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arxiv: 2605.15165 · v1 · submitted 2026-05-14 · 💻 cs.CY · stat.AP

Recognition: no theorem link

Due Process on Hold: A Queueing Framework for Improving Access in SNAP

Andrew Daw , Chloe Pache , Angela Zhou
Authors on Pith no claims yet

Pith reviewed 2026-05-15 02:58 UTC · model grok-4.3

classification 💻 cs.CY stat.AP
keywords SNAPcall centersqueueing modelsdue processsocial safety netfluid approximationendogenous congestionaccess to services
0
0 comments X

The pith

A queueing model shows that standard staffing rules understaff SNAP call centers because they ignore feedback from redials and abandonments.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a queueing framework to assess access in SNAP call centers, where congestion leads to procedural denials that violate due process rights. It argues that common staffing rules fail to account for endogenous congestion created by applicants redialing or abandoning calls, resulting in persistent understaffing. Using a fluid approximation, the model derives steady-state metrics to evaluate combined changes in staffing and service delivery. Fitting the model to court-disclosed data allows for policy recommendations that could ensure applicants have a meaningful chance to complete their applications. This approach treats access failures as system-level dynamics rather than isolated algorithmic issues.

Core claim

Standard queueing guidance from Erlang-A that does not address endogenous congestion fundamentally understaffs, which could lead to persistent shortfalls in practice. The authors develop a queueing model incorporating redials and abandonment through which backlogs generate endogenous congestion. Using a fluid approximation, they derive steady-state performance metrics to analytically characterize the impacts of bundled staffing and service delivery changes, and fit the parameters to call-center data from court documents.

What carries the argument

A queueing model that incorporates redials and abandonment to capture endogenous congestion, analyzed through fluid approximation to derive steady-state performance metrics.

Load-bearing premise

The redials and abandonment behaviors observed in the Missouri court-disclosed data are stable and representative enough to support general policy recommendations for staffing and service changes across SNAP systems.

What would settle it

A direct comparison showing whether actual call completion rates, wait times, and procedural denial rates in a SNAP call center match the model's predictions after staffing increases based on the derived metrics.

Figures

Figures reproduced from arXiv: 2605.15165 by Andrew Daw, Angela Zhou, Chloe Pache.

Figure 1
Figure 1. Figure 1: Process flow diagram of a queueing theoretic model of the SNAP call center. [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Performance metrics vs staffing levels for different potential changes in call center system design. [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Screenshot of call center dashboard B Dashboard Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
read the original abstract

The U.S. social safety net delivers essential services at mass scale, but access burdens persist, as congested contact or call centers serve as a primary mode of application completion and assistance. In Holmes v. Knodell, Missouri's SNAP call centers were so congested that nearly half of all application denials were procedural, caused by applicants' inability to complete required interviews, rather than underlying ineligibility. The judge ruled these system failures led to a violation of procedural due process. We propose a performance evaluation framework based on queueing models from operations research and management to assess and improve access in such systems. Operational access failures of call centers are distinct from prior automation failures in benefits provision. Emergent arbitrariness arises from interactions between system dynamics and access demand, rather than from an explicit algorithmic rule, making diagnosis and repair inherently system-level. We develop a queueing model that incorporates phenomena that distinguish social services from standard service domains, redials and abandonment, through which backlogs generate endogenous congestion. Standard queueing guidance from Erlang-A that does not address endogenous congestion fundamentally understaffs, which could lead to persistent shortfalls in practice. Using a fluid approximation, we derive steady-state performance metrics to analytically characterize the impacts of bundled staffing and service delivery changes. We fit model parameters to call-center data disclosed in court documents. Our queueing model can support ex-ante evaluation and design of access systems, inform policy levers for improving access, and provide evidence about whether applicants are afforded a meaningful opportunity to be served at scale.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that standard Erlang-A staffing guidance for call centers understaffs SNAP systems by ignoring endogenous congestion generated by redials and abandonment; it develops a fluid-approximation queueing model, derives steady-state performance metrics, fits redial and abandonment rates to Missouri SNAP call-center logs disclosed in Holmes v. Knodell, and uses the model to characterize the effects of bundled staffing and service-delivery changes on access and due-process compliance.

Significance. If the fitted parameters prove stable and the fluid metrics are validated, the framework supplies an analytical, system-level tool for ex-ante evaluation of access interventions in large-scale social-service contact centers, directly addressing procedural-due-process failures that arise from congestion rather than explicit rules.

major comments (2)
  1. [Abstract] Abstract and model-description section: the headline claim that Erlang-A 'fundamentally understaffs' is obtained by comparing steady-state metrics whose redial and abandonment rates are taken directly from a single court-disclosed Missouri dataset; no cross-validation, sensitivity analysis across jurisdictions, or out-of-sample check is reported, so the quantitative gap between the two models rests on untested parameter stability.
  2. [Model section] Fluid-approximation derivation: the steady-state performance metrics are stated to be derived analytically, yet the manuscript supplies neither the explicit fluid equations nor any error-bound or convergence analysis relative to the underlying stochastic process, making it impossible to assess how large the reported staffing shortfall remains under realistic parameter perturbations.
minor comments (1)
  1. [Abstract] Notation for the fluid model parameters (e.g., redial rate, abandonment probability) should be introduced once with clear definitions and then used consistently; several instances in the abstract and results paragraphs use informal phrasing that obscures the mapping to the fitted values.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the paper to incorporate additional analyses and derivations for greater transparency and robustness.

read point-by-point responses

  1. Referee: [Abstract] Abstract and model-description section: the headline claim that Erlang-A 'fundamentally understaffs' is obtained by comparing steady-state metrics whose redial and abandonment rates are taken directly from a single court-disclosed Missouri dataset; no cross-validation, sensitivity analysis across jurisdictions, or out-of-sample check is reported, so the quantitative gap between the two models rests on untested parameter stability.

    Authors: We agree that the analysis relies on parameters fitted from a single Missouri dataset disclosed in court documents from Holmes v. Knodell. This provides a grounded case study of a real-world SNAP system but limits generalizability. In the revision, we will add a dedicated sensitivity analysis varying redial and abandonment rates over plausible ranges drawn from call-center literature, and we will explicitly discuss how the staffing shortfall between the models persists under these perturbations. This addresses the concern about untested parameter stability without requiring new data sources. revision: yes

  2. Referee: [Model section] Fluid-approximation derivation: the steady-state performance metrics are stated to be derived analytically, yet the manuscript supplies neither the explicit fluid equations nor any error-bound or convergence analysis relative to the underlying stochastic process, making it impossible to assess how large the reported staffing shortfall remains under realistic parameter perturbations.

    Authors: We appreciate this observation on the presentation of the model. The fluid equations were derived using standard many-server queueing approximations for systems with abandonment and redials, but were not displayed in full in the main text. In the revised manuscript, we will include the complete set of fluid differential equations (for queue length, abandonment, and redial flows) in a new appendix subsection, along with a brief discussion of convergence to the stochastic process based on established fluid-limit results in the queueing literature. We will also report simulation-based validation of the approximation error under the fitted parameters to quantify robustness of the staffing gap. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation uses independent fluid model on externally fitted parameters

full rationale

The paper's derivation chain proceeds from a standard queueing model augmented with redials and abandonment, through a fluid approximation to obtain steady-state metrics, to numerical characterization of staffing impacts. Parameters are estimated from court-disclosed call-center logs (external data source), after which the model produces performance predictions. No equation reduces a claimed prediction to its own fitted inputs by construction, no self-citation supplies a load-bearing uniqueness result, and no ansatz is smuggled via prior work. The central claim that Erlang-A understaffs is a model-derived comparison whose quantitative content depends on the external data rather than tautological re-expression of the inputs. The analysis is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard queueing theory assumptions plus parameters fitted to limited court data; no new entities are postulated.

free parameters (1)
  • Redial and abandonment rates
    Fitted to call-center data disclosed in court documents to parameterize the model dynamics.
axioms (2)
  • domain assumption Fluid approximation yields valid steady-state performance metrics for the multi-server system with abandonment and redials
    Invoked to derive analytical characterizations of staffing impacts.
  • domain assumption Call-center dynamics can be captured by standard queueing primitives extended with redial and abandonment
    Core modeling choice distinguishing social-service call centers from conventional service systems.

pith-pipeline@v0.9.0 · 5575 in / 1439 out tokens · 60941 ms · 2026-05-15T02:58:45.365532+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

52 extracted references · 52 canonical work pages

  1. [1]

    [n. d.]. Making Government Call Centers More Human-Centered and More Efficient | New Jersey Innovation Authority. https://innovation.nj.gov/ blog/2025-06-05-callcentermodernization/

    work page 2025

  2. [2]

    https://fnsprod.azureedge.us/sites/default/files/call_center.pdf

    2011. https://fnsprod.azureedge.us/sites/default/files/call_center.pdf

    work page 2011

  3. [3]

    David Ames, Cassandra Handan-Nader, Daniel E Ho, and David Marcus. 2020. Due process and mass adjudication: crisis and reform.Stan. L. Rev.72 (2020), 1

    work page 2020

  4. [4]

    Jerry Anunrojwong, Krishnamurthy Iyer, and Vahideh Manshadi. 2023. Information design for congested social services: Optimal need-based persuasion.Management Science69, 7 (2023), 3778–3796

    work page 2023

  5. [5]

    Nitin Bakshi, Jeunghyun Kim, and Ramandeep S Randhawa. 2025. Service operations for justice-on-time: A data-driven queueing approach. Manufacturing & Service Operations Management27, 1 (2025), 305–321

    work page 2025

  6. [6]

    Benefits Tech Advocacy Hub. [n. d.]. Missouri Medicaid Home and Community Based Services Eligibility Issues. https://btah.org/case-study/missouri- medicaid-home-and-community-based-services-eligibility-issues.html

    work page

  7. [7]

    Benefits Tech Advocacy Hub. 2025. Benefits Tech Advocacy Hub. https://btah.org

    work page 2025

  8. [8]

    BenefitsCal. [n. d.]. https://benefitscal.com/Help/program-rules/calfresh-cash-aid-health-care/HCPRR?lang=en

    work page

  9. [9]

    Justin Boutilier, Jonas Oddur Jonasson, Hannah Li, and Erez Yoeli. 2024. Operational Dosage: Implications of Capacity Constraints for the Design and Interpretation of Experiments.arXiv preprint arXiv:2407.21322(2024). Manuscript submitted to ACM Due Process on Hold: A Queueing Framework for Improving Access in SNAP 17

    work page arXiv 2024

  10. [10]

    Sullivan, William F

    Lester Coffey, Richard Sullivan, Paul Doherty, Mason Erwin, Lloyd Pete Fleming, Everett Hensley, Charles McGlew, Rebecca Nichols, Douglas Sanford, Mildred Simonzi, Michael Stokes, and Jr. Sullivan, William F. 2017. Unemployment Insurance (UI) Call Center Study: Final Report. https: //oui.doleta.gov/unemploy/docs/CoffeyConsulting_UI_Call_Center_Study_Final...

    work page 2017

  11. [11]

    2025.The Supplemental Nutrition Assistance Program (SNAP)

    Congressional Research Service. 2025.The Supplemental Nutrition Assistance Program (SNAP). Technical Report R42505. Congressional Research Service. https://www.congress.gov/crs-product/R42505 Reports FY2024 participation: 41.7 million individuals in 22.2 million households per month on average. Accessed January 10, 2026

    work page 2025

  12. [12]

    Andrew Daw and Jamol Pender. 2019. New perspectives on the Erlang-A queue.Advances in Applied Probability51, 1 (2019), 268–299

    work page 2019

  13. [13]

    Sihan Ding, Maria Remerova, Robert D van der Mei, and Bert Zwart. 2015. Fluid approximation of a call center model with redials and reconnects. Performance Evaluation92 (2015), 24–39

    work page 2015

  14. [14]

    2021.A note on the level of customer support by state governments: A mystery-shopping approach

    Oeindrila Dube, Sendhil Mullainathan, and Devin G Pope. 2021.A note on the level of customer support by state governments: A mystery-shopping approach. Technical Report. National Bureau of Economic Research

    work page 2021

  15. [15]

    Nel Escher and Nikola Banovic. 2020. Exposing error in poverty management technology: A method for auditing government benefits screening tools.Proceedings of the ACM on Human-Computer Interaction4, CSCW1 (2020), 1–20

    work page 2020

  16. [16]

    2009.Markov processes: characterization and convergence

    Stewart N Ethier and Thomas G Kurtz. 2009.Markov processes: characterization and convergence. John Wiley & Sons

    work page 2009

  17. [17]

    2018.Automating inequality: How high-tech tools profile, police, and punish the poor

    Virginia Eubanks. 2018.Automating inequality: How high-tech tools profile, police, and punish the poor. Macmillan+ ORM

    work page 2018

  18. [18]

    Food and Nutrition Service. 2025. SNAP Quality Control. https://www.fns.usda.gov/snap/qc

    work page 2025

  19. [19]

    Daniel Freund, David Hausman, and Wentao Weng. 2025. Regulating Wait-Driven Requests in Queues.A vailable at SSRN 5284321(2025)

    work page 2025

  20. [20]

    Daniel Freund and Wentao Weng. 2024. The Dedicated Docket in US Immigration Courts: An analysis of fairness and efficiency properties.A vailable at SSRN 4785713(2024)

    work page 2024

  21. [21]

    Noah Gans, Ger Koole, and Avishai Mandelbaum. 2002. Telephone call centers: a tutorial and literature review.Manufacturing and Service Operations Management5, 2 (2002), 79–141

    work page 2002

  22. [22]

    worth it

    Nikhil Garg. 2025. Heterogeneous participation and allocation skews: when is choice" worth it"?arXiv preprint arXiv:2507.03600(2025)

    work page arXiv 2025

  23. [23]

    Eric Giannella, Tatiana Homonoff, Gwen Rino, and Jason Somerville. 2024. Administrative burden and procedural denials: experimental evidence from SNAP.American Economic Journal: Economic Policy16, 4 (2024), 316–340

    work page 2024

  24. [24]

    Esra Gules-Guctas. 2025. How Do Algorithmic Decision-Making Systems Used in Public Benefits Determinations Fail? Insights From Legal Challenges.Public Administration Review(2025)

    work page 2025

  25. [25]

    Pamela Herd and Donald Moynihan. 2025. Administrative Burdens in the Social Safety Net.Journal of Economic Perspectives39, 1 (2025), 129–150

    work page 2025

  26. [26]

    Daniel E Ho, Olivia Martin, Amy Perez, and Kit Rodolfa. 2025. Evaluation as due process: civil service in an automated age.Administrative Law Review77, 4 (2025)

    work page 2025

  27. [27]

    Daniel E Ho and Sam Sherman. 2017. Managing street-level arbitrariness: The evidence base for public sector quality improvement.Annual Review of Law and Social Science13, 1 (2017), 251–272

    work page 2017

  28. [28]

    Holmes, Mary, Davis, Denise, Dallas, Andrew, and Empower Missouri. 2024. Holmes et al. v. Knodell: Plaintiffs’ Suggestions in Support of Motion for Summary Judgment. Brief submitted to the U.S. District Court for the Western District of Missouri, Central Division. No. 2:22-cv-04026

    work page 2024

  29. [29]

    Tatiana Homonoff and Jason Somerville. 2021. Program recertification costs: Evidence from SNAP.American Economic Journal: Economic Policy13, 4 (2021), 271–298

    work page 2021

  30. [30]

    Susannah Howe. 2025. New Technologies, Old Rights: Litigating Public-Benefits Modernization. https://yalelawjournal.org/essay/new-technologies- old-rights-litigating-public-benefits-modernization

    work page 2025

  31. [31]

    Hilary Hoynes and Diane Whitmore Schanzenbach. 2015. US food and nutrition programs. InEconomics of means-tested transfer programs in the United States, volume 1. University of Chicago Press, 219–301

    work page 2015

  32. [32]

    Jeongwon Jo, He Zhang, Jie Cai, and Nitesh Goyal. 2025. AI Trust Reshaping Administrative Burdens: Understanding Trust-Burden Dynamics in LLM-Assisted Benefits Systems. InProceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency. 1172–1183

    work page 2025

  33. [33]

    Jae Yeon Kim, Pamela Herd, Sebastian Jilke, Donald Moynihan, and Kerry Rodden. 2025. Administrative checkpoints, burdens, and human-centered design: Increasing interview access to raise SNAP participation.Journal of Policy Analysis and Management(2025)

    work page 2025

  34. [34]

    Allison Koenecke, Eric Giannella, Robb Willer, and Sharad Goel. 2023. Popular support for balancing equity and efficiency in resource allocation: A case study in online advertising to increase welfare program awareness. InProceedings of the International AAAI Conference on Web and Social Media, Vol. 17. 494–506

    work page 2023

  35. [35]

    Zhi Liu and Nikhil Garg. 2024. Redesigning service level agreements: Equity and efficiency in city government operations.arXiv preprint arXiv:2410.14825(2024)

    work page arXiv 2024

  36. [36]

    Avi Mandelbaum, William A Massey, and Martin I Reiman. 1998. Strong approximations for Markovian service networks.Queueing Systems30 (1998), 149–201

    work page 1998

  37. [37]

    Avishai Mandelbaum and Sergey Zeltyn. 2005. The Palm/Erlang-a queue, with applications to call centers.Faculty of Industrial Engineering & Management, Technion, Haifa, Israel7, 8 (2005)

    work page 2005

  38. [38]

    Avishai Mandelbaum and Sergey Zeltyn. 2007. Service engineering in action: the Palm/Erlang-A queue, with applications to call centers. InAdvances in services innovations. Springer, 17–45. Manuscript submitted to ACM 18 Andrew Daw, Chloe Pache, and Angela Zhou

    work page 2007

  39. [39]

    Jerry L Mashaw. 1973. Management side of due process: some theoretical and litigation notes on the assurance of accuracy fairness and timeliness in the adjudication of social welfare claims.Cornell L. Rev.59 (1973), 772

    work page 1973

  40. [40]

    Caleb H Miles, Maya Petersen, and Mark J van der Laan. 2019. Causal inference when counterfactuals depend on the proportion of all subjects exposed.Biometrics75, 3 (2019), 768–777

    work page 2019

  41. [41]

    Albert L Nichols and Richard J Zeckhauser. 1982. Targeting transfers through restrictions on recipients.The American Economic Review72, 2 (1982), 372–377

    work page 1982

  42. [42]

    2023.Recoding America: why government is failing in the digital age and how we can do better

    Jennifer Pahlka. 2023.Recoding America: why government is failing in the digital age and how we can do better. Metropolitan Books

    work page 2023

  43. [43]

    Guodong Pang, Rishi Talreja, and Ward Whitt. 2007. Martingale proofs of many-server heavy-traffic limits for Markovian queues.Probability Surveys4, none (2007), 193 – 267. doi:10.1214/06-PS091

    work page doi:10.1214/06-ps091 2007

  44. [44]

    Lisa Rain and Hannah Natanson. 2025. https://www.washingtonpost.com/politics/2025/03/25/social-security-phones-doge-cuts/

    work page 2025

  45. [45]

    Rashida Richardson, Jason M Schultz, and Vincent M Southerland. 2019. Litigating Algorithms: 2019 US Report.AI Now Institute(2019)

    work page 2019

  46. [46]

    Laura Rosage. 2004. The evolution of IRS telephone quality measures.Statistics of Income. SOI Bulletin24, 3 (2004), 111–112

    work page 2004

  47. [47]

    2026.The Welfare Assembly Line: Public Servants in the Suffering City

    Josh Seim. 2026.The Welfare Assembly Line: Public Servants in the Suffering City. Univ of California Press

    work page 2026

  48. [48]

    Department of Agriculture, Economic Research Service

    U.S. Department of Agriculture, Economic Research Service. 2025. SNAP participation varied across States in fiscal year 2024. https://www.ers.usda. gov/data-products/charts-of-note/chart-detail?chartId=113053. Provides FY2024 monthly average participation and share of U.S. population (12.3%). Accessed January 10, 2026

    work page 2025

  49. [49]

    Department of Agriculture, Economic Research Service

    U.S. Department of Agriculture, Economic Research Service. 2025. Supplemental Nutrition Assistance Program (SNAP): Key Statistics and Research. https://www.ers.usda.gov/topics/food-nutrition-assistance/supplemental-nutrition-assistance-program-snap/key-statistics-and-research. Reports FY2024 average monthly participation (41.7 million) and federal spendin...

    work page 2025

  50. [50]

    Department of Agriculture, Food and Nutrition Service

    U.S. Department of Agriculture, Food and Nutrition Service. 2025. Supplemental Nutrition Assistance Program (SNAP). https://www.fns.usda.gov/ snap/supplemental-nutrition-assistance-program. Accessed January 10, 2026

    work page 2025

  51. [51]

    District Court for the Western District of Missouri

    U.S. District Court for the Western District of Missouri. 2024. Holmes et al. v. Knodell. Order Granting Plaintiffs’ Motion for Summary Judgment and Denying Defendant’s Motion for Summary Judgment. No. 2:22-cv-04026, Document 161, May 9, 2024

    work page 2024

  52. [52]

    waterfall

    Elizabeth Warren. 2025. U.S. Senator Elizabeth Warren | New War Room Investigation. https://www.warren.senate.gov/newsroom/press- releases/new-war-room-investigation-reveals-trump-admin-dishonesty-about-social-security-call-wait-times Manuscript submitted to ACM Due Process on Hold: A Queueing Framework for Improving Access in SNAP 19 A Background Additio...

    work page 2025