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Decision analysis of management judgement
DISCRETE APPLIED MATHEMATICS ELSEVIER
Discrete
Applied
Mathematics
50 (1994) 97-100
Book Announcements
Paul Goodwin and George Wright, Decision (Wiley, New York, 1991) 308 pages
Analysis
for Management
Judgement
Chapter 1: Introduction. Complex decisions. The role of decision analysis. Overview of the book. Chapter 2: Decisions Involving Multiple Objectives. Introduction. Basic terminology (Objectives and attributes. Value and utility). An office location problem. An overview of the analysis. Constructing a value tree. Measuring how well the options perform on each attribute (Direct rating. Value functions). Determining the weights of the attributes. Aggregating the benefits using the additive model. Trading benefits against costs. Sensitivity analysis. Theoretical considerations (The axioms of the method. Assumptions made when aggregating values). Conflicts between intuitive and analytic results. Chapter 3: Introduction to Probability. Introduction. Outcomes and events. Approaches to probability (The classical approach. The relative frequency approach. The subjective approach). Mutually exclusive and exhaustive events. The addition rule. Complementary events. Marginal and conditional probabilities. Independent and dependent events. The multiplication rule. Probability trees. Probability distributions. Expected values. The axioms of probability theory. Chapter 4: Decision Making under Uncertainty. Introduction. The expected monetary value (EMV) criterion. Limitations of the EMV criterion. Single-attribute utility. Interpreting utility functions. Utility functions for non-monetary attributes. The axioms of utility. More on utility elicitation. How useful is utility in practice?. Multi-attribute utility (The decanal engineering corporation. Mutual utility independence. Deriving the multi-attribute utility function. Interpreting multi-attribute utilities. Further points on multi-attribute utility). Chapter 5: Decision Trees and Injuence Diagrams. Introduction. Constructing a decision tree. Determining the optimal policy. Decision trees and utility. Decision trees involving continuous probability distributions. Practical applications of decision trees. Assessment of decision structure. Eliciting decision tree representations. Chapter 6: Applying Simulation to Decision Problems. Introduction. Monte Carlo simulation. Applying simulation to a decision problem (The elite pottery company. Plotting the two distributions, Determining the option with the highest expected utility. Stochastic dominance. The mean-standard deviation approach). Applying simulation to investment decisions (The net present value (NPV) method. Using simulation. Utility and net present value). Modeling dependence relationships (Judgmental problems. Simulating dependence relationships). Chapter 6: Appendix: The Standard Deviation. Example. References. Chapter 7: Revising Judgments in the Light of New Information. Introduction. Bayes’ theorem (Example. Answer. Another example). The effect of new information on the revision of probability judgments. Applying Bayes’ theorem to a decision problem. Assessing the value of new information (The expected value of perfect information. The expected value of imperfect information). Practical considerations, Chapter 8: The Quality of Human Judgment: Laboratory Studies. Introduction. Heuristics in probability assessment. Revision of probabilistic opinion. Individual and situational influences on decision making (Personality and decision making. Cognitive style and decision making). Contingent decision behavior. Summary. Discussion questions, References. Chapter 9: The Quality of Human Judgment: Real-world Studies. Introduction. Assessing probabilities for future events. Statistical extrapolation. Econometrics. Judgment in forecasting. Summary. Discussion questions. References. Chapter 10: Probability Assessment. Introduction. Preparing for probability assessment (Motivating. Structuring. Conditioning). Assessment methods (Assessment methods for individual probabilities. Assessment methods for probability distributions). A comparison of the assessment methods. Consistency and Elsevier Science B.V. SSDI 0166-218X(94)00002-U
98
Discrete Applied Mathematics 50 (1994)
97-100
coherence checks. Assessment of the validity of probability forecasts. Assessing probabilities for very rare events (Event trees. Fault trees. Using a log-odds scale). Chapter 11: Decisions Involving Groups of Individuals. Introduction. Mathematical aggregation. Aggregating judgments in General (Taking a simple average of the individual judgments. Taking a weighted average of the individual judgments). Aggregating probability judgments. Aggregating preference judgments (Aggregating preference orderings. Aggregating values and utilities). Unstructured group processes. Structured group processes. Decision conferencing. Summary. Discussion questions. References. Chapter 12: Resource Allocation and Negotiation Problems. Introduction. Modeling resource allocation problems (An illustrative problem. Determining the variables, resources and benefits. Identifying the possible strategies for each region. Assessing the costs and benefits of each strategy. Measuring each benefit on a common scale. Comparing the relative importance of the benefits. Identifying the costs and benefits of the packages. Sensitivity analysis). Summary of the main stages of the analysis. Negotiation models. An illustrative problem. Practical applications. Discussion questions. References. Chapter 13: Alternatiue Decision-Support Systems. Introduction. Expert systems (What is an expert system?. What is expert knowledge?. How is expert knowledge represented in expert systems?. An example of an expert system application in life underwriting). Statistical models of judgment (Recent research). Comparisons.
Robert Azencott, ed., Simulated New York, 1992) 242 pages
Annealing-Parallelization
Techniques
(Wiley,
Chapter 1: Sequential Simulated Annealing: Speed of Convergence and Acceleration Techniques (Robert Azencott). Chapter 2: A Common Large Deviations Mathematical Framework for Sequential Annealing and Parallel Annealing (Robert Azencott). Chapter 3: Rates of Convergence for Sequential Annealing: A Large Deviation Approach (Olivier Catoni). Chapter 4: Parallel Simulated Annealing: An Overview of Basic Techniques (Robert Azencott). Chapter 5: Parallel Annealing by Periodically Interacting Multiple Searches: An Experimental Study (Christine Graffigne). Chapter 6: Parallel Annealing by Periodically Interacting Multiple Searches: Acceleration Rates (Robert Azencott and Christine Graffigne). Chapter 7: Parallel Annealing by Multiple Trials: An Experimental Study on a Transputer Network (P. Roussel-Ragot and Girard Dreyfus). Chapter 8: Parallel Annealing by Multiple Trials: Experimental Study of a Chip Placement Problem Using a Sequent Machine (Bernard Virot). Chapter 9: Parallel Annealing by Multiple Trials: A Mathematical Study (Olivier Catoni and Alain Trouvt). Chapter IO: Massive Parallelization of Simulated Annealing: A Mathematical Study (Alain Trouvb). Chapter 11: Massive Parallelization of Simulated Annealing: An Experimental and Theoretical Approach for Spin-Glass Models (Isabelle Gaudron and Alain Trouve). Chapter 12: Parallel Annealing by Partitioning of Configurations: An Application to Optimal 3-D Triangulation (Christophe Lacote, Jean Mailfert and Jean-Pierre Uhry). Chapter 13: Parallel Annealing: A Comparative Study oflmplementation on Hardware Architectures (Patrick Garda). Index.
Fred Glover, Optimization 284 pages
Darwin Klingman and and their Applications
Nancy V. Phillips, Network Models in in Practice (Wiley, New York, 1992)
Chapter 1: Netform Origins and Uses: Why Modeling and Netforms Are Important. Background. Netform Modeling in the context of management science. A preview of netform applications. Chapter 2: Fundamental Models for Pure Networks. Fundamental principles. Formulating a network model from a word problem. Intuitive problem solving. Structural variations. More general networks. Algebraic statement of pure network model. Alternative conventions for network diagrams. Chapter 3: Additional Pure Network Formulation Techniques. A core example. Goal programming model conditions. The goal programming classification of target conditions and pre-emptive goal programming. Target flows on arcs with two endpoints. Modeling decreasing returns to scale. An extension of goal programming conditions. Graphical
Discrete
Applied
Mathematics
50 (1994) 97-100
Book Announcements
Paul Goodwin and George Wright, Decision (Wiley, New York, 1991) 308 pages
Analysis
for Management
Judgement
Chapter 1: Introduction. Complex decisions. The role of decision analysis. Overview of the book. Chapter 2: Decisions Involving Multiple Objectives. Introduction. Basic terminology (Objectives and attributes. Value and utility). An office location problem. An overview of the analysis. Constructing a value tree. Measuring how well the options perform on each attribute (Direct rating. Value functions). Determining the weights of the attributes. Aggregating the benefits using the additive model. Trading benefits against costs. Sensitivity analysis. Theoretical considerations (The axioms of the method. Assumptions made when aggregating values). Conflicts between intuitive and analytic results. Chapter 3: Introduction to Probability. Introduction. Outcomes and events. Approaches to probability (The classical approach. The relative frequency approach. The subjective approach). Mutually exclusive and exhaustive events. The addition rule. Complementary events. Marginal and conditional probabilities. Independent and dependent events. The multiplication rule. Probability trees. Probability distributions. Expected values. The axioms of probability theory. Chapter 4: Decision Making under Uncertainty. Introduction. The expected monetary value (EMV) criterion. Limitations of the EMV criterion. Single-attribute utility. Interpreting utility functions. Utility functions for non-monetary attributes. The axioms of utility. More on utility elicitation. How useful is utility in practice?. Multi-attribute utility (The decanal engineering corporation. Mutual utility independence. Deriving the multi-attribute utility function. Interpreting multi-attribute utilities. Further points on multi-attribute utility). Chapter 5: Decision Trees and Injuence Diagrams. Introduction. Constructing a decision tree. Determining the optimal policy. Decision trees and utility. Decision trees involving continuous probability distributions. Practical applications of decision trees. Assessment of decision structure. Eliciting decision tree representations. Chapter 6: Applying Simulation to Decision Problems. Introduction. Monte Carlo simulation. Applying simulation to a decision problem (The elite pottery company. Plotting the two distributions, Determining the option with the highest expected utility. Stochastic dominance. The mean-standard deviation approach). Applying simulation to investment decisions (The net present value (NPV) method. Using simulation. Utility and net present value). Modeling dependence relationships (Judgmental problems. Simulating dependence relationships). Chapter 6: Appendix: The Standard Deviation. Example. References. Chapter 7: Revising Judgments in the Light of New Information. Introduction. Bayes’ theorem (Example. Answer. Another example). The effect of new information on the revision of probability judgments. Applying Bayes’ theorem to a decision problem. Assessing the value of new information (The expected value of perfect information. The expected value of imperfect information). Practical considerations, Chapter 8: The Quality of Human Judgment: Laboratory Studies. Introduction. Heuristics in probability assessment. Revision of probabilistic opinion. Individual and situational influences on decision making (Personality and decision making. Cognitive style and decision making). Contingent decision behavior. Summary. Discussion questions, References. Chapter 9: The Quality of Human Judgment: Real-world Studies. Introduction. Assessing probabilities for future events. Statistical extrapolation. Econometrics. Judgment in forecasting. Summary. Discussion questions. References. Chapter 10: Probability Assessment. Introduction. Preparing for probability assessment (Motivating. Structuring. Conditioning). Assessment methods (Assessment methods for individual probabilities. Assessment methods for probability distributions). A comparison of the assessment methods. Consistency and Elsevier Science B.V. SSDI 0166-218X(94)00002-U
98
Discrete Applied Mathematics 50 (1994)
97-100
coherence checks. Assessment of the validity of probability forecasts. Assessing probabilities for very rare events (Event trees. Fault trees. Using a log-odds scale). Chapter 11: Decisions Involving Groups of Individuals. Introduction. Mathematical aggregation. Aggregating judgments in General (Taking a simple average of the individual judgments. Taking a weighted average of the individual judgments). Aggregating probability judgments. Aggregating preference judgments (Aggregating preference orderings. Aggregating values and utilities). Unstructured group processes. Structured group processes. Decision conferencing. Summary. Discussion questions. References. Chapter 12: Resource Allocation and Negotiation Problems. Introduction. Modeling resource allocation problems (An illustrative problem. Determining the variables, resources and benefits. Identifying the possible strategies for each region. Assessing the costs and benefits of each strategy. Measuring each benefit on a common scale. Comparing the relative importance of the benefits. Identifying the costs and benefits of the packages. Sensitivity analysis). Summary of the main stages of the analysis. Negotiation models. An illustrative problem. Practical applications. Discussion questions. References. Chapter 13: Alternatiue Decision-Support Systems. Introduction. Expert systems (What is an expert system?. What is expert knowledge?. How is expert knowledge represented in expert systems?. An example of an expert system application in life underwriting). Statistical models of judgment (Recent research). Comparisons.
Robert Azencott, ed., Simulated New York, 1992) 242 pages
Annealing-Parallelization
Techniques
(Wiley,
Chapter 1: Sequential Simulated Annealing: Speed of Convergence and Acceleration Techniques (Robert Azencott). Chapter 2: A Common Large Deviations Mathematical Framework for Sequential Annealing and Parallel Annealing (Robert Azencott). Chapter 3: Rates of Convergence for Sequential Annealing: A Large Deviation Approach (Olivier Catoni). Chapter 4: Parallel Simulated Annealing: An Overview of Basic Techniques (Robert Azencott). Chapter 5: Parallel Annealing by Periodically Interacting Multiple Searches: An Experimental Study (Christine Graffigne). Chapter 6: Parallel Annealing by Periodically Interacting Multiple Searches: Acceleration Rates (Robert Azencott and Christine Graffigne). Chapter 7: Parallel Annealing by Multiple Trials: An Experimental Study on a Transputer Network (P. Roussel-Ragot and Girard Dreyfus). Chapter 8: Parallel Annealing by Multiple Trials: Experimental Study of a Chip Placement Problem Using a Sequent Machine (Bernard Virot). Chapter 9: Parallel Annealing by Multiple Trials: A Mathematical Study (Olivier Catoni and Alain Trouvt). Chapter IO: Massive Parallelization of Simulated Annealing: A Mathematical Study (Alain Trouvb). Chapter 11: Massive Parallelization of Simulated Annealing: An Experimental and Theoretical Approach for Spin-Glass Models (Isabelle Gaudron and Alain Trouve). Chapter 12: Parallel Annealing by Partitioning of Configurations: An Application to Optimal 3-D Triangulation (Christophe Lacote, Jean Mailfert and Jean-Pierre Uhry). Chapter 13: Parallel Annealing: A Comparative Study oflmplementation on Hardware Architectures (Patrick Garda). Index.
Fred Glover, Optimization 284 pages
Darwin Klingman and and their Applications
Nancy V. Phillips, Network Models in in Practice (Wiley, New York, 1992)
Chapter 1: Netform Origins and Uses: Why Modeling and Netforms Are Important. Background. Netform Modeling in the context of management science. A preview of netform applications. Chapter 2: Fundamental Models for Pure Networks. Fundamental principles. Formulating a network model from a word problem. Intuitive problem solving. Structural variations. More general networks. Algebraic statement of pure network model. Alternative conventions for network diagrams. Chapter 3: Additional Pure Network Formulation Techniques. A core example. Goal programming model conditions. The goal programming classification of target conditions and pre-emptive goal programming. Target flows on arcs with two endpoints. Modeling decreasing returns to scale. An extension of goal programming conditions. Graphical