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SA73 ■ SA73 73- Suite 324- Hyatt Economics Contributed Session Chair: Lorena Berumen, Professor, Universidad Panamericana, Augusto Rodin 498, Col. Insurgentes Mixcoac, Mexico, DF, 03920, Mexico, laberumen@up.edu.mx 1 - Estimating Real Money Balances for a Selected Asian Country: A Simultaneous Equation Model Abul Jamal, Professor, Southeastern Louisiana University, College of Business, Hammond, LA, 70402, United States of America, ajamal@selu.edu, Yu Hsing Equilibrium real balances and interest rates have been the subject of extensive research. Most studies presume the supply of money to be determined by the central bank and that the interest rate is only determined by the demand for money In this paper we use both the demand for and supply of money to estimate real money balances in a selected Asian country. 2 - The Volatility Characteristics of China’s GEM Market and the Countermeasures Jie Xiong, Xi’an Jiaotong University, Postbox 2230, School of Management, No.28 Xianning West Road, Shaanxi, Xi’an, China, xiongjie@stu.xjtu.edu.cn, Ju’e Guo, Dong Qian Select the data of China’s GEM index from June 1, 2010 to October 28, 2011. EMD method is used to analyze factors that influence the fluctuations of index. Three factors are found: trend, low-frequency component and high-frequency component. Than we use the AR-EGARCH-M model to analyze the dynamic relationship between the returns and volatility of the GEM market and give the corresponding countermeasures. 3 - The Role of Product Variety and Maturity in the Market Valuation of IT Intensive Firms Wael Jabr, Assistant Professor, University of Calgary, 2500 University Dr NW, Calgary, AB, T3A6L5, Canada, wjabr@ucalgary.ca, Eric (Zhiqiang) Zheng The pervasiveness and intensive use of Information Technology in every aspect of firm processes have enabled firms to maintain a portfolio of diverse products and allowed improved performance. In this study, we address the question of how companies should best manage their portfolio of IT products under disruptive environments. We hypothesize that diversity and maturity of products play an integral part in the firm’s performance and its market value. 4 - A Panel Data Model to Explain Technical Change through IT in Latin America Lorena Berumen, Professor, Universidad Panamericana, Augusto Rodin 498, Col. Insurgentes Mixcoac, Mexico, DF, 03920, Mexico, laberumen@up.edu.mx, Jen Ai de la Cruz, Gilberto González Pèrez, Margarita Hurtado, Irma Glinz In this paper we evaluate the IT’s impact over the conformation of the export profile of Argentina, Brazil and Mexico through a dynamic panel data model. We established that the adaptation and learning process is a function of expenditure on education and innovation, infrastructure and the dynamic of consumption. <strong>Sunday</strong>, 11:00am - 12:30pm ■ SB01 01- West 101- CC Genetic Algorithms and Ant Colonies Contributed Session Chair: Maria Woodside-Oriakhi, Assistant Professor, The College of the Bahamas, Oakes Field, Nassau, GT2602, Bahamas, moriakhi@cob.edu.bs 1 - Genetic Algorithm and New Heuristic in Block Relocation Problem - Fuel Reduction Mazen Hussein, PhD Candidate, University of Wisconsin, Milwaukee, 1559 N. Prospect Ave., Apt # 305, Milwaukee, WI, 53202, United States of America, mhussein@uwm.edu, Matthew Petering This research considers a variation of the block relocation problem (BRP). A set of identically-sized items is to be retrieved from stacks in a specific order using the minimum fuel consumption. In this work we consider container weight in fuel consumption calculations and explicitly tracking the containers moves. We developed a new heuristic that is embedded in a genetic algorithm. Results show that the methodology is effective in identifying near-optimal parameter settings. INFORMS Phoenix – 2012 80 2 - Evaluation of Multi-objective Genetic Algorithms on a Preventive Maintenance Scheduling Model Kamran Moghaddam, Assistant Professor of Systems Engineering, Southern Polytechnic State University, 1100 South Marietta Parkway, Marietta, GA, 30060, United States of America, kmoghadd@spsu.edu A multi-objective optimization model to find optimal preventive maintenance and replacement schedules of a multi-component system is used to evaluate performance of different multi-objective genetic algorithms. This multi-objective, nonlinear, mixed-integer model is an NP-hard optimization model that cannot be solved using exact algorithms. The different performance metrics are used to evaluating closeness and diversity of non-dominated solutions found by multiobjective genetic algorithms. 3 - Ant Colony Optimization for the Cardinality Constrained Portfolio Problem Maria Woodside-Oriakhi, Assistant Professor, The College of the Bahamas, Oakes Field, Nassau, GT2602, Bahamas, moriakhi@cob.edu.bs This paper extends the mean-variance model of Markowitz to include the cardinality constraints This limits the number of assets held in the portfolio and sets bounds on the proportion of an asset held. An ant colony optimization heuristic algorithm is presented using data from seven real world market indices. ■ SB02 02- West 102 A- CC Joint Session: DAS/ENRE-Energy: Expert Elicitation of Energy Technologies: Theory and Practice Sponsor: Decision Analysis & Energy, Natural Res & the Environment/Energy Sponsored Session Chair: Erin Baker, University of Massachusetts, Amherst, MA, United States of America, edbaker@ecs.umass.edu 1 - Elicitations of Energy Penalties for Carbon Capture Technologies Karen Jenni, Principal, Insight Decisions, LLC, 2200 Quitman St., Denver, CO, 80212, United States of America, kjenni@insightdecisions.com, Gregory Nemet, Erin Baker We conducted elicitation interviews with 15 experts in carbon capture technologies, exploring the effects of different energy policies on the viability of and the energy penalties (EP) for 6 technologies, by 2025. Expert opinions varied substantially both within a specific technology and between technologies. A worldwide carbon pricing scenario leads to a decrease in the mean EP of 1% to 10% across the technologies; a scenario of increased US government R&D leads to a decrease of 6% to 14%. 2 - Online Expert Elicitation Tools: Development and Case Studies in Energy Technology Assessment Steve Davis, Senior Research Associate, Carnegie Institution of Washington, 260 Panama Strett, Stanford, CA, 94305, United States of America, sjdavis@dge.stanford.edu, Karen Fries We are developing innovative tools to facilitate online expert elicitation in order to reduce the time and resources required to characterize uncertainty and the sources/scope of disagreement among experts. Such characterization is critical to directing R&D efforts on technical topics. We focus on experts’ subjective judgments on energy technologies. Here we demonstrate the tools under development and present results of testing with experts on solar PV and airborne wind energy systems. 3 - Uncertainty and Surprise in Energy Forecasts: The Cost of Photovoltaics Max Henrion, CEO, Lumina Decision Systems, 26010 Highland Way, Los Gatos, CA, 95033-9758, United States of America, henrion@lumina.com, Evan Sherwin Power system planners and other energy analysts would like to know the future performance and costs of renewable and fossil energy technologies. Several research groups have used expert elicitation to assess uncertainty using probability distributions for these quantities. We compare projections for photovoltaics. Recent dramatic and unexpected cost reductions suggest systematic overconfidence in expert assessments.
4 - Harmonization and Aggregation of Energy Technology Elicitations Erin Baker, University of Massachusetts, Amherst, MA, United States of America, edbaker@ecs.umass.edu There have been a number of expert elicitations performed on energy technologies recently, including 5 studies on solar and 7 on CCS. In this project we are attempting to harmonize and then aggregate the data from disparate studies. Harmonizing the data across the elicitations has a number of specific challenges, as surveys differ along several dimensions, including the metrics elicited, the timing, and the levels of R&D funding considered. ■ SB03 03- West 102 B- CC Behavioral Decision Making Sponsor: Decision Analysis Sponsored Session Chair: Manel Baucells, Rand Corporation and University Pompeu Fabra, 1776 Main Street, Santa Monica, CA, 90401, United States of America, manel.baucells@upf.edu 1 - Reference Price Comparison, Mental Accounting, and Anomalies in Consumption-Payment Decisions Woonam Hwang, PhD Student, London Business School, Regent’s Park, London, NW1 4SA, United Kingdom, whwang.phd2009@london.edu, Manel Baucells We propose a positive theory of consumer choice. When evaluating the hedonic benefits of consumption and payment decisions, consumers engage in a double comparison: one between the benefit of consumption and the reference price, and another between the reference price and the actual price. These comparisons are governed by proposed laws of mental accounting. Under standard assumptions, our model predicts various consumer choice anomalies such as sunk cost effects and the flat-rate bias. 2 - Felicity during Anticipation and Recall Manel Baucells, Rand Corporation and University Pompeu Fabra, 1776 Main Street, Santa Monica, CA, 90401, United States of America, manel.baucells@upf.edu, Silvia Bellezza We propose a set of psychologically plausible assumptions that yield the felicity, moment-utility, function of a given episode, including the anticipation and recall. The felicity function is U-shaped during anticipation. Shortening anticipation makes the event more surprising, and leads to an increase in utility from recall. The model has prescriptive recommendations to help cope for negative events. We provide empirical evidence in favor of the main implications of the model. 3 - Model-based Correction of Overprecision Bias in Subjective Confidence Intervals Venkat Prava, Johns Hopkins University, 3400 N. Charles Street, 313 Ames Hall, Baltimore, MD, 21218, United States of America, avadhanulu@gmail.com, Robert Clemen, Benjamin Hobbs, Melissa A. Kenney Elicited confidence intervals tend to be overly narrow. We propose a model to correct this bias based on the normal and bi-normal distributions. Model parameters are estimated by adjusting expert responses using a sufficient number of questions with known answers. We apply these models to datasets of expert elicitations, testing model validity with holdout analysis and quantile-quantile plots. The bi-normal model fits better, indicating an asymmetric overprecision bias. ■ SB04 04- West 102 C- CC Uncovering Risk and Probabilities Contributed Session Chair: Saurabh Bansal, Assistant Professor, Pennsylvania State University, 405 Business Building, University Park, PA, United States of America, sub32@psu.edu 1 - Stakeholder Perspectives on Risk Scenarios for EMR Implementing Chi-Chang Chang, Assistant Professor, Chung-Shan Medical University Hospital, 110, Sec. 1, Chien-Kuo N. Rd., Taichung, Taiwan-ROC, changintw@gmail.com, Yu-Hung Cheng The purpose of this research is to elicit the risk scenarios and stakeholders’ risk priority during EMR project implementing. We adopt the intuitionistic fuzzy theory for risk management. We conducted a field study for a medical center of middle Taiwan to comment the risk of identification, analyses, measurement and INFORMS Phoenix – 2012 81 control, respectively. Based on the result of this study, we found that the risk measurement of the IFS be able to elicit the collective risk scenarios ranking of stakeholders. 2 - A New Method for Valuing Probability Assessment Brad Powley, PhD Candidate, Stanford University, 475 Via Ortega, Stanford, CA, 94305, United States of America, bpowley@stanford.edu A fundamental step in decision analysis is the encoding of probabilities. This paper introduces a method for valuing probability assessments on a continuous, uncertain quantity in a way that recognizes an expert’s inconsistency and the cost of the assessment. I will demonstrate this method using the example of a pharmaceutical company’s decision whether to market versus license a drug. 3 - Caveats of Decision Rules for Comparing Alternatives under Incomplete Preference Information Antti Punkka, Systems Analysis Laboratory, Aalto University School of Science, Otakaari 1 M, Espoo, 02150, Finland, antti.punkka@aalto.fi, Ahti Salo We show that the recommendations provided by several decision rules may depend on the normalization of the additive value function. This means, for instance, that these rules may exhibit rank reversals if the two consequences that are selected for normalizing the value function are replaced by other consequences. As a remedy, we propose the analysis of all rankings generated by those value functions that are consistent with the stated incomplete preference information. 4 - Profile-based Bayesian Method for Assessing Process Risk Chang-Ho Chin, Associate Professor, Kyung Hee University, Department of Industrial and Management, Yongin-si, Korea, Republic of, chin@khu.ac.kr, Aida Mercado Hernandez A process is a sequence of interdependent and linked activities to accomplish a predetermined goal. The early detection of process instances terminated without reaching the goal reduces the loss of resources. We propose a likelihood-based Byesian method to estimate the abnormal termination probability of process instances in real time. The estimated probability could be used as a precaution for the worst or a basis for go/no-go decision. 5 - Probability Elicitation in the Presence of Elicitation Errors Saurabh Bansal, Assistant Professor, Pennsylvania State University, 405 Business Building, University Park, PA, United States of America, sub32@psu.edu, Genaro Gutierrez We develop two analytical approaches to determine the parameters of a distribution using elicited fractile values in the presence of elicitation errors. Both approaches minimize the variance in the estimation error for the parameters of the distribution. We show that both these approaches should be preferred over approaches that ignore elicitation error and/or elicit only a specific set of fractiles. ■ SB05 SB05 05- West 103 A- CC IIE Transactions Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Jianjun (Jan) Shi, The Carolyn J. Stewart Chair and Professor, Georgia Institute of Technology, 765 Ferst Drive, Room 109, Atlanta, GA, 30332-0205, United States of America, jianjun.shi@isye.gatech.edu 1 - A Bayesian Framework for Online Parameter Estimation and Process Adjustment using Categorical Observations Kaibo Wang, Associate Professor, Tsinghua University, Department of Industrial Engineering, Beijing, China, kbwang@tsinghua.edu.cn, Jing Lin In certain manufacturing processes, low resolution categorical observations could be obtained as feasible and low-cost surrogates for accurate readings. In this work, a new online approach for parameter estimation and run-to-run process control using categorical observations is developed. The new approach is built in the Bayesian framework; it provides a convenient way to update parameter estimates when categorical observations arrive gradually in a real production scenario. 2 - Multiscale Monitoring of Autocorrelated Processes using Wavelets Analysis Kamran Paynabar, University of Michigan, Ann Arbor, MI, United States of America, kamip@umich.edu, Judy Jin, Huairui Guo This paper proposes a new method to develop multiscale monitoring control charts for an autocorrelated process that has an underlying unknown ARMA (2,1) structure. The Haar wavelet transform is used to obtain effective monitoring statistics by considering the process dynamic characteristics in both time and frequency domains. Through several simulation scenarios, we show that proposed control charts outperform other existing charts in terms of detecting changes in an autocorrelated process.
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