5 MARCH 2015 TOK LECTURE TRUTH: TNML. ECONOMICS  ECONOMISTS HAVE A VERY SHAKY RELATIONSHIP WITH TRUTH.  AT THE HEART OF THE FINANCIAL CRISIS OF 2008.

Slides:



Advertisements
Similar presentations
3. Basic Topics in Game Theory. Strategic Behavior in Business and Econ Outline 3.1 What is a Game ? The elements of a Game The Rules of the.
Advertisements

1 Intuitive Irrationality: Reasons for Unreason. 2 Epistemology Branch of philosophy focused on how people acquire knowledge about the world Descriptive.
The Structure of Networks with emphasis on information and social networks T-214-SINE Summer 2011 Chapter 16 Ýmir Vigfússon.
Simple Probability The probability of an event is the number of favorable outcomes divided by the total number of possible outcomes.
4 Why Should we Believe Politicians? Lupia and McCubbins – The Democratic Dilemma GV917.
The Scientific Method.
Chapter 10: Hypothesis Testing
Decision making and economics. Economic theories Economic theories provide normative standards Expected value Expected utility Specialized branches like.
© POSbase 2005 The Conjunction Fallacy Please read the following scenario: (by Tversky & Kahneman, 1983)Tversky & Kahneman, 1983 Linda is 31 years old,
Fallacies in Probability Judgment Yuval Shahar M.D., Ph.D. Judgment and Decision Making in Information Systems.
Judgment in Managerial Decision Making 8e Chapter 3 Common Biases
Running Experiments with Amazon Mechanical-Turk Gabriele Paolacci, Jesse Chandler, Jesse Chandler Judgment and Decision Making, Vol. 5, No. 5, August 2010.
Games, Logic, and Math Kristy and Dan. GAMES Game Theory Applies to social science Applies to social science Explains how people make all sorts of decisions.
Decision-making II judging the likelihood of events.
Behavioral Economics Chapter 30. What Is Behavioral Economics? The study of choices actually made by economic decision makers in an effort to assess the.
Heuristics and Biases. Normative Model Bayes rule tells you how you should reason with probabilities – it is a normative model But do people reason like.
Social Learning. A Guessing Game Why are Wolfgang Puck restaurants so crowded? Why do employers turn down promising job candidates on the basis of rejections.
Decision-making II judging the likelihood of events.
Reasoning with Uncertainty. Often, we want to reason from observable information to unobservable information We want to calculate how our prior beliefs.
Guessing game Guess a number 0 to 100. The guess closest to 2/3 the average number wins a prize. Ties will be broken randomly. Please write your name and.
Inductive Reasoning Bayes Rule. Urn problem (1) A B A die throw determines from which urn to select balls. For outcomes 1,2, and 3, balls are picked from.
The Basic Tools of Finance
Heuristics & Biases. Bayes Rule Prior Beliefs Evidence Posterior Probability.
“It is not a case of choosing those faces that, to the best of one’s judgment, are really the prettiest, nor even those that average opinion genuinely.
Decision Making. Test Yourself: Decision Making and the Availability Heuristic 1) Which is a more likely cause of death in the United States: being killed.
Statistics made simple Modified from Dr. Tammy Frank’s presentation, NOVA.
Risk and Uncertainty Econ 373 Environmental Economics February 8,
Basic Principles: Ethics and Business
Today’s Topic Do you believe in free will? Why or why not?
Portfolio Management Lecture: 26 Course Code: MBF702.
Good thinking or gut feeling
Decision making Making decisions Optimal decisions Violations of rationality.
Class Starter Please list the first five words or phrases that come to your mind when you hear the word : CHEMISTRY.
Chapter 2: The Scientific Method and Environmental Sciences.
Understanding Human Behavior Helps Us Understand Investor Behavior MA2N0246 Tsatsral Dorjsuren.
RISK BENEFIT ANALYSIS Special Lectures University of Kuwait Richard Wilson Mallinckrodt Professor of Physics Harvard University January 13th, 14th and.
RISK BENEFIT ANALYSIS Special Lectures University of Kuwait Richard Wilson Mallinckrodt Professor of Physics Harvard University January 13th, 14th and.
Experimental Economics NSF short course David Laibson August 11, 2005.
Classroom Games in Economics Tuvana Pastine Sept 19, 2014.
Coricelli and Nagel (2008) Introduction Methods Results Conclusion.
Copyright © Cengage Learning. All rights reserved. 8 Introduction to Statistical Inferences.
WHAT IS THE NATURE OF SCIENCE?. SCIENTIFIC WORLD VIEW 1.The Universe Is Understandable. 2.The Universe Is a Vast Single System In Which the Basic Rules.
Lecture 15 – Decision making 1 Decision making occurs when you have several alternatives and you choose among them. There are two characteristics of good.
Biological Science.
Ellsberg’s paradoxes: Problems for rank- dependent utility explanations Cherng-Horng Lan & Nigel Harvey Department of Psychology University College London.
FIN 614: Financial Management Larry Schrenk, Instructor.
LESSON TWO ECONOMIC RATIONALITY Subtopic 10 – Statistical Reasoning Created by The North Carolina School of Science and Math forThe North Carolina School.
previous next 12/1/2015 There’s only one kind of question on a reading test, right? Book Style Questions Brain Style Questions Definition Types of Questions.
Judgement Judgement We change our opinion of the likelihood of something in light of new information. Example:  Do you think.
Prepared by: A. T. M. Monawer Success in EPT Listening & Speaking Reading Writing Listening &Speaking Reading Writing.
PSY 323 – Cognition Chapter 13: Judgment, Decisions & Reasoning.
Exercise 2-6: Ecological fallacy. Exercise 2-7: Regression artefact: Lord’s paradox.
A. Judgment Heuristics Definition: Rule of thumb; quick decision guide When are heuristics used? - When making intuitive judgments about relative likelihoods.
Heuristics and Biases Thomas R. Stewart, Ph.D. Center for Policy Research Rockefeller College of Public Affairs and Policy University at Albany State University.
The Representativeness Heuristic then: Risk Attitude and Framing Effects Psychology 355: Cognitive Psychology Instructor: John Miyamoto 6/1/2016: Lecture.
1 מקורות החשיבה המדעית/מתימטית ( ) אורי לירון שיעור ראשון – חידות, מֶטָה-חידות, תהיות, וסתם שאלות מעצבנות.
WHAT IS THE NATURE OF SCIENCE?
Exercise 2-7: Regression artefact: Lord’s paradox
Ethical Decision Making
Chapter 16: Sample Size “See what kind of love the Father has given to us, that we should be called children of God; and so we are. The reason why the.
What is Logic good for? How can we understand ‘good’ or ‘utility’ or ‘value’? Intrinsic value: for its own sake Instrumental value: valued for its capacity.
PSY 323 – Cognition Chapter 13: Judgment, Decisions & Reasoning.
Pavle Valerjev Marin Dujmović
Judgment & Decision Making
The Scientific Method in Psychology
1st: Representativeness Heuristic and Conjunction Errors 2nd: Risk Attitude and Framing Effects Psychology 355:
HEURISTICS.
For Thursday, read Wedgwood
Presentation transcript:

5 MARCH 2015 TOK LECTURE TRUTH: TNML

ECONOMICS  ECONOMISTS HAVE A VERY SHAKY RELATIONSHIP WITH TRUTH.  AT THE HEART OF THE FINANCIAL CRISIS OF 2008 WAS THE VIEW THAT ECONOMISTS HAD ACHIEVED AN UNDERSTANDING OF THE WAY MARKETS WORKED AND COULD STILL PREDICT UNDER THE CONDITIONS OF UNCERTAINTY.

TRUTH IT WASN’T TRUE.

2008  THERE WAS A DISASTER. AND NOW THE TRUTH OF ECONOMIC THEORY IS GREATLY DOUBTED.

KNIGHTIAN UNCERTAINTY From Wikipedia, the free encyclopedia In economics, Knightian uncertainty is risk that is immeasurable, not possible to calculate.economics Knightian uncertainty is named after University of Chicago economist Frank Knight (1885– 1972), who distinguished risk and uncertainty in his work Risk, Uncertainty, and Profit:University of ChicagoFrank Knightriskuncertainty "Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated.... The essential fact is that 'risk' means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character; and there are far-reaching and crucial differences in the bearings of the phenomena depending on which of the two is really present and operating.... It will appear that a measurable uncertainty, or 'risk' proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all."

LEARNING 1  RISK ISN’T THE SAME AS UNCERTAINTY. WE CAN MEASURE RISK AND THEREFORE PROBABLY CONTROL FOR IT AND PREDICT DESPITE RISK. UNCERTAINTY IS DIFFERENT.

ARE OUTCOMES COLLECTIVELY RATIONAL?  EVER SINCE KEYNES DESCRIBED THE STOCK MARKETS AS DETERMINED BY THE ‘ANIMAL SPIRITS’ OF INVESTORS THERE HAS BEEN DOUBT ABOUT THE MOTIVES IN THE MARKET.  THE PERFECT MARKET HYPOTHESIS – THAT MARKETS SHOULD REFLECT REAL VALUES IF PEOPLE ARE RATIONAL FOR THE TWO REASONS ABOVE, WAS SEEN CATASTROPHICALLY TO FAIL IN 2008.

LEARNING 2  THE IDEA THAT MARKETS REFLECT TRUE VALUE IS KNOWN AS THE ‘PERFECT MARKET HYPOTHESIS’

BEAUTY CONTESTS  THE CONTESTANTS HAD TO PICK THE ‘ONE MOST LIKELY TO CATCH THE FANCY OF OTHER COMPETITORS’.

KEYNES EXPLAINS BEAUTY CONTESTS  “IT IS NOT A CASE OF CHOOSING THOSE [FACES] THAT, TO THE BEST OF ONE'S JUDGMENT, ARE REALLY THE PRETTIEST, NOR EVEN THOSE THAT AVERAGE OPINION GENUINELY THINKS THE PRETTIEST. WE HAVE REACHED THE THIRD DEGREE WHERE WE DEVOTE OUR INTELLIGENCES TO ANTICIPATING WHAT AVERAGE OPINION EXPECTS THE AVERAGE OPINION TO BE. AND THERE ARE SOME, I BELIEVE, WHO PRACTICE THE FOURTH, FIFTH AND HIGHER DEGREES.”  (KEYNES, GENERAL THEORY OF EMPLOYMENT INTEREST AND MONEY, 1936).

QUESTION 1 1. EVERYONE PICK A NUMBER 1-> IMAGINE THERE’S A PRIZE FOR CLOSEST TO 2/3 * AVERAGE CHOICE OF ROOM. PLEASE WRITE DOWN YOUR CHOICE. DISCUSS

ITERATION  ITERATIVE REASONING: EVIDENCE SUGGESTS THAT PEOPLE DO NOT WORK ACCORDING THE ITERATIVE REASONING. TWO TO FOUR ITERATIONS IS MORE LIKELY.  IN A SPECULATIVE BUBBLE THE THEORY OF THE BIGGER FOOL HOLDS.  BEAUTY CONTESTS – WHAT IS TRUTH HERE? THE OPINION OF OTHERS…

HERDING WHICH URN?  PLAYER 1: PICKS A RED BALL FROM SO DECIDES THAT URN A IS MORE LIKELY SIGNAL TO THE HERD: URN A MORE LIKELY  PLAYER 2: PICKS A BLACK BALL OUT  PRIVATE SIGNAL: URN B MORE LIKELY  HERD SIGNAL (FROM PLAYER 1): URN A MORE LIKELY  NOT SURE WHAT TO DO (50/50) BUT IMAGINE THAT PLAYER 2 DECIDES TO FOLLOW HERD AND CHOOSES URN A

INFORMATIONAL CASCADES  PLAYER 2: CHOOSES URN A  (FOLLOWING HERD SIGNAL)  PLAYER 3: PICKS A BLACK BALL SO HAS A PRIVATE SIGNAL THAT URN B’S MORE LIKELY THIS CONFLICTS WITH THE HERD SIGNAL: BOTH PLAYERS 1 & 2 HAVE PICKED A  PLAYER 3 IGNORES PRIVATE SIGNAL BECAUSE BOTH PREVIOUS PLAYERS HAVE PICKED A  SO ALSO PICKS URN A

HERDING  “INFORMATIONAL CASCADES” BUT THE HERD MIGHT CASCADE DOWN THE WRONG PATH!

LEARNING 3:  TRUTH IS A SLIPPERY CONCEPT WHEN THE ACTIONS OF OTHERS GOVERN WHAT WE BELIEVE

SECTION 2 ARE PEOPLE RATIONAL ANYWAY?

THE ULTIMATUM GAME  2 EXPERIMENTAL SUBJECTS: PROPOSER AND RESPONDER  PROPOSER GIVEN SOME MONEY, E.G. £100, WHICH THEY CAN DIVIDE AS THEY LIKE OFFERING RESPONDER ANY AMOUNT FROM £1 UP  RESPONDER CAN ACCEPT OR REJECT THE OFFER BUT IF RESPONDER REJECTS, NEITHER GETS ANYTHING

QUESTION 2:  IMAGINE YOU ARE THE PROPOSER IN THE ULTIMATUM GAME.  PLEASE WRITE DOWN WHAT SUM YOU WOULD LIKE TO OFFER ME AS RESPONDER.

DISCUSS

HEURISTICS  A RULE OF THUMB WORKS WELL WHEN IT IS CHEAPER THAN THINKING THROUGH ALL THE OPTIONS AND CALCULATING THE DECISION / GETTING EVIDENCE.

KAHNEMEN AND TVERSKY  GATHERED A LOT OF EVIDENCE FOR THE USE OF HEURISTICS IN DECISION MAKING (PROSPECT THEORY 1979). KAHNEMAN WAS GIVEN THE NOBEL MEMORIAL PRIZE FOR THIS IN FURTHER READING: THINKING, FAST AND SLOW

THE LINDA PROBLEM  LINDA IS 31 YEARS OLD, SINGLE, OUTSPOKEN AND VERY BRIGHT. SHE MAJORED IN PHILOSOPHY. AS A STUDENT, SHE WAS DEEPLY CONCERNED WITH ISSUES OF DISCRIMINATION AND SOCIAL JUSTICE, AND ALSO PARTICIPATED IN ANTI-NUCLEAR DEMONSTRATIONS. [THIS IS THE ORIGINAL FORMULATION FROM THEIR BOOK, IT IS A BIT POLITICALLY INCORRECT!]

QUESTION 3  Question 3: please circle the most likely alternative: 1. Linda is a bank teller. 2. Linda is a bank teller and is active in the feminist movement. DISCUSSION

CONJUNCTION FALLACY  AND AN EXAMPLE OF THE REPRESENTATIV ENESS HEURISTIC

QUESTION 4  IN ANOTHER EXPERIMENT, PARTICIPANTS WERE ASKED: CONSIDER A REGULAR SIX-SIDED DIE WITH FOUR GREEN FACES AND TWO RED FACES. THE DIE WILL BE ROLLED 20 TIMES AND THE SEQUENCE OF GREENS (G) AND REDS (R) WILL BE RECORDED. YOU ARE ASKED TO SELECT ONE SEQUENCE, FROM A SET OF THREE, AND YOU WILL WIN £50 IF THE SEQUENCE YOU CHOOSE APPEARS ON SUCCESSIVE ROLLS OF THE DIE.

PLEASE CHOOSE ONE 1.RGRRR 2.GRGRRR 3.GRRRRR

THE GAMBLER’S FALLACY  65% OF PARTICIPANTS CHOSE THE SECOND SEQUENCE, THOUGH OPTION 1 IS CONTAINED WITHIN IT AND IS SHORTER THAN THE OTHER OPTIONS  TVERSKY AND KAHNEMAN ARGUED THAT SEQUENCE 2 APPEARS "REPRESENTATIVE" OF A CHANCE SEQUENCE

LEARNING 4  HEURISTICS ARE SUBJECT TO SYSTEMATIC BIASES

TRY QUESTION FIVE

ANCHORING  WE BASE OUR GUESSES ON READY REFERENCE POINTS. SOMETIMES THIS IS ARBITRARY.

CONCLUSION AND DISCUSSION  THERE ARE LEVELS OF COMPLEXITY TO REACH THE TRUTH ABOUT HOW WE BEHAVE  THE ‘TRUTH’ IS NOT NECESSARILY RELEVANT TO THE RIGHT ANSWER  WE DO NOT ALWAYS DEAL WITH ‘TRUTHFUL’ INFORMATION RATIONALLY

5 MARCH 2015 TOK LECTURE TRUTH: TNML