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SA02 ■ SA02 02- West 102 A- CC Decision Analysis Applications in the Energy Industry Sponsor: Decision Analysis Sponsored Session Chair: Mazen Skaf, Partner & Managing Director, Strategic Decisions Group, 745 Emerson St, Palo Alto, CA, 94301, United States of America, MSkaf@sdg.com 1 - A Decision Quality Approach to Natural Gas Resource Uncertainty Robert Stibolt, Managing Director, Galway Group, L.P., 3050 Post Oak Blvd., Suite 1300, Houston, TX, 77056, United States of America, rstibolt@galwaygroup.com, Mazen Skaf The shale gas revolution started in North America has completely changed the dynamic of world natural gas markets. Implications are far-reaching, exciting, but also highly uncertain. For example, the shape of long-run supply curves depend on technology, geology, and geopolitics. This paper addresses these questions within a framework that embraces uncertainty while eschewing the illusion of precise prediction — a key to developing natural gas strategies that are robust. 2 - Hydrocarbon Resource Allocation Strategy for Industry Development in an Energy-rich Nation Mazen Skaf, Partner & Managing Director, Strategic Decisions Group, 745 Emerson St., Palo Alto, CA, 94301, United States of America, MSkaf@sdg.com, Thomas Seyller We present a DA-based process and system for optimizing the allocation of supplies of a hydrocarbon resource across proposed industrial projects taking into account: i) uncertainty in future supply and demand, ii) various economic, social, and environmental value measures, and iii) existing commitments for current domestic uses and industries. The value-focused approach resulted in a paradigm shift in the industrial development strategy as well as in the future focus of E&P activities. 3 - Solar Value Chain Decision Analysis in Oil and Gas Rich Countries Thomas Seyller, Sr. Engagement Manager, Strategic Decisions Group, 745 Emerson St., Palo Alto, CA, 94301, United States of America, tseyller@sdg.com, Dan Cornew We will explore how decision analysis can help oil and gas rich countries develop strategies around solar energy that maximize the total benefit to the nation – including economic value and costs, social benefits such as job creation, and environmental impacts. Strategies cover a wide range of possible incentives and mandates throughout the solar value chain, from manufacturing to deployment. ■ SA03 03- West 102 B- CC Risk and Uncertainty in Public Policy and Defense Sponsor: Decision Analysis Sponsored Session Chair: Jay Simon, Naval Postgraduate School, Defense Resources Management Institute, 699 Dyer Rd, Monterey, CA, 93943, United States of America, jrsimon@nps.edu 1 - Uncertainty Model for Rapid Updating of Target Positions Eva Regnier, Naval Postgraduate School, 699 Dyer Road, Monterey, CA, 93943, United States of America, eregnier@nps.edu, Dashi Singham In many security contexts, forces must allocate their assets based on incomplete information about targets’ positions. They frequently receive human intelligence describing past or future events related to target locations. We present an uncertainty model that allows for rapid updating, conditioning on past or future events. 2 - Ranking FDA Risks: The Medical Devices Case Robin Keller, University of California-Irvine, Irvine, CA, United States of America, lrkeller@uci.edu, Yitong Wang This talk focuses on a medical devices case study. Keller served as a decision analyst on the committee charged with suggesting how the FDA can compare risks across product categories (foods, cosmetics, drugs, etc.). The NRC book proposed a multiple objective decision analytic approach to ranking risks: “A Risk- Characterization Framework for Decision-Making at the Food and Drug Administration” is at http://dels.nas.edu/Report/Risk-Characterization- Framework-Decision/13156. INFORMS Phoenix – 2012 56 3 - Probability Segmenting: Managing Uncertainty in Military Drafts Jay Simon, Naval Postgraduate School, Defense Resources Management Institute, 699 Dyer Rd, Monterey, CA, 93943, United States of America, jrsimon@nps.edu, Jonathan Lipow When a nation chooses to enact a draft, this presents an interesting decision problem for those individuals eligible to be drafted: they can take actions which, at some cost, will reduce the probability of being selected. It is in society’s best interest to minimize these costly actions. We discuss “probability segmenting,” the idea of randomly assigning different probabilities to different individuals, as a possible method for improving the outcome at a societal level. ■ SA04 04- West 102 C- CC Advances in Decision Analysis Contributed Session Chair: Steven Kimbrough, Professor, University of Pennsylvania, 3730 Walnut St., Philadelphia, PA, 19104, United States of America, kimbrough@wharton.upenn.edu 1 - Extending the Multiscale Decision-making Model: From Two to Many Decision Alternatives Swathi Sudhaakar, Graduate Student, Virginia Tech, 116 Durham Hall, MC 0118, Blacksburg, VA, 24061, United States of America, swathips@vt.edu, Christian Wernz We generalize the multiscale decision making model by extending the agents’ action space from two to N decision alternatives. Multiscale decision theory (MSDT) models the interaction and decision interdependencies of agents in large enterprise systems. With our results, MSDT can now be applied to a wider range of applications and provides organizational and system designers with insights on how the number of decision alternatives affects agents’ decision processes. 2 - On the Axiomatization of the Satiation and Habit Formation Utility Models Ying He, PhD Candidate, University of Texas at Austin, Department Information, Risk & Operational Mg, 1 University Station, B6000, Austin, TX, 78712, United States of America, ying.he08irom@utexas.edu, James Dyer, John Butler We propose a preference condition called shifted difference independence to axiomatize a general habit formation and satiation (GHS) model. Since the GHS model can be reduced to either a general satiation model (GSa) or a general habit formation model (GHa), our theory also provides approaches to axiomatize both the GSa and the GHa model. By adding extra preference conditions, we axiomatize a GHS model with a linear habit function and a linear satiation function. 3 - Strategic Experimentation under Exclusivity Stefan Rampertshammer, Duke University, Fuqua School of Business, 100 Fuqua Drive, Durham, NC, 27516, United States of America, Stefan.Rampertshammer@duke.edu We study a model of strategic experimentation with exponential bandits in which we attach a prize and a level of exclusivity to the first success. We determine the socially optimal policy, examine equilibrium behavior, and show that the efficient outcome can be recovered in dominant strategy through a combination of exclusivity and prizes. 4 - Solution Pluralism Steven Kimbrough, Professor, University of Pennsylvania, 3730 Walnut St., Philadelphia, PA, 19104, United States of America, kimbrough@wharton.upenn.edu, Frederic Murphy Solution pluralism involves generating a plurality of optimal and near-optimal solutions for use by stakeholders. Modern heuristics and ever-advancing computational powers make solution pluralism a practicable approach to explore for near optimal solutions as a form of sensitivity analysis and multiple candidate solutions when the problem has multiple objectives. We discuss the concept and principles of solution pluralism and illustrate it with a real world application to electoral districting.
■ SA05 05- West 103 A- CC Journal of Quality Technology Invited Papers Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Daniel Apley, NorthWestern University, 2145 Sheridan Road, Evanston, IL, 60208, United States of America, apley@northwestern.edu 1 - Bayesian Modeling and Optimization of Functional Responses Affected by Noise Factors Enrique del Castillo, Distinguished Professor, Department of Industrial & Manufacturing Engineering & Department of Statistics, Penn State University, University Park, PA, United States of America, exd13@psu.edu, Bianca M. Colosimo, Hussam Alshraideh We present a Bayesian predictive modeling approach for functional response systems. The goal is to optimize the shape, or profile, of the functional response under a robust parameter design scenario in which there are controllable and noise factors. The method is illustrated with real examples and model building and diagnostics aspects of the assumed mixed effects model are discussed. Alternative modeling strategies beyond those in the JQT paper (2012, 44(2)) will be presented. 2 - Robust Leak Tests for Transmission Systems using Nonlinear Mixed-Effect Models Kamran Paynabar, University of Michigan, Ann Arbor, MI, United States of America, kamip@umich.edu, Judy Jin, John Agapiou, Paula Deeds Leakage of the transmission fluid or oil in powertrain systems can cause engine overheating and/or permanent damages. The inspection results of the amount of leakage at given testing times are sensitive to the tested part’s temperature, which varies from part to part but was not previously incorporated in the leak testing systems. The objective of this paper is to develop a robust leak testing system using nonlinear mixed-effect models, which is insensitive to the part temperature variations. 3 - A Variable-Selection-based Multivariate EWMA Chart for Process Monitoring and Diagnosis Kaibo Wang, Associate Professor, Tsinghua University, Department of Industrial Engineering, Beijing, China, kbwang@tsinghua.edu.cn, Wei Jiang, Fugee Tsung Fault detection and root cause identification are both important tasks in Multivariate Statistical Process Control. This paper proposes a Variable-Selectionbased MEWMA chart. The new chart first locates potentially out-of-control variables via variable selection, and then deploys such information in the monitoring statistics with the reduction in dimensionality providing increased sensitivity to out-of-control conditions. Its performance is studied via numerical simulations and real examples. ■ SA06 06- West 103 B- CC Efficient Learning in Stochastic Optimization Sponsor: Simulation Sponsored Session Chair: Ilya Ryzhov, University of Maryland, 4322 Van Munching Hall, College Park, MD, 21044, United States of America, iryzhov@rhsmith.umd.edu 1 - Dynamic Pricing under a General Parametric Choice Model Paat Rusmevichientong, Associate Professor, University of Southern California, Marshall School of Business, rusmevic@marshall.usc.edu, Josef Broder We consider a stylized dynamic pricing model in which a monopolist prices a product to a sequence of customers, who independently make purchasing decisions based on the price offered according to a general parametric choice model. The parameters of the model are unknown to th INFORMS Phoenix – 2012 57 SA07 e seller, whose objective is to determine a pricing policy that minimizes the regret. We characterize the regret profile for this problem. 2 - Kernelized Approximate Linear Programming Vivek Farias, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, United States of America, vivekf@mit.edu, Ciamac Moallemi, Nikhil Bhat We present a practical, non-parametric ADP algorithm that enjoys graceful, dimension-independent approximation and sample complexity guarantees by ‘kernelizing’ a recent mathematical program for ADP (the ‘smoothed’ approximate LP). A computational study on a controlled queueing network shows that our procedure is competitive with state of the art parametric ADP approaches that employ carefully tailored approximation architectures. 3 - Sequential Filtering of Glaucoma Progression Jonathan Helm, Assistant Professor, Indiana University, Kelley School of Business, ODT, 1309 East Tenth Street, Bloomington, IN, 47405, United States of America, jhelm@umich.edu, Mariel Lavieri, Mark Van Oyen, Joshua Stein, MD We develop novel filtering algorithms for monitoring the disease progression of glaucoma patients. The models are validated using patient data from two multicenter clinical trials. 4 - Ranking and Selection with Unknown Correlation Structures Huashuai Qu, University of Maryland, College Park, MD, United States of America, huashuai@math.umd.edu, Ilya Ryzhov, Michael Fu We create the first computationally tractable Bayesian statistical model for learning unknown correlations among alternatives in fully sequential ranking and selection. We can learn the unknown values and unknown correlations simultaneously with the model. A Bayesian procedure is derived to allocate simulations based on the value of information. We test the model and algorithm in a simulation study motivated by the problem of optimal wind farm placement, and obtain encouraging empirical results. ■ SA07 07- West 104 A- CC Joint Session DM/HAS: Data Mining in Health Care Sponsor: Data Mining & Health Applications Society Sponsored Session Chair: Shengfan Zhang, Assistant Professor, University of Arkansas, 4207 Bell Engineering Center, Fayetteville, AR, 72701, United States of America, shengfan@mail.uark.edu 1 - Analyzing Human Microbiome Data Paul Brooks, Assistant Professor, Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, P.O. Box 843083, Richmond, VA, 23284, United States of America, jpbrooks@vcu.edu, Microbiome Consortium The human microbiome is the community of microbes that populate habitats on and around various body sites. Advances in high-throughput sequencing facilitate investigations into the impact of the microbiome on physiology and disease. Microbiome data presents unique challenges for analysis. In this talk, we present methods for analysis that seek to address some of these challenges. 2 - Outcome and State Transition Modeling for Adaptive Interdisciplinary Pain Management Aera LeBoulluec, PhD Student/GRA, University of Texas- Arlington, 2205 Hunter Place Lane, Arlington, TX, 76006, United States of America, aera.leboulluec@mavs.uta.edu, Li Zeng, Victoria Chen In this research, we seek adaptive treatment strategies for interdisciplinary pain management using data from the Eugene McDermott Center for Pain Management at the University of Texas Southwestern Medical Center at Dallas. An adaptive dynamic programming approach is formulated to improve current and future pain outcomes. Outcome and state transition modeling handle the problem of endogeneity via an inverse probability of treatment weighting approach. 3 - Machine Learning with Operational Costs Cynthia Rudin, Assistant Professor, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, United States of America, rudin@mit.edu, Theja Tulabandhula We aim to merge theway estimation algorithms are designed with how they are used for a subsequent task. We consider the operational cost of carrying out a policy, based on a predictive model. The operational cost becomes a regularization term in the learning algorithm’s objective, allowing either an optimistic or pessimistic view of possible costs. Limiting the operational cost reduces the hypothesis space, and can thus improve generalization. We bound the complexity of such hypothesis spaces.
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