1. A is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes,

Size: px
Start display at page:

Download "1. A is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes,"

Transcription

1 1. A is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. A) Decision tree B) Graphs C) Trees D) Neural Networks (Ans.: A) Explanation: A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm.

2 2. Which of the following is a schematic model of alternatives available to the decision maker along with possible consequences and alternatives? A) Analytic Hierarchy Process B) Decision making model C) Preference Matrix D) None of the above (Ans.: D) Explanation: A Decision Tree is a schematic model of alternatives available to the decision maker, along with their possible consequences

3 3. Development of a decision tree requires the drawing of a series of nodes and branches. Which statement depicts one mistake check conceivable at a possibility node? A) No error checks are possible B) Probabilities for all branches leaving a chance node must sum to 1.0 C) The expected payoff must equal zero D) Review the data and problem statement three times (Ans.: B) Explanation: The probabilities of all branches leaving the chance nodes must sum to 1.0.

4 4. What decision-making condition must exist in order for the decision tree to be a valuable tool? A) Risk B) Certainty C) Uncertainty D) All of the above (Ans.: A) Explanation: At heart the decision tree technique for making decisions in the presence of uncertainty is really quite simple, and can be applied to many different uncertain situations. While making many decisions is difficult, the particular difficulty of making these decisions is that the results of choosing the alternatives available may be variable, ambiguous, unknown or unknowable. While it may be easy to make a decision for which the results are known, we need a rule to make decisions in an uncertain world. That rule is based on probability, the language most useful for describing and analyzing the future. If the future were certain we would probably decide to take the path that promises the highest value or lowest cost. With uncertainty, we will generally take the path which has the highest expected monetary value or lowest expected cost. These concepts combine the probability that an event will occur with the impact if it does; in other words, expected monetary value and expected cost follow the definition of project risk. Many decisions are like this in risky projects, and we often need to make a decision even if we do not know for sure how it will turn out. These can be very important decisions for the project, and making them correctly increases the possibility of project success.

5 5. How the decision tree reaches its decision? A) Single test B) Two test C) Sequence of test D) No test (Ans.: C) Explanation: A decision tree takes as input an object or situation described by a set of attributes and returns a decision the predicted out value for the input. A decision tree reaches its decision by performing a sequence of tests. Each internal node in the tree corresponds to a test of the value of one of the properties, and the branches from the node are labelled with the possible values of the test. Each leaf node in the tree specifies the value to be returned if that leaf is reached.

6 6. The objective of using decision trees is to A) Expand a Data Flow Diagram so that a user can understand it B) To specify sequence of conditions to be tested and actions to be taken C) Describe a computational procedure that can be easily understood by a person D) Use it as a tool in decision support system (Ans.: B) Explanation: Conducting analysis of decision making under uncertainty using decision trees serves several purposes: First, a decision tree is a visual representation of a decision situation. Second, the branches of a tree explicitly show all those factors within the analysis that are considered relevant to the decision. Third, and more subtly, a decision tree generally captures the idea that if different decisions were to be taken then the structural nature of a situation may have changed dramatically. This is in contrast to an Excel model with sensitivity analysis in which a change of parameters in the model does not represent any structural change to the situation. Capturing the logic and conditionality that is present in a tree would be complex to do in such modelling environments. Fourth, and arguably the most powerful, a decision tree allows for forward and backward calculation paths to happen and hence the choice of the correct decision to take is made automatically.

7 7. The decision tree equivalent of the following structured English is if C2 then if C1 then A3 else A2 endif else A1, A3 endif A) B) C)

8 D) (Ans: C) Explanation: Self Explainable

9 8. The likelihood of group A winning any game is 1/3. Group A plays group B in a competition. In the event that either group wins two games consecutively, that group is proclaimed the victor. At most three games are played in the competition and, if no group has won the competition toward the finish of three games, the competition is proclaimed a draw. What is the expected number of games in the tournament? A) 3 B) 19/9 C) 22/9 D) 25/9 (Ans.: C)

10 9. Which of the following is a valid production rule for the decision tree below? Business Appointment? Temp above 70? No Yes Decision = wear slacks No Decision = wear jeans Yes Decision = wear shorts A. IF Business Appointment= No & Temp above 70 = No THEN Decision = wear jeans B. IF Business Appointment = No & Temp above 70 = No THEN Decision = wear slacks C. IF Business Appointment = Yes & Temp above 70 = Yes THEN Decision = wear shorts D. IF Temp above 70 = No THEN Decision = wear shorts (Ans.: A)

11 10. In a certain college, 10 percent of the students are science majors. 10 percent are engineering majors. 80 percent are humanities majors. Of the science majors, 20 percent have read Newsweek. Of the engineering majors, 10 percent have read Newsweek. Of the humanities majors, 20 percent have read Newsweek. Given that a student selected at random has read Newsweek, what is the probability that that student is engineering major? A) 1/19 B) 2/19 C) 5/19 D) 9/19 (Ans.: A)

12 11. The probability of team A winning any game is 1/3. Team A plays team B in a tournament. If either team wins two games in a row, that team is declared the winner. At most four games are played and, if no team has won the tournament at the end of four games, a draw is declared. Given that the tournament lasts more than two games, what is the probability that A is the winner? A) 1/9 B) 2/9 C) 4/9 D) 5/9 (Ans.: B)

13 12. Ten percent of the students are science majors (S), 20 percent are engineering majors (E), and 70 percent are humanities majors (H). Of S,10 percent have read 2 or more articles in Newsweek, 20 percent 1 article, 70 percent 0 articles. For E, the corresponding percents are 5, 15, 80. For H they are 20, 30, 50. Given that a student has read 0 articles in Newsweek, what is the probability that the student is S or E (i.e., not H)? A) 21/58 B) 23/58 C) 12/29 D) 13/29 (Ans.: B)

14 13. Which of the accompanying techniques for selecting a strategy is predictable with risk averting behavior? A) On the off chance that two methodologies have the same expected benefit, select the one with the smaller standard deviation. B) On the off chance that two methodologies have a similar standard deviation, select the one with the smaller expected benefit. C) Select the methodology with the larger coefficient of variation. D) All of the above are correct. (Ans.: A) Explanation: Risk averse is a description of an investor who, when faced with two investments with a similar expected return (but different risks), will prefer the one with the lower risk or smaller standard deviation.

15 14. If a person's utility doubles when their income doubles, then that person is risk A) Averse. B) Neutral. C) Seeking. D) There is not enough information given in the question to determine an answer. (Ans.: B) Explanation: Risk neutral is a term used to describe the mental framework of a person when deciding where to allocate money. Given two investment opportunities, for example, a risk-neutral investor only looks at the potential gains of each investment, and ignores the potential downside risk. A risk-neutral investor, therefore, is only concerned about the expected return of his investment. A classic experiment to define a person's risk-taking appetites involves an investor faced with a choice between receiving either $100 with 100% certainty or $200 with 50% certainty. The risk-neutral investor has no preference either way, since the expected value of $100 is the same for both outcomes. In contrast, the risk-averse investor generally settles for the "sure thing" or 100% certain $100, while the risk-seeking investor opts for the 50% chance of getting $200.

16 15. Strategy A has an expected value of 10 and a standard deviation of 3. Strategy B has an expected value of 10 and a standard deviation of 5. Strategy C has an expected value of 15 and a standard deviation of 10. Which one of the following statements is true? A) A risk averse decision maker will always prefer A to B, but may prefer C to A. B) A risk neutral decision maker will always prefer C to A or B. C) A risk seeking decision maker will always prefer C to A or B. D) All of the above are correct. (Ans.: D) Explanation: Risk Aversion: Risk aversion is the behavior of humans (especially consumers and investors), when exposed to uncertainty, to attempt to reduce that uncertainty. It is the reluctance of a person to accept a bargain with an uncertain payoff rather than another bargain with more certain, but possibly lower, expected payoff. For example, a risk-averse investor might choose to put his or her money into a bank account with a low but guaranteed interest rate, rather than into a stock that may have high expected returns, but also involves a chance of losing value Risk Neutral: Risk neutral is a mindset where an investor is indifferent to risk when making an investment decision. The risk-neutral investor places himself in the middle of the risk spectrum, represented by risk-seeking investors at one end and risk-averse investors at the other. Risk-neutral measures have extensive application in the pricing of derivatives. Risk Seeking: Risk seeking is the search for greater volatility and uncertainty in investments in exchange for anticipated higher returns. Risk seekers might pursue investments such as small-cap stocks and international stocks, preferring growth investments over value investments. That being said, risk-seeking investors should conduct even greater due diligence when considering a riskier investment, due to the increased implied risk of such investments.

17 16. If a decision maker is risk averse, then the best strategy to select is the one that yields the A) Highest expected payoff. B) Lowest coefficient of variation. C) Highest expected utility. D) Lowest standard deviation. (Ans.: C) Explanation: If someone prefers to receive $B rather than playing a lottery in which expected value is $B then we say that the individual is risk averse

18 17. Circumstances that influence the profitability of a decision are referred to as A) Strategies. B) A payoff matrix. C) States of nature. D) The marginal utility of money. (Ans.: C) Explanation: Events represent possible future situations that will be the primary determinants of the eventual consequence of the decision. The situations must be mutually exclusive (no two or more events can occur simultaneously) and collectively exhaustive (the events must cover all the possibilities). An outcome over which the decision maker has little or no control e.g., lottery, cointoss, whether it will rain today.

19 18. The marginal utility of money diminishes for a decision maker who is: A) A risk seeker. B) Risk neutral. C) A risk averter. D) In a situation of uncertainty. (Ans.: C) Explanation: A risk averter displays a diminishing marginal utility of money. A risk indifferent individual has a constant marginal utility of money. A risk seeker s marginal utility of money increases.

20 19. A strategy that yields an expected monetary payoff of zero is called a A) Risk-neutral strategy. B) Fair game. C) Zero-sum game. D) Certainty equivalent. (Ans.: B) Explanation: The average outcome = a weighted average of the payoffs. A game is defined to be a situation of uncertain outcome with monetary payoffs. Betting the entire company fortune on a new product is a game. A fair game has Expected payoff = 0. I bet $1 on a (fair) coin toss. Heads, I get my $1 back + $1. Tails, I lose the $1. Expected value = i=payoffs P i $ i E[payoff] = (+$1)(1/2) + (-$1)(1/2) = 0. This is a fair game.

21 20. A risk-return trade-off function A) Shows the minimum expected return required to compensate an investor for accepting various levels of risk. B) Slopes upward for a risk averse decision maker. C) Is horizontal for a risk neutral decision maker. D) All of the above are correct. (Ans.: D) Explanation: The risk-return trade-off is the principle that potential return rises with an increase in risk. Low levels of uncertainty or risk are associated with low potential returns, whereas high levels of uncertainty or risk are associated with high potential returns. According to the risk-return trade-off, invested money can render higher profits only if the investor is willing to accept the possibility of losses.

22 21. If the market interest rate is 10% and a decision maker's risk adjusted discount rate is 12%, then the decision maker A) Is risk averse. B) Has a certainty-equivalent coefficient that is greater than one. C) Is risk neutral. D) None of the above is correct. (Ans.: A) Explanation: Risk-averse investors will assign lower values to assets that have more risk associated with them than to otherwise similar assets that are less risky.

23 22. Fred is willing to pay $1 for a lottery ticket that has an expected value of zero. This proves that Fred A) Is risk averse. B) Has a certainty-equivalent coefficient that is equal to one. C) Is risk neutral. D) None of the above is correct. (Ans.: D) Explanation: Fred is risk-seeker as a risk-neutral person would pay the expected value of the lottery i.e. zero. A risk averse would not buy the lottery where as a risk seeker would purchase the lottery.

24 23. The analysis of a complex decision situation by constructing a mathematical model of the situation and then performing a large number of iterations in order to determine the probability distribution of outcomes is called A) Sensitivity analysis. B) Expected utility analysis. C) Simulation. D) A decision tree. (Ans.: C) Explanation: Self Explainable.

25 24. A multifactor evaluation process is preferred to the analytic hierarchy process when: A. There is high confidence in determining factor weights without pairwise comparisons. B. There is low confidence in determining factor weights without pairwise comparisons. C. One desires a lesser level of computational analysis. D. One desires a greater level of computational analysis. (Ans.: A) Explanation: Definition of Multifactor Evaluation Process

26 25. Three factors are considered for a decision process. It is desired to have Factor 1 (F1) weighted as 6 times the Factor 2 (F2) weight. F2 should be 3 times the Factor 3 (F3) weight. What importance of weights should be used for a multifactor evaluation process? A. W(F1) = 0.6, W(F2) = 0.3, W(F3) = 0.1 B. W(F1) = 18/22, W(F2) = 3/22. W(F3) = 1/22 C. W(F1) = 1/22, W(F2) = 18/22, W(F3) = 3/22 D. W(F1) = 3/22, W(F2) = 1/22, W(F3) = 18/22 (Ans.: B) Explanation: This is a simple ratio problem. F1:F2 = 6:1; F2:F3 = 3:1; F1:F3 = 18:1. Therefore, F1:F2:F3 = 18:3:1. Thus, W(F1) = 18/22, W(F2) = 3/22. W(F3) = 1/22.

Making Hard Decision. ENCE 627 Decision Analysis for Engineering. Identify the decision situation and understand objectives. Identify alternatives

Making Hard Decision. ENCE 627 Decision Analysis for Engineering. Identify the decision situation and understand objectives. Identify alternatives CHAPTER Duxbury Thomson Learning Making Hard Decision Third Edition RISK ATTITUDES A. J. Clark School of Engineering Department of Civil and Environmental Engineering 13 FALL 2003 By Dr. Ibrahim. Assakkaf

More information

Learning Objectives = = where X i is the i t h outcome of a decision, p i is the probability of the i t h

Learning Objectives = = where X i is the i t h outcome of a decision, p i is the probability of the i t h Learning Objectives After reading Chapter 15 and working the problems for Chapter 15 in the textbook and in this Workbook, you should be able to: Distinguish between decision making under uncertainty and

More information

DECISION ANALYSIS. Decision often must be made in uncertain environments. Examples:

DECISION ANALYSIS. Decision often must be made in uncertain environments. Examples: DECISION ANALYSIS Introduction Decision often must be made in uncertain environments. Examples: Manufacturer introducing a new product in the marketplace. Government contractor bidding on a new contract.

More information

INSE 6230 Total Quality Project Management

INSE 6230 Total Quality Project Management INSE 6230 Total Quality Project Management Lecture 6 Project Risk Management Project risk management is the art and science of identifying, analyzing, and responding to risk throughout the life of a project

More information

Choice under risk and uncertainty

Choice under risk and uncertainty Choice under risk and uncertainty Introduction Up until now, we have thought of the objects that our decision makers are choosing as being physical items However, we can also think of cases where the outcomes

More information

Managerial Economics

Managerial Economics Managerial Economics Unit 9: Risk Analysis Rudolf Winter-Ebmer Johannes Kepler University Linz Winter Term 2015 Managerial Economics: Unit 9 - Risk Analysis 1 / 49 Objectives Explain how managers should

More information

TECHNIQUES FOR DECISION MAKING IN RISKY CONDITIONS

TECHNIQUES FOR DECISION MAKING IN RISKY CONDITIONS RISK AND UNCERTAINTY THREE ALTERNATIVE STATES OF INFORMATION CERTAINTY - where the decision maker is perfectly informed in advance about the outcome of their decisions. For each decision there is only

More information

05/05/2011. Degree of Risk. Degree of Risk. BUSA 4800/4810 May 5, Uncertainty

05/05/2011. Degree of Risk. Degree of Risk. BUSA 4800/4810 May 5, Uncertainty BUSA 4800/4810 May 5, 2011 Uncertainty We must believe in luck. For how else can we explain the success of those we don t like? Jean Cocteau Degree of Risk We incorporate risk and uncertainty into our

More information

Decision Analysis under Uncertainty. Christopher Grigoriou Executive MBA/HEC Lausanne

Decision Analysis under Uncertainty. Christopher Grigoriou Executive MBA/HEC Lausanne Decision Analysis under Uncertainty Christopher Grigoriou Executive MBA/HEC Lausanne 2007-2008 2008 Introduction Examples of decision making under uncertainty in the business world; => Trade-off between

More information

u w 1.5 < 0 These two results imply that the utility function is concave.

u w 1.5 < 0 These two results imply that the utility function is concave. A person with initial wealth of Rs.1000 has a 20% possibility of getting in a mischance. On the off chance that he gets in a mishap, he will lose Rs.800, abandoning him with Rs.200; on the off chance that

More information

Bidding Decision Example

Bidding Decision Example Bidding Decision Example SUPERTREE EXAMPLE In this chapter, we demonstrate Supertree using the simple bidding problem portrayed by the decision tree in Figure 5.1. The situation: Your company is bidding

More information

ECO303: Intermediate Microeconomic Theory Benjamin Balak, Spring 2008

ECO303: Intermediate Microeconomic Theory Benjamin Balak, Spring 2008 ECO303: Intermediate Microeconomic Theory Benjamin Balak, Spring 2008 Game Theory: FINAL EXAMINATION 1. Under a mixed strategy, A) players move sequentially. B) a player chooses among two or more pure

More information

Decision Theory. Refail N. Kasimbeyli

Decision Theory. Refail N. Kasimbeyli Decision Theory Refail N. Kasimbeyli Chapter 3 3 Utility Theory 3.1 Single-attribute utility 3.2 Interpreting utility functions 3.3 Utility functions for non-monetary attributes 3.4 The axioms of utility

More information

Unit 4.3: Uncertainty

Unit 4.3: Uncertainty Unit 4.: Uncertainty Michael Malcolm June 8, 20 Up until now, we have been considering consumer choice problems where the consumer chooses over outcomes that are known. However, many choices in economics

More information

Key concepts: Certainty Equivalent and Risk Premium

Key concepts: Certainty Equivalent and Risk Premium Certainty equivalents Risk premiums 19 Key concepts: Certainty Equivalent and Risk Premium Which is the amount of money that is equivalent in your mind to a given situation that involves uncertainty? Ex:

More information

Module 15 July 28, 2014

Module 15 July 28, 2014 Module 15 July 28, 2014 General Approach to Decision Making Many Uses: Capacity Planning Product/Service Design Equipment Selection Location Planning Others Typically Used for Decisions Characterized by

More information

Expected value is basically the average payoff from some sort of lottery, gamble or other situation with a randomly determined outcome.

Expected value is basically the average payoff from some sort of lottery, gamble or other situation with a randomly determined outcome. Economics 352: Intermediate Microeconomics Notes and Sample Questions Chapter 18: Uncertainty and Risk Aversion Expected Value The chapter starts out by explaining what expected value is and how to calculate

More information

CHAPTER 5 STOCHASTIC SCHEDULING

CHAPTER 5 STOCHASTIC SCHEDULING CHPTER STOCHSTIC SCHEDULING In some situations, estimating activity duration becomes a difficult task due to ambiguity inherited in and the risks associated with some work. In such cases, the duration

More information

Project Risk Evaluation and Management Exercises (Part II, Chapters 4, 5, 6 and 7)

Project Risk Evaluation and Management Exercises (Part II, Chapters 4, 5, 6 and 7) Project Risk Evaluation and Management Exercises (Part II, Chapters 4, 5, 6 and 7) Chapter II.4 Exercise 1 Explain in your own words the role that data can play in the development of models of uncertainty

More information

What do Coin Tosses and Decision Making under Uncertainty, have in common?

What do Coin Tosses and Decision Making under Uncertainty, have in common? What do Coin Tosses and Decision Making under Uncertainty, have in common? J. Rene van Dorp (GW) Presentation EMSE 1001 October 27, 2017 Presented by: J. Rene van Dorp 10/26/2017 1 About René van Dorp

More information

Lecture 12: Introduction to reasoning under uncertainty. Actions and Consequences

Lecture 12: Introduction to reasoning under uncertainty. Actions and Consequences Lecture 12: Introduction to reasoning under uncertainty Preferences Utility functions Maximizing expected utility Value of information Bandit problems and the exploration-exploitation trade-off COMP-424,

More information

UNCERTAINTY AND INFORMATION

UNCERTAINTY AND INFORMATION UNCERTAINTY AND INFORMATION M. En C. Eduardo Bustos Farías 1 Objectives After studying this chapter, you will be able to: Explain how people make decisions when they are uncertain about the consequences

More information

Economics Homework 5 Fall 2006 Dickert-Conlin / Conlin

Economics Homework 5 Fall 2006 Dickert-Conlin / Conlin Economics 31 - Homework 5 Fall 26 Dickert-Conlin / Conlin Answer Key 1. Suppose Cush Bring-it-Home Cash has a utility function of U = M 2, where M is her income. Suppose Cush s income is $8 and she is

More information

DECISION ANALYSIS. (Hillier & Lieberman Introduction to Operations Research, 8 th edition)

DECISION ANALYSIS. (Hillier & Lieberman Introduction to Operations Research, 8 th edition) DECISION ANALYSIS (Hillier & Lieberman Introduction to Operations Research, 8 th edition) Introduction Decision often must be made in uncertain environments Examples: Manufacturer introducing a new product

More information

Uncertainty. Contingent consumption Subjective probability. Utility functions. BEE2017 Microeconomics

Uncertainty. Contingent consumption Subjective probability. Utility functions. BEE2017 Microeconomics Uncertainty BEE217 Microeconomics Uncertainty: The share prices of Amazon and the difficulty of investment decisions Contingent consumption 1. What consumption or wealth will you get in each possible outcome

More information

FINANCIAL MANAGEMENT V SEMESTER. B.Com FINANCE SPECIALIZATION CORE COURSE. (CUCBCSSS Admission onwards) UNIVERSITY OF CALICUT

FINANCIAL MANAGEMENT V SEMESTER. B.Com FINANCE SPECIALIZATION CORE COURSE. (CUCBCSSS Admission onwards) UNIVERSITY OF CALICUT FINANCIAL MANAGEMENT (ADDITIONAL LESSONS) V SEMESTER B.Com UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION STUDY MATERIAL Core Course B.Sc. COUNSELLING PSYCHOLOGY III Semester physiological psychology

More information

Price Theory Lecture 9: Choice Under Uncertainty

Price Theory Lecture 9: Choice Under Uncertainty I. Probability and Expected Value Price Theory Lecture 9: Choice Under Uncertainty In all that we have done so far, we've assumed that choices are being made under conditions of certainty -- prices are

More information

Managerial Economics Uncertainty

Managerial Economics Uncertainty Managerial Economics Uncertainty Aalto University School of Science Department of Industrial Engineering and Management January 10 26, 2017 Dr. Arto Kovanen, Ph.D. Visiting Lecturer Uncertainty general

More information

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Decision Analysis

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Decision Analysis Resource Allocation and Decision Analysis (ECON 800) Spring 04 Foundations of Decision Analysis Reading: Decision Analysis (ECON 800 Coursepak, Page 5) Definitions and Concepts: Decision Analysis a logical

More information

Publication date: 12-Nov-2001 Reprinted from RatingsDirect

Publication date: 12-Nov-2001 Reprinted from RatingsDirect Publication date: 12-Nov-2001 Reprinted from RatingsDirect Commentary CDO Evaluator Applies Correlation and Monte Carlo Simulation to the Art of Determining Portfolio Quality Analyst: Sten Bergman, New

More information

ECON 312: MICROECONOMICS II Lecture 11: W/C 25 th April 2016 Uncertainty and Risk Dr Ebo Turkson

ECON 312: MICROECONOMICS II Lecture 11: W/C 25 th April 2016 Uncertainty and Risk Dr Ebo Turkson ECON 312: MICROECONOMICS II Lecture 11: W/C 25 th April 2016 Uncertainty and Risk Dr Ebo Turkson Chapter 17 Uncertainty Topics Degree of Risk. Decision Making Under Uncertainty. Avoiding Risk. Investing

More information

Project Risk Analysis and Management Exercises (Part II, Chapters 6, 7)

Project Risk Analysis and Management Exercises (Part II, Chapters 6, 7) Project Risk Analysis and Management Exercises (Part II, Chapters 6, 7) Chapter II.6 Exercise 1 For the decision tree in Figure 1, assume Chance Events E and F are independent. a) Draw the appropriate

More information

TIm 206 Lecture notes Decision Analysis

TIm 206 Lecture notes Decision Analysis TIm 206 Lecture notes Decision Analysis Instructor: Kevin Ross 2005 Scribes: Geoff Ryder, Chris George, Lewis N 2010 Scribe: Aaron Michelony 1 Decision Analysis: A Framework for Rational Decision- Making

More information

Chapter 05 Understanding Risk

Chapter 05 Understanding Risk Chapter 05 Understanding Risk Multiple Choice Questions 1. (p. 93) Which of the following would not be included in a definition of risk? a. Risk is a measure of uncertainty B. Risk can always be avoided

More information

ECON 459 Game Theory. Lecture Notes Auctions. Luca Anderlini Spring 2017

ECON 459 Game Theory. Lecture Notes Auctions. Luca Anderlini Spring 2017 ECON 459 Game Theory Lecture Notes Auctions Luca Anderlini Spring 2017 These notes have been used and commented on before. If you can still spot any errors or have any suggestions for improvement, please

More information

RESEARCH GROUP ADDRESSING INVESTMENT GOALS USING ASSET ALLOCATION

RESEARCH GROUP ADDRESSING INVESTMENT GOALS USING ASSET ALLOCATION M A Y 2 0 0 3 STRATEGIC INVESTMENT RESEARCH GROUP ADDRESSING INVESTMENT GOALS USING ASSET ALLOCATION T ABLE OF CONTENTS ADDRESSING INVESTMENT GOALS USING ASSET ALLOCATION 1 RISK LIES AT THE HEART OF ASSET

More information

Concave utility functions

Concave utility functions Meeting 9: Addendum Concave utility functions This functional form of the utility function characterizes a risk avoider. Why is it so? Consider the following bet (better numbers than those used at Meeting

More information

FE670 Algorithmic Trading Strategies. Stevens Institute of Technology

FE670 Algorithmic Trading Strategies. Stevens Institute of Technology FE670 Algorithmic Trading Strategies Lecture 4. Cross-Sectional Models and Trading Strategies Steve Yang Stevens Institute of Technology 09/26/2013 Outline 1 Cross-Sectional Methods for Evaluation of Factor

More information

International Financial Markets 1. How Capital Markets Work

International Financial Markets 1. How Capital Markets Work International Financial Markets Lecture Notes: E-Mail: Colloquium: www.rainer-maurer.de rainer.maurer@hs-pforzheim.de Friday 15.30-17.00 (room W4.1.03) -1-1.1. Supply and Demand on Capital Markets 1.1.1.

More information

Models & Decision with Financial Applications Unit 3: Utility Function and Risk Attitude

Models & Decision with Financial Applications Unit 3: Utility Function and Risk Attitude Models & Decision with Financial Applications Unit 3: Utility Function and Risk Attitude Duan LI Department of Systems Engineering & Engineering Management The Chinese University of Hong Kong http://www.se.cuhk.edu.hk/

More information

BEEM109 Experimental Economics and Finance

BEEM109 Experimental Economics and Finance University of Exeter Recap Last class we looked at the axioms of expected utility, which defined a rational agent as proposed by von Neumann and Morgenstern. We then proceeded to look at empirical evidence

More information

The Course So Far. Atomic agent: uninformed, informed, local Specific KR languages

The Course So Far. Atomic agent: uninformed, informed, local Specific KR languages The Course So Far Traditional AI: Deterministic single agent domains Atomic agent: uninformed, informed, local Specific KR languages Constraint Satisfaction Logic and Satisfiability STRIPS for Classical

More information

Retirement. Optimal Asset Allocation in Retirement: A Downside Risk Perspective. JUne W. Van Harlow, Ph.D., CFA Director of Research ABSTRACT

Retirement. Optimal Asset Allocation in Retirement: A Downside Risk Perspective. JUne W. Van Harlow, Ph.D., CFA Director of Research ABSTRACT Putnam Institute JUne 2011 Optimal Asset Allocation in : A Downside Perspective W. Van Harlow, Ph.D., CFA Director of Research ABSTRACT Once an individual has retired, asset allocation becomes a critical

More information

Event A Value. Value. Choice

Event A Value. Value. Choice Solutions.. No. t least, not if the decision tree and influence diagram each represent the same problem (identical details and definitions). Decision trees and influence diagrams are called isomorphic,

More information

Risk in Investment Decisions

Risk in Investment Decisions Learning Objectives: To provide conceptual understanding of risk & uncertainty. To bring out various approaches to risk measurement. To focus on methods of adjusting risks in investment decisions. Structure:

More information

Multistage decision-making

Multistage decision-making Multistage decision-making 1. What is decision making? Decision making is the cognitive process leading to the selection of a course of action among variations. Every decision making process produces a

More information

Learning Objectives 6/2/18. Some keys from yesterday

Learning Objectives 6/2/18. Some keys from yesterday Valuation and pricing (November 5, 2013) Lecture 12 Decisions Risk & Uncertainty Olivier J. de Jong, LL.M., MM., MBA, CFD, CFFA, AA www.centime.biz Some keys from yesterday Learning Objectives v Explain

More information

Measuring Risk. Expected value and expected return 9/4/2018. Possibilities, Probabilities and Expected Value

Measuring Risk. Expected value and expected return 9/4/2018. Possibilities, Probabilities and Expected Value Chapter Five Understanding Risk Introduction Risk cannot be avoided. Everyday decisions involve financial and economic risk. How much car insurance should I buy? Should I refinance my mortgage now or later?

More information

SCHEDULE CREATION AND ANALYSIS. 1 Powered by POeT Solvers Limited

SCHEDULE CREATION AND ANALYSIS. 1   Powered by POeT Solvers Limited SCHEDULE CREATION AND ANALYSIS 1 www.pmtutor.org Powered by POeT Solvers Limited While building the project schedule, we need to consider all risk factors, assumptions and constraints imposed on the project

More information

Dr. Abdallah Abdallah Fall Term 2014

Dr. Abdallah Abdallah Fall Term 2014 Quantitative Analysis Dr. Abdallah Abdallah Fall Term 2014 1 Decision analysis Fundamentals of decision theory models Ch. 3 2 Decision theory Decision theory is an analytic and systemic way to tackle problems

More information

Notes 10: Risk and Uncertainty

Notes 10: Risk and Uncertainty Economics 335 April 19, 1999 A. Introduction Notes 10: Risk and Uncertainty 1. Basic Types of Uncertainty in Agriculture a. production b. prices 2. Examples of Uncertainty in Agriculture a. crop yields

More information

Decision Trees: Booths

Decision Trees: Booths DECISION ANALYSIS Decision Trees: Booths Terri Donovan recorded: January, 2010 Hi. Tony has given you a challenge of setting up a spreadsheet, so you can really understand whether it s wiser to play in

More information

UC Berkeley Haas School of Business Economic Analysis for Business Decisions (EWMBA 201A) Fall Module I

UC Berkeley Haas School of Business Economic Analysis for Business Decisions (EWMBA 201A) Fall Module I UC Berkeley Haas School of Business Economic Analysis for Business Decisions (EWMBA 201A) Fall 2018 Module I The consumers Decision making under certainty (PR 3.1-3.4) Decision making under uncertainty

More information

SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT. BF360 Operations Research

SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT. BF360 Operations Research SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT BF360 Operations Research Unit 5 Moses Mwale e-mail: moses.mwale@ictar.ac.zm BF360 Operations Research Contents Unit 5: Decision Analysis 3 5.1 Components

More information

Microeconomics (Uncertainty & Behavioural Economics, Ch 05)

Microeconomics (Uncertainty & Behavioural Economics, Ch 05) Microeconomics (Uncertainty & Behavioural Economics, Ch 05) Lecture 23 Apr 10, 2017 Uncertainty and Consumer Behavior To examine the ways that people can compare and choose among risky alternatives, we

More information

An Introduction to Resampled Efficiency

An Introduction to Resampled Efficiency by Richard O. Michaud New Frontier Advisors Newsletter 3 rd quarter, 2002 Abstract Resampled Efficiency provides the solution to using uncertain information in portfolio optimization. 2 The proper purpose

More information

Textbook: pp Chapter 3: Decision Analysis

Textbook: pp Chapter 3: Decision Analysis 1 Textbook: pp. 81-128 Chapter 3: Decision Analysis 2 Learning Objectives After completing this chapter, students will be able to: List the steps of the decision-making process. Describe the types of decision-making

More information

Decision making under uncertainty

Decision making under uncertainty Decision making under uncertainty 1 Outline 1. Components of decision making 2. Criteria for decision making 3. Utility theory 4. Decision trees 5. Posterior probabilities using Bayes rule 6. The Monty

More information

Rational theories of finance tell us how people should behave and often do not reflect reality.

Rational theories of finance tell us how people should behave and often do not reflect reality. FINC3023 Behavioral Finance TOPIC 1: Expected Utility Rational theories of finance tell us how people should behave and often do not reflect reality. A normative theory based on rational utility maximizers

More information

Answers to chapter 3 review questions

Answers to chapter 3 review questions Answers to chapter 3 review questions 3.1 Explain why the indifference curves in a probability triangle diagram are straight lines if preferences satisfy expected utility theory. The expected utility of

More information

CASE FAIR OSTER PRINCIPLES OF MICROECONOMICS E L E V E N T H E D I T I O N. PEARSON 2012 Pearson Education, Inc. Publishing as Prentice Hall

CASE FAIR OSTER PRINCIPLES OF MICROECONOMICS E L E V E N T H E D I T I O N. PEARSON 2012 Pearson Education, Inc. Publishing as Prentice Hall PART II The Market System: Choices Made by Households and Firms PRINCIPLES OF MICROECONOMICS E L E V E N T H E D I T I O N CASE FAIR OSTER PEARSON 2012 Pearson Education, Inc. Publishing as Prentice Hall

More information

A convenient analytical and visual technique of PERT and CPM prove extremely valuable in assisting the managers in managing the projects.

A convenient analytical and visual technique of PERT and CPM prove extremely valuable in assisting the managers in managing the projects. Introduction Any project involves planning, scheduling and controlling a number of interrelated activities with use of limited resources, namely, men, machines, materials, money and time. The projects

More information

Choose between the four lotteries with unknown probabilities on the branches: uncertainty

Choose between the four lotteries with unknown probabilities on the branches: uncertainty R.E.Marks 2000 Lecture 8-1 2.11 Utility Choose between the four lotteries with unknown probabilities on the branches: uncertainty A B C D $25 $150 $600 $80 $90 $98 $ 20 $0 $100$1000 $105$ 100 R.E.Marks

More information

Decision Making. DKSharma

Decision Making. DKSharma Decision Making DKSharma Decision making Learning Objectives: To make the students understand the concepts of Decision making Decision making environment; Decision making under certainty; Decision making

More information

The Course So Far. Decision Making in Deterministic Domains. Decision Making in Uncertain Domains. Next: Decision Making in Uncertain Domains

The Course So Far. Decision Making in Deterministic Domains. Decision Making in Uncertain Domains. Next: Decision Making in Uncertain Domains The Course So Far Decision Making in Deterministic Domains search planning Decision Making in Uncertain Domains Uncertainty: adversarial Minimax Next: Decision Making in Uncertain Domains Uncertainty:

More information

Decision Analysis CHAPTER LEARNING OBJECTIVES CHAPTER OUTLINE. After completing this chapter, students will be able to:

Decision Analysis CHAPTER LEARNING OBJECTIVES CHAPTER OUTLINE. After completing this chapter, students will be able to: CHAPTER 3 Decision Analysis LEARNING OBJECTIVES After completing this chapter, students will be able to: 1. List the steps of the decision-making process. 2. Describe the types of decision-making environments.

More information

Copyright (C) 2001 David K. Levine This document is an open textbook; you can redistribute it and/or modify it under the terms of version 1 of the

Copyright (C) 2001 David K. Levine This document is an open textbook; you can redistribute it and/or modify it under the terms of version 1 of the Copyright (C) 2001 David K. Levine This document is an open textbook; you can redistribute it and/or modify it under the terms of version 1 of the open text license amendment to version 2 of the GNU General

More information

Decision making in the presence of uncertainty

Decision making in the presence of uncertainty CS 271 Foundations of AI Lecture 21 Decision making in the presence of uncertainty Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square Decision-making in the presence of uncertainty Many real-world

More information

Corporate Finance, Module 21: Option Valuation. Practice Problems. (The attached PDF file has better formatting.) Updated: July 7, 2005

Corporate Finance, Module 21: Option Valuation. Practice Problems. (The attached PDF file has better formatting.) Updated: July 7, 2005 Corporate Finance, Module 21: Option Valuation Practice Problems (The attached PDF file has better formatting.) Updated: July 7, 2005 {This posting has more information than is needed for the corporate

More information

Measuring and Utilizing Corporate Risk Tolerance to Improve Investment Decision Making

Measuring and Utilizing Corporate Risk Tolerance to Improve Investment Decision Making Measuring and Utilizing Corporate Risk Tolerance to Improve Investment Decision Making Michael R. Walls Division of Economics and Business Colorado School of Mines mwalls@mines.edu January 1, 2005 (Under

More information

8. Uncertainty. Reading: BGVW, Chapter 7

8. Uncertainty. Reading: BGVW, Chapter 7 8. Uncertainty Reading: BGVW, Chapter 7 1. Introduction Uncertainties abound future: incomes/prices/populations analysis: dose-response/valuation/climate/effects of regulation on environmental quality/longevity

More information

Sequential-move games with Nature s moves.

Sequential-move games with Nature s moves. Econ 221 Fall, 2018 Li, Hao UBC CHAPTER 3. GAMES WITH SEQUENTIAL MOVES Game trees. Sequential-move games with finite number of decision notes. Sequential-move games with Nature s moves. 1 Strategies in

More information

Iterated Dominance and Nash Equilibrium

Iterated Dominance and Nash Equilibrium Chapter 11 Iterated Dominance and Nash Equilibrium In the previous chapter we examined simultaneous move games in which each player had a dominant strategy; the Prisoner s Dilemma game was one example.

More information

Martingale Pricing Theory in Discrete-Time and Discrete-Space Models

Martingale Pricing Theory in Discrete-Time and Discrete-Space Models IEOR E4707: Foundations of Financial Engineering c 206 by Martin Haugh Martingale Pricing Theory in Discrete-Time and Discrete-Space Models These notes develop the theory of martingale pricing in a discrete-time,

More information

Chapter 18: Risky Choice and Risk

Chapter 18: Risky Choice and Risk Chapter 18: Risky Choice and Risk Risky Choice Probability States of Nature Expected Utility Function Interval Measure Violations Risk Preference State Dependent Utility Risk-Aversion Coefficient Actuarially

More information

Random Variables and Applications OPRE 6301

Random Variables and Applications OPRE 6301 Random Variables and Applications OPRE 6301 Random Variables... As noted earlier, variability is omnipresent in the business world. To model variability probabilistically, we need the concept of a random

More information

SIMULATION OF ELECTRICITY MARKETS

SIMULATION OF ELECTRICITY MARKETS SIMULATION OF ELECTRICITY MARKETS MONTE CARLO METHODS Lectures 15-18 in EG2050 System Planning Mikael Amelin 1 COURSE OBJECTIVES To pass the course, the students should show that they are able to - apply

More information

Web Extension: Continuous Distributions and Estimating Beta with a Calculator

Web Extension: Continuous Distributions and Estimating Beta with a Calculator 19878_02W_p001-008.qxd 3/10/06 9:51 AM Page 1 C H A P T E R 2 Web Extension: Continuous Distributions and Estimating Beta with a Calculator This extension explains continuous probability distributions

More information

Introduction to Decision Analysis

Introduction to Decision Analysis Introduction to Decision Analysis M.Sc. (Tech) Yrjänä Hynninen Dept of Mathematics and Systems Analysis Analytics and Data Science seminar, October 16, 2017 Learning objectives Develop an understanding

More information

Game Theory I. Author: Neil Bendle Marketing Metrics Reference: Chapter Neil Bendle and Management by the Numbers, Inc.

Game Theory I. Author: Neil Bendle Marketing Metrics Reference: Chapter Neil Bendle and Management by the Numbers, Inc. Game Theory I This module provides an introduction to game theory for managers and includes the following topics: matrix basics, zero and non-zero sum games, and dominant strategies. Author: Neil Bendle

More information

ECON FINANCIAL ECONOMICS

ECON FINANCIAL ECONOMICS ECON 337901 FINANCIAL ECONOMICS Peter Ireland Boston College April 10, 2018 These lecture notes by Peter Ireland are licensed under a Creative Commons Attribution-NonCommerical-ShareAlike 4.0 International

More information

UC Berkeley Haas School of Business Economic Analysis for Business Decisions (EWMBA 201A) Fall Module I

UC Berkeley Haas School of Business Economic Analysis for Business Decisions (EWMBA 201A) Fall Module I UC Berkeley Haas School of Business Economic Analysis for Business Decisions (EWMBA 201A) Fall 2016 Module I The consumers Decision making under certainty (PR 3.1-3.4) Decision making under uncertainty

More information

Forex Illusions - 6 Illusions You Need to See Through to Win

Forex Illusions - 6 Illusions You Need to See Through to Win Forex Illusions - 6 Illusions You Need to See Through to Win See the Reality & Forex Trading Success can Be Yours! The myth of Forex trading is one which the public believes and they lose and its a whopping

More information

Finance Concepts I: Present Discounted Value, Risk/Return Tradeoff

Finance Concepts I: Present Discounted Value, Risk/Return Tradeoff Finance Concepts I: Present Discounted Value, Risk/Return Tradeoff Federal Reserve Bank of New York Central Banking Seminar Preparatory Workshop in Financial Markets, Instruments and Institutions Anthony

More information

How to Consider Risk Demystifying Monte Carlo Risk Analysis

How to Consider Risk Demystifying Monte Carlo Risk Analysis How to Consider Risk Demystifying Monte Carlo Risk Analysis James W. Richardson Regents Professor Senior Faculty Fellow Co-Director, Agricultural and Food Policy Center Department of Agricultural Economics

More information

TOPIC: PROBABILITY DISTRIBUTIONS

TOPIC: PROBABILITY DISTRIBUTIONS TOPIC: PROBABILITY DISTRIBUTIONS There are two types of random variables: A Discrete random variable can take on only specified, distinct values. A Continuous random variable can take on any value within

More information

Risk Video #1. Video 1 Recap

Risk Video #1. Video 1 Recap Risk Video #1 Video 1 Recap 1 Risk Video #2 Video 2 Recap 2 Risk Video #3 Risk Risk Management Process Uncertain or chance events that planning can not overcome or control. Risk Management A proactive

More information

36106 Managerial Decision Modeling Decision Analysis in Excel

36106 Managerial Decision Modeling Decision Analysis in Excel 36106 Managerial Decision Modeling Decision Analysis in Excel Kipp Martin University of Chicago Booth School of Business October 19, 2017 Reading and Excel Files Reading: Powell and Baker: Sections 13.1,

More information

New financial analysis tools at CARMA

New financial analysis tools at CARMA New financial analysis tools at CARMA Amir Salehipour CARMA, The University of Newcastle Joint work with Jonathan M. Borwein, David H. Bailey and Marcos López de Prado November 13, 2015 Table of Contents

More information

16 MAKING SIMPLE DECISIONS

16 MAKING SIMPLE DECISIONS 247 16 MAKING SIMPLE DECISIONS Let us associate each state S with a numeric utility U(S), which expresses the desirability of the state A nondeterministic action A will have possible outcome states Result

More information

G5212: Game Theory. Mark Dean. Spring 2017

G5212: Game Theory. Mark Dean. Spring 2017 G5212: Game Theory Mark Dean Spring 2017 Modelling Dynamics Up until now, our games have lacked any sort of dynamic aspect We have assumed that all players make decisions at the same time Or at least no

More information

Web Extension: Abandonment Options and Risk-Neutral Valuation

Web Extension: Abandonment Options and Risk-Neutral Valuation 19878_14W_p001-016.qxd 3/13/06 3:01 PM Page 1 C H A P T E R 14 Web Extension: Abandonment Options and Risk-Neutral Valuation This extension illustrates the valuation of abandonment options. It also explains

More information

Introduction. Two main characteristics: Editing Evaluation. The use of an editing phase Outcomes as difference respect to a reference point 2

Introduction. Two main characteristics: Editing Evaluation. The use of an editing phase Outcomes as difference respect to a reference point 2 Prospect theory 1 Introduction Kahneman and Tversky (1979) Kahneman and Tversky (1992) cumulative prospect theory It is classified as nonconventional theory It is perhaps the most well-known of alternative

More information

How Risky is the Stock Market

How Risky is the Stock Market How Risky is the Stock Market An Analysis of Short-term versus Long-term investing Elena Agachi and Lammertjan Dam CIBIF-001 18 januari 2018 1871 1877 1883 1889 1895 1901 1907 1913 1919 1925 1937 1943

More information

Homework 1: Preferences, Lotteries, Expected Value and Expected Utility

Homework 1: Preferences, Lotteries, Expected Value and Expected Utility Homework 1: Preferences, Lotteries, Expected Value and Expected Utility Solution Guide 1. Study your own preferences (a) In each case, indicate if you prefer one of the goods over the other or if you are

More information

Chapter 15 Trade-offs Involving Time and Risk. Outline. Modeling Time and Risk. The Time Value of Money. Time Preferences. Probability and Risk

Chapter 15 Trade-offs Involving Time and Risk. Outline. Modeling Time and Risk. The Time Value of Money. Time Preferences. Probability and Risk Involving Modeling The Value Part VII: Equilibrium in the Macroeconomy 23. Employment and Unemployment 15. Involving Web 1. Financial Decision Making 24. Credit Markets 25. The Monetary System 1 / 36 Involving

More information

Name. Answers Discussion Final Exam, Econ 171, March, 2012

Name. Answers Discussion Final Exam, Econ 171, March, 2012 Name Answers Discussion Final Exam, Econ 171, March, 2012 1) Consider the following strategic form game in which Player 1 chooses the row and Player 2 chooses the column. Both players know that this is

More information

Optimization of a Real Estate Portfolio with Contingent Portfolio Programming

Optimization of a Real Estate Portfolio with Contingent Portfolio Programming Mat-2.108 Independent research projects in applied mathematics Optimization of a Real Estate Portfolio with Contingent Portfolio Programming 3 March, 2005 HELSINKI UNIVERSITY OF TECHNOLOGY System Analysis

More information

Full Monte. Looking at your project through rose-colored glasses? Let s get real.

Full Monte. Looking at your project through rose-colored glasses? Let s get real. Realistic plans for project success. Looking at your project through rose-colored glasses? Let s get real. Full Monte Cost and schedule risk analysis add-in for Microsoft Project that graphically displays

More information

Formulating Models of Simple Systems using VENSIM PLE

Formulating Models of Simple Systems using VENSIM PLE Formulating Models of Simple Systems using VENSIM PLE Professor Nelson Repenning System Dynamics Group MIT Sloan School of Management Cambridge, MA O2142 Edited by Laura Black, Lucia Breierova, and Leslie

More information