Rutgers University: Department of MSIS Seminar Schedule
All the seminars are held on Fridays at 11am unless otherwise noted. For more information, please contact Mert Gurbuzbalaban.
Seminar Organizer(s): Mert Gurbuzbalaban and Thomas Lidbetter.
Spring 2017:
 Feb 24: No seminar due to Rutgers Research Day.
 March 3: Nikhil Vellodi (NYU)
 Time: 11am, Location: Newark 1WP502
 Video streaming available at: New Brunswick, 100RR4095

Title: Cheap Talk in MultiProduct BargainingAbstract: I study a game in which a buyer and a seller bargain over multiple products. The buyer has private valuations over the seller’s product range, and can communicate these through cheaptalk messages. In the benchmark model a fully revealing, welfaremaximizing equilibrium exists. However, a wellknown refinement selects a nonempty set of partially informative equilibria, which I characterize fully. In particular, the set contains the exante buyer optimal equilibrium. These results hold for a broad class of preferences, and rest on a natural tradeoff faced by the buyer – reveal his type in order to secure his preferred good, or hide his type in order to secure a better price. As goods become less substitutable, the buyer reveals more information, whilst his share of the surplus changes nonmonotonically.
 March 10: Mengdi Wang (Princeton)
 Time: 11am, Location: New Brunswick 100RR3095
 Video streaming available at: Newark – 1WP302
 Abstract: TBA
 March 20 (Tuesday): Stan Uryasev (Florida)
 Time: 11am , Location: TBA
 Video streaming available at: TBA
 Title: Buffered Probability of Exceedance (bPOE): Mathematical Properties and Applications
 Abstract: (Joint work with Matt Norton and Alexander Mafusalov) The Probability of Exceedance (POE) is frequently used to measure uncertainties in outcomes. For instance, POE is used to estimate probability that assets of a company fall below liabilities. POE measures only the frequency of outcomes and ignores magnitude of outcomes. POE counts outcomes exceeds the threshold, and it “does not worry” about the amount by which each outcome exceeds the threshold. POE is lumping together all threshold exceedance events, potentially “hiding” quite large and very troublesome outcomes. Moreover, POE has poor mathematical properties when used to characterize discrete distributions of random values (e.g., when distributions are defined by observed historical data). POE for discrete distributions is a discontinuous function of control variables, making it difficult to analyze and optimize.This presentation discusses a new probabilistic characteristic called Buffered Probability of Exceedance (bPOE). With bPOE, it is possible to count outcomes close to a threshold value, rather than only outcomes exceeding the threshold. To be more precise, bPOE counts tail outcomes averaging to some specific threshold value. For instance, 4% of landfalling hurricanes in US have cumulative damage exceeding $50 billion (i.e., POE = 0.04 for threshold=$50 billion). It is estimated, that the average damage from the worst 10% of hurricanes is $50 billion. In terms of bPOE, we say bPOE=0.1 for threshold=$50 billion. bPOE shows that largest damages having magnitude around $50 billion have frequency 10%. bPOE can be considered as an important supplement to POE. We think that bPOE should be routinely calculated together with POE. This example shows that bPOE exceeds POE, which is why it is called Buffered Probability of Exceedance. The positive difference, bPOEPOE, can be interpreted as some “buffer.” The bPOE concept was recently developed as an extension of Buffered Probability of Failure (introduced by Rockafellar and Royset). bPOE has been derived from Conditional ValueatRisk (CVaR) characteristic of uncertainty. Actually, bPOE is an inverse function of CVaR and it inherits a majority of the exceptional mathematical properties of CVaR (which is a so called “coherent measure of risk”). Similar to CVaR, minimization of bPOE can be reduced to convex and Linear Programming.We will discuss two applications of bPOE concept. The first application considers the Cash Matching of a Bond Portfolio. We minimize bPOE that assets exceed liabilities. The second application uses bPOE in data mining. Currently, the Area Under the Receiver Operating Characteristics Curve (AUC) is standardly used to evaluate classification models. AUC can be presented as the probability that some discrete random value is below zero. We explored so called Buffered AUC (bAUC) as a counterpart of the standard AUC.
 March 24: TBA
 Time: 11am, Location: 1WP1027 / 100RR4095
 Abstract: TBA
 March 31: Eugene Feinberg (Stonybrook)
 Time: 11am, Location: TBA
 Abstract: TBA
 April 14th: Melike Gursoy (Rutgers)
 Time: 11am, Location: TBA
 Abstract: TBA
 April 21st: Waheed Bajwa (Rutgers)
 Time: 11am, Location: TBA
 Abstract: TBA
 May 5th: Lisa Hellerstein (NYU)
 Time: 11am, Location: 1WP1027 / 100RR4095
 Abstract: TBA