Revelation Principle; Quasilinear Utility

Similar documents
PIER Working Paper

A Note on Unawareness and Zero Probability

Matching Theory and Practice

Chapter 14. From Randomness to Probability. Probability. Probability (cont.) The Law of Large Numbers. Dealing with Random Phenomena

Beliefs under Unawareness

A Functional Representation of Fuzzy Preferences

Qeauty and the Books: A Response to Lewis s Quantum Sleeping Beauty Problem

Unawareness and Strategic Announcements in Games with Uncertainty

Contests with Ambiguity

Logic and Artificial Intelligence Lecture 0

ECE 331 Digital System Design

ECE 301 Digital Electronics

Real-Time Systems Dr. Rajib Mall Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur

Lecture 16: Feedback channel and source-channel separation

All Roads Lead to Violations of Countable Additivity

Figure 9.1: A clock signal.

Designing the US Incentive Auction

Prudence Demands Conservatism *

Okasha, S. (2016). On the Interpretation of Decision Theory. Economics and Philosophy, 32, DOI: /S

Musical Sound: A Mathematical Approach to Timbre

Sequential Decision Making with Adaptive Utility

PART II METHODOLOGY: PROBABILITY AND UTILITY

Game Theory a Tool for Conflict Analysis of the Nigeria Minimum Wage Situation

COSC3213W04 Exercise Set 2 - Solutions

Chapter 12. Synchronous Circuits. Contents

Module 8 : Numerical Relaying I : Fundamentals

Randomness for Ergodic Measures

Section 1 The Portfolio

SIX STEPS TO BUYING DATA LOSS PREVENTION PRODUCTS

A repetition-based framework for lyric alignment in popular songs

SYMPOSIUM ON MARSHALL'S TENDENCIES: 6 MARSHALL'S TENDENCIES: A REPLY 1

Understanding the True Cost of Cable Cuts

ORF 307: Lecture 14. Linear Programming: Chapter 14: Network Flows: Algorithms

Pandering to Persuade

DIGITAL TELEVISION: MAINTENANCE OF ANALOGUE TRANSMISSION IN REMOTE AREAS PAPER E

The Impact of Media Censorship: Evidence from a Field Experiment in China

Tape. Tape head. Control Unit. Executes a finite set of instructions

ORTHOGONAL frequency division multiplexing

EconS Preferences

Lecture 3: Nondeterministic Computation

Anthropic decision theory for self-locating beliefs

Scientific Philosophy

Experiment # 12. Traffic Light Controller

Outline. Why do we classify? Audio Classification

I Don t Want to Think About it Now: Decision Theory With Costly Computation

PUBLIC NOTICE FOR PARTICIPATION IN THE APULIA FILM FORUM 11 th - 13 th October Monopoli (Italy)

Interactive Methods in Multiobjective Optimization 1: An Overview

The Convention on Biological Diversity and its Protocols Status of Implementation. Secretariat of the Convention on Biological Diversity

Thursday 29 March list of shortlisted entrants published online (close of business)

Keywords: Gauthier, coordination, cooperation, salience, focal points

Telephone, Cable TV, Radio Contract San Diego Convention Center

Vector-Valued Image Interpolation by an Anisotropic Diffusion-Projection PDE

Invitation to Melodifestivalen 2019

Return to: CRPA Entertainment Showcase. 135 Day St., 2nd Floor, 2H, Newington, CT Ph: Fx:

Safety Rules Parts Check Lists and Photos Cable Diagrams for Various Crane Configurations Step by Step Instructions Tips for Packaging and Storage

ECE 301 Digital Electronics

CSC258: Computer Organization. Combinational Logic

Math and Music. Cameron Franc

Building Your DLP Strategy & Process. Whitepaper

Modeling Scientific Revolutions: Gärdenfors and Levi on the Nature of Paradigm Shifts

THINKING AT THE EDGE (TAE) STEPS

Burning Out in Sequential Elimination Contests*

ELEC 310 Digital Signal Processing

22/9/2013. Acknowledgement. Outline of the Lecture. What is an Agent? EH2750 Computer Applications in Power Systems, Advanced Course. output.

<> 6-11 DECEMBER 2017 <> COMPETITION RULES AND REGULATIONS

WHAT WE WANT SPECIFIC GUIDELINES

THE MONTY HALL PROBLEM

Labelling. Friday 18th May. Goldsmiths, University of London. Bayesian Model Selection for Harmonic. Labelling. Christophe Rhodes.

MGT602 Online Quiz#1 Fall 2010 (525 MCQ s Solved) Lecture # 1 to 12

Music Emotion Recognition. Jaesung Lee. Chung-Ang University

Figure 1: Feature Vector Sequence Generator block diagram.

OVERVIEW OF THE MOVIE BUSINESS

TAKE-TWO INTERACTIVE INTERACTIVE SOFTWARE QUIZ

PUBLIC NOTICE FOR PARTICIPATION IN THE APULIA FILM FORUM 16 th - 18 th November Vieste (Italy)

A Statistical Framework to Enlarge the Potential of Digital TV Broadcasting

(Refer Slide Time: 1:45)

What You Need to Know About Addressing GDPR Data Subject Rights in Primo

Machina Research. M2M Communications for Policy Makers

Ways to Enhance Positive Thought Patterns Adapted from: Change Your Brain, Change your Life by Daniel G. Amen, MD Written by: Alwlynn Lamp, M.Ed.

<> 4-9 DECEMBER 2018 <> COMPETITION RULES AND REGULATIONS

ELCT201: DIGITAL LOGIC DESIGN

Introduction. The report is broken down into four main sections:

ABSTRACT SUBMISSION GUIDELINES 2018 Oral or Poster Communications

1: University Department with high profile material but protective of its relationship with speakers

ELIGIBLE INTERMITTENT RESOURCES PROTOCOL

Ideological and Political Education Under the Perspective of Receptive Aesthetics Jie Zhang, Weifang Zhong

RULES & GUIDELINES 1. APPLICATIONS 4. COMPETITOR NOTIFICATION 2. PAYMENT TERMS 3. ELIGIBILITY

Emotional Decision-Makers and Anomalous Attitudes towards Information

Terms of Use and The Festival Rules

ambiguity aversion literature: A critical assessment

Game Theory 1. Introduction & The rational choice theory

Lecture 10 Popper s Propensity Theory; Hájek s Metatheory

Invitation to Submit Songs for Eurovision Australia Decides, the selection show for Australia s entry to the Eurovision Song Contest 2019

CAUI-4 Chip to Chip and Chip to Module Applications

The word digital implies information in computers is represented by variables that take a limited number of discrete values.

Outline. Introduction to number systems: sign/magnitude, ones complement, twos complement Review of latches, flip flops, counters

Week 14 Music Understanding and Classification

Cinemex Expansion Strategies to Reduce the Distance Market with Cinepolis

Multiple Strategies to Analyze Monty Hall Problem. 4 Approaches to the Monty Hall Problem

Piano and Instrumental

Transcription:

Revelation Principle; Quasilinear Utility Lecture 14 Revelation Principle; Quasilinear Utility Lecture 14, Slide 1

Lecture Overview 1 Recap 2 Revelation Principle 3 Impossibility 4 Quasilinear Utility 5 Risk Attitudes Revelation Principle; Quasilinear Utility Lecture 14, Slide 2

Bayesian Game Setting Extend the social choice setting to a new setting where agents can t be relied upon to disclose their preferences honestly. Start with a set of agents in a Bayesian game setting (but no actions). Definition (Bayesian game setting) A Bayesian game setting is a tuple (N, O, Θ, p, u), where N is a finite set of n agents; O is a set of outcomes; Θ = Θ 1 Θ n is a set of possible joint type vectors; p is a (common prior) probability distribution on Θ; and u = (u 1,..., u n ), where u i : O Θ R is the utility function for each player i. Revelation Principle; Quasilinear Utility Lecture 14, Slide 3

Mechanism Design Definition (Mechanism) A mechanism (for a Bayesian game setting (N, O, Θ, p, u)) is a pair (A, M), where A = A 1 A n, where A i is the set of actions available to agent i N; and M : A Π(O) maps each action profile to a distribution over outcomes. Thus, the designer gets to specify the action sets for the agents (though they may be constrained by the environment) the mapping to outcomes, over which agents have utility can t change outcomes; agents preferences or type spaces Revelation Principle; Quasilinear Utility Lecture 14, Slide 4

Implementation in Dominant Strategies Definition (Implementation in dominant strategies) Given a Bayesian game setting (N, O, Θ, p, u), a mechanism (A, M) is an implementation in dominant strategies of a social choice function C (over N and O) if for any vector of utility functions u, the game has an equilibrium in dominant strategies, and in any such equilibrium a we have M(a ) = C(u). Revelation Principle; Quasilinear Utility Lecture 14, Slide 5

Implementation in Bayes-Nash equilibrium Definition (Bayes Nash implementation) Given a Bayesian game setting (N, O, Θ, p, u), a mechanism (A, M) is an implementation in Bayes Nash equilibrium of a social choice function C (over N and O) if there exists a Bayes Nash equilibrium of the game of incomplete information (N, A, Θ, p, u) such that for every θ Θ and every action profile a A that can arise given type profile θ in this equilibrium, we have that M(a) = C(u(, θ)). Revelation Principle; Quasilinear Utility Lecture 14, Slide 6

Properties Forms of implementation Direct Implementation: agents each simultaneously send a single message to the center Indirect Implementation: agents may send a sequence of messages; in between, information may be (partially) revealed about the messages that were sent previously like extensive form We can also insist that our mechanism satisfy properties like the following: individual rationality: agents are better off playing than not playing budget balance: the mechanism gives away and collects the same amounts of money truthfulness: agents honestly report their types Revelation Principle; Quasilinear Utility Lecture 14, Slide 7

Lecture Overview 1 Recap 2 Revelation Principle 3 Impossibility 4 Quasilinear Utility 5 Risk Attitudes Revelation Principle; Quasilinear Utility Lecture 14, Slide 8

Revelation Principle It turns out that any social choice function that can be implemented by any mechanism can be implemented by a truthful, direct mechanism! Consider an arbitrary, non-truthful mechanism (e.g., may be indirect) Revelation Principle; Quasilinear Utility Lecture 14, Slide 9

Revelation Principle strategy s 1 (θ 1 ) type θ 1 M strategy s n (θ n ) type θ n Original Mechanism outcome It turns out that any social choice function that can be implemented by any mechanism can be implemented by a truthful, direct mechanism! Consider an arbitrary, non-truthful mechanism (e.g., may be indirect) Revelation Principle; Quasilinear Utility Lecture 14, Slide 9

Revelation Principle strategy s 1 (θ 1 ) type θ 1 M strategy s n (θ n ) type θ n Original Mechanism outcome It turns out that any social choice function that can be implemented by any mechanism can be implemented by a truthful, direct mechanism! Consider an arbitrary, non-truthful mechanism (e.g., may be indirect) Recall that a mechanism defines a game, and consider an equilibrium s = (s 1,..., s n ) Revelation Principle; Quasilinear Utility Lecture 14, Slide 9

Revelation Principle strategy s (θ 1 1 type θ ) 1 M strategy s (θ n n type θ ) n s 1 (s (θ )) 1 1 M Original Mechanism ( s n (s (θ )) n n New Mechanism outcome We can construct a new direct mechanism, as shown above This mechanism is truthful by exactly the same argument that s was an equilibrium in the original mechanism The agents don t have to lie, because the mechanism already lies for them. Revelation Principle; Quasilinear Utility Lecture 14, Slide 10

Computational Criticism of the Revelation Principle computation is pushed onto the center often, agents strategies will be computationally expensive e.g., in the shortest path problem, agents may need to compute shortest paths, cutsets in the graph, etc. since the center plays equilibrium strategies for the agents, the center now incurs this cost if computation is intractable, so that it cannot be performed by agents, then in a sense the revelation principle doesn t hold agents can t play the equilibrium strategy in the original mechanism however, in this case it s unclear what the agents will do Revelation Principle; Quasilinear Utility Lecture 14, Slide 11

Discussion of the Revelation Principle The set of equilibria is not always the same in the original mechanism and revelation mechanism of course, we ve shown that the revelation mechanism does have the original equilibrium of interest however, in the case of indirect mechanisms, even if the indirect mechanism had a unique equilibrium, the revelation mechanism can also have new, bad equilibria So what is the revelation principle good for? recognition that truthfulness is not a restrictive assumption for analysis purposes, we can consider only truthful mechanisms, and be assured that such a mechanism exists recognition that indirect mechanisms can t do (inherently) better than direct mechanisms Revelation Principle; Quasilinear Utility Lecture 14, Slide 12

Lecture Overview 1 Recap 2 Revelation Principle 3 Impossibility 4 Quasilinear Utility 5 Risk Attitudes Revelation Principle; Quasilinear Utility Lecture 14, Slide 13

Impossibility Result Theorem (Gibbard-Satterthwaite) Consider any social choice function C of N and O. If: 1 O 3 (there are at least three outcomes); 2 C is onto; that is, for every o O there is a preference profile [ ] such that C([ ]) = o (this property is sometimes also called citizen sovereignty); and 3 C is dominant-strategy truthful, then C is dictatorial. Revelation Principle; Quasilinear Utility Lecture 14, Slide 14

What does this mean? We should be discouraged about the possibility of implementing arbitrary social-choice functions in mechanisms. However, in practice we can circumvent the Gibbard-Satterthwaite theorem in two ways: use a weaker form of implementation note: the result only holds for dominant strategy implementation, not e.g., Bayes-Nash implementation relax the onto condition and the (implicit) assumption that agents are allowed to hold arbitrary preferences Revelation Principle; Quasilinear Utility Lecture 14, Slide 15

Lecture Overview 1 Recap 2 Revelation Principle 3 Impossibility 4 Quasilinear Utility 5 Risk Attitudes Revelation Principle; Quasilinear Utility Lecture 14, Slide 16

Quasilinear Utility Definition (Quasilinear preferences) Agents have quasilinear preferences in an n-player Bayesian game when the set of outcomes is O = X R n for a finite set X, and the utility of an agent i given joint type θ is given by u i (o, θ) = u i (x, θ) f i (p i ), where o = (x, p) is an element of O, u i : X Θ R is an arbitrary function and f i : R R is a strictly monotonically increasing function. Revelation Principle; Quasilinear Utility Lecture 14, Slide 17

Quasilinear utility u i (o, θ) = u i (x, θ) f i (p i ) We split the mechanism into a choice rule and a payment rule: x X is a discrete, non-monetary outcome p i R is a monetary payment (possibly negative) that agent i must make to the mechanism Implications: Revelation Principle; Quasilinear Utility Lecture 14, Slide 18

Quasilinear utility u i (o, θ) = u i (x, θ) f i (p i ) We split the mechanism into a choice rule and a payment rule: x X is a discrete, non-monetary outcome p i R is a monetary payment (possibly negative) that agent i must make to the mechanism Implications: u i (x, θ) is not influenced by the amount of money an agent has agents don t care how much others are made to pay (though they can care about how the choice affects others.) Revelation Principle; Quasilinear Utility Lecture 14, Slide 18

Quasilinear utility u i (o, θ) = u i (x, θ) f i (p i ) We split the mechanism into a choice rule and a payment rule: x X is a discrete, non-monetary outcome p i R is a monetary payment (possibly negative) that agent i must make to the mechanism Implications: u i (x, θ) is not influenced by the amount of money an agent has agents don t care how much others are made to pay (though they can care about how the choice affects others.) What is f i (p i )? Revelation Principle; Quasilinear Utility Lecture 14, Slide 18

Lecture Overview 1 Recap 2 Revelation Principle 3 Impossibility 4 Quasilinear Utility 5 Risk Attitudes Revelation Principle; Quasilinear Utility Lecture 14, Slide 19

Fun game Look at your piece of paper: it contains an integer x Go around the room offering everyone the following gamble: they pay you x you flip a coin: heads: they win and get paid 2x tails: they lose and get nothing. Players can accept the gamble or decline. Answer honestly (imagining the amounts of money are real) play the gamble to see what would have happened. Keep track of: Your own bank balance from others gambles you accepted. The number of people who accepted your offer. Revelation Principle; Quasilinear Utility Lecture 14, Slide 20

Risk Attitudes How much is $1 worth? What are the units in which this question should be answered? Revelation Principle; Quasilinear Utility Lecture 14, Slide 21

Risk Attitudes How much is $1 worth? What are the units in which this question should be answered? Utils (units of utility) Revelation Principle; Quasilinear Utility Lecture 14, Slide 21

Risk Attitudes How much is $1 worth? What are the units in which this question should be answered? Utils (units of utility) Different amounts depending on the amount of money you already have Revelation Principle; Quasilinear Utility Lecture 14, Slide 21

Risk Attitudes How much is $1 worth? What are the units in which this question should be answered? Utils (units of utility) Different amounts depending on the amount of money you already have How much is a gamble with an expected value of $1 worth? Revelation Principle; Quasilinear Utility Lecture 14, Slide 21

Risk Attitudes How much is $1 worth? What are the units in which this question should be answered? Utils (units of utility) Different amounts depending on the amount of money you already have How much is a gamble with an expected value of $1 worth? Possibly different amounts, depending on how risky it is Revelation Principle; Quasilinear Utility Lecture 14, Slide 21

Risk Neutrality 8 Protocols for Strategic Agents: Mechanism Design 34/015 u u 34/5 34/215 $ /21 / /01 $ (a) Risk neutrality (b) Risk neutrality: fair lottery u :;678< :;6< u :;698< Revelation Principle; Quasilinear Utility Lecture 14, Slide 22

u u 34/5 Risk Aversion 34/215 $ /21 / /01 $ (a) Risk neutrality (b) Risk neutrality: fair lottery u :;678< :;6< u :;698< $ 698 6 678 $ (c) Risk aversion (d) Risk aversion: fair lottery u u Revelation Principle; Quasilinear Utility AB=>?C Lecture 14, Slide 23

:;678< Recap Revelation Principle Impossibility :;6< Quasilinear Utility Risk Attitudes u u :;698< Risk Seeking $ 698 6 678 $ (c) Risk aversion (d) Risk aversion: fair lottery u u AB=>?C $ AB=C AB=@?C =@? = =>? $ (e) Risk seeking (f) Risk seeking: fair lottery Figure 8.3 Risk attitudes: Risk aversion, risk neutrality, risk seeking, and in each case, utility for the outcomes of a fair lottery. c Shoham and Leyton-Brown, 2006 Revelation Principle; Quasilinear Utility Lecture 14, Slide 24