Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T18:02:10.757657Z
Paper Citation Record · LEDGER
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications
As of 16 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2502.12024.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-18T20:32:54.607173Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T20:36:50.334353Z
Reference resolution
18 of 18 outbound references displayed
- verified exact0
- verified fuzzy12
- unresolved2
- parse uncertain0
- malformed identifier3
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 5ad239d8-2a47-4c9d-a4f9-b65036e47b8b · outbound
This paper cites Social Learning under Platform Influence: Consensus and Persistent Disagreement.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Social Learning under Platform Influence: Consensus and Persistent Disagreement
Reference 1
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-09T18:02:10.820026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9efa59cf-8846-4913-b710-15eb4169d0da · outbound
This paper cites The projected policy gradient update rule which is expressed as: σh+1 =P P(S×A) (σh +γ h∇σV(σ h)) (7) whereγ h denotes the learning rate at iterationh.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications The projected policy gradient update rule which is expressed as: σh+1 =P P(S×A) (σh +γ h∇σV(σ h)) (7) whereγ h denotes the learning rate at iterationh
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:10.998927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6e61bc08-8f39-415f-80db-667ee7792907 · outbound
This paper cites Specifically, we assume thatM:P(X)→ M ⊂R n, whereMis a compact set.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Specifically, we assume thatM:P(X)→ M ⊂R n, whereMis a compact set
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:10.965994Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 64e901fd-c6e0-4e73-933e-ce090097ccfc · outbound
This paper cites Fatou’s lemma for weakly converging measures under the uniform integrability condition,.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Fatou’s lemma for weakly converging measures under the uniform integrability condition,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:11.076881Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fb64b93e-0089-4ea3-af23-b84fe42ee1d6 · outbound
This paper cites , H, yielding policy ˆgt(x, mt).
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications , H, yielding policy ˆgt(x, mt)
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:10.982107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c5995228-3f0e-4dcf-80d0-257cab68247b · outbound
This paper cites The scalar interaction function is the mean field limit of this finite retailers case.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications The scalar interaction function is the mean field limit of this finite retailers case
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:10.948065Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1181dc12-3a48-4f50-907c-c5c7eb8efa87 · outbound
This paper cites A similar regenerative framework for arrivals and departures is discussed in other MFE models (e.g., Iyer et al.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications A similar regenerative framework for arrivals and departures is discussed in other MFE models (e.g., Iyer et al
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:10.932782Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7a177bcd-c3f0-4d6c-817d-3d51ae22b3dd · outbound
This paper cites an unresolved cited work.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Unresolved cited work
Reference 13
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-09T18:02:10.918121Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation daf0e88d-92a9-42ab-8449-ff786d0bd999 · outbound
This paper cites Proof of Theorem 2.Lettbe some iteration of the algorithm.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Proof of Theorem 2.Lettbe some iteration of the algorithm
Reference 14
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-09T18:02:10.902300Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a72c0d99-8450-4929-8b36-184610ebfa3e · outbound
This paper cites Hence, ∆V (0, a) = (1−δ)h′ 1(a)V(1, m) + (−h′ 1(a) +δh ′ 1(a))V(0, m) =h′ 1(a)(1−δ)∆V(0, m).
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Hence, ∆V (0, a) = (1−δ)h′ 1(a)V(1, m) + (−h′ 1(a) +δh ′ 1(a))V(0, m) =h′ 1(a)(1−δ)∆V(0, m)
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:10.885155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7a5dae62-f853-49ae-b01e-c880edf5ecd6 · outbound
This paper cites Part (ii) shows thatgis increasing inx.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Part (ii) shows thatgis increasing inx
Reference 16
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-09T18:02:10.869577Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7eb58694-7e0f-41db-9cd4-632f149a4341 · outbound
This paper cites This is typically satisfied in many practical settings, including cases whereM(s) represents the expected value operator.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications This is typically satisfied in many practical settings, including cases whereM(s) represents the expected value operator
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:10.853164Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3d667cd5-b756-4378-8288-28caf067737d · outbound
This paper cites an unresolved cited work.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T18:02:10.836828Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1adbdcca-e93f-4406-803d-1bad3e53007e · outbound
This paper cites Learning in Mean Field Games: A Survey.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Learning in Mean Field Games: A Survey
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T18:02:10.698396Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 827568a2-fa13-4310-a5e4-a308a611d147 · outbound
This paper cites A general framework for learning mean-field games,.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications A general framework for learning mean-field games,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:11.046730Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f9645c4c-5d96-4ca9-9850-ae63ca8a11ca · outbound
This paper cites Learning while playing in mean-field games: Convergence and optimality,.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Learning while playing in mean-field games: Convergence and optimality,
Reference 1265
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:11.016491Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 762b2620-a6d1-4ef2-88c6-8a12c87dc46f · outbound
This paper cites Your uber is arriving: Managing on-demand workers through surge pricing, forecast communication, and worker incentives,.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Your uber is arriving: Managing on-demand workers through surge pricing, forecast communication, and worker incentives,
Reference 3235
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:11.062154Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8cd5c702-f470-4c3f-abbe-fcc955169ba9 · outbound
This paper cites Markov perfect industry dynamics with many firms,.
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications Markov perfect industry dynamics with many firms,
Reference 6715
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T18:02:11.031744Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
Observation 958600f5-5495-4825-99af-35c385e310be · inbound
Approximation of Discrete-Time Infinite-Horizon Mean-Field Equilibria via Finite-Horizon Mean-Field Equilibria cites this paper.
Approximation of Discrete-Time Infinite-Horizon Mean-Field Equilibria via Finite-Horizon Mean-Field Equilibria Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T20:36:50.336970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.