DECISION THEORY & DECISION TREE

Slides:



Advertisements
Similar presentations
Decision Analysis (Decision Tables, Utility)
Advertisements

Decision Theory.
To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 3-1 © 2006 by Prentice Hall, Inc. Upper Saddle River, NJ Prepared by.
Chapter 3 Decision Analysis.
Decision Analysis Chapter 3
1 Decision Analysis What is it? What is the objective? More example Tutorial: 8 th ed:: 5, 18, 26, 37 9 th ed: 3, 12, 17, 24 (to p2) (to p5) (to p50)
12-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Decision Analysis Chapter 12.
Chapter 14 Decision Analysis. Decision Making Many decision making occur under condition of uncertainty Decision situations –Probability cannot be assigned.
Introduction to Management Science
Introduction to Decision Analysis
Introduction to Management Science
Decision Theory.
Copyright 2009 John Wiley & Sons, Inc. Beni Asllani University of Tennessee at Chattanooga Operations Management - 6 th Edition Chapter 1 Supplement Roberta.
1 DSCI 3223 Decision Analysis Decision Making Under Uncertainty –Techniques play an important role in business, government, everyday life, college football.
Chapter 3 Decision Analysis.
Managerial Decision Modeling with Spreadsheets
2000 by Prentice-Hall, Inc1 Supplement 2 – Decision Analysis A set of quantitative decision-making techniques for decision situations where uncertainty.
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin An Introduction to Decision Making Chapter 20.
DSC 3120 Generalized Modeling Techniques with Applications
Operations and Supply Chain Management, 8th Edition
Chapter 7 Decision Analysis
3 Decision Analysis To accompany Quantitative Analysis for Management, Twelfth Edition, by Render, Stair, Hanna and Hale Power Point slides created by.
Decision Analysis Chapter 12.
Decision Theory is a body of knowledge and related analytical techniques Decision is an action to be taken by the Decision Maker Decision maker is a person,
Part 3 Probabilistic Decision Models
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 18-1 Business Statistics: A Decision-Making Approach 6 th Edition Chapter.
Copyright 2006 John Wiley & Sons, Inc. Beni Asllani University of Tennessee at Chattanooga Operations Management - 5 th Edition Chapter 2 Supplement Roberta.
Decision Analysis Chapter 3
Decision Making Under Uncertainty and Under Risk
Decision analysis: part 1 BSAD 30 Dave Novak Source: Anderson et al., 2013 Quantitative Methods for Business 12 th edition – some slides are directly from.
Chapter 1 Supplement Decision Analysis Supplement 1-1.
CD-ROM Chap 14-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition CD-ROM Chapter 14 Introduction.
Decision Analysis Chapter 3
8-1 CHAPTER 8 Decision Analysis. 8-2 LEARNING OBJECTIVES 1.List the steps of the decision-making process and describe the different types of decision-making.
Module 5 Part 2: Decision Theory
“ The one word that makes a good manager – decisiveness.”
An Introduction to Decision Theory (web only)
Chapter 3 Decision Analysis.
Decision Theory Decision theory problems are characterized by the following: 1.A list of alternatives. 2.A list of possible future states of nature. 3.Payoffs.
1 1 Slide Decision Theory Professor Ahmadi. 2 2 Slide Learning Objectives n Structuring the decision problem and decision trees n Types of decision making.
To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. Supplement S2 Decision Analysis To.
12-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Decision Analysis Chapter 12.
Operations Research II Course,, September Part 5: Decision Models Operations Research II Dr. Aref Rashad.
Decision Analysis Mary Whiteside. Decision Analysis Definitions Actions – alternative choices for a course of action Actions – alternative choices for.
Welcome Unit 4 Seminar MM305 Wednesday 8:00 PM ET Quantitative Analysis for Management Delfina Isaac.
Fundamentals of Decision Theory Chapter 16 Mausam (Based on slides of someone from NPS, Maria Fasli)
12-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Decision Analysis Chapter 12.
Decision Analysis.
Example We want to determine the best real estate investment project given the following table of payoffs for three possible interest rate scenarios. Interest.
Decision Analysis Pertemuan Matakuliah: A Strategi Investasi IT Tahun: 2009.
Chapter 12 Decision Analysis. Components of Decision Making (D.M.) F Decision alternatives - for managers to choose from. F States of nature - that may.
DECISION MODELS. Decision models The types of decision models: – Decision making under certainty The future state of nature is assumed known. – Decision.
Chap 18-1 Business Statistics: A Decision-Making Approach 6 th Edition Chapter 18 Introduction to Decision Analysis.
QUANTITATIVE TECHNIQUES
McGraw-Hill/Irwin Copyright © 2008 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 4 Decision Analysis Building the Structure for Solving.
Decision Analysis Chapter 12.
OPERATIONS MANAGEMENT: Creating Value Along the Supply Chain,
Welcome to MM305 Unit 4 Seminar Larry Musolino
Chapter 19 Decision Making
Decision Analysis Chapter 12.
Supplement: Decision Making
Prepared by Lee Revere and John Large
MNG221- Management Science –
نظام التعليم المطور للانتساب
نظام التعليم المطور للانتساب
Decision Analysis Support Tools and Processes
Decision Analysis Decision Trees Chapter 3
Decision Analysis.
Applied Statistical and Optimization Models
Presentation transcript:

DECISION THEORY & DECISION TREE Components of Decision Making Decision Making Under Uncertainty (Without Probabilities) Decision Making Under Risk (With Probabilities) Decision Analysis with Additional Information Utility

The Six Steps in Decision Theory Clearly define the problem at hand List the possible alternatives Identify the possible outcomes List the payoff or profit of each combination of alternatives and outcomes Select one of the mathematical decision theory models Apply the model and make your decision

Decision Analysis Components of Decision Making A state of nature is an actual event that may occur in the future. A payoff table is a means of organizing a decision situation, presenting the payoffs from different decisions given the various states of nature. Payoff Table

Types of Decision-Making Environments Type 1: Decision-making under certainty decision-maker knows with certainty the consequences of every alternative or decision choice Decision-making under uncertainty (without probability) The decision-maker does not know the probabilities of the various outcomes Type 2: Decision-making under risk (with probability) The decision-maker does know the probabilities of the various outcomes

Decision Analysis Decision Making without Probabilities Decision situation: An investor wants to decide which of the three property to buy. Decision-Making Criteria: Maximax, Maximin, Minimax, Minimax Regret, Hurwicz, Equal Likelihood (Laplace) Payoff Table for the Real Estate Investments

Decision Making without Probabilities The Maximax Criterion In the maximax criterion the decision maker selects the decision that will result in the maximum of maximum payoffs; an optimistic criterion. Payoff Table Illustrating a Maximax Decision

Decision Making without Probabilities The Maximin Criterion In the maximin criterion the decision maker selects the decision that will reflect the maximum of the minimum payoffs; a pessimistic criterion. Payoff Table Illustrating a Maximin Decision

Decision Making without Probabilities The Minimax Regret Criterion Regret is the difference between the payoff from the best decision and all other decision payoffs. The decision maker attempts to avoid regret by selecting the decision alternative that minimizes the maximum regret. Regret Table Illustrating the Minimax Regret Decision

Decision Making without Probabilities The Hurwicz Criterion - The Hurwicz criterion is a compromise between the maximax and maximin criterion. - A coefficient of optimism, , is a measure of the decision maker’s optimism. - The Hurwicz criterion multiplies the best payoff by  and the worst payoff by 1- ., for each decision, and the best result is selected. Decision Values Apartment building $50,000(.4) + 30,000(.6) = 38,000 Office building $100,000(.4) - 40,000(.6) = 16,000 Warehouse $30,000(.4) + 10,000(.6) = 18,000

Decision Making without Probabilities The Equal Likelihood Criterion - The equal likelihood ( or Laplace) criterion multiplies the decision payoff for each state of nature by an equal weight, thus assuming that the states of nature are equally likely to occur. Decision Values Apartment building $50,000(.5) + 30,000(.5) = 40,000 Office building $100,000(.5) - 40,000(.5) = 30,000 Warehouse $30,000(.5) + 10,000(.5) = 20,000

Decision Making without Probabilities Summary of Criteria Results - A dominant decision is one that has a better payoff than another decision under each state of nature. - The appropriate criterion is dependent on the “risk” personality and philosophy of the decision maker. Criterion Decision (Purchase) Maximax Office building Maximin Apartment building Minimax regret Apartment building Hurwicz Apartment building Equal liklihood Apartment building

Decision Making without Probabilities Solutions with QM for Windows (1 of 2)

Decision Making without Probabilities Solutions with QM for Windows (2 of 2)

Decision Making under Risk (with Probabilities) Expected Value Expected value is computed by multiplying each decision outcome under each state of nature by the probability of its occurrence. EV(Apartment) = $50,000(.6) + 30,000(.4) = 42,000 EV(Office) = $100,000(.6) - 40,000(.4) = 44,000 EV(Warehouse) = $30,000(.6) + 10,000(.4) = 22,000

Decision Making with Probabilities Expected Opportunity Loss The expected opportunity loss is the expected value of the regret for each decision. The expected value and expected opportunity loss criterion result in the same decision. EOL(Apartment) = $50,000(.6) + 0(.4) = 30,000 EOL(Office) = $0(.6) + 70,000(.4) = 28,000 EOL(Warehouse) = $70,000(.6) + 20,000(.4) = 50,000

Decision Making with Probabilities Solution of Expected Value Problems with QM for Windows

Decision Making with Probabilities Expected Value of Perfect Information The expected value of perfect information (EVPI) is the maximum amount a decision maker would pay for additional information. EVPI equals the expected value given perfect information minus the expected value without perfect information. EVPI equals the expected opportunity loss (EOL) for the best decision.

Decision Making with Probabilities EVPI Example Decision with perfect information: $100,000(.60) + 30,000(.40) = $72,000 Decision without perfect information: EV(office) = $100,000(.60) - 40,000(.40) = $44,000 EVPI = $72,000 - 44,000 = $28,000 EOL(office) = $0(.60) + 70,000(.4) = $28,000

Decision Making with Probabilities EVPI with QM for Windows

Decision Making with Probabilities Decision Trees A decision tree is a diagram consisting of decision nodes (represented as squares), probability nodes (circles), and decision alternatives (branches).