Latest Seminars

Democratizing Venture Capital Financing for Innovation: Crowdfunding Under Intellectual Property Rights Governance
Mr Zhitao Yin, J. Mack Robinson College of Business, Georgia State University

Date 21.12.2018
Time 10:30 am ‐ 12:00 noon
Venue ISOM Conference Room 4047, LSK Business Building

Optimal Duration of Innovation Contests
Dr Gizem Korpeoglu, School of Management, University College London

This paper studies the optimal duration and the optimal award scheme of an innovation contest where an organizer elicits solutions to an innovation‐related problem from a group of agents.  Each agent can improve  her  solution  by  exerting  costly effort  but  the  quality  of  her solution  also  depends  on  an  output uncertainty.  We  show,  consistent  with  recent empirical  evidence,  that  the  optimal  contest  duration  and  the optimal total award are positively correlated.  This is because both the optimal contest duration and the optimal total award increase with the agent's output uncertainty and decrease with the marginal impact of the agent's effort on the quality of her solution.  A managerial insight from this result is that the optimal contest duration may increase with the novelty and the sophistication of solutions that the organizer seeks.  More interestingly, we  show  that  it  is  optimal  for  the  organizer  to  give multiple  awards  when  the  organizer  has  low  urgency  in obtaining solutions.  This result may explain why many contests on platforms give multiple awards.

Date 12.12.2018
Time 11:00 am ‐ 12:15 pm
Venue Room 1003, LSK Business Building

Data Analytics for Order Assignment in Last Mile Delivery
Mr Sheng Liu, Department of Industrial Engineering and Operations Research, University of California, Berkeley

Food delivery market is rising across the world. In China, retailing giants are providing fast food delivery service from their physical stores. A key challenge for them is to improve the on‐time performance of delivery services. Working with a major food delivery service provider in China, we develop a data‐driven optimization framework to minimize the expected delivery delay. Driven by the real‐world data set, we propose a machine learning approach to predict the actual travel distance considering drivers' behaviors. Combined with the travel distance prediction, our optimization framework is flexible and yields significantly better result than existing models that assume drivers follow the shortest‐distance routes.

Date 11.12.2018
Time 11:00 am ‐ 12:15 pm
Venue Room 1003, LSK Business Building

Effects of Disclosing Sponsorship on User Engagement in Social Media
Dr Zike Cao, Rotterdam School of Management, Erasmus University

Date 07.12.2018
Time 11:00 ‐ 12:30 pm
Venue Conference Room 5047, LSK Business Building

The Path and Opportunities Toward Hydrogen Energy Development
Prof. Hong Chen, Shanghai Advanced Institute of Finance

Hydrogen has been considered by some the ultimate fuel and energy carrier.  President Bush declared in his 2003 State of the Union Address, “With a new national commitment, our scientists and engineers will overcome obstacles to taking these cars from laboratory to showroom, so that the first car driven by a child born today could be powered by hydrogen, and pollution‐free.” He provided a multibillion dollar research initiative to develop hydrogen technology. With the advice from Dr. Steven Chu, then the Secretary of U.S. Department of Energy, President Obama cut funding for research into hydrogen fuel cells in 2010. In an interview with MIT Technology Review, Dr. Steven Chu expressed skeptical of hydrogen and suggested that the hydrogen energy require “Four Miracles” to happen.

On the other hand, Japan has continued its push toward the fuel cell technology. Toyota commercialized the first fuel cell electric car Mirai in 2014. Chinese government has also announced the plans to build hydrogen infrastructure to support about 50,000 zero emissions fuel cell electric vehicles (FCEVs) by 2025 and 1 million FCEVs in service by 2030.

In this talk, I would like to address the following questions:

  • Why is the hydrogen energy important?
  • Have the Four Miracles happened?
  • What is the path toward the hydrogen energy development?
  • What are the challenges and opportunities for the hydrogen energy development?


Moreover, I will introduce one breakthrough technology that would make happen the Four Miracles. This is a disruptive technology that will make the hydrogen become the main stream energy and make the earth really clean. As a participant in the development and the promotion of the commercialization of this technology, I will also present the progress of its development.

Date 30.11.2018
Time 3:00 ‐ 4:15 pm
Venue ISOM Conference Room 4047, LSK Business Building

Driving Precision Health Care through Heterogeneous Outcome Analysis
Mr Guihua Wang, Stephen M. Ross School of Business, University of Michigan

This study addresses the challenges of generating patient‐centric information about hospital quality and analyzes the impact of information on enabling patients to receive better care. Methodologically, we develop a new Instrumental Variable (IV) tree approach by incorporating an IV into a tree‐based method to correct for potential endogeneity issues in heterogeneous treatment effect analysis using observational data. Empirically, we designate hospitals as different treatments and apply the IV tree to study the outcome differences between thirty‐five New York hospitals for cardiovascular surgeries. We found that the outcome differences between hospitals are heterogeneous across different patients. By comparing scenarios with patient‐centric and population‐average information, we show that 80% of patients can benefit from using patient‐centric information and their complications can be reduced by 67.4%. We also illustrate how patient‐centric information can enhance pay‐for‐performance programs offered by payers and guide hospitals in targeting quality improvement efforts.

Date 28.11.2018
Time 3:00 ‐ 4:15 pm
Venue Room 3005, LSK Business Building

Fake News Propagation and Detection: A Sequential Model
Prof. Yiangos Papanastasiou, Haas School of Business, University of California, Berkeley

In the wake of the 2016 US presidential election, social media platforms are facing increasing pressure to combat the propagation of “fake news” (i.e., articles whose content is fabricated). Motivated by recent attempts in this direction, we consider the problem faced by a social media platform that is observing the sharing actions of a sequence of rational agents and is dynamically choosing whether to conduct an inspection (i.e., a “fact‐check”) of an article whose validity is ex ante unknown. We first characterize the agents’ inspection and sharing actions and establish that in the absence of any platform intervention, the agents’ news‐sharing process is prone to the proliferation of fabricated content, even when the agents are intent on sharing only truthful news. We then study the platform’s inspection problem. We find that because the optimal policy is adapted to crowdsource inspection from the agents, it exhibits features that may appear a priori nonobvious; most notably, we show that the optimal inspection policy is nonmonotone in the ex ante probability that the article being shared is fake. We also investigate the effectiveness of the platform’s policy in mitigating the detrimental impact of fake news on the agents’ learning environment. We demonstrate that in environments characterized by a low (high) prevalence of fake news, the platform’s policy is more effective when the rewards it collects from content sharing are low relative to the penalties it incurs from the sharing of fake news (when the rewards it collects from content sharing are high in absolute terms).

Date 23.11.2018
Time 11:00 am ‐ 12:15 pm
Venue Room 3003, LSK Business Building

Beauty and Signaling in Online Matching Markets: Evidence from a Randomized Field Experiment
Ms Lanfei Shi, Robert H. Smith School of Business, University of Maryland

Date 20.11.2018
Time 12:00 noon ‐ 1:30 pm
Venue ISOM Conference Room 4047, LSK Business Building

Anticipated Regret During Auctions: Empirical Evidence from Ebay
Prof. Serdar Simsek, Naveen Jindal School of Management, University of Texas at Dallas

Winner and loser regrets are defined as regretting for paying too much in case of winning an auction and regretting for not bidding high enough in case of losing it, respectively.  In this paper, we develop a structural model for common value auctions which accounts for bidders' learning and their anticipation of winner and loser regrets in an online auction platform.  Using a large data set from eBay and empirical Bayesian estimation method, we quantify the bidders' anticipation of regret in various product categories, and investigate the role of experience in explaining the bidders' regret and learning behaviors.  We also show how our results can be utilized by online auction platforms.  In particular, our counterfactual analyses show that shutting down the bidder regret via appropriate notification policies can increase eBay's revenue significantly.  This is joint work with Meisam Hejazi Nia (HomeAway) and Özalp Özer (UT Dallas).

Date 20.11.2018
Time 3:00 ‐ 4:15 pm
Venue Room 3001, LSK Business Building

Joint Statistics Seminar - Limiting Distribution of Outlier Singular Vectors of Low-rank Matrices with Additive Random Noise
Dr. Ke Wang, Department of Mathematics, HKUST

In this talk, we consider the matrix model Y=S+X where S is a low-rank deterministic matrix, representing the signal, and X is a random noise.  It is a central task in high dimensional data analysis to understand how the spectral properties of S are altered with a small random perturbation.  We give a precise description of the limiting distribution of the angles between the outlier singular vectors of Y with their counterparts, the leading singular vectors of S.  It turns out that the limiting distribution depends on the structure of S and the distribution of X, and thus it is non-universal.

This talk is based on a joint work with Zhigang Bao and Xiucai Ding.

Date 16.11.2018
Time 2:00 pm - 3:00 pm
Venue Room 4047 (LSK Business Building)

“Marshmallow Pricing”: Effects of “Wait‐and‐Not‐Pay” Schemes on the Monetization of Hedonic Digital Content
Ms Angela Choi, College of Business, Korea Advanced Institute of Science and Technology

Date 31.10.2018
Time 10:00 ‐ 11:30 am
Venue ISOM Conference Room 4047, LSK Business Building

Joint Statistics Seminar - Portfolio Construction Based on Stochastic Dominance and Empirical Likelihood
Prof. Thierry Post, Graduate School of Business, Nazarbayev University

This study develops a portfolio optimization method based on the Stochastic Dominance (SD) decision criterion and the Empirical Likelihood (EL) estimation method.  SD and EL share a distribution-free assumption framework which allows for dynamic and non-Gaussian multivariate return distributions.  The SD/EL method can be implemented using a two-stage procedure which first elicits the implied probabilities using Convex Optimization and subsequently constructs the optimal portfolio using Linear Programming.  The solution asymptotically dominates the benchmark and optimizes the goal function in probability, for a class of weakly dependent processes.  A Monte Carlo simulation experiment illustrates the improvement in estimation precision using a set of conservative moment conditions about common factors in small samples.  In an application to equity industry momentum strategies, SD/EL yields important out-of-sample performance improvements relative to heuristic diversification, Mean-Variance optimization, and a simple 'plug-in' approach.

Keywords: Stochastic Dominance, Empirical Likelihood, Portfolio optimization, Momentum strategies

Joint works with Selcuk Karabati and Stelios Arvanitis.

Date 26.10.2018
Time 2:00pm – 3:00pm
Venue Room 1014 (LSK Business Building)

A Re‐solving Heuristic with Uniformly Bounded Loss for Network Revenue Management
Dr. He Wang, Georgia Institute of Technology

We consider the classical “network revenue management” problem, where a firm has limited resources and needs to irrevocably accept or reject customer requests in order to maximize expected revenue.  We study a class of re‐solving heuristics for this problem.  These heuristics periodically re‐optimize an approximation of the problem known as the deterministic linear program (DLP), where random customer arrivals are replaced by their expectations.  We find that, in general, frequently re‐solving the DLP produces the same order of revenue loss as one would get without re‐solving, which scales as the square root of the problem size.  However, by re‐solving the DLP at a few selected points in time, we design a new re‐solving heuristic, whose revenue loss is bounded by a constant that is independent of the problem size.

 (Joint work with PhD student Pornpawee Bumpensanti. Paper is available at:  https://arxiv.org/abs/1802.06192)

Date 26.10.2018
Time 11:00 am ‐ 12:15 pm
Venue LSK G003

Picture Perfect: An Image Mining of Advertising Content and Its Effects on Social Targeting
Ms Hyunji So, College of Business, Korea Advanced Institute of Science and Technology

Date 24.10.2018
Time 10:00 ‐ 11:30 am
Venue ISOM Conference Room 4047, LSK Business Building

Social Learning from Online Reviews withProduct Choice
Dr Stefano Vaccari, LUISS‐GuidoCarli

Product choice when consumers engage in social learning has significant implications on learning outcomes and on the information accumulation rate. In many practical settings, consumers have a choice on which product to buy, if any, among several possible alternatives. The quality of these products may be unknown to consumers, but online platforms provide product reviews so that, as time goes by, customers accumulate information about the products' quality. This paper studies a model where consumers estimate the quality of products from online binary product reviews (like/dislike), and subsequently make a choice among competing alternatives using a multinomial logit model. The consumer learning model is naive, i.e., consumers take the ratio of likes over the total number of reviews as a proxy for quality. We explore the impact of choice on the learning outcome, and show that consumers correctly learn the ranking of the product qualities, but not the actual quality vector. We provide the conditions that allow consumers to get arbitrarily close to the truth and characterize the consistency of their product choices relative to the full information benchmark. Using a large market (fluid model) approximation, we study how choice and product parameters affect learning speed and derive some intuition on the primitives that matter the most. Finally, we address the following platform control problem: assuming that consumers suffer some search cost to go down the list of displayed products, which order of products should the platform use to speed up learning and purchases? Without search costs, the platform has no leverage to accelerate learning, but if search costs exist, and are significant, e.g., most people do not see past the top 10 or so options, then (a) disregarding the search cost leads to significantly optimistic results in terms of information accumulation speed, and (b) by carefully selecting the order in which product options are displayed, the platform may in fact reduce the time‐to‐learn even when compared to the case where there are no search costs.

Date 11.10.2018
Time 1:45 ‐ 3:00 pm
Venue Room 2003, LSK Business Building