Behavioral Finance Economics 437.

Slides:



Advertisements
Similar presentations
Utility theory U: O-> R (utility maps from outcomes to a real number) represents preferences over outcomes ~ means indifference We need a way to talk about.
Advertisements

Mixed Strategies.
Behavioral Finance Kahneman March 29, 2012 Behavioral Finance Economics 437.
CHAPTER 13: Binomial Distributions
Decision-Making under Uncertainty – Part I Topic 4.
Behavioral Finance Uncertain Choices February 18, 2014 Behavioral Finance Economics 437.
Utility Axioms Axiom: something obvious, cannot be proven Utility axioms (rules for clear thinking)
CHAPTER 14 Utility Axioms Paradoxes & Implications.
1 Utility Theory. 2 Option 1: bet that pays $5,000,000 if a coin flipped comes up tails you get $0 if the coin comes up heads. Option 2: get $2,000,000.
Judgment and Decision Making in Information Systems Utility Functions, Utility Elicitation, and Risk Attitudes Yuval Shahar, M.D., Ph.D.
Lecture 4 on Individual Optimization Risk Aversion
Behavioral Finance Introduction January 13, 2015 Behavioral Finance Economics 437.
Notes – Theory of Choice
PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005 Normative Decision Theory A prescriptive theory for how decisions should be made.
Stat 245 Recitation 11 10/25/2007 EA :30am TA: Dongmei Li.
Decision Making as Constrained Optimization Specification of Objective Function  Decision Rule Identification of Constraints.
Behavioral Finance Law of One Price Jan 26, 2012 Behavioral Finance Economics 437.
Behavioral Finance Other Noise Trader Models Feb 3, 2015 Behavioral Finance Economics 437.
Behavioral Finance EMH and Critics Jan 15-20, 2015 Behavioral Finance Economics 437.
Behavioral Finance EMH Definitions Jan 24, 2012 Behavioral Finance Economics 437.
PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005 Rational Decision Making.
Judgment and Decision Making in Information Systems Probability, Utility, and Game Theory Yuval Shahar, M.D., Ph.D.
The Development of Decision Analysis Jason R. W. Merrick Based on Smith and von Winterfeldt (2004). Decision Analysis in Management Science. Management.
Decision making Making decisions Optimal decisions Violations of rationality.
Economic evaluation of health programmes Department of Epidemiology, Biostatistics and Occupational Health Class no. 9: Cost-utility analysis – Part 2.
Lecture 8—Probability and Statistics (Ch. 3) Friday January 25 th Quiz on Chapter 2 Classical and statistical probability The axioms of probability theory.
Random Variables Section 3.1 A Random Variable: is a function on the outcomes of an experiment; i.e. a function on outcomes in S. For discrete random variables,
Markets, Firms and Consumers Lecture 4- Capital and the Firm.
RISK BENEFIT ANALYSIS Special Lectures University of Kuwait Richard Wilson Mallinckrodt Professor of Physics Harvard University January 13th, 14th and.
CHAPTER 12: General Rules of Probability Lecture PowerPoint Slides The Basic Practice of Statistics 6 th Edition Moore / Notz / Fligner.
A Stochastic Expected Utility Theory Pavlo R. Blavatskyy June 2007.
Lecture 3 on Individual Optimization Uncertainty Up until now we have been treating bidders as expected wealth maximizers, and in that way treating their.
Decision theory under uncertainty
Chapter 16: Making Simple Decision March 23, 2004.
Expected Value, Expected Utility & the Allais and Ellsberg Paradoxes
Behavioral Finance EMH & Surveys Jan Behavioral Finance Economics 437.
Chapter 16 March 25, Probability Theory: What an agent should believe based on the evidence Utility Theory: What the agent wants Decision Theory:
Behavioral Finance Preferences Part II Feb18 Behavioral Finance Economics 437.
1 BAMS 517 – 2011 Decision Analysis -IV Utility Failures and Prospect Theory Martin L. Puterman UBC Sauder School of Business Winter Term
Behavioral Finance Introduction January 21, 2016 Behavioral Finance Economics 437.
Rationality Myth How & Why People Make Weird Choices.
Risk Efficiency Criteria Lecture XV. Expected Utility Versus Risk Efficiency In this course, we started with the precept that individual’s choose between.
Behavioral Finance Biases Feb 23 Behavioral Finance Economics 437.
Behavioral Finance Preferences Part I Feb 16 Behavioral Finance Economics 437.
Von Neumann-Morgenstern Lecture II. Utility and different views of risk Knightian – Frank Knight Risk – known probabilities of events Uncertainty – unknown.
Behavioral Finance Economics 437.
Ralf Möller Universität zu Lübeck Institut für Informationssysteme
CHAPTER 14: Binomial Distributions*
Behavioral Finance Economics 437.
Making Simple Decisions
CHAPTER 1 FOUNDATIONS OF FINANCE I: EXPECTED UTILITY THEORY
Decisions under risk Sri Hermawati.
Games of pure conflict two person constant sum
Lecture 3 Axioms & elicitation.
Risk Chapter 11.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Vincent Conitzer Utility theory Vincent Conitzer
Behavioral Finance Economics 437.
Behavioral Finance Economics 437.
Presentation transcript:

Behavioral Finance Economics 437

Choices When Alternatives are Uncertain Lotteries Choices Among Lotteries Maximize Expected Value Maximize Expected Utility Allais Paradox

What happens with uncertainty Suppose you know all the relevant probabilities Which do you prefer? 50 % chance of $ 100 or 50 % chance of $ 200 25 % chance of $ 800 or 75 % chance of zero

Lotteries A lottery has two things: A set of (dollar) outcomes: X1, X2, X3,…..XN A set of probabilities: p1, p2, p3,…..pN X1 with p1 X2 with p2 Etc. p’s are all positive and sum to one (that’s required for the p’s to be probabilities)

For any lottery We can define “expected value” p1X1 + p2X2 + p3X3 +……..pNXN But “Bernoulli paradox” is a big, big weakness of using expected value to order lotteries So, how do we order lotteries?

“Reasonableness” Four “reasonable” axioms: Completeness: for every A and B either A ≥ B or B ≥ A (≥ means “at least as good as” Transitivity: for every A, B,C with A ≥ B and B ≥ C then A ≥ C Independence: let t be a number between 0 and 1; if A ≥ B, then for any C,: t A + (1- t) C ≥ t B + (1- t) C Continuity: for any A,B,C where A ≥ B ≥ C: there is some p between 0 and 1 such that: B ≥ p A + (1 – p) C

Conclusion If those four axioms are satisfied, there is a utility function that will order “lotteries” Known as “Expected Utility”

For any two lotteries, calculate Expected Utility II p U(X) + (1 – p) U(Y) q U(S) + (1 – q) U(T) U(X) is the utility of X when X is known for certain; similar with U(Y), U(S), U(T)

Allais Paradox Choice of lotteries Lottery A: sure $ 1 million Or, Lottery B: 89 % chance of $ 1 million 1 % chance of zero 10 % chance of $ 5 million Which would you prefer? A or B

Now, try this: Choice of lotteries Lottery C Or, Lottery D: 89 % chance of zero 11 % chance of $ 1 million Or, Lottery D: 90 % chance of zero 10 % chance of $ 5 million Which would you prefer? C or D

Back to A and B If you prefer B to A, then Choice of lotteries Lottery A: sure $ 1 million Or, Lottery B: 89 % chance of $ 1 million 1 % chance of zero 10 % chance of $ 5 million If you prefer B to A, then .89 (U ($ 1M)) + .10 (U($ 5M)) > U($ 1 M) Or .10 *U($ 5M) > .11*U($ 1 M)

And for C and D If you prefer C to D: Choice of lotteries Lottery C 89 % chance of zero 11 % chance of $ 1 million Or, Lottery D: 90 % chance of zero 10 % chance of $ 5 million If you prefer C to D: Then .10*U($ 5 M) < .11*U($ 1M)

So, if you prefer B to A and C to D It must be the case that: .10 *U($ 5M) > .11*U($ 1 M) And .10*U($ 5 M) < .11*U($ 1M)

First Mid-Term Exam: Tuesday, Feb 20, 2018 In Wilson Auditorium, 9:30 – 10:45 AM All readings, lectures and powerpoint slides through and including Thursday, Feb 8th Readings, specifically: Shleifer: Chapters 1 and 2 Articles by Black, Shiller, Malkiel, Fama Burton and Shah, pp. 1-51 But not Kahneman: Thinking: Fast and Slow Not lectures on Feb 13 and Feb 15

The End