Latest Seminars

Using Domain Adaptation Transfer Learning to Resolve Label-Lacking Problem: An Application to Deception Prediction
Mr. Ka Chung NG Boris, Ph.D. student, ISOM

Date 18.11.2020
Time 10:00 am - 11:30 am
Venue LSK 3003 / Zoom (mixed-mode)

Online Pricing with Offline Data: Phase Transition and Inverse Square Law
Dr Jinzhi Bu, Massachusetts Institute of Technology

Classical statistical learning distinguishes between offline learning and online learning. Motivated by the idea of bridging the gap between these two different types of learning tasks, this work investigates the impact of pre-existing offline data on the online learning in the context of a dynamic pricing problem. We consider a seller offering a single product with an infinite amount of inventory over a selling horizon. The demand in each period is determined by the price of the product according to a linear demand model with unknown parameters. We assume that the seller has some pre-existing offline data before the start of the selling horizon, and wants to utilize both the preexisting offline data and the sequentially-revealed online data to minimize the regret of the online learning process. We characterize the joint effect of the size, location and dispersion of the offline data on the optimal regret of the online learning. Our results reveal surprising transformations of the optimal regret rate with respect to the size of the offline data, which we refer to as phase transitions. In addition, our results also demonstrate that the location and dispersion of the offline data have an intrinsic effect on the optimal regret, which is quantified via the inverse-square law.

Date 18.11.2020
Time 9:00 - 10:30 pm
Venue Online via Zoom

The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify
Mr. David HOLTZ, MIT Sloan School of Management

Date 11.11.2020
Time 9:00 am - 10:30 am (Hong Kong Time)
Venue Zoom

The Value of Humanization in Customer Service
Mr. Yang GAO, University of Rochester

Date 09.11.2020
Time 9:00 am - 10:30 am (Hong Kong Time)
Venue Zoom

Omnichannel Assortment Optimization under the Multinomial Logit Model with a Features Tree
Dr Venus Lo, The City University of Hong Kong

We consider the assortment optimization problem of a retailer who operates both a physical store and an online store. Products are described by their features and we capture the relationship between the products and the features with a tree. Non-leaf vertices correspond to features and leaf vertices correspond to products, so that the path from the root to a leaf describes the features that make up a product. A customer observes a feature if any product with that feature is offered in the physical store. A customer is either a physical store customer or an online store customer, and each customer chooses amongst the products offered in her respective store. However, an online store customer also visits the physical store to try out the products. The utilities of products in the online store are revised based on the features that an online customer sees in the physical store. The retailer offers the full assortment of products in the online store, and the goal is to find an assortment to offer in the physical store that maximizes the total expected revenue from both types of customers.

First, we consider the case with only online store customers, so that the physical store serves as a showroom for customers to try out products. We give an efficient algorithm to find the optimal assortment to display in the physical store. Second, we consider a mix of customers. The assortment optimization problem is NP-hard and we give a fully polynomial-time approximation scheme (FPTAS). Via numerical experiments, we demonstrate that our model can approximate the case where the products are arbitrary combinations of features without a tree structure and our FPTAS performs much better than its theoretical guarantee.

This is joint work with Professor Huseyin Topaloglu at Cornell University

Date 06.11.2020
Time 10:30 am - 11:45 am
Venue Online via Zoom

Sympathy to the Seemingly Needy: A Large-Scale Field Experiment on Social Influence and Non-Social Signals in Medical Crowdfunding
Miss Yun Young HUR, Georgia Institute of Technology

Date 02.11.2020
Time 9:00 am - 10:30 am (Hong Kong Time)
Venue Zoom

When Sharing Economy Meets Traditional Business: Coopetition between Ride-Sharing Platforms and Car-Rental Firms
Mr. Chenglong ZHANG, University of Texas at Dallas

Date 28.10.2020
Time 10:00 am - 11:30 am (Hong Kong Time)
Venue Zoom

3D Printing and Product Assortment Strategy
Dr Duo Shi, The Chinese University of Hong Kong, Shenzhen

3D printing, as a production technology, distinguishes from conventional technologies in three characteristics: design freedom, i.e., it can handle certain product designs that conventional technologies cannot; quality differentiation, i.e., for the same product design, it might achieve a different quality, higher or lower than that of conventional technologies; and natural flexibility, i.e., it is endowed with capacity flexibility without sacrificing operational efficiency. This paper investigates the joint impact of these characteristics when a firm selects conceptual designs to form its product assortment, taking into account each design's production technology choice from 3D printing and two conventional technologies: dedicated and traditional flexible. Some designs can be handled by any technology (generic), whereas the others are specific to 3D printing (3D-specific). The firm selects designs to be handled by each technology and then invests accordingly in technology adoption, product development, capacity, and production. We characterize the structure of the optimal assortment based on the popularity of each design. Within the sets of generic designs and 3D-specific designs, respectively, the most popular designs should be selected into the assortment; under a mild condition, the optimal assortment comprises the most popular ones among all the designs. Within the optimal assortment, 3D printing should handle the less popular generic designs than conventional technologies. We further demonstrate that a greater design freedom or higher quality of 3D printing may reduce product variety. In the absence of design freedom and quality differentiation, natural flexibility by itself always enhances product variety; by contrast, the traditional flexible technology may reduce product variety. Numerical study shows that 3D printing tends to be more valuable when popularities of the generic designs have a lower Gini index and when popularities of the 3D- specific designs have a higher Gini index.

Date 23.10.2020
Time 10:30 am - 11:45 am
Venue Online via Zoom

Observational vs. Experimental Data When Learning Intervention Policies
Mr. Carlos FERNÁNDEZ-LORÍA, New York University

Date 19.10.2020
Time 9:00 am - 10:30 am (Hong Kong Time)
Venue Zoom

The Secret to Finding Love: A Field Experiment of Choice Structure in Online Dating Platform
Dr. Hyungsoo Lim

Date 15.06.2020
Time 3:00 pm - 4:30 pm
Venue Zoom

An Economic Analysis of Difficulty Adjustment Algorithms in Proof-of-Work Blockchain Systems
Prof. Shunya Noda, Vancouver School of Economics, University of British Columbia

Date 21.05.2020
Time 10:30 am - 12:00 pm
Venue Zoom

High Dimensional Covariance Matrix Estimation by Penalizing the Matrixlogarithm Transformed Likelihood
Dr Anita Wang

Date 03.04.2020
Time 2:00 pm - 3:15 pm
Venue

Price Competition Based on Relative Prices and Applications to Medicare Market
Mr Lijian Lu, Columbia University

We consider price competition models for oligopolistic markets, in which the consumer reacts to relative rather than absolute prices, where the relative price is defined as the difference between the absolute price and a given reference value. Such settings arise, for example, when the full retail price earned by the “retailer” is reduced by virtue of a third party offering a subsidy or a rebate or in prospect theoretical models in which customers establish a reference price and base their choices on the differentials with respect to the reference price. When choosing among the various competing options, the consumer trades off the net price paid with various other product or service attributes, as in standard price competition models. The reference price may be exogenously specified and pre-announced to the competing firms. Alternatively, it may be endogenously determined, as a function of the set of absolute prices selected by the competing firms, for example the lowest or the second lowest price. We review five different application areas where the above model structure arises. We then characterize the equilibrium behavior under a general reference value scheme of the above type; this in a base model, where we assume that the consumer choice model is of the general MultiNomialLogit (MNL) type. We also derive comparison results for the price equilibria that arise under alternative subsidy schemes. These comparisons have important implications for the design of subsidy schemes.

We proceed to apply our results to the Medicare insurance market, both in terms of its existing structure, as well as in terms of various proposals to redesign the program, in particular the Wyden-Ryan plan. We show that implementation of the latter plan in 2010 would have reduced the capitation rates, on average by 18.5% and enabled savings of 16.2% in the governments’ costs. These numbers are significantly larger than traditional estimates obtained under the assumption that the plans’ premia and market shares would not be affected by the new capitation rate scheme. For beneficiaries continuing to opt for the traditional Medicare plan, the average monthly cost is roughly $64.

Date 12.03.2020
Time 2:00 pm - 3:15 pm
Venue

When Popularity Meets Position: Disentangling Popularity and Position Using an Experimental Approach
Ms. Qianran JIN, Jenny, McGill University

Date 06.02.2020
Time 10:30 am - 12:00 pm
Venue ISOM Conference Room, LSK 4047

To Compete or Contract? Assessing the Effectiveness of Contests in Online Labor Market
Dr. Jiahui MO, Assistant Professor, Nanyang Technological University

Date 31.01.2020
Time 10:30 am - 12:00 pm
Venue ISOM Conference Room, LSK 4047