DECISION THEORY.  It’s deals with a very scientific and quantitative way of coming to decision.  It has 4 phases. 1.Action or acts. 2.State of nature.

Slides:



Advertisements
Similar presentations
Decision Theory.
Advertisements

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.
14 DECISION THEORY CHAPTER. 14 DECISION THEORY CHAPTER.
Module 16 – Decision Theory
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)
Lesson 9.1 Decision Theory with Unknown State Probabilities.
20- 1 Chapter Twenty McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.
Chapter 3 Decision Analysis.
Introduction to Decision Analysis

Chapter 18 Statistical Decision Theory Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Statistics for Business and Economics 7 th.
Decision Theory.
LECTURE TWELVE Decision-Making UNDER UNCERTAINITY.
Chapter 21 Statistical Decision Theory
Chapter 3 Decision Analysis.
Ch 7 Decision theory Learning objectives: After completing this chapter, you should be able to: 1.Outline the characteristics of a decision theory approach.
Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter Twenty An Introduction to Decision Making GOALS.
Managerial Decision Modeling with Spreadsheets
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin An Introduction to Decision Making Chapter 20.
DSC 3120 Generalized Modeling Techniques with Applications
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or.
Chapter 7 Decision Analysis
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,
1 1 Slide Decision Analysis Professor Ahmadi. 2 2 Slide Decision Analysis Chapter Outline n Structuring the Decision Problem n Decision Making Without.
CHAPTER 19: Decision Theory to accompany Introduction to Business Statistics fourth edition, by Ronald M. Weiers Presentation by Priscilla Chaffe-Stengel.
DECISION THEORY Decision theory is an analytical and systematic way to tackle problems A good decision is based on logic.
Topic 2. DECISION-MAKING TOOLS
ISMT 161: Introduction to Operations Management
Decision Making Under Uncertainty and Under Risk
Operations Management Decision-Making Tools Module A
To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 3-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 3 Fundamentals.
1 1 Slide © 2005 Thomson/South-Western EMGT 501 HW Solutions Chapter 12 - SELF TEST 9 Chapter 12 - SELF TEST 18.
Chapter 8 Decision Analysis n Problem Formulation n Decision Making without Probabilities n Decision Making with Probabilities n Risk Analysis and Sensitivity.
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)
An Introduction to Decision Theory
3-1 Quantitative Analysis for Management Chapter 3 Fundamentals of Decision Theory Models.
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.
Advanced Project Management Project Risk Management Ghazala Amin.
Chapter 9 - Decision Analysis - Part I
Decision Analysis Steps in Decision making
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.
Decision Theory McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Lecture 6 Decision Making.
Models for Strategic Marketing Decision Making. Market Entry Decisions To enter first or to wait Sources of First-Mover Advantages –Technological leadership.
Fundamentals of Decision Theory Chapter 16 Mausam (Based on slides of someone from NPS, Maria Fasli)
Decision Theory McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Decision Analysis.
Decision Theory Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.
Chapter 8 Decision Analysis n Problem Formulation n Decision Making without Probabilities n Decision Making with Probabilities n Risk Analysis and Sensitivity.
1 1 Slide © 2005 Thomson/South-Western Chapter 13 Decision Analysis n Problem Formulation n Decision Making without Probabilities n Decision Making with.
QUANTITATIVE TECHNIQUES
DECISION THEORY & DECISION TREE
Decisions under uncertainty and risk
Chapter Twenty McGraw-Hill/Irwin
Decisions Under Risk and Uncertainty
Welcome to MM305 Unit 4 Seminar Larry Musolino
Determinants of Effective Capacity
Decision Theory Dr. T. T. Kachwala.
Steps to Good Decisions
Supplement: Decision Making
MNG221- Management Science –
نظام التعليم المطور للانتساب
نظام التعليم المطور للانتساب
Presentation transcript:

DECISION THEORY

 It’s deals with a very scientific and quantitative way of coming to decision.  It has 4 phases. 1.Action or acts. 2.State of nature or events or outcome. 3.Pay off and pay off table or pay off matrix. Decision  A decision problem may be represented by tree diagram

 Decision making problems deals with the selection of single act from a set of acts.  There can be 2 or more acts denoted by A1,A2,A3….An. action space A = {A1 A2 A3 ……….An}  Decision tree of acts  Tabular form of reprehensive acts Action or acts action acts A1 A2 ………An A1 A2. An

 Each act is associated with one or more events or state of natures.  There events are the outcome of consequence of an act.  Events are denoted by E1 E2 ….En  E = {E1 E2 ………En} is a set of events.  Tree diagram of events.  Tabular form of events. Events or state of natures E E1 E2 ………En E1 E2. En

 In decision problems it is required to measure the degree to which the decision maker’s objectives is achieved.  Monetary value is used or a measure to represent achievement or lack of achievement.  This monetary gain or loss is called a pay off.  Pay off is expressed as profit, loss cost satisfaction etc. Pay off & pay off table E A E1E2……En A1P11P12P1n A2P21P22P2n..... AmAm1Am2.Amn PAY OFF TABLE TREE DIAGRAM OF PAY OFF

 Once a pay off table is read no its turn to some decision.  There are 3 decisions making situations. 1.Decision under uncertainty.(without problem) 2. Decision under risk.(with problem) 3.Decision under certainty. Decision making situations

 The probabilities of the states of nature is not known.  Decision is taken on the basis of 4 criteria. 1.Maxi min or mini max 2.Maxi max or mini min 3.Mini max reg. 4.Laplace. Decision under uncertainty.(without problem)

 Maxi min => maximize the minimum  Minimax => minimize the maximum  Maximin : find the pay off using maximin  Minimum profit/pay off for  Miximum pay off of minimum profit.  A2 act is chosen. Pacimistic approach A18 A240 A3-25 E A E1E2E3 A A A A240

 Minimax :- find the pay off using minimax  Maximum cost minimax of maximum cost = 100 A3 act is chosen. A1700 A2900 A3100 E A E1E2E3 A A A

 Maximax => maximum of maximum profit (optimistic approach)  Maximum pay off =  Maximum of maximum pay off = A1 = act is chosen according to maximax Minimum criteria. Minimine pay off minimum of minimum cost = A3 act is chosen according to minimum A17 A24 A36 E A E1E2E3E4 A A A A1 = 7 A3 = -7 A1-5 A2-4 A3-7

2) 1)Cal the maximum of E (regret pay off) 3)take max of each row max reg. minimum of 4) take minimum of this (max of reg. pay off) A3 act is chosen Minimax regret or minimax opportunity loss E A E1E2E3E4 A A A E A E1E2E3E4 A A A E118 E216 E317 E416 A17 A26 A35

  200  Find the average pay off for each act.  Find the maximum av from step(1) A1 is chosen. Laplase(equally likely criteria) E A E1E2E3AV A1200 A A

 In such problems uncertainty is there but probability is given may be from past experience.  In such problems 2 methods are used: 1.Using EMV(expected monetary value) 2.Using EOL(expected opportunity loss) Decision making under risk(probability given)

 A baker buys veg cutlet at rs.2 & sell it for rs.5. at the end of the day unsold veg cutlets are given to the poor for free of cost.  The following table shows the sales of veg cutlets during the past 100 days.   total = 100days Decision under risk by(EMV) method consider Daily sale No. of days

 Now the question is how many veg cutlets the baker has to stock every day in order to maximise his profit?  The 4 events are: E1 = demand for 10 cutlets E2 = E3 = E4 =  The 4 acts are: A1 = stock of 10 cutlets  profit on 1 cutlet = rs.3 A2 = A3 = A4 =

net profit is called conditional pay off Conditional pay off for each act event combination Pay off for A1.E1 = 10×3 = 30 Pay off for A1.E2 = 10×3 = 30 as 50 on Pay off for A2.E1 = 10×3 -2 = 28 Pay off for A2.E2 = 11×3 =33 as soon. P(selling 10 cutlets) = 15/100= 0.15 P(selling 11 cutlets) = 20/100= 0.20 P(selling 12 cutlets) = 40/100= 0.40 P(selling 13 cutlets) = 25/100= 0.25 E A E1 10 E2 11 E3 12 E A A A A

 Expected conditional pay off is given by the multiplying each conditional pay off by the corresponding probability, expected conditional pay off for A1.E1 = 30(.15) = 4.5 expected conditional pay off for A1.E2 = 30(.20) = 6 expected conditional pay off for A1.E3 = 30(.40) = 12 expected conditional pay off for A1.E4 = 30(.25) = 7.5 And so on… Table for expected conditional pay off E A E1E2E3E4 A A A A

EMV (expected monetary value) for A1 = = 30 EMV (expected monetary value) for A2 = 32.5 EMV (expected monetary value) for A3 = 33.5 EMV (expected monetary value) for A4 = 32.5 Since, EMV is maximum for act 3 i.e. A3 = 33.5 act A3 is chosen. i.e. 12 veg cutlets are to be stocked every day for maximum profit

It is same as (EMV) method only the difference is;  After finding the conditional pay off regret pay off has to found. This new table is called conditional opportunity loss table(COL).  The product of col and the corresponding probability given expected COL.  The sum of all expected COL is act wise given EOL.  The minimum of EOL is selected as or act. Decision under risk by(EOL) method

 A newspaper boy purchases magazines at rs.3 each & sales them at rs.5 each. He cannot return the unsold magazines. The probability distribution of the demand for the magazine is given below.  Determine how many copies of magazines should he purchases daily by EOL method Demand probability

demand stock  conditional pay off table For conditional pay off; cp = 3 & sp = 5 Profit is rs.2 on each magazine. conditional pay off for A1E1 = 16 × 2 = 32 A2E1 = 32 – 3 = 29 A3E1 = 32 – 6 = 26 E A E1 16 E2 17 E3 18 E4 19 E5 20 A A A A A

CONDITIONAL OPPORTUNITY LOSS TABLE  For E1 = (32~x), x Є E1  For E2 = (34~x), x Є E2 and so on… E A E1E2E3E4E5 A A A A A

 Expected COL = p(E) × COL  minimum EOL A3 is chosen 18 magazine should be purchased Both COL & EMV are same result E A E1E2E3E4E5EOL A A A A A

THANK YOU