IORA - Institute of Operations Research and Analytics

DAO-ISEM-IORA Seminar Series: Guillaume Roels
2025-11-14 10:00 - 11:30 (Asia/Singapore)
Name of Speaker

Guillaume Roels

Schedule

14 Nov 2025, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

HSS 4-1

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/SWlclpbGT6eV8q9w_dkijA

Title

You, Me, or We? Co-Productive Principal-Agent Dynamics

Abstract

Problem Definition: Projects are often initiated by a single person – a principal – who then decides whether to execute it on their own (Single Execution) or to partner with someone else – an agent. If an agent is hired, the project execution may be joint (Joint Execution) or undertaken only by the agent (Delegated Execution). How do the resulting co-productive dynamics compare to what would be optimal to do?
Methodology/Results: We consider a co-productive principal-agent model with endogenous team formation. With financial transfers, as is typical in inter-organizational contexts, joint execution happens less frequently than optimally. Moreover, the optimal contract under joint execution turns out to be a 50%-50% equity split under mild conditions. When financial transfers are not allowed, as is typical in intra-organizational contexts, there might be too much joint execution and too little delegation if the agent is very efficient. Overall, the inefficiency created by moral hazard under delegated and joint execution is much less important than that created by the principal’s ability to engage in project hoarding and not form a team.
Managerial Implications: The problem of under-delegation should really be framed as a problem of project hoarding, i.e., principals do not partner enough. Moreover, it is only relevant within – and not between – organizations. When principals partner with an agent, they might delegate too much if agents are inefficient or contribute too much if agents are efficient. Although the principal-agent and team-production literature have essentially focused on setting incentives for effort exertion within a given operating mode, a more critical issue appears to induce principals to form a team (or not), leading to inefficient operating modes.

About the Speaker

Guillaume Roels is the Timken Chaired Professor of Global Technology and Innovation at INSEAD. His research lies on the interface of operational excellence, people-centric operations, and the management of services. Recent work has focused on collaborative dynamics in organizations, the design of service experiences, and customer ownership in service systems. Prior to joining INSEAD, Guillaume was an Associate Professor at the UCLA Anderson School of Management. He received an MS degree in Management Engineering and a DEA in Management from the Catholic University of Louvain, Belgium, and a PhD in Operations Research from MIT.
He is currently serving as the Editor-in-Chief of Service Science, an INFORMS journal was a Department Editor at M&SOM. He also served as the President of the M&SOM Technology, Innovation, and Entrepreneurship (TIE) Specific Interest Group (SIG) and the President of the M&SOM Service Management (SIG). Recent research awards include a finalist position on the 2023 POMS College of Service Operations Management Best Student Paper Competition, a finalist position in the 2023 INFORMS Social Media Analytics Best Student Paper Competition, and a second place in the 2023 INFORMS Service Science Cluster Best Paper Competition.

DAO-ISEM-IORA Seminar Series: Seungki Min
2026-01-30 10:00 - 11:30
Name of Speaker

Seungki Min

Schedule

30 Jan 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ1 0302

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/psLI6qAmQPyKNZ3DOtqRvw

Title

An Information-Theoretic Analysis of Nonstationary Bandit Learning

Abstract

In many real-world bandit learning problems, the underlying environment evolves over time, requiring decision-makers to continually acquire information and adapt their action selection accordingly. In this talk, I study Bayesian formulations of nonstationary bandit problems, where environmental dynamics are modeled as stochastic processes, and develop an information-theoretic framework for analyzing attainable performance.
Our analysis yields generic regret upper bounds that extend classical results from stationary Bayesian bandits to nonstationary settings. A key insight is that the entropy rate of the optimal action process naturally quantifies the intrinsic difficulty introduced by nonstationarity. I further connect our results to existing frequentist analyses of nonstationary bandits, showing that several well-known regret bounds in the literature can be recovered as special cases within our unified framework.

About the Speaker

Seungki Min is an Assistant Professor of Operations Management at Seoul National University Business School. His research focuses on bandit optimization and reinforcement learning, with an emphasis on principled frameworks for dynamic decision problems arising in business and engineering applications, including online platforms, pricing, and finance. His research has appeared in Operations Research, Management Science, and leading AI/ML conferences such as ICML and NeurIPS. He earned his Ph.D. from Columbia Business School. Prior to academia, he worked in high-frequency trading domain.

DAO-ISEM-IORA Seminar Series: Francis de Véricourt
2026-02-06 10:00 - 11:30
Name of Speaker

Francis de Véricourt

Schedule

6 Feb 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

HSS 4-2

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/KcwXsVRZSI2rLe4DvkXNFQ

Title

Beyond the Black Box: Unraveling the Role of Explainability in Human-AI Collaboration

Abstract

Explainable Artificial Intelligence (AI) models have been proposed to mitigate overreliance and underreliance on AI, which reduce the effectiveness of human-AI collaborative tools. Yet, empirical evidence is mixed, and the impact of explainable AI on a decision-maker (DM)’s cognitive load and fatigue is often ignored. This paper offers a theoretical perspective on these issues. We develop an analytical model that incorporates the defining features of human and machine intelligence, capturing the limited but flexible nature of human cognition with imperfect machine recommendations. Crucially, we represent how AI-based explanations influence the DM’s belief in the algorithm’s predictive quality. Our results indicate that explainable AI has varying effects depending on the level of explainability it provides. While low explainability levels have no impact on decision accuracy and reliance levels, they lessen the cognitive burden of the DM. In contrast, higher explainability levels enhance accuracy by improving overreliance but at the expense of increased underreliance. Further, the relative impact of explainability (c.f. a black-box system) is higher when the DM is more cognitively constrained, the decision task is sufficiently complex or when the stakes are lower. Importantly, higher explainability levels can escalate the DM’s cognitive burden and hence overall processing time and fatigue, precisely when explanations are most needed, i.e. when the DM is pressed for time to complete a complex task and doubts the machine’s quality. Our study elicits comprehensive effects of explainability on decision outcomes and cognitive effort, enhancing our understanding of designing effective human-AI systems in diverse decision-making environments.

About the Speaker

Francis de Véricourt is Professor of Management Science and the founding Academic Director of the Institute for Deep Tech Innovation (DEEP) at ESMT Berlin. He also holds the Joachim Faber Chair in Business and Technology, and is the co-author of Framers, a Penguin Random House book listed on Financial Times’ Best Books. He lived and worked in France, USA, Germany and Singapore.

Francis was the first Associate Dean of Research and holder of the President’s Chair at ESMT Berlin. He held faculty positions at Duke University and INSEAD, where he was the Paul Dubrule Chaired professor in Sustainable Development, and was a post-doctoral researcher at Massachusetts Institute of Technology (MIT). His general research interest is in the area of decision science, analytics and operations, with applications in health care, sustainability and human-AI interaction. He is the author of numerous academic articles in prominent management, analytics and economics journals such as Management Science, Operations Research, American Economics Review and others. He received several outstanding research awards and is currently an Area Editor at Operations Research.

Francis has been the recipient of many teaching awards for delivering classes to MBA and Executive MBA students at ESMT and INSEAD. He has extensive experience in executive education and corporate learning solutions, and is a regular speaker in academic and industry forums.

DAO-ISEM-IORA Seminar Series: Lu Jiaqi
2026-02-13 10:00 - 11:30
Name of Speaker

Lu Jiaqi

Schedule

13 Feb 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ1 0302

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/FGZiBt3mT9CHxWWr-w6t5Q

Title

Bandit Allocational Instability

Abstract

About the Speaker

Jiaqi Lu is an assistant professor in the School of Data Science and the School of Management and Economics (joint appointment) at the Chinese University of Hong Kong, Shenzhen. Her research aims at understanding when and how do agents’ colliding incentives and complex dynamics lead to market inefficiencies, and how to mitigate them. The types of applications usually involve matching platforms and supply chain. For example, recently, she studies bandit algorithms’ unintended side effect on downstream tasks, such as allocational instability in platform operations and sample bias in post-policy inference. Her papers typically appear in Journals including Management Science, Operations Research, Mathematics of Operations Research, and conferences such as ACM EC and WINE.
Jiaqi Lu obtained her Ph.D. in the Decision, Risk, and Operations division at Columbia Business School, and her B.E. in Industrial Engineering, B.A. in English (double major) at Tsinghua University.

DAO-ISEM-IORA Seminar Series: Georgina Hall
2026-02-27 10:00 - 11:30
Name of Speaker

Georgina Hal

Schedule

27 Feb 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ1 0302

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/MSVeTEDGTSGxi0TGgyLmNg

Title

Sum of Squares Submodularity

Abstract

We introduce the notion of t-sum of squares (sos) submodularity, which is a hierarchy, indexed by t, of sufficient algebraic conditions for certifying submodularity of set functions. We show that, for fixed t, each level of the hierarchy can be verified via a semidefinite program of size polynomial in n, the size of the ground set of the set function. This is particularly relevant given existing hardness results around testing whether a set function is submodular (Crama, 1989). We derive several equivalent algebraic characterizations of t-sos submodularity and identify submodularity-preserving operations that also preserve t-sos submodularity. We further present a complete classification of the cases for which submodularity and t-sos submodularity coincide, as well as examples of t-sos-submodular functions. We demonstrate the usefulness of t-sos submodularity through three applications: (i) a new convex approach to submodular regression, involving minimal manual tuning; (ii) a systematic procedure to derive lower bounds on the submodularity ratio in approximate submodular maximization, and (iii) improved difference-of-submodular decompositions for difference-of-submodular optimization.
This is joint work with Anna Deza (Georgia Tech).

About the Speaker

Georgina Hall is an Assistant Professor at INSEAD in the Decision Sciences Area. Her research focuses on convex relaxations of NP-hard problems, particularly those that arise in polynomial optimization and problems on graphs. Prior to joining INSEAD in 2019, she was a postdoctoral student at INRIA. She completed her PhD in Operations Research and Financial Engineering at Princeton University in 2018. She is the recipient of the 2018 INFORMS Optimization Society Young Researcher’s Prize and the 2020 Information Theory Society Paper Award, among other awards.

DAO-ISEM-IORA Seminar Series: Peng Sun
2026-03-11 10:00 - 23:30
Name of Speaker

Peng Sun

Schedule

11 Mar 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ2 0511

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/8WAQ86W5TMW5MZzaiDBNiQ

Title

Optimal Push, Pull, and Failure Funding for Global Health

Abstract

Malaria and tuberculosis each cause over half a million deaths annually, yet commercial incentives to develop treatments for these and other diseases concentrated in low-income countries remain weak. Governments and nonprofits address this gap through push (e.g., grants) and pull (e.g., prizes) mechanisms. We propose a third approach: the funder pays only if the firm fails, reimbursing part of its testing costs. This failure insurance is optimal when markets are large enough to reward success but too small to justify initial investment. We model the problem as an infinite-dimensional optimization problem with adverse selection and moral hazard constraints, and use duality theory to characterize optimal funding mechanisms. Failure insurance is preferred for tuberculosis if testing costs are below $1 billion. For most tropical diseases, including malaria, the optimal policy is pull funding with supplemental push support. These results challenge current push-heavy practice and offer broader insights for global health and innovation policy.

About the Speaker

Peng Sun is a JB Fuqua Professor in the Decision Sciences area at the Fuqua School of Business, Duke University. He researches mathematical theories and models for resource allocation decisions under uncertainty, and incentive issues in dynamic environments. His work spans a range of applications areas, from operations management, economics, finance, marketing, to health care and sustainability. He has served as a Department Editor at Management Science, and an Associate Editor at Operations Research.

DAO-ISEM-IORA Seminar Series: Robert Shumsky
2026-03-18 10:00 - 11:30
Name of Speaker

Robert Shumsky

Schedule

18 Mar 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ2 0511

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/fjT0SX_zQPCBsBv0ciBjsw

Title

Use it or Slowly Lose it: Expertise Atrophy with Organizational AI Usage

Abstract

As organizations adopt generative AI, its use can improve productivity but reliance can lead to atrophy of worker knowledge and skills over time. The challenge is how to incentivize human oversight and maintain long-run expertise. Using a principal-agent framework, we study optimal incentive design when workers can exert costly effort to verify and correct imperfect AI output, where effort both improves current performance and preserves expertise. A central managerial challenge is that improving AI quality makes oversight harder to motivate, since acceptable outcomes increasingly occur even when workers shirk. Consequently, profit-maximizing compensation can be non-monotonic in AI quality, skill, or return on effort, and organizations may even be better off, in terms of profitability, with worse AI systems. More subtle implications arise when skills decay with AI reliance. First, due to contracting frictions, we find that firms may (rationally) allow expertise to deteriorate by substituting higher effort from non-experts for expertise, leading to significant performance losses compared to a system in which both effort and expertise can be prescribed. Second, when tasks are relatively less complex with short learning curves and high returns on effort for low-skilled workers, then the risk of skill atrophy can mitigate these frictions. For such tasks workers are self-motivated to preserve expertise, so that higher rates of potential skill loss can, counterintuitively, increase profit. These insights highlight a managerial “danger zone” in which low-to-moderate skill decay is easily overlooked yet leads to substantial long-term losses, underscoring when proactive investment in human expertise is most valuable.

About the Speaker

Robert Shumsky is a Professor of Operations Management at the Tuck School of Business at Dartmouth and is faculty co-director of Health Care Management Education at Dartmouth. His research focuses on the improvement of service operations, and he has written about capacity estimation and control, how to allocate work to improve quality, and how to coordinate service supply chains. He has conducted research on the U.S. air traffic management system and studied transportation operations for state agencies and the Federal Aviation Administration. He has also served as a consultant for both manufacturing and service operations, including call centers and health care providers. Professor Shumsky has published articles in many academic journals including Operations Research, Management Science, and the Proceedings of the National Academy of Science. He currently serves in various editorial positions for several academic journals. He received his PhD degree in Operations Research from MIT.

DAO-ISEM-IORA Seminar Series: Ignacio Rios
2026-03-20 10:00 - 23:30
Name of Speaker

Ignacio Rios

Schedule

20 Mar 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ1 0302

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/-QjsYdlrQXyy-bdAfa6zlg

Title

Designing Effective Fundraising Campaigns: The Role of Incentives and Solicitation Mechanisms

Abstract

Charitable donations are a vital source of funding for nonprofit organizations, enabling them to carry out their mission of addressing social issues and providing support to those in need. To boost contributions, third parties often donate large sums that fundraisers use to incentivize individual donations, with one-to-one matching being the most common mechanism. However, alternative designs may lead to even higher contributions. This paper investigates the effectiveness of two design choices in the context of fundraising: (i) the incentive mechanism, focusing on the two most prevalent ones (i.e., matching and gift unlock); and (ii) the solicitation mechanism, i.e., whether donations occur simultaneously or sequentially. We introduce a stylized game-theoretical model where a fundraiser decides the design choices and corresponding design parameters to maximize overall donations. Following the fundraiser’s decision, donors make their one-time contribution. For each design choice, we characterize the equilibrium donations and find the fundraiser’s optimal policy. We find that gift unlock consistently outperforms matching. Moreover, sequential solicitation is the optimal choice with gift unlock, whereas simultaneous solicitation yields higher overall contributions with matching. Furthermore, our simulations indicate that the effectiveness of gift unlock is robust to peer effects and donor participation uncertainty. Our findings indicate that fundraisers should prioritize gift unlock over matching, align the selected incentive mechanism with the optimal solicitation format, and calibrate campaign parameters to maximize donation outcomes.

About the Speaker

Ignacio Ríos is an Assistant Professor of Operations Management at the Jindal School of Management, University of Texas at Dallas. He holds a Ph.D. in Operations, Information, and Technology and an M.A. in Economics from Stanford University, as well as degrees in Operations Management and Industrial Engineering from the University of Chile. His research expertise lies in behavioral market design, with a focus on how incentives, information, allocation rules and users’ behavior shape outcomes in markets without money. Ignacio has played a leading role in the reform of Chile’s school choice and college admissions systems, and also in designing other two-sided matching markets. His work has been recognized with numerous awards, including the Poets & Quants “40 Under 40 Best Business School Professors” distinction, the IFORS Prize for OR in Development, and the BOM Best Paper Award.

DAO-ISEM-IORA Seminar Series: Yael Grushka-Cockayne
2026-03-27 10:00 - 11:30
Name of Speaker

Yael Grushka-Cockayne

Schedule

27 Mar 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

HSS 4-2

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/51hGI1hiRe-T473GjiQA1w

Title

Decision-making with Ordinal Ratings

Abstract

Experts often provide judgments on an ordinal scale, which are easy to generate and are intuitive. Ordinal ratings, however, are not trivial to aggregate across multiple experts, as they provide neither the strict preference ordering of a ranking, nor the intensity of preference of cardinal scores. In addition, ordinal rating judgments often map to a broad set of outcomes, which are not expressed through the ordinal, discrete set of choices elicited. In this way, ordinal ratings also neglect to express the degree of uncertainty that may exist when rankings are interpreted as forecasts. We offer a framework for mapping ordinal ratings to continuous outcome distributions, allowing for the aggregation of ratings and the expression of the uncertainty that may exist in the forecasts. Finally, our framework allows for rendering the aggregate distributional forecasts back to the original ordinal scale, providing again an intuitive set of judgements, to be used by the decision maker. We demonstrate our framework in the context of National Football League (NFL) scout assessments of players performance. These assessments, treated as forecasts, are utilized by general managers when making player selection decisions in the annual NFL draft.

About the Speaker

Yael Grushka-Cockayne
Landmark Communication Incorporated Professor of Business Administration, Vice Dean and Senior Associate Dean for Professional Degree Programs, Academic Co-Director of the LaCross Institute for AI.
Professor Yael Grushka-Cockayne’s research and teaching activities focus on data science, artificial intelligence, forecasting, project management and behavioral decision-making. Her research is published in numerous academic and professional journals, and she is a regular speaker at international conferences in the areas of decision analysis, project management and management science. Prof. Grushka-Cockayne is an award-winning teacher, winning the Darden Morton Leadership Faculty Award in 2011, the University of Virginia’s Mead-Colley Award in 2012, the Darden Outstanding Faculty Award in 2013 and 2022, University of Virginia All University Teaching Award in 2015, the Faculty Diversity Award in 2013 and 2018, and the Transformational Faculty Award in 2024. Prof. Grushka-Cockayne teaches the core “Decision Analysis” course, an elective she designed on project management, an elective on data science and a new course on coding with ChatGPT.
Before starting her academic career, she worked in San Francisco as a marketing director of an Israeli ERP company. As an expert in the areas of project management, Prof. Grushka-Cockayne has served as a consultant to international firms in the aerospace and pharma industries. She is a UVA Excellence in Diversity fellow and a member of INFORMS, the President of the Decision Analysis Society, and a member of the Operational Research Society and the Project Management Institute (PMI). She served an associate editor at Management Science and is currently as associate editor at Operation Research.
Grushka-Cockayne was named one of “21 Thought-Leader Professors” in Data Science. Her course “Fundamentals of Project Planning and Management” Coursera MOOC has over 300,000 enrolled, across 200 countries worldwide. Her “Data Science for Business” Harvard Online course, launched in 2021, has taught hundreds of learners around the world.
