I2SDS Seminars: Spring 2014

Estimation and Mitigation of Downside Risks in Project Portfolio Selection

Speaker: Janne Kettunen, The George Washington University


When projects are selected based on uncertain ex ante estimates about how much value they will yield ex post, projects whose values have been overestimated are more likely to be selected. Thus, the estimated value of the project portfolio, expressed as the sum of value estimates for selected projects, tends to be higher than the realized portfolio value obtained as the sum of ex post values of these projects. It is known that the resulting overestimation of expected portfolio value can be eliminated by employing revised value estimates based on Bayesian updating (Vilkkumaa et al., 2014). In this work, we show that the uncertainties in estimating projects’ values, combined with the selection of a subset of projects, has major implications for the development of risk estimates about portfolio value. First, if downside risks are measured in terms of lower percentiles of the distribution of portfolio value, the estimates will have systematic upward or downward bias depending on correlations between project values and between estimation errors. Second, even if Bayesian updating of value estimates in many cases improves the accuracy of risk estimates, it will not yield unbiased estimates. Third, to improve the accuracy of risk estimates, we propose the use of calibration curves which can be derived by analyzing past selection processes or by simulating the portfolio selection process. We consider the introduction of risk constraints as well, but show that this approach may yield risk estimates which are too optimistic in that the estimated portfolio values in lower percentiles are well above their actual levels.

Friday, May 9th, 3:00 PM – 4:00 PM
Duques Hall, Room 553 (801 22nd Street NW)

Visualizing Survey Operations

Speaker: Fred Highland, Lockheed Martin Information Systems and Global Solutions


Modern survey data collection systems must balance cost and quality while supporting multiple response modes (paper, internet, telephone and personal interview) and addressing unpredictable respondent behavior. The next generation of survey systems utilizes adaptive methods to address these issues adding additional dynamics to already complex systems and raising new challenges to operations management. The presentation will discuss the problem of visualizing this complex collection of information and how it can be used to manage future survey operations. It will provide an overview of modern survey systems and adaptive methods, a review of previous survey management approaches and operations concepts, and begin the discussion on visualizing the operation of the next generation of multi-modal adaptive survey systems.

Thursday, May 8th, 3:00 PM – 4:00 PM
Duques Hall, Room 353 (801 22nd Street NW)

Monthly Clinic Assignments for Residents

Speaker: Jonathan F. Bard, University of Texas at Austin


Upon receiving their degree, medical school graduates enter residencies or training programs in specific specialties. As part of this training, each intern and resident, collectively called housestaff, must spend one or two half-day sessions a week in their assigned continuity clinic. The exact amount of time is a function of their current monthly rotation. In fact, it is the variable clinic hour requirements that drive the scheduling process, and is what distinguishes this problem from most personnel scheduling problems. From the program director’s point of view, the objective is to both maximize clinic hours and minimize the number of violations of a prioritized set of goals while ensuring that certain clinic-level and individual constraints are satisfied. The corresponding problem is formulated as an integer goal program and a three-phase methodology is proposed to find solutions. After pre-processing, a commercial solver is used to obtain tentative solutions and then improvements are made in a post-processing step. The effectiveness of the methodology is demonstrated by analyzing eight monthly rosters provided by the Internal Medicine Residency Program at the University of Texas Health Science Center in San Antonio. On average, we were able to assign up to 7.62% more clinic sessions with far fewer violations of the goals than were seen in the actual schedules worked.

Monday, May 5th, 11:00 AM – 12:00 PM
Duques Hall, Room 553 (801 22nd Street NW)

Counter-terrorism Decisions Using Asymmetrically Prescriptive/Descriptive Game Theory

Speaker: Jason R. W. Merrick, Virginia Commonwealth University


Counter-terrorism decisions have been an intense area of research in recent years. Both decision analysis and game theory have been used to model such decisions, and more recently approaches have been developed that combine the techniques of the two disciplines. In this talk, we discuss techniques from decision analysis and game theory and more recent hybrid approaches, intelligent adversary risk analysis and adversarial risk analysis. We continue by questioning the descriptive validity of the adversarial parts of the model. Classical game theory assumes Expected Utility Theory preferences. However, a growing body of work suggests that while EUT is the best prescriptive model of preferences, it is not the best descriptive model. Prospect theory is presently the leading descriptive theory of choice under uncertainty, but prior work in game theory has only considered special cases of prospect theory for risk. We propose an asymmetrical prescriptive/descriptive approach to game theory that uses a hybrid of expected utility and prospect theory preferences over risk. Our results are applicable to decision analysis situations where one is advising a client decision maker what they should do in a competitive, interactive situation, while modeling what the other decision makers will do. We study the effects of this approach in several sequential decisions, including whether to screen containers entering the US for radioactive materials, how to an incumbent company should respond to a new entrant in their market, and price setting in a simple supply chain.

Friday, April 25th, 3:30 PM – 4:30 PM
Duques Hall, Room 353 (801 22nd Street NW)

An Empirical Analysis of Price, Quality, and Incumbency in Procurement Auctions

Speaker:Professor Tunay Tunca, Department of Decision, Operations, and Information Technology at Robert H. Smith School of Business, University of Maryland


The use of multi-attribute auctions for procurement of products and services when both price and quality matter is becoming more frequent. Such auctions often employ scoring rules and are open-ended in winner determination. Yet there is a significant gap in the literature on studying the efficiency of these procurement mechanisms. In this paper, providing a theoretical model and utilizing data from legal service procurement auctions, we study how open-ended scoring auctions can be used effectively in procurement, and demonstrate the roles supplier quality and incumbency play in this process. We demonstrate that open-ended auctions can generate substantial savings to a buyer without compromising quality. We study the underlying mechanism and show how the auction format can work to achieve such performance. We find that the buyer’s revealed preferences significantly differ from her stated preferences. Finally, we contribute to the understanding of the role of incumbency in procurement auctions by providing evidence that what may be perceived as incumbency bias can in fact be a revelation of preference for quality.

Friday, April 18th, 11:00 AM – 12:00 PM
Duques Hall, Room 353 (801 22nd Street NW)

Mathematical Programming Approaches for Multi-vehicle Path Coordination Under Communication Constraints

Speaker:Professor Hande Benson, Decision Sciences Department, Drexel University


We present a mathematical programming approach for generating time-optimal velocity profiles for a group of vehicles that must follow fixed and known paths while maintaining communication connectivity. Each vehicle is required to arrive at its goal as quickly as possible and stay in communication with a certain number of other vehicles in the arena throughout its journey. This problem arises frequently in emergency response, particularly search-and-rescue efforts, in routing fleets of driverless vehicles, and in urban security and warfare applications. We formulate the centralized problem as a discrete-time mixed-integer nonlinear programming problem (MINLP) with constraints on vehicle kinematics, dynamics, collision avoidance, and communication connectivity. We investigate the efficient solution of the MINLP and the scalability of the proposed approach by testing scenarios involving up to fifty (50) vehicles. Finally, we present results on the corresponding decentralized problem.

Friday, April 4th, 2:30 PM – 3:30 PM
Duques Hall, Room 453 (801 22nd Street NW)

Infrastructure Network Protection

Speaker:Melike Baykal-Gürsoy, Department of Industrial and Systems Engineering, Rutgers University


Network security against possible attacks involves making decisions under uncertainty. In this talk, we present game-theoretic models of allocating defense effort among nodes of a network. We consider both the static and dynamic games. We derive the unique equilibrium strategy pair in closed form for a simple static game. We consider the case that the network’s defender does not know the adversary’s motivation for intruding on the network – e.g., to bring the maximal damage to the network or to infiltrate into the network for other purposes. We illustrate and analyze the consequences of taken this uncertainty into account with a simple Bayesian game model. We show how information about this factor can be used to increase the efficiency of the optimal protection strategy. We also prove that the attack strategy has node-sharing structure. Presentation will conclude with a discussion of future research.

Friday, Feb 28th, 11:00 AM – 12:00 PM
Location: Duques Hall, Room 453 (801 22nd Street NW)

Incorporating unobserved heterogeneity in Weibull survival models: A Bayesian approach

Speaker:Mark Steel, Department of Statistics, University of Warwick, UK


We propose flexible classes of distributions for survival modelling that naturally deal with both the presence of outlying observations and unobserved heterogeneity. We present the family of Rate Mixtures of Weibull distributions, for which a random effect is introduced through the rate parameter. This family contains i.a. the well-known Lomax distribution and can accommodate flexible hazard functions. Covariates are introduced through an Accelerated Failure Time model and we explicitly take censoring into account. We construct a weakly informative prior that combines the structure of the Jeffreys prior with a proper (informative) prior. This prior is shown to lead to a proper posterior distribution under mild conditions. Bayesian inference is implemented by means of a Metropolis-within-Gibbs algorithm. The mixing structure is exploited in order to provide an outlier detection method. Our methods are illustrated using two real datasets, one concerning bone marrow transplants and another on cerebral palsy.

Thursday, Feb 13th, 4:00 pm – 5:00 pm
Location: Phillips Hall, Room 109 (801 22nd Street NW)

Overcoming the Planning Fallacy

Speaker:Yael Grushka-Cockayne, Darden School of Business, University of Virginia


How an organization manages its projects is critical to its success. Yet, firms routinely experience the Planning Fallacy: projects are delivered late, over-budget, or with reduced scope. We investigate project performance and describe our work with UK Department of Transport, Network Rail on how to plan for the planning fallacy. Using concepts from foresting and aggregating expert opinions, we propose methods for overcoming the fallacy.

Friday, January 31th, 11:00AM-12:00PM
Location: Duques 453 (2201 G Street, NW)

Storming Towards Scalable Bayesian Sequential Inference

Speaker: Simon Wilson, School of Computer Science and Statistics Trinity College, Dublin, Ireland


In this talk I describe our initial work with implementing sequential inference methods using Storm. Storm is an open source, distributed, fault tolerant framework for the processing of streaming data. It provides a simple Java interface which supports the creation of powerful streaming algorithms which are automatically parallelised and distributed across a computational cluster. I will briefly describe how Storm works and show how to implement some simple sequential inference algorithms. In conclusion, I discuss how suitable Storm is for implementing Bayesian sequential algorithms such as the particle filter.

Friday, January 10th, 11:00AM-12:00PM
Location: Funger 620 (2201 G Street, NW)