2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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208A-MCC
Spatial Risk and Decision Analysis
Sponsored: Decision Analysis
Sponsored Session
Chair: Gilberto Montibeller, Loughborough University, Loughborough,
United Kingdom,
1 - Spatial Risk Analysis In Emergency Management
Nikolaos Argyris, Loughborough University, Loughborough,
United Kingdom, Simon French
In any emergency, there is a great deal of uncertainty, often geographical
or spatio-temporal uncertainty. For instance, an industrial accident may lead to a
plume of contamination, putting populations at risk downwind. The path of a
hurricane provides another example that is of obvious concern to emergency
managers. We consider how analysts can communicate spatio-temporal
uncertainty to those handling the crisis. We review the somewhat limited
literature on the representation of spatial uncertainty on maps. We note that
many cognitive issues arise and that the potential for confusion is high. We then
make some suggestions based upon the idea of presenting multiple scenarios.
2 - Spatial Preference Functions For Risk Analysis
Jay Simon, American University, ,
L Robin Keller
When outcomes are defined over a geographic region, measures of spatial risk
regarding these outcomes can be more complex than traditional measures of risk.
One of the main challenges is the need for a cardinal preference function that
incorporates the spatial nature of the outcomes. We explore preference conditions
that will yield the existence of spatial measurable value and utility functions, and
discuss their application to spatial risk analysis.
3 - Multi-criteria Spatial Risk Analysis For Resource
Allocation Decisions
Gilberto Montibeller, Full Professor of Management Science,
Loughborough University, Loughborough University,
United Kingdom, , Valentina Ferretti
There is a broad literature on spatial multi-criteria evaluation in the
environmental domain and some attempts of conducting risk analysis in this
context. Most of these attempts neither employ a proper decision analytical
framework nor provide a clear conceptualization for allocating resource on
mitigating actions. To address these weaknesses we conceptualize a multi-criteria
spatial risk analysis assessment, which may support spatial decision-making
processes. The framework employs expected multi-attribute utility and portfolio
decision analysis concepts in a spatial context. A case study on flooding
evaluation and defense building illustrates its application in practice.
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208B-MCC
Modeling of Uncertainty and Preference in
Decision Analysis
Sponsored: Decision Analysis
Sponsored Session
Chair: Christopher Hadlock, Austin, TX, United States,
Co-Chair: Robert Hammond, Chevron, Houston, TX, United States,
1 - Johnson Quantile-parameterized Distributions
Christopher Hadlock, The University of Texas at Austin,
J. Eric Bickel
It is common practice in decision analysis to elicit quantiles of continuous
uncertainties, and then fit a continuous probability distribution to the
corresponding probability-quantile pairs. This process is inconvenient because it
requires access to a curve-fitting process, and the best-fit distribution will often
not honor the assessed points. By strategically extending the Johnson Distribution
System, we design the new J-QPD distribution system, which is directly
parameterized by and honors any symmetric percentile triplet of low-base-high
assessments in conjunction with known support bounds, eliminating the need to
apply a fit procedure.
2 - The Metalog Distributions
Thomas Keelin, Keelin Reeds Partners, 770 Menlo Avenue,
Suite 230, Menlo Park, CA, 94025, United States,
The metalog distributions constitute a new system of continuous univariate
probability distributions designed for flexibility, simplicity, and ease of use. The
system includes quantile-parameterized unbounded, semi-bounded, and bounded
distributions, each of which offers shape flexibility that compares favorably with
Pearson distributions and others. Applications in fish biology and hydrology show
how metalogs enable unprecedented insight into CDF data. A decision analysis
application shows metalogs aided a decision that would have been made wrongly
based on traditional discrete methods.
3 - Reexamining The Viability Of Scoring Rules
Zachary Smith, The University of Texas at Austin,
J. Eric Bickel
There are a number of widely used proper scoring rules used to elicit and rank
expert opinions. However, not all rules have the property of being additive, in the
sense that the score for marginal distributions and joint distributions are
comparable. Scoring rules without this property are sensitive to the presentation
of information as well as the information itself. We characterize scoring rules that
are additive, and consider practical implications for some commonly-used rules.
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209A-MCC
Panel: Systemic Risk Issues in Counterparty Risk and
Central Clearing
Invited: Risk and Compliance
Invited Session
Moderator: Agostino Capponi, Columbia University, 500 West 120th
street, New York, NY, 10027, United States,
1 - Panel on Systemic Risk Issues In Counterparty Risk And
Central Clearing
Agostino Capponi, Columbia University,
The panel is formed by six leading experts in the area of systemic risk and central
clearing counterparties. The discussion will be centered on the economics of
clearinghouses and their role in promoting financial stability. Pro and cons of
central clearing will be highlighted and possible unintended consequences will be
discussed.
2 - Panelist
John Birge, Chicago Booth School of Business,
3 - Panelist
Akhtarur Siddique, Office of Comptroller of Currency,
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209B-MCC
Dynamic Pricing with Substitution, Learning and
Reference Price Effects
Sponsored: Revenue Management & Pricing
Sponsored Session
Chair: Candace Arai Yano, University of California-Berkeley, IEOR
Dept. and Haas School of Business, Berkeley, CA, 94720, United States,
1 - Optimal Use And Replenishment Of Substitutable Raw Materials
In Non-Stationary Capacitated Systems With Dynamic Price
Izak Duenyas, University of Michigan-Ann Arbor,
We consider a make-to-order setting where a firm can use either of two kinds of
materials (or their mixture) to produce an end product using a shared production
line with stochastic capacity. The materials are substitutable but one has a higher
conversion rate and the other is cheaper, and their availability is uncertain. We
show that a Use-down-to/Balancing Production Policy and modified Order-up-to
Ordering Policy is optimal. Although the optimal policy is hard to compute using
brute-force due to curse of dimensionality, we use its structure to develop an
algorithm that solves for it efficiently. We also conduct sensitivity analysis of the
optimal policy and find counter intuitive results.
2 - A Squared-coefficient-of-variation Rule For Learning And Earning
N. Bora Keskin, Duke University, Durham, NC, United States,
Consider a price-setting firm that sells products over a continuous time horizon.
The firm is uncertain about the sensitivity of demand to price changes and
updates its prior belief on an unobservable sensitivity parameter by observing
demand responses. We derive and solve a PDE to show how the value of learning
should be projected onto prices in an optimal fashion.
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