DAO-ISEM-IORA Seminar Series: Peng Sun
2026-03-11 10:00 - 23:30 (Asia/Singapore)
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.

DAO-ISEM-IORA Seminar Series: Park Sinchaisri
2026-04-10 10:00 - 11:30
Name of Speaker

Park Sinchaisri

Schedule

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

Venue

BIZ1 0204

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/oo0ElW4xSIu9BcdsAyKQ2A

Title

Algorithmic Advice, Human Compliance, and Learning

Abstract

Problem definition:Organizations increasingly deploy algorithmic tools to support complex operational decisions,raising a practical design question: how should these tools be built when designers care not only about immediate performance, butalso about preserving and building human skill that remains valuable when advice is unavailable, imperfect, or requires genuineoversight? We study how theprecisionof algorithmic advice shapes this trade-off.Methodology/results:We develop a stylized modelof advice-taking and learning. The model characterizes a reward-learning frontier: precise, action-level advice is easier to implementand improves payoffs while available through higher compliance, whereas broad, strategic advice requires interpretation, inducesgreater exploration, and generates knowledge that is portable, even when decision environments differ. We test the model’s predictionsin two online experiments in an electric-vehicle routing and charging task, representing typical characteristics of sequential decisiontasks. Consistent with the theory, precise numerical advice delivers the strongest gains during the advice phase, whereas broaderadvice can yield more robust performance after advice is removed, specifically if the new environment differs substantially, butnot completely. We use inverse reinforcement learning to recover interpretable latent objective components from action traces,distinguishing transient compliance from persistent internalization.Managerial implications:Our results provide design guidancefor advice systems that balance short-run operational efficiency with the development of long-run human capability. They also helpvalidate inverse reinforcement learning as an effective tool for estimating human behaviors in complex sequential tasks

About the Speaker

Park Sinchaisri is an Assistant Professor of Operations and IT Management at the Haas School of Business, University of California, Berkeley. His research draws on operations management, economics, machine learning, and behavioral science to study human decision-making in complex environments, design human-AI systems that improve decision-making, and develop strategies for managing the future of work. His work has been published in Management Science and Manufacturing & Service Operations Management, and has also appeared in leading human-computer interaction venues including CSCW. He received his PhD in Operations, Information and Decisions and an AM in Statistics from the Wharton School of the University of Pennsylvania, an SM in Computational Science and Engineering from MIT, and an ScB in Computer Engineering and Applied Mathematics-Economics from Brown University. Originally from Bangkok, Thailand, he hopes his research can help address urban challenges and improve outcomes for marginalized workers.

DAO-ISEM-IORA Seminar Series: Kwok-Hao Lee
2026-04-17 10:00 - 11:30
Name of Speaker

Kwok-Hao Lee

Schedule

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

Venue

HSS 3-1

Link to register
(via Zoom)

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

Title

Two-Sided Markets Shaped By Platform-Guided Search

Abstract

We investigate concerns that vertically integrated platforms like Amazon steer demand towards their own offers via algorithmic prominence, potentially harming consumers. On Amazon, for each product, the Buybox prominence algorithm selects one seller to feature, influencing which offers consumers consider. Using novel Amazon sales and Buybox (prominence) data, we estimate a structural model capturing the effects of such algorithmic prominence on consumer choices, seller pricing, and entry. We find that the platform can indeed steer demand as 95% of consumers consider only the Buybox offer. The Buybox is highly price-elastic (−21), but skews towards Amazon’s own offers, which are featured as frequently as observably similar offers priced 5% cheaper. Still, as consumers prefer these offers, this skew does not amount to self-preferencing in the sense of harming consumers: consumer surplus is roughly maximized at the estimated Amazon Buybox advantage, which balances higher prices against showing consumers their preferred offers.

About the Speaker

Lee Kwok Hao is an industrial organisation economist working at the intersection of digital markets and the smart city. He uses administrative and platform data to study how algorithms and policy rules govern search, matching, pricing, and allocation, with a focus on transportation systems and public housing. As an Assistant Professor (Presidential Young Professor) at the Department of Strategy & Policy at the National University of Singapore (NUS) Business School, Kwok Hao has been a recipient of the Social Science and Humanities Research Fellowship under the Social Science Research Council of Singapore. Previously, Kwok Hao was a Presidential Fellow at the NUS Business School, during which he spent a postdoctoral stint at the Cowles Foundation at Yale University. He obtained my PhD from Princeton University after formative years at the University of Chicago and Washington University in St. Louis.

DAO-ISEM-IORA Seminar Series: Yanwei Jia
2026-04-24 10:00 - 11:30
Name of Speaker

Yanwei Jia

Schedule

24 Apr 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/QpOphmoURVCrbzjftZht4g

Title

When to Quit a Venture: Normative Theory and Structural Identification of Decoupled Belief and Decision

Abstract

Understanding how agents learn and make decisions under uncertainty is a fundamental question in many fields, with applications including real options, R&D, and entrepreneurial ventures. The conventional approach formulates this learning process as an optimal stopping problem within a Bayes framework, assuming agents possess the cognitive sophistication to continuously update their beliefs based on statistical principles, thereby rigidly locking their decisions to these updated beliefs and forcing a strict, deterministic threshold rule. This paper develops a continuous-time reinforcement learning framework for sequential experimentation that formally separates beliefs from actions. By decoupling the evaluation and policy processes, we provide a unifying framework that yields both normative benchmarks and flexible positive dynamics. Normatively, using the workhorse Gaussian bandit model, we prove that by properly tuning learning rates, the framework achieves a logarithmic regret bound, matching the efficiency of Bayesian rationality. Positively, the decoupled policy generates distinct and testable predictions, such as experience-driven, path-dependent quitting dynamics, even when the belief is consistent with its Bayesian counterpart. Crucially, we prove the structural identifiability of these hidden learning dynamics. By utilizing the method of simulated moments, we demonstrate how this framework can be structurally estimated directly from censored observational field data and extended to general jump-diffusion bandits.

About the Speaker

Yanwei Jia is an assistant professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong. He obtained his Ph.D. degree from the National University of Singapore in 2020, and B.Sc. from Tsinghua University in 2016. Prior to joining CUHK in 2023, he was an associate research scientist and adjunct assistant professor in the Department of Industrial Engineering and Operations Research at Columbia University. His research interest falls broadly into financial decision-making problems and uses the structural approach to study the decision making and information aggregation mechanism.

DAO-ISEM-IORA Seminar Series: Diwakar Gupta
2026-05-20 10:00 - 11:30
Name of Speaker

Diwakar Gupta

Schedule

20 May 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/Wne_hIgXQ-qf4uJZ1Frn5Q

Title

The Impact of List Diving on Post-Transplant Outcomes

Abstract

Transplant programs often turn down deceased-donor kidney offers for their top-ranked potential transplant recipients (PTRs) and utilize them for lower-ranked PTRs. This practice is known as list diving. PTRs in the US are ranked based on an evidence- and consensus-based policy that reflects the collective judgment of stakeholders in the kidney transplant ecosystem. Because list diving upends the consensus ranking, concerns have been raised about its impact on the national system. In this talk, I will present an empirical analysis of the Organ Procurement and Transplantation Network (OPTN) data to quantify the impact of list diving decisions on short- and long-term survival of PTRs after accounting for the endogeneity of such decisions. I will also illustrate the impact of list diving on skipped candidates. The analysis shows that list diving worsens 1 and 3-year survival for transplants performed by centers with significant within-DSA competition. A significant minority of first-ranked skipped candidates do not receive transplants and either die or are removed from waitlist. Their average wait time is 8 months longer. The study highlights the need for greater oversight over transplant programs’ utilization decisions.

About the Speaker

Diwakar Gupta holds an appointment as the INBA–Stuart Centennial Professor of Information, Risk, and Operations Management at the McCombs School of Business of The University of Texas at Austin. His research spans healthcare operations, supply chain finance and risk management, and inventory and revenue management. He serves as the departmental editor of healthcare operations department at the Naval Research Logistics journal and as senior editor of healthcare operations management department at the POMS journal. He teaches healthcare analytics at UT Austin.

DAO-ISEM-IORA Seminar Series: George Shanthikumar
2026-05-22 10:00 - 11:30
Name of Speaker

George Shanthikumar

Schedule

22 May 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/nhLjyspHTpuCwf5OfrR7rg

Title

Full Space (FS) Relative Valuations, Risk Measures and Valuations of Risky Prospects

Abstract

This paper proposes a unified mathematical infrastructure for the valuation of risky prospects:the Full-Space (FS) Relative Valuation framework. This framework generalizes and synthesizes disparate valuation paradigms in economics, finance, and insurance into a single, coherent structure. We demonstrate that the FS framework is both general and tractable. Its core representation is characterized by necessary and sufficient conditions (i.e., modularity on the lattice of quantile or distribution functions) and we show it can be generalized to represent any valuation functional as an infimum over FS-type kernels. The framework also possesses desirable theoretical properties (e.g., monotonicity, concave order preservation) and admits practical representations, including certainty-equivalent construction and a computationally appealing Fenchel-type supremum form. By integrating these features, FS valuations offer a powerful and versatile foundation for modeling and optimization across disciplines.

About the Speaker

Professor Shanthikumar joined the Krannert faculty in 2009. Prior to coming to Purdue, he was a Chancellor’s Professor of Industrial Engineering and Operations Research at the University of California, Berkeley. His research interests are in integrated interdisciplinary decision making, model uncertainty and learning, production systems modeling and analysis, queueing theory, reliability, scheduling, semiconductor yield management, simulation stochastic processes, and sustainable supply chain management. He has written or co-written more than 250 papers on these topics. He is a co-author (with John A. Buzacott) of the book Stochastic Models of Manufacturing Systems and a co-author (with Moshe Shaked) of the books Stochastic Orders and Their Applications and Stochastic Orders.
He was a co-editor of Flexible Services & Manufacturing Journal and is (or was) a member of the editorial boards of the Asia-Pacific Journal of Operations Research, IEEE Transactions on Automation Sciences and Engineering, IIE Transactions, International Journal of Flexible Management Systems, Journal of Discrete Event Dynamic Systems, Journal of the Production and Operations Management Society, Operations Research, Operations Research Letters, OPSEARCH, Probability in the Engineering and Information Sciences, and Queueing Systems: Theory and Applications.
Professor Shanthikumar has extensively consulted for various companies, including Applied Materials (AMAT), Bellcore, IBM, KLA-Tencor, NTT (Japan), Intel, Intermolecular, ReelSolar, Safeway, and Southern Pacific. Through KLA-Tencor, he has worked on joint development projects for Advanced Micro Devices, IBM, Intel, LSI, Motorola, Texas Instruments, Toshiba, Fujitsu, Taiwan Semiconductor Manufacturing Company, and UMC.

DAO-ISEM-IORA Seminar Series: Jiheng Zhang
2026-06-04 10:00 - 11:30
Name of Speaker

Jiheng Zhang

Schedule

4 Jun 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/wv9WK1alQ6SrTblAvPtglw

Title

AI for OR and OR for AI

Abstract

This talk explores the interplay between Operations Research (OR) and Large Language Models (LLMs) through two complementary research directions.

In the first part, we study the scheduling of LLM inference workloads across large GPU clusters. LLM inference involves two phases: a compute-intensive prefill phase that processes user input, and a memory-bound decode phase that generates output tokens. When these phases share GPU resources, prefill tasks throttle concurrent decodes, creating state-dependent contention that is further complicated by workload heterogeneity across applications. We formulate this as a multiclass many-server queueing network with state-dependent service rates, grounded in empirical iteration-time measurements. We analyze the fluid approximation and solve steady-state linear programs that characterize optimal resource allocation. We design gate-and-route policies that regulate prefill admission and decode routing, and prove their asymptotic optimality in the many-GPU limit. We further extend the framework to incorporate service level indicators such as latency and fairness. Numerical experiments demonstrate that our policies outperform standard serving heuristics.

In the second part, we consider the reverse direction: using LLMs to automate OR analysis. Formulating optimization models from natural language and generating executable solver code could reduce reliance on scarce expert knowledge, but LLMs suffer from probabilistic inconsistency and existing methods face a data efficiency dilemma. We propose OR-R1, a framework that integrates supervised fine-tuning with Test-Time Group Relative Policy Optimization (TGRPO). TGRPO extracts reliable training signals from unlabeled data by generating multiple candidate solutions and treating the solver-verified consensus as the ground truth. We provide theoretical guarantees for gradient convergence and show that this voting-based proxy consistently maximizes true solution accuracy. OR-R1 requires only 10% of the training data used by prior methods, yet achieves state-of-the-art accuracy across eight OR benchmarks.

About the Speaker

Jiheng Zhang is a Professor in the Department of Industrial Engineering and Decision Analytics at HKUST, where he also holds a joint appointment in the Department of Mathematics. His research interests include stochastic modeling and optimization, statistical learning, numerical methods, and algorithms, with applications in operations management, large communication networks, and financial technology. He serves as an associate editor for several top journals, including Operations Research, Stochastic Systems, and Probability in the Engineering and Informational Sciences. Since 2018, he has been the Director of the EPI-One Lab, leading various applied projects with industry partners such as Huawei and Webank. He holds several patents in areas such as large-scale production planning and blockchain consensus mechanism design. He earned his Ph.D. in Operations Research from the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology in 2009. He also holds an M.S. in Mathematics from Ohio State University and a B.S. in Mathematics from Nanjing University.

DAO-ISEM-IORA Seminar Series: Zi Yang Kang
2026-08-06 10:00 - 11:30
ame of Speaker

Zi Yang Kang

Schedule

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

Venue

BIZ1 0204

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/fs48EzhDRsWzBkChYW84dg

Title

Topping Up and Optimal Redistribution (joint with Mitchell Watt)

Abstract

This paper studies how topping up—allowing recipients of in-kind transfers to supplement subsidized consumption in a private market—affects optimal redistribution. Consumers can access a competitive private market, while a social planner offers an alternative nonlinear price schedule. We show that the effect of topping up depends on the correlation between redistributive priority and demand. When the correlation is positive, topping up does not affect the optimal mechanism. When the correlation is negative, topping up weakens screening and reduces redistribution. At the extensive margin, topping up reduces the set of environments in which intervention is optimal. At the intensive margin, topping up weakly reduces both the scope of a free public option and the mass of consumers served, and shifts redistribution away from the consumers with the highest redistributive priority. We characterize the optimal mechanisms and show how topping up changes the comparative statics of optimal redistribution with respect to redistributive priorities.

About the Speaker

I am an Assistant Professor of Economics at the University of Toronto. I work on mechanism design, market design, and industrial organization. My research applies a theoretical lens to policy design and evaluation. I am particularly interested in “partial mechanism design,” where a policymaker can design only part of the market.

DAO-ISEM-IORA Seminar Series: Safak Yucel
2026-08-12 10:00 - 11:30
Name of Speaker

Safak Yucel

Schedule

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

Venue

BIZ2-413C

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/XSWqNnAUScaE9tpYkW5BhQ

Title

Delivery Terms for Voluntary Carbon Offsets

Abstract

In addition to abating their emissions, companies purchase carbon offsets for voluntary decarbonization targets, such as reaching net-zero emissions. A carbon offset represents emissions reduction achieved by a developer through investments in projects, such as forestry and renewable energy. Companies purchase offsets under two delivery terms. The first is forward delivery, where the buyer orders a certain quantity before sellers invest and yield uncertainty realizes. The second is prompt delivery, where sellers invest and uncertainty realizes, before the buyer orders offsets. Motivated by the importance of choosing the right delivery term for buyers and developers, in this paper, we investigate economic and environmental implications of these delivery terms. Our results offer several managerial insights: A buyer should prefer forward delivery if investment cost is low, but prompt delivery otherwise. Interestingly, the delivery term that leads to a lower cost for the buyer may actually require higher abatement. Second, developers should prefer forward delivery for low-cost projects as well. One would expect that the delivery term that enables developers to make higher investment results in higher profit. However, we show that prompt delivery may lead to higher profit despite lower investment. Finally, environmental groups should promote forward delivery only for a project with a low investment cost. We find that the delivery term that leads to a higher abatement by the buyer may not result in a greater reduction in emissions. This then suggests that, contrary to the common practice, focusing only on the buyer’s abatement may be misguided, and environmental groups should instead also take developers’ investment into account in determining which delivery term to promote

About the Speaker

Safak Yucel is an associate professor of Operations Management (with tenure) at the McDonough School of Business, Georgetown University. Prof. Yucel is also the associate director of the Business of Sustainability Initiative. His main research interests are in sustainable operations, with a focus on renewable energy. He is also interested in the economic and environmental implications of new business models. Prof. Yucel’s research has appeared in leading journals, including Management Science and Manufacturing and Service Operations Management. He has worked at the National Renewable Energy Laboratory of the U.S. Department of Energy, prior to receiving his Ph.D. in Operations Management from Duke University’s Fuqua School of Business.
