Latest Seminars

Social Learning and Polarization on Content Platforms
Dr Dongwook Shin, The Hong Kong University of Science and Technology

This paper investigates the nature of social learning (SL) on content platforms and its impact on optimal content design. The content provider can choose low- or high-quality content and, in addition, may also polarize (as opposed to keeping it neutral) the content in favor of some consumers while opposing other consumers’ opinions/preferences. On the content platform, SL manifests as consumers’ inference about the unknown quality of content using the history of past consumption, based on which they make consumption decisions. We specify a behavioral model of SL that accounts for and illustrates the impact of false consensus effect (FCE) --- a cognitive bias wherein consumers project their own preference onto others --- on the SL outcome. In this environment, we find that whether the SL mechanism reveals the true quality of content depends largely on the interaction between content polarization and the degree of the FCE. Depending on the extent of the interaction, SL may be incomplete or even cursed in the sense that beliefs converge to a limit where history offers no information about quality. The optimal content design internalizes the SL dynamics and as a result, we find that quality and polarization can be used as substitutes by the content provider. In particular, content may be polarized to mask its low quality. Interestingly, we also find that SL increases the incentive for the content provider to increase quality compared to a benchmark without SL. Furthermore, we find parametric regimes in which SL may not be beneficial to consumers, but is in fact preferred by the content platform (and vice versa). In this sense, the value of SL may be misaligned between the platform and its consumers.

Date 02.12.2021
Time 4:30 - 5:00 PM
Venue Room G012, LSK Business Building, HKUST

Understanding Customer Risk Adjustment in Commercial Banks: A Machine Learning Approach
Prof. Sean Xin XU, Associate Dean School of Economics & Management Tsinghua University

Understanding customers’ risk preferences is important for financial firms to effectively target offerings and manage relationships; however, assessing customers’ risk preference, particularly at scale and over time, is challenging. Lacking for efficient measures, we have limited understanding on risk preferences and the associated decision-making. Especially, little research has been carried out on when and how customers adjust their portfolio from the perspective of managing risk. To fill this research gap, we employ machine learning techniques to leverage rich data on customers’ behavior that is commonly available to commercial banks to measure customers’ risk preferences. Using real-world data from a large commercial bank, we first identify customers’ risk preferences over time. Then, with the developed measure, we provide theoretical insights on how customers’ risk preferences, current portfolio risk and attention (i.e., observe financial products) jointly affect their portfolio adjustment. Further, a field experiment in a marketing campaign shows that, telemarketers doubled their conversion rates with our measure. These results suggest that the risk preference measure generated by machine learning has significant theoretical and practical implications.

Date 30.11.2021
Time 2:30 - 4:00 pm
Venue Zoom ID: 931 1775 4236 (Passcode: 164650)

Optimal Budget Allocation With Online Ad Campaign
Miss Huijun Chen, ISOM, HKUST

This paper investigates how the presence of the spillover and carryover effects in the multi-channel ad campaign affects the budget allocation decisions of a marketing agency, which strives to maximize the total expected number of clicks or conversions over the campaign. A salient feature of the problem is that the market agency only has access to aggregate data such that the effectiveness of different online advertising channels cannot be estimated using standard methods that typically require individual-level data. The authors propose a data augmentation method for estimating the microlevel consumer advertising response models using aggregate data. The essence of this approach is to simulate latent state dynamics such that the generated data is consistent with the observed aggregate data. The authors then demonstrate the validity of the method using actual channel-level advertising campaign data from an online fashion retailer in Korea. Lastly, the authors study a fluid mean-field formulation and derive key structural insights on the optimal budget allocation policies, which are leveraged to design an implementable budget allocation policy.

Date 19.11.2021
Time 10:30 - 11:45 AM
Venue Room 4047, LSK Business Building

Optimizing Initial Screening for Colorectal Cancer Detection with Adherence Behavior
Dr Zhichao Zheng, Singapore Management University

Cancer remains one of the leading causes of human death, and early detection is the key to reducing mortality. To detect cancer in the early stages, two-stage screening programs are widely adopted in practice. Individuals receiving positive outcomes in the first-stage (initial) test are recommended to undergo a second-stage test for further diagnosis. The initial test design—i.e., selecting cutoffs to report test outcomes—is crucial for screening effectiveness (i.e., cancer detection) and efficiency (i.e., second-stage capacity costs). However, not all individuals who receive positive outcomes follow up with the second-stage test; evidence shows that adherence behavior is closely associated with the cutoff used in the initial test. This paper studies the initial test design in the context of colorectal cancer (CRC) screening to balance the trade-off between screening effectiveness and efficiency and takes into account individuals’ guideline adherence behavior.

We adopt a Bayesian persuasion framework with information avoidance to model the initial test design and individuals’ response to screening guidelines. We analytically prove that under certain conditions, an initial test using a single cutoff (i.e., a dichotomous test) is optimal for screening follow-up maximization, and a continuous test (i.e., showing exact readings of the biomarker) is optimal for screening effectiveness maximization. We apply the framework to Singapore’s CRC screening guideline design and calibrate the model using various sources of data, including a nationwide survey in Singapore. Our results suggest that compared with the current practice, increasing the cutoff to the level that maximizes expected follow-ups by cancer patients can detect 969 more CRC incidences and prevent 37,820 colonoscopies, which are the second-stage test for CRC screening. Aiming only for high-sensitivity initial tests using lower cutoffs (as in the current practice) can backfire and lead to large numbers of unnecessary colonoscopies and low follow-up rates from cancer patients. We further explore the benefits of using different cutoffs for different subpopulations and use an interpretable clustering technique to construct implementable rules for partitioning the population. We demonstrate that using a lower cutoff for males older than 60 and females older than 70 (high-risk and high-adherence groups) and a higher cutoff for the rest of the screening population (low-risk and low-adherence groups) can further improve screening effectiveness and efficiency.

Date 12.11.2021
Time 10:30 - 11:45 am
Venue Zoom ID: 981 9920 2378 (passcode 767205)

The Limits of Bundling: High Demand with Limited Inventory
Dr Tarek Abdallah, Northwestern University

There is an increased interest in bundle selling mechanisms especially with the rise of subscription services. This rise was mainly fueled by the success of subscription services in the digital markets where inventory is unlimited. However, recently there is a slew of subscriptions services that emerged in the retail industry where inventory is limited. In this paper, we take a first step towards understanding the impact of key operational metrics such as inventory levels and limited selling horizons on the optimal bundle selling strategy. We study a dynamic bundle pricing problem when the firm is selling multiple items but with limited inventory. We propose a new scaling regime to study this problem, called high-demand regime, where we scale the arrival rate in order to capture markets where demand is high but inventory is limited. Our results highlight a fundamental limitation of bundling in such markets. Firms should avoid bundling fast moving items together and should rather sell them separately (or bundle fast moving items with slow moving items). Moreover, depending on the tail of the valuation distribution, the firm should either consider static pricing of the items or dynamic pricing. We provide closed form solutions for the static and dynamic pricing policies.

Date 05.11.2021
Time 10:30 - 11:45 AM
Venue Zoom ID: 958 7450 2573 (passcode 419621)

An Optimal Greedy Heuristic with Minimal Learning Regret for the Markov Chain Choice Model
Mr Wentao Lu, ISOM

We study the assortment optimization problem and show that local optima are global optima for all discrete choice models that can be represented by the Markov Chain model. We develop a forward greedy heuristic that finds an optimal assortment for the Markov Chain model and runs in $O(n^2)$ iterations. The heuristic has performance bound $1/n$ for any regular choice model which is best possible among polynomial heuristics. We also propose a backward greedy heuristic that is optimal for Markov chain model and requires fewer iterations. Numerical results show that our heuristics  performs significantly better than the estimate then optimize method and the revenue-ordered assortment heuristic when the ground truth is a latent class multinomial logit choice model. Based on the greedy heuristics, we develop a learning algorithm that enjoys asymptotic optimal regret for the Markov chain choice model and avoids parameter estimations, focusing instead on binary comparisons of revenues.

Date 29.10.2021
Time 10:30 - 11:45 AM
Venue Room 4047, LSK Business Building

Contextual Optimization: Bridging Machine Learning and Operations
Dr Adam Elmachtoub, Columbia University

Many operations problems are associated with some form of a prediction problem. For instance, one cannot solve a supply chain problem without predicting demand. One cannot solve a shortest path problem without predicting travel times. One cannot solve a personalized pricing problem without predicting consumer valuations. In each of these problems, each instance is characterized by a context (or features). For instance, demand depends on prices and trends, travel times depend on weather and holidays, and consumer valuations depend on user demographics and click history. In this talk,  we review recent results on how to solve such contextual optimization problems, with a particular emphasis on techniques that blend the prediction and decision tasks together.

Date 22.10.2021
Time 10:30 - 11:45 AM
Venue Zoom ID: 940 9210 1521 (passcode 801626)

When Should the Regulator Allow/Prohibit Inter-Temporal Transfer of Emission Permits?
Mr Xingyu Fu, PhD candidate, ISOM, HKUST

Emission permits are widely adopted to combat climate change and regulatory authorities sometimes allow for the inter-temporal banking and borrowing of emission permits so that firms can flexibly respond to market uncertainties. We find that such time flexibility may lead to poor social performance, especially when the production cost fluctuation is sufficiently large. This result is failed to be captured by the classic simplified assumption where firms cannot sub-exercise emission permits. Furthermore, we demonstrate that the inter-temporal permits transfer should be prohibited when the market is at the red ocean stage or when the pollutant generated relatively instant damage. Lastly, we analyze some restricted permits transfer policies such as transfer discount and transfer cap, which are shown to dominate both the taxation and the non-transferable permits in terms of social welfare.

Date 15.10.2021
Time 10:30 - 11:45 AM
Venue Room 4047, LSK Business Building

Creation or Destruction? STEM OPT Extension and Employment of Information Technology Professionals
Prof. Min-Seok Pang, Temple University

Information technology (IT) professionals play an important role in firms' IT investments, innovation, and entrepreneurship, contributing to significant economic growth in the U.S. The use of temporary work visas and related immigration policies has attracted a significant controversy and policy debates in the U.S. On the one hand, foreign IT professionals complement domestic IT professionals by facilitating innovation and entrepreneurship. On the other hand, the foreign IT professionals substitute the domestic counterparts by intensifying labor market competition. In this study, we focus on an extension in the Optional Practical Training (OPT) program for STEM graduates from U.S. institutions. Specifically, we explore the effects of the OPT extension on the number and wage of domestic workers in STEM occupations and how these effects differ between IT and non-IT STEM occupations. Our results demonstrate that an increase in the supply of foreign IT professionals from the OPT extension boosts the employment of domestic IT professionals. This study contributes to the information systems, labor economics, and public policy literature by quantifying the impacts of a policy change on the employment of IT professionals and provides rich implications for policymakers.

Date 13.10.2021
Time 9:00am - 10:30am (Hong Kong Time)
Venue Zoom ID: 964 9603 7857 (Passcode: 550542)

Eliciting Human Judgment for Prediction Algorithms
Dr Song-Hee Kim, Seoul National University

Even when human point forecasts are less accurate than data-based algorithm predictions, they can still help boost performance by being used as algorithm inputs. Assuming one uses human judgment indirectly in this manner, we propose changing the elicitation question from the traditional direct forecast (DF) to what we call the private information adjustment (PIA): how much the human thinks the algorithm should adjust its forecast to account for information the human has that is unused by the algorithm. Using stylized models with and without random error, we theoretically prove that human random error makes eliciting the PIA lead to more accurate predictions than eliciting the DF. However, this DF-PIA gap does not exist for perfectly consistent forecasters. The DF-PIA gap is increasing in the random error that people make while incorporating public information (data that the algorithm uses) but is decreasing in the random error that people make while incorporating private information (data that only the human can use). In controlled experiments with students and Amazon Mechanical Turk workers, we find support for these hypotheses.

Joint work with Rouba Ibrahim (University College London) and Jordan Tong (University of Wisconsin- Madison).

Date 08.10.2021
Time 10:30 - 11:45 am
Venue Zoom ID: 990 9153 1838 (passcode 118249)

Platform Tokenization: Financing, Governance, and Moral Hazard
Dr Alex Yang, London Business School

This paper highlights two channels through which blockchain-enabled tokenization can  alleviate moral hazard frictions between founders, investors, and users of a platform: token financing and decentralized governance. We consider an entrepreneur who uses outside financing and exerts private effort to build a platform, and users who decide whether to join in response to the platform’s dynamic transaction fee policy. We first show that raising capital by issuing tokens rather than equity mitigates effort under-provision because the payoff to equity investors depends on profit, whereas the payoff to token investors depends on transaction volume, which is less sensitive to effort. Second, we show that decentralized governance associated with tokenization eliminates a potential holdup of platform users, which in turn alleviates the need to provide users with incentives to join, reducing the entrepreneur’s financing burden. The downside of tokenization is that it puts a cap on how much capital the entrepreneur can raise. Namely, if tokens are highly liquid, i.e., they change hands many times per unit of time, their market capitalization is small relative to the NPV of the platform profits, limiting how much money one can raise by issuing tokens rather than equity. If building the platform is expensive, this can distort the capacity investment. The resulting trade-off between the benefits and costs of tokenization leads to several predictions regarding adoption. (Link to paper:

Date 24.09.2021
Time 10:30 - 11:45 AM
Venue Case Room 1005, LSK Business Building

Tight Guarantees for Multi-unit Prophet Inequalities and Online Stochastic Knapsack
Dr Will Ma, Columbia University

Prophet inequalities are a useful tool for designing online allocation procedures and comparing their performance to the optimal offline allocation. In the basic setting of $k$-unit prophet inequalities, the magical procedure of Alaei (2011) with its celebrated performance guarantee of $1-1/sqrt(k+3)$ has found widespread adoption in mechanism design and general online allocation problems in online advertising, healthcare scheduling, and revenue management. Despite being commonly used for implementing a fractional allocation in an online fashion, the tightness of Alaei’s procedure for a given $k$ has remained unknown. In this paper we resolve this question, characterizing the tight bound by identifying the structure of the optimal online implementation, and consequently improving the best-known guarantee for $k$-unit prophet inequalities for all $k>1$.

We also consider the more general online stochastic knapsack problem where each individual allocation can consume an arbitrary fraction of the initial capacity. Here we introduce a new “best-fit” procedure for implementing a fractionally-feasible knapsack solution online, with a performance guarantee of $1/( 3+ e^(-2) ) ~ 0.319$, which we also show is tight with respect to the standard LP relaxation. This improves the previously best- known guarantee of 0.2 for online knapsack.

Our analysis differs from existing ones by eschewing the need to split items into “large” or “small” based on capacity consumption, using instead an invariant for the overall utilization on different sample paths.

Finally, we refine our technique for the unit-density special case of knapsack, and improve the guarantee from 0.321 to 0.3557 in the multi-resource appointment scheduling application of Stein et al. (2020).

(Joint work with Jiashuo Jiang and Jiawei Zhang)

Date 17.09.2021
Time 10:30 - 11:45 am
Venue Zoom ID: 966 4342 3419 (passcode 117631)

Signaling Quality with Return Insurance: Theory and Empirical Evidence
Dr Man Yu, Department of ISOM, HKUST

This paper examines an innovative return policy, return insurance, emerging on various shopping platforms such as and Return insurance is underwritten by an insurer and can be purchased by either a retailer or a consumer. Under such insurance, the insurer partially compensates consumers for their hassle costs associated with product return. We analyze the informational roles of return insurance when product quality is the retailer's private information, consumers infer quality from the retailer's price and insurance adoption, and the insurer strategically chooses insurance premiums.

We show that return insurance can be an effective signal of high quality. When consumers have little confidence about high quality and expect a significant gap between high and low qualities, a high-quality retailer can be differentiated from a low-quality retailer solely through its adoption of return insurance. We confirm, both analytically and empirically with a data set consisting of over 10,000 sellers on, that return insurance is more likely adopted by higher-quality sellers under information asymmetry. Furthermore, we find that the presence of the third party (i.e., the insurer) leads to double marginalization in signaling, which strengthens a signal's differentiating power and sometimes renders return insurance a preferred signal, in comparison with free return, whereby retailers directly compensate for consumers' return hassles. As an effective and costly signal of quality, return insurance may also improve consumer surplus and reduce product returns. Its profit advantage to the insurer is most pronounced under significant quality uncertainty.

Date 10.09.2021
Time 10:30 - 11:45 am
Venue Zoom ID: 998 2990 2117 (passcode 203368)

Harnessing Geolocation Information in Mobile Health Apps
Prof. Jason CHAN, University of Minnesota

Date 25.08.2021
Time 9:00 - 10:30 AM (Hong Kong Time)
Venue Meeting ID: 968 1411 0875 (Passcode: 752909)

Incentivizing Commuters to Carpool: A Large Field Experiment with Waze
Dr Maxime Cohen, McGill University

Traffic congestion is a serious global issue. A potential solution, which requires zero investment in infrastructure, is to convince solo car users to carpool. In this paper, we leverage the Waze Carpool service and run the largest ever digital field experiment to nudge commuters to carpool. Our field experiment involves more than half a million users across four U.S. states between June 10 and July 3, 2019. We identify users who can save a significant commute time by carpooling through the use of a high- occupancy vehicle (HOV) lane, users who can still use a HOV lane but have a low time saving, and users who do not have access to a HOV lane on their commute. We send them in-app notifications with different framings: mentioning the HOV lane, highlighting the time saving, emphasizing the monetary welcome bonus (for users who do not have access to a HOV lane), and a generic carpool invitation. We find a strong relationship between the affinity to carpool and the potential time saving through a HOV lane. Specifically, we estimate that mentioning the HOV lane increases the click-through rate (i.e., proportion of users who clicked on the button inviting them to try the carpool service) and the on-boarding rate (i.e., proportion of users who signed up and created an account with the carpool service) by 133-185% and 64-141%, respectively relative to a generic invitation. We conclude by discussing the implications of our findings for carpool platforms and public policy.

(Joint work with Michael-David Fiszer, Avia Ratzon, and Roy Sasson)

Date 20.08.2021
Time 9:00 - 10:15 AM
Venue Zoom ID: 915 4092 8440 (passcode 064840)