Risk, Importance and Value of Information
Speaker: Emanuele Borgonovo, Bocconi University, Department of Decision Sciences
Decisions involving significant budgetary and public consequences expose decision makers to complex cognitive tasks. The decision making process can be simplified by the specification of an acceptable level of risk. Once a decision has been made, the knowledge of events whose occurrence significantly impacts the baseline level of risk is gathered through risk importance measures. However, risk importance measures fail to convey information before a decision is made. We introduce a value of information approach that bridges this gap leading to a new importance measure. Our results establish an explicit link between risk metrics, risk importance measures and acceptable risk targets. We obtain analytically the expression of value of information as a function of the acceptable risk and of the probability of evidence, specifying the regions where it is increasing and decreasing. This result adds to previous literature on the dependence between value of information and its determinants. The new importance measure presents several advantages: it does not impose additional computational burden, it is computed without specifying the decision maker utility function, it makes importance measures, for the first time, usable also in a pre-decision setting, augmenting the palette of tools available in a relevant class of complex decision analysis problems. A realistic application illustrates the managerial insights.
Joint work with Alessandra Cillo, Bocconi University, Department of Decision Sciences and IGIER
Thursday, July 16th, 11:30 am – 12:30 pm
Control of a Fleet of Vehicles to Collect Uncertain Information in a Threat Environment
Speaker: Rajan Batta, State University of New York at Buffalo, Department of Industrial & Systems Engineering
We study the problem of controlling a fleet of vehicles to search and collect information reward within a specified mission time from a set of regions characterized by uncertain reward and a threat environment. We seek a decentralized time-allocation policy using pre-calculated routes to maximize the total reward. We demonstrate that sharing regions among vehicles is beneficial. However, shared regions make the decentralized time-allocation problem computationally intractable. To overcome this, we develop an approximate formulation using an independency assumption. This approximate model allows us to decompose, by vehicle, the time-allocation problem, and obtain an easily implementable policy that takes on a Markovian form. We derive a tight upper bound for the decentralized time-allocation policy using the obtained Markovian policy. We also develop a sufficient condition under which the approximate formulation becomes exact. A numerical study establishes the computational efficiency of the method, where only a few CPU seconds are needed for problems with a planning horizon of 300 time units and 40 regions, and demonstrates the benefit of using a region-sharing strategy. The numerical study also examines the fleet’s workload sharing behavior with respect to the cooperation factor (which measures the fused information reward gained from sharing), the mission duration and the search sequence.
Wednesday, May 6th, 5:00 pm – 6:00 pm
The Illusion of a Portfolio “Rebalancing Bonus”
Speaker: Michael Edesess, City University of Hong Kong, SEEM Department
The vast majority of financial advisors who advise individual and institutional clients on their investments recommend that the clients periodically “rebalance” their portfolios to restore their asset allocation after market movements cause it to drift. But careful investigation can find no evidence that the practice either enhances expected return or helps to control or reduce risk. Beneath the flawed reasoning that long advocated that rebalancing added a “bonus” lie three interesting mathematical inequalities. These can be shown to a high level of certainty using computer simulations to be true, but they appear not to have been mathematically proven as yet.
Tuesday, April 28th, 11:00 am – 12:00 pm
Modeling Durations using Estimating Functions
Speaker: Nalini Ravishanker, Department of Statistics, University of Connecticut, Storrs
Accurate modeling of patterns in inter-event durations is important in several applications because patterns in elapsed times between events contain valuable information. Since the Autoregressive Conditional Duration (ACD) model was first proposed, several classes of duration models have been studied in the literature. Developing fast and accurate methods for estimation based on long duration series is still an ongoing research problem. The framework of martingale estimating functions (Godambe, 1985) provides an optimal approach for developing inference for linear and nonlinear time series based on information on the first two conditional moments of the observed process. In situations where information about higher-order conditional moments of the process is also available, combined (linear and quadratic) estimating functions are more informative. This talk describes the approach in the context of nonlinear durations modeling. Recursive equations based on the nonlinear estimating functions and which permit fast, online estimation of parameters with large data sets are derived. Since the accuracy of the solutions to the recursive formulas benefits immensely from good starting values of the parameters, an approach for determining such starting values is proposed, and demonstrated in the context of the Log ACD models that are popular for durations modeling. A simulation study and an example of inter-event durations for IBM stock prices are used to illustrate the approach. Extensions to other classes of nonlinear time series models is also discussed.
This is joint work with A. Thavaneswaran, University of Manitoba.
Friday, April 24th, 11:00 am – 12:00 pm
Consumer Behavior, Revenue Management, and the Design of Loyalty Programs
Speaker: So Yeon Chun, Georgetown University, McDonough School of Business
While originally viewed as marketing efforts, consumer reward loyalty programs have grown substantially in size and scope during the last two decades, to the extent that they now significantly interact with other firm functions, including operations, accounting and finance.
In the first part of the talk, we consider a question regarding the design of loyalty programs, which has been at the forefront of recent changes in the airline industry: should frequent-flyer status be awarded based on the money spent or miles flown? We present a model for strategic consumers’ decision and endogenously derive the demand as a function of prices, loyalty program design, and premium status qualification requirements. We then discuss firm’s optimal pricing and design decisions, and provide managerial implications. [Based on joint work with Anton Ovchinnikov (Queen’s)]
In the second part of the talk, we consider a long-term dynamic management of loyalty programs, and study the problem of optimally setting the cash prices and point requirements (point prices). We develop a model that captures complex interactions between loyalty programs on several firm functions, such as the effect of loyalty programs on sales revenues, rewards redemptions, servicing costs, and earnings. We then discuss the structure of optimal policy, and provide managerial insights and prescriptive recommendations.
Based on joint work with Dan Iancu (Stanford) and Nikolaos Trichakis (HBS).
Friday, April 17th, 11:00 am – 12:15 pm
Project Management Decisions with Uncertain Targets
Speaker: Jeffrey M. Keisler, College of Management, University of Massachusetts Boston
Sophisticated quantitative techniques for project management, notably PERT/CPM, attempt to minimize the risk that the project will fail to meet fixed requirements. But requirements themselves often vary, necessitating qualitative techniques to get or keep projects on track. This new work replaces the assumption of fixed requirements with new assumptions that allow for (1) a fully decision analytic treatment of project management decision making under uncertainty that (2) can be easily incorporated into existing project management techniques.
Friday, April 10th, 11:00 am – 12:00 pm
A Flexible Observed Factor Model with Separate Dynamics for the Factor Volatilities and Their Correlation Matrix
Speaker: Sujit K. Ghosh, NC State University & SAMSI
In this article, we consider a novel regression model with observed factors. To allow for the prediction of future observations, we model the observed factors using a flexible multivariate stochastic volatility (MSV) structure with separate dynamics for the volatilities and the correlation matrix. The correlation matrix of the factors is time varying, and its evolution is described by an inverse Wishart process. We develop an estimation procedure based on Bayesian Markov chain Monte Carlo methods, which has two major advantages compared to existing methods for similar models in the literature. First, the procedure is computationally more efficient. Second, it can be applied to calculate the predictive distributions for future observations. We compare the proposed model with other multivariate volatility models using Fama-French factors and portfolio weighted return data. The result shows that our model has better predictive performance.
Friday, April 3rd, 11:10 am – 12:00 pm
Advice Overextension: How and when do people provide advice?
Speaker: Robin Dillon-Merrill, Georgetown University, McDonough School of Business
Many decision models rely on judgments from subject-matter experts (SMEs). The Department of Homeland Security has a model that requires over a thousand probability assessments from experts on topics ranging from weapon types, to border and transportation issues. In 2010, 7 SMEs from the Intelligence Community provided all of the required assessments to complete the model. While there is little doubt these 7 SMEs had knowledge depth in their fields of expertise, it is almost certain that some “extension” beyond their expertise base occurred. Much research has studied advice taking from experts, but relatively little is known about why people provide advice when they are not an expert on the subject. I will discuss several different behavioral laboratory studies that we have recently conducted that demonstrate that people are too willing to provide advice often extending beyond their expertise. I will also discuss the use of the helping power motivation scale (Frieze and Boneva, 2001) as a possible explanatory mechanism.
Friday, February 20th,11:00 am – 12:00 noon
The Potential of Servicizing as a Green Business Model
Speaker: Vishal Agrawal, Georgetown University, McDonough School of Business
It has been argued that servicizing business models, under which a firm sells the functionality of a product rather than the product itself, are environmentally beneficial. The main arguments are: First, under servicizing the firm charges customers based on the product usage. Second, the quantity of products required to meet customer needs may be smaller because the firm may be able to pool customer needs. Third, the firm may also have an incentive to offer products with higher efficiency. In this paper, we investigate the economic and environmental potential of servicizing business models. We endogenize the firm’s choice between a pure sales, a pure servicizing, or a hybrid model with both sales and servicizing options, the pricing decisions and, the resulting customer usage. We consider two extremes of pooling efficacy, viz., weak versus strong pooling. We find that under weak pooling servicizing leads to higher production impact but lower use impact. In contrast, under strong pooling when a hybrid business model is more profitable, it is also environmentally superior. However, a pure servicizing model may be environmentally inferior because it may not only lead to higher use impact but also a larger quantity of products even under strong pooling. We also examine the firm’s efficiency choice and find that, contrary to conventional wisdom, under servicizing the firm does not always offer higher efficiency products. Furthermore, we show that while under sales a more efficient product leads to higher customer usage, under servicizing it may actually lead to lower usage.
Friday, February 6th,11:00 am – 12:00 noon
Don’t Count on Poisson! Introducing the Conway-Maxwell-Poisson Distribution to Model Count Data
Speaker: Kimberly Sellers, Georgetown University, Department of Mathematics
Count data have become widely pervasive in various applied fields requiring data collection, including surveys, environmental studies, disease surveillance, and genetic studies. Classical statistical methods surrounding count data center around the Poisson distribution and associated methodologies, whose assumption is that the mean and variance equal. Real data, however, violate this basic principle in that the dataset displays some form of dispersion. The Conway-Maxwell-Poisson (COM-Poisson) distribution is a flexible alternative for count data that not only contains three classical distributions as special cases, but can more broadly accommodate either over- and under-dispersion. As a result, it has served as a motivating distribution for generalizing many classical statistical methods to allow for dispersion, including regression analysis, control chart theory, and stochastic processes. This talk will highlight some of these areas, and demonstrate their use in various applications.
Friday, January 30th, 11:15 am –12:15 pm
- Center for the Connected Consumer
- Center for Entrepreneurial Excellence (CFEE)
- Center for International Business Education and Research (CIBER)
- Senior Research Scholars & Fellows
- Center for Latin American Issues (CLAI)
- Center for Real Estate & Urban Analysis (CREUA)
- Professional Services
- European Union Research Center (EURC)
- Global Financial Literacy Excellence Center (GFLEC)
- The Growth Dialogue
- Institute for Brazilian Issues
- Institute for Corporate Responsibility
- Institute for Integrating Statistics in Decision Sciences
- International Institute of Tourism Studies
- Korean Management Institute (KMI)