UNSW | BUSINESS SCHOOL | SCHOOL OF ECONOMICS Calling the shots Experimental evidence on significant aversion to non-existing strategic risk Ben Greiner.

Slides:



Advertisements
Similar presentations
Game Theory. I What is Game theory? The Theory of Games and Economic Behaviour by John von Neumann and Oskar Morgenstern (1944). Especially one institution:
Advertisements

The Basics of Game Theory
Nagel: Unraveling in Guessing Games: An Experimental Study Economics 328 Spring 2005.
Mixed Strategies.
Ultimatum Game Two players bargain (anonymously) to divide a fixed amount between them. P1 (proposer) offers a division of the “pie” P2 (responder) decides.
1 Game Theory. 2 Definitions Game theory -- formal way to analyze interactions among a group of rational agents behaving strategically Agents – players.
Welcome to this experiment on decision making Centre for Ecological and Evolutionary Studies dept. of Theoretical Biology.
1 On the Methodology of Inequity Aversion Theory.
Overview Fundamentals
Game Theory 1. Game Theory and Mechanism Design Game theory to analyze strategic behavior: Given a strategic environment (a “game”), and an assumption.
This paper reports an experimental study based on the popular Chinos game, in which three players, arranged in sequence, have to guess the total number.
Experimental evidence of the emergence of aesthetic rules in pure coordination games Federica Alberti University of East Anglia ESA World Meeting 2007.
Short introduction to game theory 1. 2  Decision Theory = Probability theory + Utility Theory (deals with chance) (deals with outcomes)  Fundamental.
4 Why Should we Believe Politicians? Lupia and McCubbins – The Democratic Dilemma GV917.
Judgment in Managerial Decision Making 8e Chapter 4 Bounded Awareness
GAME THEORY.
GAME THEORY By Ben Cutting & Rohit Venkat. Game Theory: General Definition  Mathematical decision making tool  Used to analyze a competitive situation.
1 Duke PhD Summer Camp August 2007 Outline  Motivation  Mutual Consistency: CH Model  Noisy Best-Response: QRE Model  Instant Convergence: EWA Learning.

B OUNDED R ATIONALITY in L ABORATORY B ARGAINING with A SSYMETRIC I NFORMATION Timothy N. Cason and Stanley S. Reynolds Economic Theory, 25, (2005)
BEE3049 Behaviour, Decisions and Markets Miguel A. Fonseca.
Games as Systems Administrative Stuff Exercise today Meet at Erik Stemme
Advanced Microeconomics Instructors: Wojtek Dorabialski & Olga Kiuila Lectures: Mon. & Wed. 9:45 – 11:20 room 201 Office hours: Mon. & Wed. 9:15 – 9:45.
Motivations and observed behaviour: Evidence from ultimatum bargaining experiment Elena Tougareva Laboratory of Social and Economic Psychology, Institute.
Introduction to Game Theory and Behavior Networked Life CIS 112 Spring 2009 Prof. Michael Kearns.
Punishing Unacceptable Behavior Janhavi Nilekani and Sarah Ong.
Playing Unfair: Punishment in Bargaining and Negotiations Deborah Kay Elms IPES Conference November 14, 2008.
The Agencies Method for Coalition Formation in Experimental Games John Nash (University of Princeton) Rosemarie Nagel (Universitat Pompeu Fabra, ICREA,
Lecture 2B Experimental Methods for Business Strategy In this session you will design a game on your own laptop and have your colleagues log on as subjects.
A Study of Computational and Human Strategies in Revelation Games 1 Noam Peled, 2 Kobi Gal, 1 Sarit Kraus 1 Bar-Ilan university, Israel. 2 Ben-Gurion university,
Grether and Plott: Economic Theory of Choice and the Preference Reversal Phenomenon Economics 328 Spring 2004.
Sections 4-1 and 4-2 Review and Preview and Fundamentals.
Economics for Leaders The Ultimatum Game. Proposal Selection Form Proposer Identification Code __________________ Circle a proposal: 9/1 8/2 7/3 6/4 5/5.
The Development of Decision Analysis Jason R. W. Merrick Based on Smith and von Winterfeldt (2004). Decision Analysis in Management Science. Management.
Learning Incentive Schemes for the Working Poor Catherine Eckel University of Texas, Dallas Cathleen Johnson CIrANO Claude Montmarquette University of.
Proposal Selection Form Proposer Identification Code __________________ Circle a proposal: 19/1 18/2 17/3 16/4 15/5 14/6 13/7 12/8 11/9 10/10 9/11 8/12.
“Life must be understood backward, but … it must be lived forward.”
Course Behavioral Economics Alessandro InnocentiAlessandro Innocenti Academic year Lecture 14 Fairness LECTURE 14 FAIRNESS Aim: To analyze the.
1 The Determinants of Managerial Decisions Under Risk Martin G. Kocher University of Innsbruck Ganna Pogrebna Columbia University Matthias Sutter University.
Experimental Economics NSF short course David Laibson August 11, 2005.
Experimental evidence of the emergence of aesthetic rules in pure coordination games Federica Alberti (Uea) Creed/Cedex/Uea Meeting Experimental Economics.
NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani 1 Yohei Mitani Institute of Behavioral Science University of Colorado, Boulder Nicholas.
Check whether these things are on your desk. If not, please raise your hand. –Pen –Receipt –“Summary of the experiment” Fill in the receipt following the.
The Wonderful World… of Probability. When do we use Probability?
1 What is Game Theory About? r Analysis of situations where conflict of interests is present r Goal is to prescribe how conflicts can be resolved 2 2 r.
Justice and Fairness Karl Schurter Interdisciplinary Center for Economic Science.
Lecture 12. Game theory So far we discussed: roulette and blackjack Roulette: – Outcomes completely independent and random – Very little strategy (even.
Testing theories of fairness— Intentions matter Armin Falk, Ernst Fehr, Urs Fischbacher February 26, 2015.
6.5 Find Expected Value MM1D2d: Use expected value to predict outcomes. Unit 4: The Chance of Winning!
1 Welcome to CASSEL Welcome to the CASSEL Lab, and thank you for participating in today’s experiment. It is very important that you do not touch the computer.
Introduction to Game Theory Presented by 蘇柏穎 2004/12/9 2004/12/9.
M9302 Mathematical Models in Economics Instructor: Georgi Burlakov 0.Game Theory – Brief Introduction Lecture
Games of pure conflict two-person constant sum games.
On Investor Behavior Objective Define and discuss the concept of rational behavior.
By: Donté Howell Game Theory in Sports. What is Game Theory? It is a tool used to analyze strategic behavior and trying to maximize his/her payoff of.
Experiments and “Rational” Behavior, 5/1/07. Beauty Contest Game Each person choose a number from 0 to 100. We will average these numbers. The person.
John D. Hey LUISS & University of York Julia A. Knoll University of Düsseldorf Strategies in Dynamic Decision Making An Experimental Investigation on the.
Yu-Hsuan Lin Catholic University of Korea, Korea University of York, U.K. 5 th Congress of EAAERE, Taipei, 06 th – 07 th August 2015.
OVERCOMING COORDINATION FAILURE THROUGH NEIGHBORHOOD CHOICE ~AN EXPERIMENTAL STUDY~ Maastricht University Arno Riedl Ingrid M.T. Rohde Martin Strobel.
Von Neumann-Morgenstern Lecture II. Utility and different views of risk Knightian – Frank Knight Risk – known probabilities of events Uncertainty – unknown.
Correlated equilibria, good and bad: an experimental study
Proposal Selection Form
Game Theory M.Pajhouh Niya M.Ghotbi
Behavioral economics Chapter 30
DESIGN ISSUES, CHOICES Between vs. within-subject design?
CHAPTER 1 FOUNDATIONS OF FINANCE I: EXPECTED UTILITY THEORY
ECON 100 Lecture 7 Monday, February 25.
Lecture 12.
Rational Decisions and
Behavioral economics Chapter 30
Presentation transcript:

UNSW | BUSINESS SCHOOL | SCHOOL OF ECONOMICS Calling the shots Experimental evidence on significant aversion to non-existing strategic risk Ben Greiner

Strategic Risk  “Regular” risk: Uncertainty about an outcome of a random event Known or unknown (guessed, subjective) probabilities  Strategic risk: Uncertainty about strategies/actions other people are going to choose Subjective probabilities  In (standard) game theory there is no strategic uncertainty Beliefs are correct in equilibrium, and equilibrium strategies are best responses to beliefs

An experiment

A B B A: $?? B: $?? A: $?? B: $?? A: $?? B: $?? A: $?? B: $??

A B B A: $80 B: $20 A: $0 B: $0 A: $50 B: $50 A: $0 B: $0

A B B A: $80 B: $20 A: $0 B: $0 A: $50 B: $50 A: $50 B: $0

A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 50 B: 0 A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 0 B: 0  Ultimatum Game  Reverse Impunity Game

A B B A: € 6.40 B: € 1.60 A: € 0.00 B: € 0.00 A: € 4.00 B: € 4.00 A: € 4.00 B: € 0.00 A B B A: € 6.40 B: € 1.60 A: € 0.00 B: € 0.00 A: € 4.00 B: € 4.00 A: € 4.00 B: € 4.00  Ultimatum Game  Reverse Impunity Game Experiment 1

Experiment 1: Procedures  One-shot experiment, pie size € 8, 145 participants  Conducted in foyer of student restaurant: Students participant before lunch Get paid after lunch  Participants decided in all (altogether 5) games and both roles (strategy method)  Payoff: 10-sided dice, thrown for 2 randomly matched subjects, each number implies a particular game and role assignment, game played out and paid out

A B B A: € 6.40 B: € 1.60 A: € 0.00 B: € 0.00 A: € 4.00 B: € 4.00 A: € 4.00 B: € 0.00 A B B A: € 6.40 B: € 1.60 A: € 0.00 B: € 0.00 A: € 4.00 B: € 4.00 A: € 4.00 B: € 4.00  Ultimatum Game  Reverse Impunity Game Experiment 1 55%28% 99.3% 91.0% 93.8%

Experiment 2: Beliefs  2 years after first experiment in large classroom  135 subjects were asked to predict frequencies of choices in 1st experiment  Instructions included all procedural information and original instructions of 1st experiment  Incentive compatible payment (step-function approximately following quadratic scoring rule)

Guessed Acceptance Rates of Unequal Split Proposals Experiment 2: Beliefs  Ultimatum Game  Reverse Impunity Game ~ 94.4%92.8% 91.0%93.8% Exp 1 observed Exp 2 Average Guess 99% Exp 2 Median Guess ~

≥ 98.8%96.6% 99.3% Guessed Acceptance Rates of Equal Split Proposals Experiment 2: Beliefs Exp 1 observed Exp 2 Average Guess 100% Exp 2 Median Guess ~  Ultimatum Game  Reverse Impunity Game

> 79.0%51.6% 55.2%28.3% Guessed Share of Unequal Split Proposals Experiment 2: Beliefs Exp 1 observed Exp 2 Average Guess 89.5%48.1%Exp 2 Median Guess >  Ultimatum Game  Reverse Impunity Game

A B B A: € 12.0 B: € 3.0 A: € 0.0 B: € 0.0 A: € 7.5 B: € 7.5 A: € 7.5 B: € 0.0 A B B A: € 12.0 B: € 3.0 A: € 0.0 B: € 0.0 A: € 7.5 B: € 7.5 A: € 0.0 B: € 0.0  Ultimatum Game  Reverse Impunity Game Experiment 3

Experiment 3: Play method  214 participants invited to laboratory  Role lottery  Responders leave lab to next room  56 / 51 proposer-responder pairs in UG / RIG  Pen & paper

A B B A: € 12.0 B: € 3.0 A: € 0.0 B: € 0.0 A: € 7.5 B: € 7.5 A: € 7.5 B: € 0.0 A B B A: € 12.0 B: € 3.0 A: € 0.0 B: € 0.0 A: € 7.5 B: € 7.5 A: € 0.0 B: € 0.0  Ultimatum Game  Reverse Impunity Game Experiment 3 64%39% 94%100% 70% 87% 55%28%

A B B A: $ 31 B: $ 15 A: $ 10 B: $ 10 A: $ 23 B: $ 23 A: $ 23 B: $ 23 A B B A: $ 31 B: $ 15 A: $ 10 B: $ 10 A: $ 23 B: $ 23 A: $ 0 B: $ 0  Ultimatum Game  Reverse Impunity Game Experiment 4: Enter or not  ENTER THE GAME or STAY OUT?  Both ENTER: roles assigned 50/50; Both OUT: $20, $20  One enters, other stays out: ENTER= proposer, OUT= responder

Experiment 4: Procedures  66 / 52 participants in Ultimatum / Reverse Impunity  Computerized, all decisions at computer screen, zTree  Pairs of 2 participants anonymously decided simultaneously about entering dropped out or not were informed about random move, if necessary proceeded and played Ultimatum / Rev. Impunity got paid in cash

Experiment 4: Results 72% 77% 89% 75% 100% 83% 56% 96% 79%

A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 0 B: 0  Ultimatum Game  Reverse Impunity Game A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 50 B: 0  Although equal splits are almost never rejected equal splits are not expected to be rejected  a non-neglible share of people behaves as if there is a non-neglible chance that an equal split will be rejected.

A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 0 B: 0  Ultimatum Game  Reverse Impunity Game A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 50 B: 0  Behavior is not compatible with a consistency between strategies and rational beliefs implies extreme risk aversion / pessimism when dealing with people (strategic risk), as opposed to lotteries (regular risk)

 Thank you.

A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 0 B: 0  Ultimatum Game  Reverse Impunity Game A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 50 B: 0  Behavior is not compatible with a consistency between strategies and rational beliefs Social preferences Errors Risk Aversion

A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 0 B: 0  Ultimatum Game  Reverse Impunity Game A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 50 B: 0  Maximin *behavior*?  Von Neumann & Morgenstern: “...the rules of rational behaviour must provide definitely for the possibility of irrational conduct on the part of others”  Selten (2001): vNM “do not pin down what to be expected from other players”, in vNM’s pre-Nash concept “players concentrate on what can be assured in a game”

A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 0 B: 0  Ultimatum Game  Reverse Impunity Game A B B A: 80 B: 20 A: 0 B: 0 A: 50 B: 50 A: 50 B: 0  Strategic ambiguity aversion Gilboa, Schmeidler – non-additive beliefs / capacities Chateauneuf, Eichberger & Grant (2007) – non-extreme-outcome additive capacities Eichberger, Kelsey (, Schipper) (2010, 2008, 2009) – compare to empirical / experimental data