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1999 | Book

The Problem Solving Process Read chapter Read first chapter

Decision Modeling in Policy Management

An Introduction to the Analytic Concepts

Author: Giampiero E. G. Beroggi

Publisher: Springer US

Included in: Professional Book Archive

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About this book

The last decade has experienced major societal challenges at the intersection of technological systems and policy making. Prevalent examples are the liberalization of energy and telecommunications markets, the public aversion towards nuclear power plants, the development of high-speed trains, the debates about global warming and sustainability, the development of intelligent vehicle systems, and the controversies concerning the location of waste depositories, airports, and energy systems. These challenges, coupled with the call from industry for a systems-engineering oriented approach to policy analysis, motivated Delft University of Technology to launch the first European School of Systems Engineering, Policy Analysis. and Management (SEPA). The purpose was to educate engineering oriented policy analysts in bridging the gap between engineering systems and policy decision making processes, both for the public and private sector. Up to now, more than 500 first-year students and 30 Ph.D. students have enrolled in the program. In 1993, I set up a class called Quantitative Methods for Problem Solving which had to address the most relevant issues in decision making for policy management, such as linear and non-linear optimization, multiattribute utility theory, multicriteria decision making, concepts from game theory, outranking relations, and probabilistic influence diagrams.

Table of Contents

Frontmatter
Chapter I. The Problem Solving Process
Abstract
Problems are situations that everyone, including managers and decision makers, face many times each day; however, there is no generally agreed-upon definition of what a problem is. What may be a problem for one person is not necessarily a problem for another. Problems appear at some moment in time, remain present for a certain period, and eventually disappear. Problems get resolved by ‘natural’ means or through human intervention. Sometimes problems remain undetected, while other situations are assessed as problems when in fact they are not.
Giampiero E. G. Beroggi
Chapter II. The Analytic Modeling Process
Abstract
Problems occur and must be solved in the real world environment. This environment can be perceived and described as a collection of elements and their relations. The elements are persons, organizations, cities, etc., all interacting with one another. The collection of these elements and their relations is called a system. To describe and solve a problem, a model of the system is developed. A model is an abstraction of a system under investigation, made by an observer of the system. This observer can be an analyst, or any other person who studies the system to derive some conclusions about its behavior. The construction of the model and the identification of the criteria, goals, scenarios, decision makers, and actions depend strongly on the perception of the model builder. Human perception of the system under investigation may be influenced by factors unrelated to the problem, such as the analyst’s mood, recent experiences, or personal attitudes toward the problem. Although the analytic approach to modeling is based on rules, axioms, and formalisms, modeling will always remain a critical craft skill [Willemain, 1995].
Giampiero E. G. Beroggi
Chapter III. Descriptive Assessment — Criteria and Weights
Abstract
In Chapter II we introduced the descriptive approach to preference elicitation which is based on paired comparisons of the items to be ordered from most to least preferred. These items may be alternatives, criteria, decision makers, goals, or scenarios (to assess their likelihood). In the first part of this chapter we will address descriptive approaches for determining the preferences for criteria, called weights. These descriptive preference elicitation concepts will also be used in subsequent chapters to determine the preferences or importances of alternatives (Chapter IV) and of decision makers (Chapter IX).
Giampiero E. G. Beroggi
Chapter IV. Descriptive Assessment — Alternatives and Ranking
Abstract
In this chapter, we address such problems as the selection of the best suited construction project, the prioritization of a set of feasible hospital locations, the ranking of a set of transportation policies, and the identification of promising risk abatement strategies. All of these decision problems involve binary decision variables, where x j =1 if alternative a j is chosen and x j =0 otherwise.
Giampiero E. G. Beroggi
Chapter V. Values and Normative Choice
Abstract
The normative approach to decision analysis assumes that the decision maker’s subjective preference structure can be captured by an m-dimensional preference function v(e1,…,e m ), as opposed to the descriptive approach which is based on paired comparisons. This m-dimensional preference function will be called m-dimensional value function. Other authors, especially economists, call it utility function. However, we will reserve the term utility for subjective normative preference functions in the presence of uncertainty (Chapter VII).
Giampiero E. G. Beroggi
Chapter VI. Choices Under Uncertainty
Abstract
Decision making under uncertainty means that the alternatives are evaluated for different scenarios. A scenario was defined in Chapter I, Section 2.4, as a condition of the system under investigation, upon which all assessments and evaluations depend, but which is not under the control of the decision maker. For example, one could assess the benefit of a public transportation system for the four scenarios (1) high demand and strong economy, (2) low demand and strong economy, (3) high demand and weak economy, and (iv) low demand and weak economy.
Giampiero E. G. Beroggi
Chapter VII. Uncertainty and Normative Choice
Abstract
In Chapter V we discussed value functions to rank alternatives under certainty. The purpose of this chapter is to discuss an extension of the concepts of value theory to a normative approach for decision making under uncertainty: utility theory. In the presence of certainty, the decision maker’s dilemma refers to the tradeoffs between different criteria. For example, imagine a contest where the winner can choose one prize out of the set of all prizes, the second ranked person can then choose from the remaining prizes, and so on. If we assume that all consequences of the prices (e.g., monetary value, satisfaction, etc.) are known for sure, the persons choosing from the set of prizes are facing a typical choice situation under certainty. Examples of such prices are a TV set, a stereo, a bicycle, etc., with multidimensional value-tags attached to them. The problem is then simply to trade off preferences for different criteria, such as gains, satisfaction, and quality.
Giampiero E. G. Beroggi
Chapter VIII. Sequential Decision Making
Abstract
In this chapter we will focus on sequential decision problems that must be made in the presence of informed uncertainty; that is, uncertainty that can be quantified with probability distributions (also called decision making under risk). These sequential decisions will result in a sequence of actions, also referred to as policies.
Giampiero E. G. Beroggi
Chapter IX. Multi-Actor Decision Making
Abstract
A decision maker has been defined in Chapter I as a person who participates in the assessment of the decision options and in the choice process. People who are not decision makers but are relevant to the problem solving process are called stakeholders. Actor is a generic term that refers to both decision makers and stakeholders. When multiple actors participate in the decision making process we have a multiactor setting. Being able to make a decision means being able to assess the alternatives under consideration and to make a choice. Thus, we would expect that multiple decision makers might disagree in their assessments as well as in their choices.
Giampiero E. G. Beroggi
Chapter X. Constraint-Based Policy Optimization
Abstract
The previous chapters addressed extensively decision problems where the decision variables could take on binary values and where the objective was to find the best alternative out of the set of feasible alternatives. In this chapter we will address decision problems with binary-, integer-, and real-valued decision variables. This means that we see the decision variable x j as the intensity of employing alternative a j For example, imagine a project manager who must decide how many hours employees should spend on a certain project. One does not want to know which employees to assign to that project, but for how many hours each employee should be assigned to the project. The number of hours for each employee are the decision variables and the time-unit for each employee the basic alternative. A solution to this problem could be to have one employee work 5 hours (x1=5), another 7 hours (x2=7), and a third 8.5 hours (x3=8.5).
Giampiero E. G. Beroggi
Backmatter
Metadata
Title
Decision Modeling in Policy Management
Author
Giampiero E. G. Beroggi
Copyright Year
1999
Publisher
Springer US
Electronic ISBN
978-1-4615-5599-5
Print ISBN
978-0-7923-8331-4
DOI
https://doi.org/10.1007/978-1-4615-5599-5