REVIEW 3 major objections 5 minor 7 references
Making a Case for Research Collaboration Between Artificial Intelligence and Operations Research Experts
T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read A three-workshop series establishes that artificial intelligence and operations research are complementary fields whose collaboration can be deliberately grown through funding, education, long-term programs, aligned venues, and shared…
desk verdict Not a research paper but a solid, transparent workshop report that deserves peer review with genre-appropriate expectations. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The organizing device is the 'challenge problem': a large-scale societal or industrial problem, solicited from both communities, that cannot be cleanly solved by either field alone and that breakout groups use to surface integration strategies. The report also runs on the complementary pairing of AI's data-driven learning with OR's model-driven optimization as the engine that makes hybrid work superior, and on the five recommendations as the lever that makes such pairing routine. Challenge problems do the argumentative work by giving the collaboration claim concrete shape and justifying the funding, education, venue, and benchmark proposals that follow.
What would settle it
A concrete test would be to count cross-community co-authored papers and benchmark usage in the five years after the recommended joint funding and venue policies launch; if these rates do not rise relative to comparable single-discipline research, the claim that these structural changes drive collaboration is undercut.
Extended reading notes
Core claim
On the report's own terms, the central discovery is that the obstacles to AI/OR collaboration are structural and therefore removable. The workshops produced six challenge-problem areas, including causal inference for the opioid epidemic, generative AI combined with OR/MS, multi-agent learning in AI-powered supply networks, data scarcity and privacy in healthcare, and the integration of OR and AI through optimization together with the optimality-explainability tradeoff; each one is said to require both data-driven AI methods and model-driven OR methods. From these problems the report distills five recommendations for future action. The claim is that if these recommendations are implemented, the two fields will not merely coexist but will jointly produce solutions to large-scale societal decision problems that neither could produce alone.
Load-bearing premise
The load-bearing premise is that the invited experts who took part in the workshops adequately represent the needs and priorities of the broader AI and OR communities, so the challenge problems and recommendations they selected are the right ones for the field.
Editorial extensions
If this is right
- If funding agencies adopt joint AI/OR programs with mandated co-principal investigators, interdisciplinary proposals would no longer be filtered out by single-discipline review panels.
- If summer schools, speaker series, and co-advising become standard, a generation of researchers will be trained to speak both the data-driven and model-driven languages.
- If promotion and tenure policies count cross-community venues appropriately, rational career incentives will align with collaboration rather than against it.
- If shared benchmark datasets with human and societal dynamics are built, algorithms from AI and OR can be compared on equal ground, and competition can drive joint progress.
- If long-term residential programs are held, sustained work on challenge problems can mature into publications and durable partnerships rather than one-off meetings.
Reading between the lines
- A testable extension: the report's recommendations imply that the rate of AI/OR cross-community co-authorship and cross-citation is currently lower than the complementarity of the fields would justify; that gap could be measured before and after the proposed policies are introduced.
- The report leaves implicit that generative AI's 'democratization of optimization' may be the fastest payoff of AI/OR collaboration, since it converts natural-language business problems into solvable models without requiring users to be OR experts.
- A natural next step beyond the report would be a community-curated benchmark suite in the style of the mentioned problem libraries, but extended with the social and human dynamics the report calls for.
- If the structural diagnosis is right, isolated research grants without changes to venues and incentives will underperform; the report's logic could be tested by comparing outcomes of joint funding with and without aligned venue policies.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This manuscript is the final report of a three-workshop series (2021–2024) organized by INFORMS, ACM SIGAI, and the Computing Community Consortium to promote collaboration between artificial intelligence (AI) and operations research (OR). It summarizes the first two workshops on methods, applications, and trustworthy AI; describes the third workshop's 'challenge problems' (13 accepted proposals grouped into six topic areas); and presents five recommendations: joint funding opportunities, joint education, long-term research programs, aligned conferences/journals, and joint benchmark creation. The report also documents concrete outcomes, including the AI-SCORE summer school and an INFORMS–AAAI memorandum of understanding.
Significance. If the recommendations were adopted and effective, the report could influence funding agencies, academic departments, and professional societies to reduce structural barriers between AI and OR. The manuscript is valuable as a historical record of a coordinated community effort: it provides transparent documentation of the workshop series, names organizers and participants, cites prior workshop reports, and describes real outcomes such as the AI-SCORE summer school and the INFORMS–AAAI collaboration. However, the report's central claim—that these five institutional changes will substantially increase collaboration and maximize societal impact—is an assertion rather than a demonstrated result. The evidence base is the opinions of a self-selected invited group, and no external validation or comparison is provided. The significance of the recommendations therefore rests on an unexamined representativeness assumption.
major comments (3)
- [Executive Summary and Section 6] The report's central claim that the five recommendations will 'maximize societal impact' (Executive Summary, p. 4) and 'strengthen collaboration' (Section 6, p. 26) is not supported by evidence beyond the views of workshop participants. Section 5.3 states that 13 challenge proposals were accepted from an open call, but it does not report the number of submissions, the selection criteria, or any demographic or community-coverage information. The appendix lists participants, but there is no sampling frame or rationale for why this group represents the broader AI and OR communities. This is a load-bearing empirical premise: funding agencies, promotion committees, and journal editors are being asked to change policies based on these recommendations. A membership survey, a systematic review of prior collaboration interventions, or bibliometric evidence of benefits would be needed to substantiate the generalization. Without such evidence, the recommendations are better framed as hypotheses or as the informed opinions of a specific group rather than as a 'unified strategic research vision.'
- [Section 6.3 and Section 6.1] The recommendations in Section 6.1 and Section 6.3 reference 'Section 7' for the challenge problems and topics, but the manuscript has no Section 7; the challenge problems appear in Section 5.3. This internal cross-reference error suggests the recommendations were drafted against a different outline and prevents readers from tracing the proposed funding and research programs to the specific challenges they are meant to address. The report should be revised so that all citations point to existing sections.
- [Section 4.1] The claim that the AI-SCORE summer school goals were 'well-achieved, as evidenced by the high level of engagement and solutions proposed' and that 'student feedback was strongly positive' is anecdotal. No survey instrument, response rate, comparison group, or criteria for 'success' are provided. Since this outcome is used to support Recommendation 2 (joint education), the report should either present evaluative data (e.g., pre/post assessments, participant surveys, follow-up publications) or soften the claim to a descriptive account of activities and impressions.
minor comments (5)
- [Section 6.2, p. 27] The text says 'help showcase the power of integrating OR and CI' but should presumably read 'OR and AI'; this typo occurs in a key sentence about the value of co-advising.
- [Section 6.3, p. 27] 'the The Institute for Mathematical and Statistical Innovation (IMSI)' contains a duplicated definite article; the sentence should be rephrased.
- [Appendix, Workshop 3 participants] Thiago Serra is listed in the author affiliations as University of Iowa but in the appendix as Bucknell University; the affiliation should be consistent across the manuscript.
- [Reference list] Several entries are incomplete or inconsistent: for example, 'Islam, M. S. (2021)' is cited without a journal or venue, and in-text citations to 'Islam et al.' do not always match the reference list entries. The reference list should be carefully checked against the in-text citations.
- [Section 1, p. 6] The Background section says the goals 'articulated in 2020 are still very relevant' but does not restate them; readers must infer the goals from the later text. Restating them would improve readability.
Circularity Check
No circularity: the report is a workshop-consensus document whose recommendations are explicitly synthesized from workshop discussions, with no derivation chain to reduce.
full rationale
This is a community-consensus report rather than a technical derivation. It makes no quantitative predictions, fits no parameters, invokes no uniqueness theorems, and presents no mathematical result that could be equivalent to its inputs by construction. The five recommendations are explicitly presented as outcomes of the three workshops, so the relationship between workshop discussion and the final recommendations is declarative synthesis, not a hidden reduction. The report states that the challenge problems were 'gathered through an open call for proposals to the community' and that 13 were accepted and combined into six topic areas; thus the challenge list is transparently a selection from submitted proposals, not a result claimed to follow from first principles. References to earlier workshop reports (Das et al., 2021; Dickerson et al., 2023) are historical records of the same workshop series and are not used as load-bearing external proof for the report's conclusions. The report contains no equation-level self-definition, no fitted parameter renamed as a prediction, and no self-citation chain used to force a conclusion. A possible limitation is that the participant group may not be representative of the broader AI and OR communities, but that is an empirical-generalizability concern, not circular reasoning under the criteria used here. Therefore the circularity score is 0.
Assumptions & free parameters
assumptions (3)
- domain assumption AI and OR have complementary strengths and their collaboration improves decision-making.
- domain assumption Workshop participants' perspectives are representative of the AI and OR communities.
- domain assumption Cultural differences are a barrier to collaboration that can be mitigated by institutional mechanisms.
Cite this review
Pith. "Pith review of Making a Case for Research Collaboration Between Artificial Intelligence and Operations Research Experts." pith.science (2026). https://pith.science/paper/UMPPSOET
@misc{pith2026250721076,
author = {Pith},
title = {Pith review of: Making a Case for Research Collaboration Between Artificial Intelligence and Operations Research Experts},
year = {2026},
howpublished = {\url{https://pith.science/paper/UMPPSOET}},
note = {Machine review of arXiv:2507.21076}
}
read the original abstract
In 2021, INFORMS, ACM SIGAI, and the Computing Community Consortium (CCC) hosted three workshops to explore synergies between Artificial Intelligence (AI) and Operations Research (OR) to improve decision-making. The workshops aimed to create a unified research vision for AI/OR collaboration, focusing on overcoming cultural differences and maximizing societal impact. The first two workshops addressed technological innovations, applications, and trustworthy AI development, while the final workshop highlighted specific areas for AI/OR integration. Participants discussed "Challenge Problems" and strategies for combining AI and OR techniques. This report outlines five key recommendations to enhance AI/OR collaboration: 1) Funding Opportunities, 2) Joint Education, 3) Long-term Research Programs, 4) Aligning Conferences/Journals, and 5) Benchmark Creation.
Reference graph
Works this paper leans on
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[1]
Causal Inference and the Opioid Epidemic The opioid crisis in the United States has reached alarming proportions, as evidenced by staggering statistics. In 2021, the number of individuals who lost their lives due to drug overdoses surpassed six times the figures recorded in 1999. Even more concerning, the year 2021 witnessed a distressing 16% surge in drug...
work page 2021
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[2]
Bringing Together Generative AI and Operations Research/Management Science Today’s Generative AI (GenAI) models, including Large Language Models (LLMs) such as ChatGPT (OpenAI, n.d.) and Gemini (Gemini, n.d.), are the culmination of decades of AI research and are providing amazing, advanced AI capabilities through natural language Computing Community Cons...
work page 2023
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[3]
Understanding Multi-Agent Interaction and Learning in AI-Powered Supply Networks The presence of algorithmic agents is rapidly increasing across markets. Supply chains offer a prime example of this trend. The 2020 pandemic significantly accelerated the adoption of algorithmic agents in these markets (Andrade, Frongillo, & Piliouras, 2021), highlighting the ...
work page 2020
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[4]
Data Scarcity and Privacy in Healthcare Machine learning algorithms are increasingly prevalent in high and low-risk settings, providing predictions that can be used within existing decision-making frameworks. In a healthcare setting, ML can analyze vast patient data, allowing subsequent models to merge seamlessly into organizational workflows. For example,...
work page 2020
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[5]
What are the compelling ‘big’ ideas that require AI and OR collaboration?
WORKSHOP III: GRAND CHALLENGES FOR AI/OR COLLABORATIONS 5.1. Ideas Resulting From the First Two Workshops Leading to Workshop III In the first two workshops, we explored how cross-pollination of OR and AI methods and techniques could benefit each discipline, and approaches to enable this collaborative understanding. The next question is, “What are the compe...
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[6]
Integrating OR and AI Through the Optimization Lens and the Tradeoff Between Optimality and Explainability One of the issues fundamental to all application problems discussed in the earlier subsections of this report is the effective and capable optimization solution methods that underpin all AI methods. Combining AI and OR can improve upon "traditional" me...
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[7]
RECOMMENDATIONS FOR FUTURE ACTIONS Ultimately, this workshop series sought to strengthen collaboration between the AI and OR communities. While this report specifically focuses on the AI and OR communities, our recommendations more broadly apply to collaboration with related disciplines, including causal inference, economics, statistics, and others. In all...
Reviewed August 7, 2026 · model on record in the stance chip above.
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