Jay Simon

Decision Analysis with Geographically Varying Outcomes: Preference Models and Illustrative Applications

This paper presents decision analysis methodology for decisions based on data from geographic information systems. The consequences of a decision alternative are modeled as distributions of outcomes across a geographic region. We discuss conditions that may conform with the decision makers preferences over a specified set of alternatives; then we present specific forms for value or utility functions that are implied by these conditions. Decisions in which there is certainty about the consequences resulting from each alternative are considered first; then probabilistic uncertainty about the consequences is included as an extension. The methodology is applied to two hypothetical urban planning decisions involving water use and temperature reduction in regional urban development, and fire coverage across a city. These examples illustrate the applicability of the approach and the insights that can be gained from using it.

Decision Making with Prostate Cancer: A Multiple-Objective Model with Uncertainty

A patient who has been diagnosed with prostate cancer must make a difficult treatment decision. The available alternatives have varying cure rates and probabilities of side effects over a period of many years. This paper describes a well-informed, objective, and personalized model for comparing treatments to help a patient make the best treatment decision.

A Value-Focused Approach to Energy Transformation in the United States Department of Defense

The United States Department of Defense (DoD) has identified its energy requirements as a key vulnerability and in recent years has taken substantial initiatives to improve its energy profile. As part of this process, DoD leaders have issued guidance documents outlining goals and objectives relating to energy. These documents are intended to inform many different decisions at strategic, managerial, and operational levels. They specify a wide range of objectives that overlap only partially, while identical terms appear in many documents, but with inconsistent definitions. In this paper, we review 44 strategic guidance documents and apply a value-focused thinking approach to identify and define explicitly a comprehensive set of common objectives for energy decisions in the DoD. The objectives and associated definitions are intended to facilitate horizontal and vertical communication within the DoD. In addition, the objectives we define suggest possible metrics that may be comparable across services and in some cases may be aggregated across organizational levels.

A Multiattribute Sealed-Bid Procurement Auction with Multiple Budgets for Government Vendor Selection

This paper offers a new approach to government vendor selection decisions in major public procurements. A key challenge is for government purchasing agents to select vendors that deliver the best combination of desired nonprice attributes at realistic funding levels. The mechanism proposed in this paper is a multiattribute first-price, sealed-bid procurement auction. It extends traditional price-only auctions to those in which competition takes place exclusively over attribute bundles. The model is a multiattribute auction in which a set of possible budget levels is specified. This model reveals the benefits of defining a procurement alternative in terms of its value to the buyer over a range of possible expenditures, rather than as a single point in budget-value space. This new approach leads to some interesting results. In particular, it suggests that in a fiscally constrained environment, the traditional approach of eliminating dominated alternatives could lead to suboptimal decisions. Finally, an extension of the model explicitly examines the buyers decision problem under budget uncertainty by applying a utility function assessed over the value measure.

Complexity and Self-Sustainment in Disaster Response Supply Chains

Governmental organizations play a major role in disaster relief operations. Supply chains set up to respond to disasters differ dramatically in many dimensions that affect the cost of relief efforts. One factor that has been described recently is self-sustainment, which occurs when supplies consumed by intermediate stages of a supply chain must be provided via the chain itself because they are not locally available. This article applies the concept of self-sustainment to response supply chains. A mathematical model of a self-sustaining response supply chain is developed. Analysis of this model yields insights about the relationships and interactions among self-sustainment, speed of disaster onset, dispersion of impact, and the cost of the relief efforts.

Using Means Objectives to Present Risk Information

When making decisions involving alternatives with risk, individuals are often unable to express or view the possible outcomes in terms of a fundamental objective. In many cases, using a means objective is more practical or more accessible. However, to apply information about a means objective correctly, a decision maker must first translate it into information about a fundamental objective. This paper presents and discusses the results of two experiments regarding decision makers’ preferences and decision process when information is presented either in terms of a means objective or a fundamental objective. We find that individuals are somewhat more likely to choose a risky alternative when information is expressed in terms of means objectives than in terms of fundamental objectives, and that this difference is not significantly smaller among individuals with greater quantitative ability. Individuals are also better able to articulate their decision process when given information related to fundamental objectives than they are with information related to means objectives. In addition, we find that individuals who focus on the uncertainty involved in a decision are more likely to choose a sure thing, whereas individuals who focus on consequences are more likely to choose a gamble.

The fuel multiplier in multi-stage supply chains

Fuel requirements on the battlefield impose direct costs associated with the resources necessary to transport the fuel and protect logistics assets, in addition to indirect energy security costs. Estimating the enterprise-wide demand for fuel associated with fuel consumption on the battlefield is a challenging, but necessary, step to making good decisions. This paper presents a modeling framework for estimating the enterprise-wide fuel requirements associated with a multistage fuel supply chain, demonstrating a multiplicative increase in fuel demand with additional stages, and examining the fuel impact of protecting the supply chain.

From the Editors---Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus

This is the final issue under this Editor-in-Chief, so this column is fittingly coauthored with the associate editors, whose terms also end with this issue, to emphasize their major role in the leadership of the journal. We first introduce incoming Editor-in-Chief Rakesh K. Sarin, briefly review this years operations, and thank our editorial board and referees. Then we move on to this issues five research articles. In our first article, Ralph L. Keeney presents “Value-Focused Brainstorming.” Next, Kenneth C. Lichtendahl Jr. and Samuel E. Bodily develop models for “Multiplicative Utilities for Health and Consumption.” Then, Luis V. Montiel and J. Eric Bickel present “A Simulation-Based Approach to Decision Making with Partial Information.” Our fourth article, by Kash Barker and Kaycee J. Wilson, is “Decision Trees with Single and Multiple Interval-Valued Objectives.” Our final article, by Anton Kuhberger and Christian Wiener, is on “Explaining Risk Attitude in Framing Tasks by Regulatory Focus: A Verbal Protocol Analysis and a Simulation Using Fuzzy Logic.”

Preference Functions for Spatial Risk Analysis: Preference Functions for Spatial Risk Analysis

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. We also present a simple example on household freshwater usage across regions to demonstrate how such functions can be assessed and applied.

An application of the multiple knapsack problem: The self-sufficient marine

Self-Sufficiency (SS) is the ability to maintain capability without external support or aid. Operations in austere environments with limited functional infrastructure and logistical support, which are common in humanitarian assistance and disaster relief as well as military operations, must be self-sufficient. In this paper, we explore the challenges of SS in the United States Marine Corps (USMC). Marines engage in a wide variety of expeditionary operations, and must function without logistical support for long stretches of time. They face competing constraints, including the load that a squad can carry, mission requirements, resources required for sustainment, and the extent to which resources can be shared. We extend the knapsack problem in several ways to model a Marine squads decisions regarding what items to carry and how to distribute them. The Office of Naval Research found the models and the results to be significant as baseline analysis for the resource demands of a self-sufficient squad. Though the data and scenarios are USMC-specific, the challenges of SS can be found in any expeditionary undertakings or operations in austere environments.