Latest Seminars

The Economics of Cyber Crime
Mr. Xiaofan LI, University of Texas at Austin

Date 09.01.2020
Time 10:30 am - 12:00 pm
Venue ISOM Conference Room, LSK 4047

Nudging Drivers to Safety: Evidence from a Field Experiment
Mr Vivek Choudhary, Department of Technology & Operations Management, INSEAD, Singapore

Driving is an integral component of many operational systems and any small improvement in driving quality can have significant effect on accidents, traffic, pollution, and the economy in general. However, the complexity and multidimensionality of driving as a task make it challenging to improve. With motor vehicles at the core of their business processes, many industries employ financial and non-financial incentives for their drivers to promote better driving. Such interventions are often expensive and not very effective as evident from accident statistics. Instead, we devise and test a behavioral intervention called a nudge in a field experiment. We use telematics technology (i.e., real-time sensor data in a mobile device such as accelerometer and gyroscope) to measure driving performance as well as to deliver nudges to the drivers via notifications. Leveraging a smartphone application launched by our industry partners, we sent three types of performance nudges to drivers, indicating how they performed with respect to their personal best, personal average, and latest driving performance. We are the first to study effectiveness of different types of nudges and compare their relative strength in the context of driving in a field experiment. We find that personal best and personal average nudges improve driving performance, on average, by 18.17% and 18.71% standard deviations of the performance scores calculated by the application respectively, translating into an increase in the inter-accident time by nearly 1.8 years, while also improving driving performance consistency (as measured by the coefficient of variation of the performance score). We further study heterogeneity of this effect using generalized random forest. We show that high-performing drivers who are not frequent feedback seekers benefit the most from personal best nudges, while low-performing drivers who are also frequent feedback seekers benefit the most from the personal average nudges. Using these findings, we construct personalized nudges that outperform either of these nudges.

Date 03.01.2020
Time 3:00 pm - 4:15 pm
Venue Room G003, LSK Business Building

Joint Statistics Seminar - Sufficient Dimension Reduction for Classification
Prof. Xin CHEN, Southern University of Science and Technology

We propose a new sufficient dimension reduction approach designed deliberately for high-dimensional classification.  This novel method is named maximal mean variance (MMV) stimulated by the mean variance index first proposed by Cui, Li and Zhong (2015) which measures the dependence between a categorical random variable with multiple classes and a continuous random variable.  Our method requires quite mild restrictions on the predicting variables and keeps the model-free advantage without the need to estimate the link function. Consistency of the MMV estimator is established under regularity conditions for both fixed and diverging dimension (p) cases and the number of the response classes can also be allowed to diverge with the sample size n.  We also construct the asymptotic normality for the estimator when the dimension of the predicting vector is fixed.  Furthermore, although without any definite theoretical proof, our method works pretty well when p << n.  Surprising classification efficiency gain of the proposed method is verified by numerical studies.

Date 02.01.2020
Time 11:00 am – 12:00 noon
Venue Room 5047 (LSK Business Building)

“Un”Fair Machine Learning Algorithms
Dr. Manmohan ASERI, Visiting Assistant Professor, Tepper School of Business, Carnegie Mellon University, USA

Date 15.11.2019
Time 10:30 am - 12:00 pm
Venue Case Room G001, G/F, LSK Business Building

Blockchain Adoption for Combating Deceptive Counterfeits
Dr Hubert Pun, Professor and PhD Program Coordinator, Ivey Business School, Western University

In this paper, we study combating deceptive counterfeiting using blockchain technology. When a product is tagged with a non-duplicable unique identifier customers know whether a product is authentic or fake. However, customers have privacy concerns while using blockchain. We consider a market with a manufacturer and a deceptive counterfeiter. The manufacturer can either use blockchain or signal through pricing to validate product authenticity. The government can incentivize blockchain adoption by providing subsidy to the manufacturer while optimizing social welfare. We find that without government subsidy, blockchain should be used only when the counterfeit quality is intermediate or when customers have intermediate distrust about products in the market. When customers have serious distrust about products, differential pricing strategy is more effective than blockchain. With subsidy, however, we show that differential pricing strategy should never be used. Blockchain can be more effective than differential pricing strategy in eliminating post-purchase regret, and our result advocates that government should participate in the manufacturer’s blockchain adoption decision because this benefits customers and society.

Key words: blockchain, customer privacy, deceptive counterfeit, government subsidy

Date 11.11.2019
Time 11:00 am - 12:15 pm
Venue Room G001, LSK Business Building