a7e5-e28b3310c4ec · outbound

This paper cites Consensus-based participatory budgeting for legitimacy: Decision support via multi-agent reinforcement learning.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Consensus-based participatory budgeting for legitimacy: Decision support via multi-agent reinforcement learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:18.617158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 93268363-765b-4ec2-8bb4-3e0635a0bb1e · outbound

This paper cites θ-learning: An algorithm for the self-organisation of collective self-governance.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach θ-learning: An algorithm for the self-organisation of collective self-governance

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:18.469181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 78b30f34-9e41-439c-9153-1447810aefc0 · outbound

This paper cites Different modelling purposes.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Different modelling purposes

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:18.249092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 675cb03a-440e-4fe2-a859-8cd26f565e59 · outbound

This paper cites Albrecht, Filippos Christianos, and Lukas Schafer.Multi-Agent Reinforcement Learning: Foundations and Modern Approaches.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Albrecht, Filippos Christianos, and Lukas Schafer.Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:18.056935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation df11faa7-4b94-45d8-b703-7385c8b0a8ad · outbound

This paper cites Action branching architectures for deep reinforcement learning.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Action branching architectures for deep reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:17.852139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cf45c4c5-212e-4885-8289-8c6576b540bd · outbound

This paper cites Cumulative voting: The value of minority shareholder voting rights.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Cumulative voting: The value of minority shareholder voting rights

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:17.654549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 478cb40d-112d-4f7e-83ff-70ad0e186fb8 · outbound

This paper cites Proportional participatory budgeting with additive utilities.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Proportional participatory budgeting with additive utilities

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:17.459952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 08cf9338-e174-4979-9544-7c0a8e570418 · outbound

This paper cites Rusu, Joel Veness, Marc G.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Rusu, Joel Veness, Marc G

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:17.261766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 553dd072-059b-48d6-8c2f-938b75a30598 · outbound

This paper cites The importance of experience replay database composition in deep reinforcement learning.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach The importance of experience replay database composition in deep reinforcement learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:17.059782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e6895825-1c25-481f-a78f-7f8a69471201 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Understanding the difficulty of training deep feedforward neural networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T14:54:14.652106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66f7e0a4-3169-46af-b38a-b3c3676d0089 · outbound

This paper cites Prioritized experience replay.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Prioritized experience replay

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:16.849768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d5ed89a5-386b-46a7-8fbd-2604e244f01f · outbound

This paper cites Communication-enabled deep reinforcement learning to optimise energy-efficiency in uav-assisted networks.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Communication-enabled deep reinforcement learning to optimise energy-efficiency in uav-assisted networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:16.661967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 06a0d87a-c012-448f-a9b5-0ca8b72bb497 · outbound

This paper cites Pabulib: A Participatory Budgeting Library.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Pabulib: A Participatory Budgeting Library

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:54:15.554753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation feefe8c2-4fb1-48f2-acc4-a9846038040d · outbound

This paper cites Welfare engineering in multiagent systems.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Welfare engineering in multiagent systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:16.497693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b38e533e-229d-430f-aa01-6d22ca4e5da7 · outbound

This paper cites Fairness in long-term participatory budgeting.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Fairness in long-term participatory budgeting

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:16.289436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f8e8fb20-35c7-40c4-bc80-46325ee9445d · outbound

This paper cites The nature of the social agent.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach The nature of the social agent

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:16.088534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c997794f-cd53-4cd9-841f-4285cf6bdd3a · outbound

This paper cites Gareth Polhill, Christina Semeniuk, and Frithjof Stöppler.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Gareth Polhill, Christina Semeniuk, and Frithjof Stöppler

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:15.888885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5028298f-af39-4cb2-9ff8-75f16fe099f2 · outbound

This paper cites Generating synthetic bitcoin transactions and predicting market price movement via inverse reinforcement learning and agent-based modeling.

Fair Compromises in Participatory Budgeting: a Multi-Agent Deep Reinforcement Learning Approach Generating synthetic bitcoin transactions and predicting market price movement via inverse reinforcement learning and agent-based modeling

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:54:15.719399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.