IORA - Institute of Operations Research and Analytics

DAO-IORA Seminar Series – Jing Wu
2023-03-03 10:00 - 11:30 (Asia/Singapore)
Prof. Jing Wu is an Associate Professor in the Department of Decision Sciences and Managerial Economics at the Chinese University of Hong Kong (CUHK) Business School. He receives his Ph.D. (major in operations management, minor in economics & finance) and MBA from the University of Chicago Booth School of Business and his bachelor’s degree in Electronic Engineering from Tsinghua University. Prof. Wu’s primary research fields are the operations-finance interface, global supply chains, FinTech, and business intelligence. His papers are published in leading journals such as Management Science, M&SOM, and POMS. His articles appear in business magazines such as MIT Sloan Management Review, the Economist, and Forbes. In particular, his quantitative research findings on the supply chain impact of COVID-19 and the Trade War have been reported by over 400 media outlets in over 20 countries worldwide. He is a Senior Editor for Production and Operations Management and serves on the Editorial Board for Journal of Operations Management. He has been a committee/track chair for leading international academic conferences such as INFORMS and POMS meetings. Before academia, he worked as a quantitative strategist at Deutsche Bank New York.

Name of Speaker
Jing Wu

Schedule
Friday 3 March 2023, 10.00am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1 Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZYkc-yurjsuGtSdKCtZD62yxhugAKzE-6Nh

Title
The Golden Revolving Door: Hedging through Hiring Government Officials

Abstract
Using both the onset of the US-China trade war in 2018 and the most recent Russia-Ukraine conflict and associated trade tensions, we show that government-linked firms increase their importing activity by roughly 33% (t=4.01) following the shock, while non-government linked firms trading to the same countries do the opposite, decreasing activity. These increases appear targeted, in that we see no increase for government-linked supplier firms generally to other countries (even countries in the same regions) at the same time, nor of these same firms in these regions at other times of no tension. In terms of mechanism, government supplier-linked firms are nearly twice as likely to receive tariff exemptions as equivalent firms doing trade in the region who are not also government suppliers. More broadly, these effects are increasing in level of government connection. For example, firms that are geographically closer to the agencies to which they supply increase their imports more acutely. Using micro-level data, we find that government supplying firms that recruit more employees with past government work experience also increase their importing activity more – particularly when the past employee worked in a contracting role. Lastly, we find evidence that this results in sizable accrued benefits in terms of firm-level profitability, market share gains, and outsized stock returns.

DAO-IORA Seminar Series – Karthyek Murthy
2023-03-17 10:00 - 11:30
Karthyek Murthy serves as an Assistant Professor in Singapore University of Technology & Design. His research interests lie in data-driven operations research. Prior to joining SUTD, he was a postdoctoral researcher in Columbia University IEOR department. His research has been recognised with 2021 INFORMS Junior Faculty Forum (JFIG) Paper competition (Third place), 2019 WSC Best Paper Award, and IBM PhD fellowship. Karthyek serves as an Associate Editor for the INFORMS journal Stochastic Systems and as a judge for the INFORMS Nicholson student paper competition.

Name of Speaker
Karthyek Murthy

Schedule
Friday 17 March 2023, 10.00am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1 Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0pd-mrqT8sHdHKwZT0EH5wYD4D-WJxsVNx

Title
Locally robust models for optimization under tail-based data imbalance

Abstract
Many problems in operations and risk management require the familiar “estimate, then optimize” workflow involving a model estimation from data in the first step before plugging in the trained model to solve various optimization tasks. In this talk, we first give the ingredients for constructing locally robust optimization formulations in which the first step model estimation has no effect, locally, on the optimal solutions. Then delving specifically into optimization problems affected by tail-based data imbalance, we show that this local sensitivity translates to improved out-of-sample performance freed from the first-order impact of model errors caused by model selection and misspecification biases that are especially difficult to avoid when performing estimation with imbalanced data. The key ingredient in achieving this local robustness is a novel debiasing procedure that adds a non-parametric bias correction term to the objective. The debiased objective retains convexity, and the imputation of the correction term relies only on a non-restrictive large deviations behavior conducive for transferring knowledge from representative data-rich regions to the datascarce tail regions suffering from imbalance. The bias correction gets determined by the extent of model error in the estimation step and the specifics of the stochastic program in the optimization step, thereby serving as a scalable “smart-correction” step bridging the disparate goals in estimation and optimization. Besides showing the empirical effectiveness of the proposed formulation in real datasets, the numerical experiments bring out the utility of locally robust solutions in tackling model errors and shifts in distribution between training and deployment.

DAO-IORA Seminar Series – Saed Alizamir
2023-03-23 10:00 - 11:30
Saed Alizamir is an Associate Professor of Operations Management at Yale School of Management. He joined Yale in 2013 after receiving a PhD in Decision Sciences from Duke University’s Fuqua School of Business. Professor Alizamir’s research interests lies in the area of social responsibility and public sector operations. In his research, he focuses on problems in public policy that involve private-public interactions and dynamic decision-making. The goal of his research is to provide normative recommendations that inform better policy decisions, especially in areas where not enough data exists to run full-fledged empirical studies. He has worked on government subsidy instruments in renewable energy industry and electric vehicle markets, agricultural supply chains, demand management in residential electricity sector, and optimal control of diagnostic systems such as nurse triage. In 2021, Professor Alizamir was named as one of the World’s Best 40 Under 40 Business School Professors by Poets & Quants. He serves as an Associate Editor for the Operations Research journal, and co-chaired the M&SOM cluster for the INFORMS conference in 2019. At Yale, he teaches MBA level courses in core Operations, Managing Sustainable Operations, and Quantitative Decision Models.

Name of Speaker
Saed Alizamir

Schedule
Thursday 23 March 2023, 10am – 11.30am

Venue
I4-01-03 Seminar Room

Registration Link
https://nus-sg.zoom.us/meeting/register/tZMkfuuurTwjGdfG2nYqcDCFI8vVGYD-qMR0

Title
Search Delegation Policies for Compliance Enforcement

Abstract

Enforcement of regulatory compliance over time often involves intermittent search in the form of inspection in order to reveal the compliance state of the regulated entity. To enable cost-effective enforcement of environmental compliance standards, regulatory agencies encourage production firms to voluntarily discover and correct compliance violations. Although such self-regulation activities often bring desired benefits, they create nontrivial challenges. To study this tradeoff, we develop a model that captures the interactions between a regulator and a firm that unfold over time. Because constant monitoring is prohibitive, the regulator and the firm perform costly inspections to discover the compliance state of production. If the regulator detects noncompliance, the firm is required to pay penalty and restore compliance. To avoid penalty, the firm performs self-inspections to preemptively detect noncompliance and restore compliance without reporting the action to the regulator. We show that inefficiency caused by the firm’s private action is amplified if the regulator adopts a policy of requiring permanent restoration. Under such a policy, the firm’s self-inspections may leave the regulator and the environment worse off. By contrast, self-inspections always bring a net benefit to the regulator if repeated temporary restorations are allowed. We also find that, due to self-inspections, a paradoxical situation arises where the regulator prefers mandating permanent restoration despite having a small chance of enforcing it.

DAO-IORA Seminar Series – Philip Zhang
2023-03-31 10:00 - 11:30
Renyu (Philip) Zhang has been an Associate Professor (with tenure) at the Department of Decision Sciences and Managerial Economics, The Chinese University of Hong Kong Business School since September 2022. He is also an economist and Tech Lead at Kwai, one of the world’s largest online video-sharing and live-streaming platforms. Philip’s recent research focuses on developing data science methodologies (e.g., data-driven optimization, causal inference, and machine learning) to evaluate and optimize the operations strategies in the contexts of online platforms and marketplaces, sharing economy, and social networks, especially their recommendation, advertising, pricing, and matching policies. His research works have appeared in top business journals such as Management Science, Operations Research, and Manufacturing & Service Operations Management, and have been recognized by various research awards of the INFORMS and POMS communities. His research projects have been funded by various funding agencies including HK RGC, NSFC, SMEC, and STCSM. Philip serves as a Senior Editor for Production and Operations Management, and an Associate Editor for Naval Research Logistics. He has also developed data science and economics frameworks to evaluate and optimize the user growth strategy and the platform ecosystem of Kwai. Prior to joining CUHK, Philip was an Assistant Professor of Operations Management at New York University Shanghai between 2016 and 2022. Please visit Philip’s personal website for more about him: https://rphilipzhang.github.io/rphilipzhang/

Name of Speaker
Zhang Renyu, Philip

Schedule
Friday 31 March 2023, 10.00am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1 Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZEtduirqT4iG9L-B7cNDO-sjXX8YFw9Csoz

Title
Deep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence

Abstract
Large-scale online platforms launch hundreds of randomized experiments (a.k.a. A/B tests) every day to iterate their operations and marketing strategies, while the combinations of these treatments are typically not exhaustively tested. It triggers an important question of both academic and practical interests: Without observing the outcomes of all treatment combinations, how to estimate the causal effect of any treatment combination and identify the optimal treatment combination? We develop a novel framework combining deep learning and doubly robust estimation to estimate the causal effect of any treatment combination for each user on the platform when observing only a small subset of treatment combinations. Our proposed framework (called debiased deep learning, DeDL) exploits Neyman orthogonality and combines interpretable and flexible structural layers in deep learning. We prove theoretically that this framework yields efficient, consistent, and asymptotically normal estimators under mild assumptions, thus allowing for identifying the best treatment combination when only observing a few combinations. To empirically validate our method, we then collaborate with a large-scale video-sharing platform and implement our framework for three experiments involving three treatments where each combination of treatments is tested. When only observing a subset of treatment combinations, our DeDL approach significantly outperforms other benchmarks to accurately estimate and infer the average treatment effect (ATE) of any treatment combination, and to identify the optimal treatment combination.

DAO-IORA Seminar Series – Javad Nasiry
2023-04-28 10:00 - 11:30
Javad Nasiry is a professor of Operations Management at McGill’s Desautels Faculty of Management where he joined in 2019. He is the director of Sustainable Growth Initiative (SGI) which is a cross-faculty initiative to mobilize the talent and expertise within McGill University to help businesses move towards more socially and environmentally sustainable business models.
His main research interests are in behavioural operations, supply chain management, sustainability, retail operations, and operations-marketing interface. His research in sustainable operations focuses on the environmental consequences of new business models in apparel, renewable energy, and agriculture industries.
Prior to joining McGill, he was an associate professor of operations management in the School of Business and Management at the Hong Kong University of Science and Technology (HKUST) where he joined in 2010.

Name of Speaker
Javad Nasiry

Schedule
Friday 28 April 2023, 10.00am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1 Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZEsfuihqDgqHNL63FOwX-SW-XU0E_mfDTdE

Title
Sustainability in the fast fashion industry

Abstract
We establish a much-needed link between the fast fashion business model and its environmental consequences. A fast fashion system allows firms to react quickly to changing consumer demand by replenishing inventory (via quick response) and introducing more fashion styles. We study the environmental impact of the fast fashion business model by analyzing its implications for product quality, variety, and inventory decisions. We find that a key driver of low product quality in the fast fashion industry is the firm’s incentive to offer variety to hedge against uncertain fashion trends. When variety is endogenous, quality decreases as consumers become more sensitive to fashion or as the cost of introducing new styles decreases.
We assess the effectiveness of three environmental initiatives (waste disposal regulations, consumer education, and production tax schemes) in countering the environmental impact of fast fashion.

DAO-IORA Seminar Series - Chun So Yeon
2023-05-12 10:00 - 11:30
So Yeon Chun is an Associate Professor of Technology & Operations Management at INSEAD. So Yeon’s data-driven research focuses on the interface between operations and marketing in consumer loyalty reward programs (consumer choices, point currencies and monetization, customer lifetime values, and consumer behavior experiments), revenue management (pricing and forecasting), and risk management (risk measures, portfolio optimization) with applications in industries such as retail, transportation, finance, and hospitality. So Yeon holds a PhD in Operations Research and an MS in Applied Statistics from the School of Industrial and Systems Engineering at the Georgia Institute of Technology.

Name of Speaker
Chun So Yeon

Schedule
Friday 12 May 2023, 10.00am – 11.30am

Venue
BIZ1-0202

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0vdO-sqDwvHtaug9Pj_1n9e035FOi3xDX-

Title
Point vs. Money: Monetization of Loyalty Currency and Consumer Payment Choice Behavior

Abstract
In recent years, companies have made various changes to the design and operational management of their loyalty programs to further monetize their virtual currency, or points, by making them more like money. However, it remains unclear whether consumers treat points as they treat money when deciding whether to use them to pay for a purchase. In this talk, we aim to address this question and explore how and why consumers spend loyalty points differently than money. In the first part of the talk, we investigate the important factors that influence consumers’ decisions to pay with either points or money, and we examine how a co-branded credit card partnership affects consumers’ attitudes toward point currency. We develop a model of consumers’ payment decisions and estimate it using proprietary transaction data from a major US airline company through a hierarchical Bayesian framework. In the second part of the talk, we present a series of behavioral experiments that focus on the design and operation of the exchange rate between points and money. We examine the behavioral bias that consumers exhibit toward point currency and explore whether the effects of the exchange rate design are unique to loyalty currencies or more generally apply to other foreign currencies.

DAO-IORA Seminar Series - Bar Light
2023-08-04 10:00 - 11:30
Bar Light is an assistant professor in the Department of Statistics and Operations Research in Tel Aviv University’s School of Mathematics. Bar was previously a Postdoctoral Researcher at Microsoft Research focusing on market design and designing ad-auctions. Bar obtained a PhD in Operations Research from Stanford university. His research mainly focuses on market design for platforms, the analysis of large markets and systems, and dynamic optimization.

Name of Speaker
Bar Light

Schedule
4 August 2023, 10.00am – 11.30am

Venue
BIZ1-0202

Link to Register
https://nus-sg.zoom.us/meeting/register/tZYkcOCgqTwrGNaBBd0TAVKzv8j4hP53YiPw

Title
Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence

Abstract
We study the aggregate welfare and individual regret guarantees of dynamic pacing algorithms in the context of repeated auctions with budgets. Such algorithms are commonly used as bidding agents in Internet advertising platforms. We show that when agents simultaneously apply a natural form of gradient-based pacing, the liquid welfare obtained over the course of the learning dynamics is at least half the optimal expected liquid welfare obtainable by any allocation rule. Crucially, this result holds without requiring convergence of the dynamics, allowing us to circumvent known complexity-theoretic obstacles of finding equilibria. This result is also robust to the correlation structure between agent valuations and holds for any core auction, a broad class of auctions that includes first-price, second-price, and generalized second-price auctions. For individual guarantees, we further show such pacing algorithms enjoy dynamic regret bounds for individual value maximization, with respect to the sequence of budget-pacing bids, for any auction satisfying a monotone bang-for-buck property.

DAO-IORA Seminar Series - Michelle Wu
2023-08-10 10:00 - 11:30
Michelle Xiao Wu is a Co-Director in the Data Science Lab at MIT Institute for Data, Systems, and Society (IDSS). Before joining IDSS, she was an Assistant Professor at Carson College of Business, Washington State University. She received her Ph.D. (major in operations management, minor in economics) and MBA from the University of Chicago Booth School of Business and an M.Sc degree in Physics from Northwestern University.
Her research interests focus on operations management in the digital economy, including pricing for e-commerce platforms, digital content release, and the sharing economy. Her other research interests include machine learning, the operations-finance interface, and supply chain management. Her papers are published in leading journals such as Management Science, M&SOM, JMIS, and IJPR.
In her consulting experience with various companies, she provides implementable methods and strategies to optimize operational decisions, in manufacturing, e-commerce, and brick & mortar retail.

Name of Speaker
Michelle Wu

Date
10 August 2023, 10am – 11.30am

Venue
BIZ1-0206

Registration Link
https://nus-sg.zoom.us/meeting/register/tZAlcuqgpz4iEtYiqIsqyAVij6uSyYM-4GWH

Title
Empowering Businesses with Data, Analytics, and Automation

Abstract
We present our work with a global online fashion retailer, Zalando, as an example of how a global retailer can utilize massive amount of data to optimize price discount decisions over a large number of products in multiple countries on a weekly basis.

DAO-IORA Seminar Series - Yan Zhenzhen
2023-08-25 10:00 - 11:30
Dr. Zhenzhen Yan is an assistant professor at School of Physical and Mathematical Sciences, Nanyang Technological University. She joined SPMS since 2018. Before that, she received her PhD in Management Science from the National University of Singapore, and her BSc and MSc in Management Science, Operations Research from the National University of Defense and Technology in China. Her research interests mainly focus on the interplay between optimization and data analytics. Her first line of research is to solve various operations management problems and engineering problems from the distributionally robust perspective, including supply chain design and operations, and healthcare operations. The second line is to develop data-driven optimization approaches with applications to e-commerce operations and resource allocation. Her work has been published in leading operations management journals including Management Science, Operations Research, MSOM and POMS, and top AI conferences including Neurips, UAI and AAAI. Her work has received media coverage in various outlets including the Straits Times and ScienceDaily etc. She currently serves as an Associate Editor of Decision Sciences.

Name of Speaker
Yan Zhenzhen

Schedule
25 August 2023, 10am

Venue
BIZ1 03-07

Registration Link (Zoom)
https://nus-sg.zoom.us/meeting/register/tZUvcOCqrD4uG9eJgleNTFTjjnTADqikckwd

Title
Sample-Based Online Generalized Assignment Problem with Unknown Poisson Arrivals

Abstract
We study an edge-weighted online stochastic Generalized Assignment Problem with unknown Poisson arrivals. We provide a sample-based multi-phase algorithm by utilizing both pre-existing offline data (named historical data) and sequentially revealed online data. The developed algorithm employs the concept of exploration-exploitation to dynamically learn the arrival rate and optimize the allocation decision. We establish its parametric performance guarantee measured by a competitive ratio. We further provide a guideline on fine tuning the parameters under different sizes of historical data based on the established parametric form. By analyzing a special case which is a classical online weighted matching problem, we also provide a novel insight on how the historical data’s quantity and quality (measured by the number of underrepresented agents in the data) affect the trade-off between exploration and exploitation in online algorithms and their performance. Finally, we demonstrate the effectiveness of our algorithms numerically.

DAO-IORA Seminar Series - Yaron Shaposhnik
2023-09-08 10:00 - 11:30
Yaron Shaposhnik is an Assistant Professor of Information Systems and Operations Management at the Simon School of Business in the University of Rochester. Most broadly, he is interested in the optimization and analysis of mathematical models that capture real world problems, and in developing decision support tools that leverage analytics to improve operations.

Name of Speaker
Yaron Shaposhnik

Schedule
8 September 2023, 10am – 11.30am

Registration Link
https://nus-sg.zoom.us/meeting/register/tZcsdu6vrzwsGdC8u6_NyrnGumir0pnyZY21

Title of Talk
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation

Abstract
We develop a method for understanding specific predictions made by (global) predictive models by constructing (local) models tailored to each specific observation (these are also called “explanations” in the literature). Unlike existing work that “explains” specific observations by approximating global models in the vicinity of these observations, we fit models that are globally-consistent with predictions made by the global model on past data. We focus on rule-based models (also known as association rules or conjunctions of predicates), which are interpretable and widely used in practice. We design multiple algorithms to extract such rules from discrete and continuous datasets, and study their theoretical properties. Finally, we apply these algorithms to multiple credit-risk models trained on the Explainable Machine Learning Challenge data from FICO and demonstrate that our approach effectively produces sparse summary-explanations of these models in seconds. Our approach is model-agnostic (that is, can be used to explain any predictive model), and solves a minimum set cover problem to construct its summaries.

DAO-IORA Seminar Series - Teo Chung Piaw & Wang Quanmeng
2023-09-15 10:00 - 11:30
Name of speakers
Teo Chung Piaw & Wang Quanmeng

Schedule
15 September 2023, 10am – 11.30am

Venue
BIZ1 – 0205

Registration
https://nus-sg.zoom.us/meeting/register/tZIofumopzgoGtUtQiqUDX1hBKnar2DU7wzJ

Title of talk
Last mile innovations: The case of the Locker Alliance Network

Abstract
In this talk, we’ll explore a collection of academic research we’ve conducted, funded by IMDA, focusing on Singapore’s “Locker Alliance Network” (LAN). This government-led initiative aims to establish a network of public lockers in residential areas and community hubs to improve the efficiency of last-mile parcel deliveries. Our research tackles key operational questions, such as the ideal density, coverage, and impact of the LAN.
To address these questions, we’ve employed locker usage data from a commercial courier service to calibrate a model that gauges how walking distance and other variables influence customer preferences for locker pickups versus traditional home or office deliveries. Additionally, we’ve created a facility location model that leverages existing parcel delivery data to optimize the LAN’s design. Contrary to traditional thinking, our results indicate that peak parcel volume areas are not necessarily the best locations for lockers. Instead, our model recommends an optimal coverage radius of 250 meters for the LAN in Singapore. One unique challenge we faced was the absence of home-office pair information for residents, leading us to develop a new type of facility location model where the choice set is unknown. Our findings suggest that under realistic assumptions—namely, that home delivery will always be more popular than locker pickup—the lack of this specific information has minimal impact on the performance of our locker facility location model.
In related research, we’ve also examined the LAN’s effects on routing efficiency and conducted empirical tests to understand how exposure and popularity influence adoption choices. We also discuss how the challenges in this public facility (that it is interoperable and used by many different LSPs) are partially addressed due to a “nested” pattern in the optimal solution to the facility location model.

About the speakers
Chung Piaw Teo is Provost’s Chair Professor in NUS Business School and Executive Director of the Institute of Operations Research and Analytics (IORA) in the National University of Singapore, and concurrently a co-director in the SIA-NUS Digital Aviation Corp Lab. With a focus on optimization and supply chain management, Professor Teo is trying to bridge the gap between theoretical research and practical applications of OR and Analytics in business and engineering.
He was a fellow in the Singapore-MIT Alliance Program, an Eschbach Scholar in Northwestern University (US), Professor in Sungkyunkwan Graduate School of Business (Korea), and a Distinguished Visiting Professor in YuanZe University (Taiwan). He is department editor for MS (Optimization), and a former area editor for OR (Operations and Supply Chains). He was elected Fellow of INFORMS and Chang Jiang Scholar (China) in 2019. He has also served on several international committees such as the Chair of the Nicholson Paper Competition (INFORMS, US), member of the LANCHESTER and IMPACT Prize Committee (INFORMS, US), Fudan Prize Committee on Outstanding Contribution to Management (China), and recently chaired the EIC search committee for Operations Research, an INFORMS journal.
Quanmeng Wang is a research fellow at Institute of Operations Research and Analytics, where he also earned his PhD. His research mainly focus on model development for operation problems in logistics. He participated in several research projects collaborated with industry partner of IORA, including a leading express company of China and a government public service sector of Singapore.

DAO-IORA Seminar Series - Alex Yang
2023-09-22 10:00 - 11:30
Name of speaker
S. Alex Yang

Schedule
22 September 2023, 10am – 11.30am

Venue
I4-01-03 Seminar Room

Registration
https://nus-sg.zoom.us/meeting/register/tZ0sdOCuqzkoGNS7Zb8Byg3Kg8GrkOoxwvH8

Title of talk
Crowd-judging on Two-sided Platforms: An Analysis of In-group Bias

Abstract
Disputes over transactions on two-sided platforms are common and usually arbitrated through platforms’ customer service departments or third-party service providers. This paper studies crowd-judging, a novel crowd-sourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. Using a rich dataset from the dispute resolution center at Taobao, a leading Chinese e-commerce platform, we aim to understand this innovation and propose and analyze potential operational improvements, with a focus on in-group bias (buyer jurors favor the buyer, likewise for sellers). Platform users, especially sellers, share the perception that in-group bias is prevalent and systematically sways case outcomes as the majority of users on such platforms are buyers, undermining the legitimacy of crowd-judging. Our empirical findings suggest that such concern is not completely unfounded: on average, a seller juror is approximately 10% likelier (than a buyer juror) to vote for a seller. Such bias is aggravated among cases that are decided by a thin margin, and when jurors perceive that their in-group’s interests are threatened. However, the bias diminishes as jurors gain experience: a user’s bias reduces by nearly 95% as their experience grows from zero to the sample-median level. Incorporating these findings and juror participation dynamics in a simulation study, the paper delivers three managerial insights. First, under the existing voting policy, in-group bias influences the outcomes of no more than 2% of cases. Second, simply increasing crowd size, either through a larger case panel or aggressively recruiting new jurors, may not be efficient in reducing the adverse effect of in-group bias. Finally, policies that allocate cases dynamically could simultaneously mitigate the impact of in-group bias and nurture a more sustainable juror pool.
Link to paper: https://pubsonline.informs.org/doi/10.1287/mnsc.2023.4818

About the speaker
S. Alex Yang is an Associate Professor of Management Science and Operations at London Business School. Alex holds a PhD and an MBA from the University of Chicago Booth School of Business, an MS from Northwestern University, and a BS from Tsinghua University. Alex’s primary research focus is on the interface of operations management and finance, especially in trade credit, supply chain finance, and FinTech. His recent research focuses on platform governance and operations and value chain management and innovation. Alex’s research has appeared in academic journals in operations and finance, such as Management Science, M&SOM, and Journal of Financial Economics, and has received several best paper awards. He is the associate editor of several academic journals. An award-winning teacher, Alex has taught on the MBA, EMBA, and executive education programmes in universities and business schools around the world. Beyond research and teaching, Alex has working and consulting experience in banks, Fintech and technology companies, hedge funds, airlines, and international organizations.
https://www.london.edu/faculty-and-research/faculty-profiles/y/yang-s
https://salexyang.com

DAO-IORA Seminar Series - Mabel Chou, Sun Qinghe, Li Wei
2023-10-06 10:00 - 11:30
Name of speakers
Mabel C. Chou, Sun Qinghe, Li Wei

Schedule
6 October 2023, 10am – 11.30am

Venue
Hon Sui Sen Memorial Library, Seminar Room 4-7

Zoom link
https://nus-sg.zoom.us/meeting/register/tZAlcu2pqzkvHNA_ldzyBArQnujX1H_xk8Tr

Title of talk
Data driven bunker procurement planning: working with the maritime industry

Abstract
In this presentation, we will recount our journey collaborating with the maritime industry, discussing the challenges we encountered and elucidating how we transformed these challenges into gratifying experiences and impactful contributions. We will use our work on bunker procurement decisions with a global container shipping company as an example to illustrate the impact we made and the lessons we learned.
Bunker refueling decisions in international shipping are crucial operational choices. Each ship acts like a movable storage unit navigating through diverse markets, procuring bunker fuels from different ports to sustain its voyage. This involves grappling with challenges posed by varying bunker fuel prices over time and locations. To tackle this challenge, we propose data-driven structure-prescriptive (SP) approaches that combine the strengths of modern machine learning with the insights from traditional OR modeling and optimization. Instead of predicting future marine fuel prices, our approach directly learns the optimal refueling policy from data and adapts refueling decisions to the current market conditions, including fuel prices, crude oil price, NYSE index, etc.
Our focus lies in leveraging the well-established understanding that the optimal refueling decision adheres to a state-dependent base-stock refueling policy. This decision depends on factors such as the port of call, fuel tank capacity, market conditions, and is finite-valued, depending on the vessel’s schedule and voyage. We provide a practical framework to incorporate these structural properties into data-driven decision-making for bunker refueling operations. The proposed SP approaches successfully recovered the “true” optimal refueling policy in synthetic simulations. Moreover, our experiments unveiled that incorporating more structural properties into the learning process significantly improved the out-of-sample (OOS) performance. In the case study, we compared our proposed SP approach with the firm’s existing operation, resulting in a noteworthy reduction of fuel expenses, which amounts to approximately 2.52 million USD per year in savings for a fleet of six ships.
In addition, to facilitate our collaboration with industry, we propose an eXplainable multi-stage bunker procurement planning (X-BPP) framework for the maritime industry. In this presentation, we will showcase this framework, discuss its performance, and share the lessons we learn in implementing the system.

About the speakers
Mabel C. Chou is an associate professor in the Analytics and Operations department at National University of Singapore (NUS). She received the B.Sc. degree in mathematics from National Taiwan University, the M.Sc. degree in mathematics and Ph.D. degree in industrial engineering and management sciences from Northwestern University. Her research focuses on production scheduling and supply chain analysis. Her current research interest is in the application of optimization tools and business analytics for engineering, service, and supply chain management problems. She is an associate editor for Operations Research, a senior editor for Production and Operations Management and an associate editor for Pacific Journal of Optimization. She has also consulted for companies such as GSK, Caterpillar, P&G, SIA Engineering Company, National University Hospital, Tan Tock Seng Hospital, Lenovo, Supreme Components International, etc.
Sun Qinghe is an Assistant Professor at the Department of Logistics and Maritime Studies (LMS), PolyU Business School. Her research combines data with optimization to provide insights into risk management within supply chain systems, particularly within the maritime logistics sector. Qinghe received her Ph.D. in Operations Research from the National University of Singapore (NUS) in 2022, jointly advised by Mabel Chou and Qiang Meng, and her B.Sc. in Maritime Studies from Nanyang Technological University (NTU), Singapore.
Li Wei is a Research Fellow at the National University of Singapore’s Institute of Operations Research and Analytics, jointly advised by Mabel C. Chou and Chen Ying. He has a broad interest in model development for Financial Forecasting-related problems and his research is often motivated by industry initiatives. He obtained his Ph.D. in Computational Finance from the Norwegian University of Science and Technology before joining NUS.

DAO-IORA Seminar Series - Zhou Zhengyuan
2023-11-20 10:00 - 11:30
Name of Speaker
Zhengyuan Zhou

Schedule
20 November 2023, 10am – 11.30am

Venue
BIZ1 – 0302

Link to Register
https://nus-sg.zoom.us/meeting/register/tZIudu6qrTorE90DBkeYzCo1WC_rQEUdCldn

Title
Optimal No-Regret Learning in Repeated First-Price Auctions

Abstract
First-price auctions have very recently swept the online advertising industry, replacing second-price auctions as the predominant auction mechanism on many platforms for display ads bidding. This shift has brought forth important challenges for a bidder: how should one bid in a first-price auction, where unlike in second-price auctions, it is no longer optimal to bid one’s private value truthfully and hard to know the others’ bidding behaviors? In this paper, we take an online learning angle and address the fundamental problem of learning to bid in repeated first-price auctions. We discuss our recent work in leveraging the special structures of the first-price auctions to design minimax optimal no-regret bidding algorithms.

About the Speaker
Zhengyuan Zhou is currently an assistant professor in New York University Stern School of Business, Department of Technology, Operations and Statistics. Before joining NYU Stern, Professor Zhou spent the year 2019-2020 as a Goldstine research fellow at IBM research. He received his BA in Mathematics and BS in Electrical Engineering and Computer Sciences, both from UC Berkeley, and subsequently a PhD in Electrical Engineering from Stanford University in 2019. His research interests lie at the intersection of machine learning, stochastic optimization and game theory and focus on leveraging tools from those fields to develop methodological frameworks to solve data-driven decision-making problems.

DAO-IORA Seminar Series- Karen Zheng
2023-11-28 10:00 - 11:30
Name of Speaker
Yanchong (Karen) Zheng

Schedule
28 November 2023, 10am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1, Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0vf-GpqzwvHN1xiFo9IOFYhdZZS-yp1RcZ

Title
Improving Farmers’ Welfare via Digital Agricultural Platforms

Abstract
In order to improve the welfare of smallholder farmers, multiple countries (e.g., Ethiopia and India) have launched digital agricultural platforms to transform traditional markets. However, there is still mixed evidence regarding the impact of these platforms and more generally how they can be leveraged to enable more efficient agricultural supply chains and markets. In this talk, we describe a body of work that provides the first rigorous impact analysis of such a platform and demonstrates how innovative price discovery mechanisms could be enabled by digital agri-platforms in resource-constrained environments. The work is focused on the Unified Market Platform (UMP) that connects all the agricultural wholesale markets in the state of Karnataka, India. Our impact assessment shows that the launch of the UMP has significantly increased the modal prices of certain commodities (2.6%-6.5%), while prices for other commodities have not changed. The analysis highlights operational and market factors that contribute to the variable impact of UMP on prices. Motivated by these insights, we collaborate closely with the Karnataka government to design, implement, and assess the impact of a new two-stage auction on the UMP. To ensure implementability and protect farmers’ revenue, the design process is guided by practical operational considerations as well as semi-structured interviews with a majority of the traders in the field. A new behavioral auction model informed by the field insights is developed to determine when the proposed two-stage auction can generate a higher revenue for farmers than the traditional single-stage, first-price, sealed-bid auction. The new auction mechanism was implemented on the UMP for a major market of lentils in February 2019. By March 2020, commodities worth more than $19 million (USD) had been traded under the new auction. A difference-in-differences analysis demonstrates that the implementation has yielded a significant 3.6% price increase (corresponding to a 55%-94% profit gain), benefiting over 20,000 farmers who traded in the treatment market.
This talk is based on joint work with Retsef Levi (MIT), Somya Singhvi (USC), Manoj Rajan (ReMS) and his team in Karnataka, India.
Papers: The talk will cover the following two papers with a focus on the second one:
The impact of unifying agricultural wholesale markets on prices and farmers’ profitability, with Levi, Rajan, Singhvi. PNAS, February 4, 2020, 117(5) 2366-2371. https://doi.org/10.1073/pnas.1906854117
Improving Farmers’ Income on Online Agri-platforms: Evidence from the Field, with Levi, Rajan, Singhvi. https://ssrn.com/abstract=3486623

About the Speaker
Yanchong (Karen) Zheng is the George M. Bunker Professor and an Associate Professor of Operations Management at the MIT Sloan School of Management. Her recent research focuses on two general topics: (I) the design of incentives, technologies, and behavioral interventions to enhance efficiency, welfare, and sustainability in food and agriculture systems, with a focus on smallholder supply chains; and (II) the role of information transparency in driving environmentally and socially responsible behaviors. In her research, Zheng employs a behavior-centric, data-driven, field-based approach, and she collaborates with both public and private partners on the ground to create positive impacts to society.

DAO-IORA Seminar Series - Chen Xi
2024-01-02 10:00 - 11:30
Name of Speaker
Chen Xi

Schedule
2 January 2024, 10am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1, Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0tcuygqTstGtXSwYItVg9uaq46sPHW8Akl

Title
Digital Privacy in Personalized Pricing and New Directions in DeFI

Abstract
Abstract: This talk has two parts. The first part is on digital privacy in personalized pricing. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this talk, we consider a dynamic pricing problem with an unknown demand function of posted prices and personalized information. By leveraging the fundamental framework of differential privacy, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customers’ information and purchasing decisions. This is joint work with Prof. Yining Wang and Prof. David Simchi-Levi.
The second part is on my very recent research in decentralized finance. I will first discuss my work on delta hedging liquidity positions on the automated market maker (Uniswap V3) and then highlights some open problems in decentralized finance.

About the Speaker
Xi Chen is the Andre Meyer Full professor at Stern School of Business, New York University, who is also an affiliated professor at Computer Science and Center for Data Science. Before that, he was a Postdoc in the group of Prof. Michael Jordan at UC Berkeley and obtained his Ph.D. from the Machine Learning Department at Carnegie Mellon University.
He studies high-dimensional machine learning, online learning, large-scale stochastic optimization, and applications to operations management and FinTech. Recently, he started a new research line on blockchain technology and decentralized finance. He is a recipient of COPSS Leadership Academy, Elected Member of International Statistical Insititute (ISI), The World’s Best 40 under 40 MBA Professor by Poets & Quants, and Forbes 30 under 30 in Science.

DAO-IORA Seminar Series - Zhao Jinglong
2024-01-12 10:00 - 11:30
Name of Speaker
Zhao Jinglong

Schedule
12 January 2024, 10am – 11.30am

Venue
BIZ1-0203

Link to Register

https://nus-sg.zoom.us/meeting/register/tZEpcOGopz4jE91kG6vFGQ78zjTCRdz9iGFZ

Title
Adaptive Neyman Allocation

Abstract
In experimental design, Neyman allocation refers to the practice of allocating subjects into treated and control groups, potentially in unequal numbers proportional to their respective standard deviations, with the objective of minimizing the variance of the treatment effect estimator. This widely recognized approach increases statistical power in scenarios where the treated and control groups have different standard deviations, as is often the case in social experiments, clinical trials, marketing research, and online A/B testing. However, Neyman allocation cannot be implemented unless the standard deviations are known in advance. Fortunately, the multi-stage nature of the aforementioned applications allows the use of earlier stage observations to estimate the standard deviations, which further guide allocation decisions in later stages. In this paper, we introduce a competitive analysis framework to study this multi-stage experimental design problem. We propose a simple adaptive Neyman allocation algorithm, which almost matches the information-theoretic limit of conducting experiments. Using online A/B testing data from a social media site, we demonstrate the effectiveness of our adaptive Neyman allocation algorithm, highlighting its practicality especially when applied with only a limited number of stages.

About the Speaker
Jinglong Zhao is an Assistant Professor of Operations and Supply Chain Management at Questrom School of Business at Boston University. He works at the interface between optimization and econometrics. His research leverages discrete optimization techniques to design field experiments with applications in online platforms. Jinglong completed his PhD in Social and Engineering Systems and Statistics at Massachusetts Institute of Technology.

DAO-IORA Seminar Series - Qin Hanzhang
2024-01-19 10:00 - 11:30
Name of Speaker
Qin Hanzhang

Schedule
19 January 2024, 10am – 11.30am

Venue
I4-01-03 Seminar Room

Link to Register
https://nus-sg.zoom.us/meeting/register/tZEpce-orz8rEtZcddjRXnFf3EACPJSUMiAt

Title
Lower Sample Complexity of Reinforcement Learning for Structured MDPs: Evidence from Inventory Control

Abstract
I will discuss the important open problems of 1) What is the sample complexity (i.e., how may number of data samples is needed) of learning nearly optimal policy for multi-stage stochastic inventory control when the underlying demand distribution is initially unknown; and 2) How to compute such a policy when the required number of data samples are given. For the first half of the talk, without considering fixed ordering cost, I will start answering the questions from the backlog setting via SAIL, a novel SAmple based Inventory Learning algorithm. Then, results for the more practical lost-sales setting will be discussed, including the first sample complexity result for this more challenging setting with only mild assumptions (that ensures quality data), by leveraging both recent developments of variance reduction techniques for reinforcement learning and the structural properties of the dynamic programming formulation for inventory control settings. Numerical simulations show that SAIL significantly outperforms competing methods in terms of inventory cost minimization. Then in the second half, I will discuss several recent developments on sample complexity related to all three types of the MDP formulations (finite-horizon MDPs, infinite-horizon discounted/average-cost MDPs) for inventory control with fixed ordering cost. Somewhat surprisingly, in all three cases, it is found that sample complexity of the most naïve plug-in estimators is strictly lower than the “best possible” bounds derived for general MDPs. The first half will be based on joint work with David Simchi-Levi (MIT) and Ruihao Zhu (Cornell), and the second half will be based on joint work with Boxiao Chen (UIC), Xiaoyu Fan (NYU), Michael Pinedo (NYU) and Zhengyuan Zhou (NYU).

About the Speaker
Hanzhang Qin is an Assistant Professor at the Department of Industrial Systems Engineering and Management at NUS. He is also an affiliated faculty member at the NUS Institute for Operations Research and Analytics. His research was recognized by several awards, including INFORMS TSL Intelligent Transportation Systems Best Paper Award and MIT MathWorks Prize for Outstanding CSE Doctoral Research. Before joining NUS, Hanzhang spent one year as a postdoctoral scientist in the Supply Chain Optimization Technologies Group of Amazon NYC. He earned his PhD in Computational Science and Engineering under supervision of Professor David Simchi-Levi, and his research interests span stochastic control, applied probability and statistical learning, with applications in supply chain analytics and transportation systems. He holds two master’s, one in EECS and one in Transportation both from MIT. Prior to attending MIT, Hanzhang received two bachelor degrees in Industrial Engineering and Mathematics from Tsinghua University.

DAO-IORA Seminar Series - Zhan Ruohan
2024-01-26 10:00 - 11:30
Name of Speaker
Zhan Ruohan

Schedule
26 January 2024, 10am – 11.30am

Venue
I4-01-03

Link to Register
https://nus-sg.zoom.us/meeting/register/tZApdeirrD8uGdK8Z3tiW6Can4XywK0kVD4C

Title
Estimation and Inference under Recommender Interference

Abstract
In digital platforms, recommender systems (RecSys) are essential for aligning content with viewer preferences. This work considers the evaluation of RecSys updates, referred to as “treatments”, by analyzing their “global treatment effect” (GTE) – the expected overall benefit of universally applying these treatments. Our focus is on treatments targeting content creators. We utilize A/B experiments on the creator side and identify that the conventional difference-in-mean estimator is biased for GTE estimation, due to interference among creators competing for visibility. To address this challenge, we introduce a semi-parametric model that combines a parametric choice model, designed to streamline the recommendation process, with a nonparametric component that employs machine learning to account for the heterogeneity among viewers and content. Using this model, we approximate GTE with a doubly robust estimator that satisfies Neyman orthogonality, ensuring consistency and asymptotic normality, and supporting hypothesis testing. We show the robustness and semiparametric efficiency of our estimator even under model mis-specification. We demonstrate the efficacy of our method through simulations and practical applications on a leading short video platform. This is joint work with Shichao Han, Yuchen Hu, and Zhenling Jiang.

About the Speaker
Ruohan Zhan is an assistant professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology. She earned her PhD from Stanford University and her BS from Peking University. Specializing in causal inference, statistics, and machine learning, Ruohan develops new methods to solve problems from online marketplaces, particularly on challenges related to causal effect identification, economic analysis, experimentation and operations. Her research has been published in top-tier journals including Management Science and Proceedings of National Academy of Sciences, as well as leading machine learning conferences including NeurIPS, ICLR, WWW, and KDD.

DAO-IORA Seminar Series - Canberk Ucel
2024-02-02 10:00 - 11:30
Name of Speaker
Canberk Ucel

Schedule
2 February 2024, 10am – 11.30am

Venue
I4-01-03 Seminar Room

Link to Register
https://nus-sg.zoom.us/meeting/register/tZcvcuivqDMjGdS7y3CPbCx_Y701qvieVxVJ

Title
The Value of Advice: Evidence from Thousands of Smallholder Farms in the Philippines

Abstract
Increasing the productivity of Philippine coconut farms that are well below world standards could improve the livelihoods of 3.4 million farming families, most suffering poverty. Government and supporting organizations have long promoted Good Agricultural Practices (GAPs), which decades of public research suggests would double farm productivity with little capital investment, but have failed to achieve widespread adoption and productivity gains. We study the role of access to change agents in facilitating GAP adoption and effective implementation using proprietary data on the productivity, granular farming practices and characteristics of 1,998 smallholders. Our quantitative analysis leverages the pseudo-exogenous variation in agricultural extension office locations to find that being within 8 kilometers of an extension office is associated with greater awareness of central recommendations for 7 of 8 GAPs, increased adoption rates for three most effective GAPs, and 36% higher productivity, on average, on otherwise comparable farms. Our post-hoc analysis further suggests that physical interactions enable change agents to support complex practice adoption and local implementation decisions. Moreover, we find significant heterogeneity in the effects of agent access, and offer facility reallocation and farm visit schedules to improve service coverage and effectiveness using existing agent capacity. Our results suggest that supporting organizations should integrate change agent support or otherwise focus on developing better customized farming advice, integrate farmer feedback, and assist smallholders with the finer details of implementation leveraging emerging information technologies. Evidence-based provision of advisory services, extended beyond the Philippine context, could potentially benefit two billion people worldwide dependent on smallholder farms, and redound benefits to small heterogenous firms that dominate vital functions in other industries. We suggest new avenues for research on data-driven, evidence-based improvements in the provision and design of advisory services, e.g. related to optimal facility allocation and agent visit schedules and the development and communication of effective operational recommendations.

About the Speaker
Canberk Ucel is an Assistant Professor at Bilkent University in Turkey and a Visiting Scholar at INSEAD. He completed his PhD in Operations, Information and Decisions at the Wharton School at the University of Pennsylvania in 2022, and also holds an undergraduate degree in Industrial Engineering from Bilkent University. He is strongly interested in studying operational and organizational issues in understudied industries facing complex social, economic and environmental challenges, and his current research focuses on the agriculture industry, which contributes significantly to environmental conservation and economic development, and employs most of the world’s poorest workers. His research, which has been recognized by several academic awards, leverages proprietary, granular farm operations data he collected through several industry partnerships he built during his doctoral studies, as well as extensive field work, to generate practical recommendations for farmers, companies and policymakers to advance key economic, social and environmental goals. He strives to translate his research into positive change in the industry, including through large-scale randomized controlled field trials, and advocates agriculture as a fruitful context for managerial and operational research with potential to generate significant societal impact. His teaching experience spans various MBA, graduate and undergraduate courses at Wharton and Bilkent related to operations, supply chains, and data analytics and statistics, and includes writing teaching cases, migrating courses online, and designing and teaching new class sessions and courses.

DAO-IORA Seminar Series - Kai Hoberg
2024-02-16 10:00 - 11:30
Name of Speaker
Kai Hoberg

Schedule
16 February 2024, 10am – 11.30am

Venue
I4-01-03 Seminar Room

Link to Register
https://nus-sg.zoom.us/meeting/register/tZYuceusrjouE9P7Pu7BAYbWh7quOmNJ-xcw

Title
Using (inaccurate) data to drive better supply chain decision making

Abstract
More and more data is available to improve supply chain decision making but it needs to be carefully applied considering human limitations. Against this background, I will present two studies that focus on the role of human judgment in supply chain management decision making, first exploring the influence of planners’ adjustments to AI-generated demand forecasts and second examining the effectiveness of human decision-making in inventory management subject to inaccurate data. Study 1 investigates the role of human judgment in demand forecasting. We analyze planners’ adjustments to AI-generated forecasts using a dataset containing 30 million SKU-store-day level forecasts and associated variables. We employ random forest and decision tree approaches to understand the drivers and quality of adjustments. Our findings suggest product characteristics such as price, freshness, and discounts are important factors in adjustments. Large positive adjustments are frequent but often inaccurate, while large negative adjustments are accurate but less common which indicates behavioral biases. In Study 2, we focus on decisions made under the inaccurate inventory data due to shrinkage and loss. We explore the trade-off between cleaning inventory data centrally and allowing decision makers to adjust ordering decisions based on their judgment. In light of human biases in decision making, we present a set of hypotheses on the cleaning-adjustment trade-off and test them in a laboratory setting. The study raises questions about the effectiveness of normative models in determining whether to clean data centrally or rely on decision makers’ judgments, providing insights into optimizing human knowledge utilization in supply chain management.

About the Speaker
Kai Hoberg is Professor of Supply Chain and Operations Strategy at the Kühne Logistics University in Hamburg. His research focuses on supply chain analytics, the role of technology in supply chains, and supply chain strategy. His research findings have been published in academic journals like Journal of Operations Management, Production and Operations Management or Journal of Supply Chain Management. Kai was a visiting researcher at international universities such as the National University of Singapore, Cornell University, the Israel Institute of Technology and the University of Oxford. Prior to his return to academia, he was a project manager in the operations team at Booz & Company. For the past 10 years he has supported the McKinsey Supply Chain practice in teaching and research. His team at KLU is closely working industry partners such as Bayer, Procter & Gamble, Jungheinrich or Infineon.

DAO-IORA Seminar Series - Mohamed Mostagir
2024-02-26 10:00 - 11:30
Name of Speaker
Mohamed Mostagir

Schedule
26 February 2024, 10am – 11.30am

Venue
BIZ1- 0203

Link to Register
https://nus-sg.zoom.us/meeting/register/tZUrc–gqj4uE9WGbEbpRoBVA4DGCZes88wN

Title
A Theory of Ghosting

Abstract
Ghosting is a phenomenon where communication between two parties abruptly stops after one side becomes deliberately unresponsive. This occurs in a variety of settings, but the term entered the mainstream after its usage to describe an important aspect of the dating experience that has been sparsely studied. Both online dating platforms and their users report that ghosting is one of the primary drivers hurting user experience and preventing good outcomes. We develop a model of ghosting and study the efficacy of different policies that platforms have implemented to deal with this problem.

About the Speaker
Mohamed Mostagir is an associate professor of Technology and Operations at the University of Michigan Ross School of Business. He is interested in social learning and belief formation and their applications in a wide variety of settings.

DAO-IORA Seminar Series - Kimon Drakopoulos
2024-03-08 10:00 - 11:30
Name of Speaker
Kimon Drakopoulos

Schedule
8 March 2024, 10am – 11.30am

Venue
BIZ1- 0206

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0scuyqrT4vGtDnHl1AToXlPUExri0y7Suq

Title
Blockchain Mediated Persuasion

Abstract
An ex-post informed Sender wishes to persuade a rational Bayesian Receiver to take a desired action, as in the classic Bayesian Persuasion model studied by Kamenica and Gentzkow (2011). However, we consider settings in which Sender cannot reliably commit to a signal mechanism. An alternative approach is to consider a trustworthy mediator that receives a reported state of the world from Sender and then, based on this report, generates a signal realization for Receiver. Such mediation can be implemented via costly blockchain technology. Surprisingly, we show that this cost differentiated mediation succeeds where free mediation fails. By requiring Sender to pay the mediator for different signal realizations, we can effectively incentivize them to truthfully report, which in turn allows for beneficial persuasion to take place. Joint with Justin Mulvany, Irene Lo

About the Speaker
Kimon Drakopoulos is an Associate Professor in Business Administration at the Data Sciences and Operations department at the USC Marshall School of Business. His research focuses on the operations of complex networked systems, social networks, stochastic modeling, game theory and information economics. Kimon is currently serving in the high level advisory committee to the Greek government on AI regulation and implementation. In 2020 he served as the Chief Data Scientist of the Greek National COVID-19 Scientific taskforce and a Data Science and Operations Advisor to the Greek Prime Minister. He has been awarded the Wagner Prize for Excellence in Applied Analytics and the Pierskalla Award for contributions to Healthcare Analytics.

DAO-IORA Seminar Series - Arnoud den Boer
2024-03-11 10:00 - 11:30
Name of Speaker
Arnoud den Boer

Schedule
11 March 2024, 10am – 11.30am

Venue
BIZ1- 0304

Link to Register

https://nus-sg.zoom.us/meeting/register/tZcrdeytpjgsGtEen4nnptCLRFl85dvfKFvR

Title
Can price algorithms learn to form a cartel?

Abstract
Can price algorithms learn to form a cartel instead of compete against each other, potentially leading to higher consumer prices and lower social welfare? The question is controversial among economists and competition policy regulators. One the one hand, concerns have been expressed that self-learning price algorithms do not only make it easier to form price cartels, but also that this can be achieved within the boundaries of current antitrust legislation – raising the question whether the existing competition law needs to be adjusted to mitigate undesired algorithmic collusion. On the other hand, a number of economists believe that algorithms learning to collude is science fiction, except by using forms of signaling or communication that are already illegal, and argue that there is no need to change antitrust laws. Motivated by this discussion, I will present work that shows that under some market conditions, price algorithms can learn to collude. Based on joint work with Janusz Meylahn, Thomas Loots, Maarten Pieter Schinkel, Ali Aouad.

About the Speaker
Arnoud is Associate Professor at the Korteweg-de Vries Institute for Mathematics of the University of Amsterdam. He studied Mathematics at Utrecht University (2006), Mathematics for Industry at Eindhoven University of Technology (2008) and wrote his PhD thesis `Dynamic Pricing and Learning’ (2013) about data-driven price algorithms at the CWI Centrum for Wiskunde and Computer Science in Amsterdam. Arnoud’s research focuses on the interface of learning and optimization, with applications in dynamic pricing and revenue management. He is the recipient of several awards and grants, including the 2015 Gijs de Leve prize for best PhD Thesis in operations research defended in the Netherlands in the period 2012-2014, personal grants from the Dutch Science Foundation, and the INFORMS Revenue Management & Pricing Section Prize. Arnoud serves as editor for Management Science, M&SOM, and POMS, is board member of the Euro Working Group on Pricing and Revenue Management and board member of the INFORMS Revenue Management and Pricing Section.

DAO-IORA Seminar Series - Zhang Fuqiang
2024-03-13 10:00 - 11:30
Name of Speaker
Zhang Fuqiang

Schedule
13 March 2024, 10am – 11.30am

Venue
BIZ1-0307

Link to Register

https://nus-sg.zoom.us/meeting/register/tZUvdu2grDItHdZl1LhIH4lgx0-lY42S7eks

Title
The Bright Side of Price Volatility in Global Commodity Procurement

Abstract
This paper studies two competing firms’ choices between the contingent-price contract (CPC) and fixed-price contract (FPC) in global commodity procurement. The FPC price is determined when signing the contract, whereas the CPC price is pegged to an underlying index and remains open until the delivery date. Under both contracts, each firm determines its order quantity based on the updated belief about the market demand. The unrealized CPC price correlates with the market demand, allowing a firm to update its belief about the CPC price using demand information, thereby generating a price-learning effect. We find that, contrary to conventional wisdom, a larger price volatility could benefit the firms, and, under differentiated contracts, a firm might benefit from the improvement of forecast accuracy at its rival. We further show that the price-learning effect plays a critical role in the firms’ contract choices. First, significant price volatility forces the firms to pursue the responsiveness of the CPC. Second, the firms may adopt differentiated contracts to enhance their responses to market changes and dampen competition, and a higher competition intensity more likely leads to contract differentiation. Third, the firms in a small market seek responsiveness and contract differentiation rather than cost efficiency. This study reveals the bright side of price volatility and takes a step toward understanding the effect of two-dimensional information updating.

About the Speaker
Fuqiang Zhang is the Dan Broida professor of Supply Chain, Operations, and Technology (SCOT) at Olin Business School, Washington University in St. Louis. He also serves as the SCOT area chair and academic director of MBA programs at Olin. Professor Zhang obtained his Ph.D. in Managerial Science and Applied Economics from the Wharton School, University of Pennsylvania. His research interests focus on supply chain and technology innovation, consumer analytics in operations management, and sustainable operations. In recent years, he has been working on research topics that are driven by empirical data. Professor Zhang’s research has appeared in top-tier academic journals such as Management Science, Manufacturing & Service Operations Management, Operations Research, Marketing Science, and Production and Operations Management.

DAO-IORA Seminar Series - Zhu Weiming
2024-03-19 10:00 - 11:30
Name of Speaker
Zhu Weiming

Schedule
19 March 2024, 10am – 11.30am

Venue
BIZ1-0206

Link to Register
https://nus-sg.zoom.us/meeting/register/tZcvdu-rpzkuHNTHWtUz_oEaagwlXP1_FVHp

Title
Extracting Efficiency from Chaos: Rider Behavior, Performance and Negative Externality in Dockless Bike Sharing Systems

Abstract
In recent years, urban bike-sharing systems developed in two main forms: Dock-based systems that rely on fixed location docks to park at and pick up bicycles from, and Dockless systems which allow users to pick up and drop off bikes anywhere. By eliminating reliance on fixed docks, dockless systems provide significantly improved mobility and convenience to riders, yet they have downsides such as high operational costs and occupying valuable sidewalk space. Collaborating with a major dockless bike-sharing platform, we empirically analyze riders’ economic incentives, explore the efficiency and measure the negative externality of this business model. Specifically, we address the following questions: (i) What is the impact of the number of bicycles in the system on efficiency? (ii) How does the efficiency of dockless and dock-based systems compare? (iii) How can bike-share company minimize the negative externalities generated during service provision through smart relocation?

About the Speaker
Weiming Zhu is an Associate Professor in Innovation and Information Management at HKU Business School. Weiming obtained his bachelor’s degree in Physics from HKUST and Ph.D. in Operations Management from the Robert H. Smith School of Business at University of Maryland. Prior to joining the University of Hong Kong, he was an Associate Professor in IESE’s Department of Production, Technology and Operations Management. Weiming has also been a visiting professor in the Institute for Data, Systems, and Society (IDSS) at Massachusetts Institute of Technology and Kellogg School of Management at Northwestern University. Weiming’s research interests include operations in the platform economy, urban mobility, and supply chain finance. His work has been published at Management Science, M&SOM and Journal of International Economics, and has been recognized in M&SOM, POMS, Service Science and CSAMSE best paper award competitions.

DAO-IORA Seminar Series - Sasa Zorc
2024-03-22 10:00 - 11:30
Name of Speaker
Sasa Zorc

Schedule
22 March 2024, 10am – 11.30am

Venue
BIZ1-0206

Link to Register

https://nus-sg.zoom.us/meeting/register/tZIpce2srDwrHdyCUf-w1Rtdh_3c-YlU9nTz

Title
Search with Recall and Gaussian Learning

Abstract
The classic sequential search problem rewards the decision maker with the highest sampled value, minus a cost per sample. If the sampling distribution is unknown, then a Bayesian decision maker faces a complex balance between exploration and exploitation. We solve the stopping problem of sampling from a Normal distribution with unknown mean and unknown variance and a conjugate prior, a longstanding open problem. The optimal stopping region may be empty (it may be optimal to continue the search regardless of the offer one receives, especially at the early stages), or it may consist of one or two bounded intervals. While a single reservation price cannot describe the optimal rule, we do find a standardized reservation rule: stop if and only if the standardized value of the current offer is sufficiently high relative to the standardized search cost. We also introduce the index function, which provides a computationally practical way to implement the standardized stopping rule for any given prior, sampling history, and sampling horizon.

About the Speaker
Sasa Zorc is an assistant professor at the Darden School of Business, University of Virginia. Sasa obtained his PhD in Management from INSEAD. He studies incentives in multi-agent systems such as health care and matching markets (both centralized and decentralized). Methodologically, his research relies on stochastic dynamic games, search theory, dynamic mechanism design, contract theory and data-driven simulations.

DAO-IORA Seminar Series - Wang Guihua
2024-04-05 10:00 - 11:00
Name of Speaker
Wang Guihua

Schedule
5 April 2024, 10am – 11am

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0ucOmorj8sEtZEDn8rf411mXUZJEvpgeaO

Title
The Spillover Effect of Suspending Non-essential Surgery: Evidence from Kidney Transplantation

Abstract
Organ transplantation is a life-saving procedure for patients with end-stage organ disease; delaying transplantation can have life-or-death consequences. Between March and April 2020, in the midst of the COVID-19 pandemic, multiple state governors issued orders to temporarily suspend non-essential surgery. Although such suspensions were not intended for essential surgery (e.g., deceased-donor kidney transplants), the literature on service operations management suggests that these suspensions may have either a positive or a negative spillover effect on essential surgery, depending on whether hospitals maintain or reduce resources in response to such suspensions. Motivated by this dichotomy, we estimate the potential spillover effect of suspending nonessential surgery on deceased-donor kidney transplantation. Through analyzing a dataset of all U.S. kidney transplant procedures, we observe a steep decline in the transplant volume in almost all states during the early months of the pandemic. However, states that suspended nonessential surgery experienced steeper declines than those that did not. Using a difference-in-differences approach, we estimate a state-level suspension of non-essential surgery led to a 23.6% reduction in transplant volume. This negative spillover effect is particularly pronounced for low-efficiency transplant centers with long cold ischemia times (CITs), but less so for high-efficiency centers. Our mediation analysis shows 38.7% of the spillover effect can be attributed to the change in healthcare employment. Our study suggests that in the event of a future public health crisis, policymakers should consider more nuanced approaches to securing the healthcare workforce critical to supporting essential services, especially for transplant centers with long CITs.

About the Speaker
Guihua is an Assistant Professor of Operations Management and a Sydney Smith Hicks Faculty Fellow at the Naveen Jindal School of Management, The University of Texas at Dallas. He obtained his PhD from the University of Michigan, MSc from the Georgia Institute of Technology, MSc and BEng from the National University of Singapore. Prior to his PhD study, Guihua worked as a supervisor of the industrial engineering department at UPS Asia headquartered in Singapore. Guihua’s research focuses on the intersection of empirical econometrics and machine learning with application to personalized healthcare. More specifically, Guihua has developed new empirical machine learning techniques such as instrumental variable forest and first-difference causal tree for heterogeneous treatment effect analysis using observational healthcare data. Guihua’s research has been published at Management Science, Manufacturing & Service Operations Management, Production and Operations Management, Advances in Applied Probability, Surgery, and Annals of Thoracic Surgery, and received media coverage by Associate Press, Crain’s Detroit, Houston Chronicle, Medical Xpress, National Interest, NPR, PRI, Science Daily, Simply Flying, The Conversation, and Yahoo! News. His research was named a finalist of both the Pierskalla Best Paper Award and the MSOM Service Management SIG Best Paper Award, a runner-up of both the Financial Times Responsible Business Education Award and the INFORMS Service Science Best Cluster Paper Award, the winner of the INFORMS Health Applications Society Student Paper Competition, two-time finalists of the MSOM Student Paper Competition, and a finalist of the INFORMS Service Section Best Student Paper Competition.

DAO-IORA Seminar Series - Michael Freeman
2024-04-12 10:00 - 11:30
Name of Speaker
Michael Freeman

Schedule
12 April 2024, 10am – 11.30am

Venue
Hon Sui Sen Memorial Library HSS 3-1

Link to Register
https://nus-sg.zoom.us/meeting/register/tZIofu-hqTosGdL3UD4H6gyub6ROFYIUrSpf

Title
From full-time to flexi: Investigating the impact of workforce shifts in primary care on service provision and quality

Abstract
Primary care practices worldwide are experiencing a shift away from traditional full-time roles toward increased part-time and temporary staffing models. This transformation necessitates understanding the implications for primary healthcare delivery. Our paper provides an empirical analysis examining the evolving work patterns and schedules of general practitioners (GPs) in the UK over the past decade. Using primary care records, we first examine how GP work volume impacts healthcare service utilization and patient outcomes. We also explore factors that may mediate this relationship. Going further, given the seeming inevitability of more flexible staffing models, our investigation also aims to inform effective workforce strategies that optimise healthcare delivery. Specifically, we examine scheduling approaches that may allow primary care practices to adapt to part-time and locum GPs. Our findings aim to inform healthcare leaders seeking to adapt to emerging flexible staffing models while maintaining high-quality and accessible primary care.

About the Speaker
Michael Freeman is an Assistant Professor of Technology and Operations Management at INSEAD. His research focuses on healthcare operations, leveraging large empirical datasets and rigorous analysis to uncover actionable insights that inform practices to enhance healthcare delivery. This work centers on two key themes – improving patient routing and care continuity and assessing the impacts of organisational changes and predictive technologies on healthcare productivity and quality. He has published several papers in Management Science and M&SOM, and his research has also been recognized with various awards, including winning the 2016 MSOM Student Paper Competition. His research has also earned attention from media outlets (BBC, The Guardian, and others) and healthcare leaders, including the UK’s Royal College of Emergency Medicine and College of General Practitioners. Alongside research, Michael is an award-winning teacher at INSEAD, where he directs and teaches in executive education, the EMBA, MBA, and PhD programmes.

DAO-IORA Seminar Series - Tauhid Zaman
2024-05-31 10:00 - 11:30
Name of Speaker
Tauhid Zaman

Schedule
31 May 2024, 10am – 11.30am

Venue
BIZ1- 0307

Link to Register
https://nus-sg.zoom.us/meeting/register/tZMlduChqDoiGNbvqS_7q_XH-E66BMzMMYIl

Title
Social Media Suspensions and Shadow Banning: Political Bias or Genuine Disinformation Control?

Abstract
Social media platforms like Twitter and TikTok wield significant influence over the visibility and reach of content, raising critical questions: Are certain voices marginalized based on political leanings, or are platform interventions primarily aimed at mitigating the spread of disinformation? Recent discussions highlight allegations of political bias in account suspensions and “shadow banning,” a subtle mechanism where certain content is less visible to users, often without their knowledge.
Drawing from a field study on Twitter, we investigate these claims. Our findings suggest that while some account suspensions may appear biased, there is equally strong evidence indicating that these actions are strategic efforts to combat the spread of disinformation.
We then delve into the issue of shadow banning, demonstrating how this mechanism can be used to shape opinions into arbitrary distributions by solving a linear program. Through simulations on real social network topologies, we show that if shadow banning were driven by partisanship, its implementation could be so subtle that overt political biases remain undetected. This underscores the imperative for rigorous transparency and oversight in the actions of social media platforms.
This talk is based on the following papers:

Analysis: Trade-offs between reducing misinformation and politically-balanced enforcement on social media: https://osf.io/preprints/psyarxiv/ay9q5
Shaping Opinions in Social Networks with Shadow Banning: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0299977

About the Speaker
Tauhid is an Associate Professor of Operations Management at the Yale School of Management. He received his BS, MEng, and PhD degrees in electrical engineering and computer science from MIT. His research focuses on information operations problems in the context of social media. Some of the topics he has studied include combating online extremists, identifying bots, and designing influence campaigns. His broader interests cover data driven approaches to investing in startup companies, algorithmic sports betting, and biometric data. His work has been featured in the Wall Street Journal, Wired, Mashable, the LA Times, Bloomberg, and Time Magazine.
Website: https://www.zlisto.com/
