SlideShare uma empresa Scribd logo
1 de 35
DATA MINING AND MACHINE LEARNING
                                                                 IN A NUTSHELL



                                         GAME THEORY,
                                                         AN INTRODUCTION

                                                    Mohammad-Ali Abbasi
                                                          http://www.public.asu.edu/~mabbasi2/

                                     SCHOOL OF COMPUTING, INFORMATICS, AND DECISION SYSTEMS ENGINEERING
                                                         ARIZONA STATE UNIVERSITY

              Arizona State University
                                                                http://dmml.asu.edu/
Data Mining and Machine Learning Lab
                                         Data Mining and Machine Learning- in a nutshell         An Introduction to Game Theory   1
Agenda

 • History
 • Introduction to Game Theory
 • Type of Games
        – Dominant Games
        – Nash Equilibrium
        – Multiple Equilibrium
 • Game Time


                Arizona State University
  Data Mining and Machine Learning Lab
                                           Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   2
History

  • Interdisciplinary (Economic and Mathematic)
    approach to the study of human behavior
  • Founded in the 1920s by John von Neumann
  • 1994 Nobel prize in Economics awarded to
    three researchers
  • “Games” are a metaphor for wide range of
    human interactions


                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   3
What is a Game

  • Game theory is concerned with situations in
    which decision-makers interact with one
    another,
  • and in which the happiness of each participant
    with the outcome depends not just on his or
    her own decisions but on the decisions made
    by everyone.




                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   4   4
A Game!

  • Ten of you go to a restaurant


  • If each of you pays for your own meal…
         – This is a decision problem


  • If you all agree to split the bill...
         – Now, this is a game



                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   5
Restaurant Decision-Making

  • Bill splitting policy changes incentives.
                                              May I recommend that with the Bleu
                                                 Cheese for ten dollars more?

                                                                                                      Sure!


                                                                                                       It is only
                                                                                                    a dollar more
                                                                                                        for me!




                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   6
Decision theory vs. Game theory

  • Decision Theory
         – You are self-interested and selfish

  • Game Theory
         – So is everyone else




                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   7   7
Applications
  • Market:
         – pricing of a new product when other firms have similar new products
         – deciding how to bid in an auction
  • Networking:
         – choosing a route on the Internet or through a transportation networks
  • Politic:
         – Deciding whether to adopt an aggressive or a passive stance in
           international relations
  • Sport:
         – choosing how to target a soccer penalty kick and choosing how to
           defend against
         – Choosing whether to use performance-enhancing drugs in a
           professional sport




                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   8   8
Introduction to Game Theory


                                  •      Review a Game
                                  •      Characteristics
                                  •      Rules
                                  •      Assumptions
              Arizona State University
Data Mining and Machine Learning Lab
                                         Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   9
The Prisoner’s Dilemma

  • Two burglars, Jack and Tom, are captured and
    separated by the police
  • Each has to choose whether or not to confess and
    implicate the other
  • If neither confesses, they both serve one year for
    carrying a concealed weapon
  • If each confesses and implicates the other, they
    both get 4 years
  • If one confesses and the other does not, the
    confessor goes free, and the other gets 8 years
                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   10
Prisoners dilemma

  • Introduction


                                                                                            Tom
                                                                                        Not    Confess
                                                                                      Confess

                                  Not Confess                                         -1, -1               -8, 0
  Jack
                                                 Confess                                0, -8           -4, -4
                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell     An Introduction to Game Theory   11
Jack’s Decision Tree



                                        If Tom Confesses                                      If Tom Does Not Confess

                                                     Jack                                                  Jack

                                            Confess      Not Confess                              Confess        Not Confess

                                4 Years in                          8 Years in                                          1 Years in
                                                                                               Free
                                  Prison                              Prison                                              Prison

                           Best
                                                                                                      Best
                       Strategy
                                                                                                      Strategy




                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell                      An Introduction to Game Theory   12
Basic elements of a Game

  • Players
         – Everyone who has an effect on your earnings
  • Strategies
         – Actions available to each player
         – Define a plan of action for every contingency
  • Payoffs
         – Numbers associated with each outcome
         – Reflect the interests of the players


                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   13
Assumptions in the Game Theory
  • Player
         – We assume that each player knows everything about the
           structure of the game
         – Player don’t know about another’s decision
         – Each player knows the rules of the game
         – Players are rational and expert
  • Strategy
         – Each player has two or more well-specified choices
         – Each player chooses a strategy to maximize his own payoff
         – Every possible combination of strategies available to the players
           leads to a well-defined end-state (win, loss, draw) that
           terminates the game
  • Payoff
         – everything that a player cares about is summarized in the
           player's payoffs
                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   14
Basic Games

  • games with only two players
         – We can apply it on any number of players
  • simple, one-shot games
         – Simultaneously, Independent and only once
         – Not dynamic




                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   16
Types of Games


                                  • Dominant Games
                                  • Nash Equilibrium
                                  • Multiple Equilibrium
              Arizona State University
Data Mining and Machine Learning Lab
                                         Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   17
Prisoner’s Dilemma



                                        If Tom Confesses                                      If Tom Does Not Confess

                                                     Jack                                                  Jack

                                            Confess      Not Confess                              Confess        Not Confess

                                4 Years in                          8 Years in                                          1 Years in
                                                                                               Free
                                  Prison                              Prison                                              Prison

                           Best
                                                                                                      Best
                       Strategy
                                                                                                      Strategy




                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell                      An Introduction to Game Theory   18
Dominant strategy

  • A players has a dominant strategy if that
    player's best strategy does not depend on
    what other players do.
                                                               P1(S,T) >= P1 (S’, T)


  • Strict Dominant strategy
                                                         P1(S,T) > P1 (S’, T)
  • Games with dominant strategies are easy to
    play
         – No need for “what if …” thinking
                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   19
Prisoner's Dilemma

  • Strategies must be undertaken without the
    full knowledge of what other players will do.


  • Players adopt dominant strategies,
  • BUT they don't necessarily lead to the best
    outcome.




                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   20
If only one player has Strictly dominant Strategy

  • Players: Firm A and Firm B
         – Produce a new product
                     • Options: Low Price and Upscale
                     • 60% of people would prefer low price and 40% high
                       price
                     • Firm A is dominant and can gets 80% of market




                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   21
Marketing Strategy

  • Dominant Games


                                                                                      Firm B
                                                                               Low Price   Upscale

                                                        Low
                                                             .48, .12                                  .6, .4
                                                       Price
  Firm A
                                             Upscale                              .4, .6          .32, .08

                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   22
A three client Game

  • Two Firms: Firm 1 and Firm 2
  • Three Clients: Client A, B and C
  • Conditions:
         – If two firms apply for same client can get half of its
           business
         – Firm 1 is too small to attract a business -> payoff =
           0
         – If firm 2 approaches to B or C on its own, it will
           take all their business (their business is worth 2)
         – A is larger client and its business is worth 8. they
           can work with it if both of them target it.
                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   23
Marketing Strategy

  • Nash Equilibrium

                                                                                              Firm 2
                                                                           A                     B                  C

                                                      A             4, 4                      0, 2             0, 2

  Firm 1                                              B             0, 0                      1, 1             0, 2

                                                     C              0, 0                      0, 2             1, 1
                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell       An Introduction to Game Theory   24
Nash Equilibrium

  • A Nash equilibrium is a situation in which
    none of them have dominant Strategy and
    each player makes his or her best response
         – (S, T) is Nash equilibrium if S is the best strategy to
           T and T is the best strategy to S



  • John Nash shared the 1994 Nobel prize in
    Economic for developing this idea!


                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   25
Multiple Equilibriums



                                  • Coordination Game
                                  • The Hawk-Dove Game

              Arizona State University
Data Mining and Machine Learning Lab
                                         Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   26
Coordination Game



                                                                                Your Partner
                                                                            Power Point Keynote

                                                   Power
                                                                                       1, 1              0, 0
                                                    Point
  You
                                             Keynote                                   0, 0              1, 1


                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   27
Other samples of Coordination Game

  • Using Metric units of measurement of English
    Units
  • Two people trying to find each other in a
    crowded mall with two entrance
  • …


  • These games has more than one Nash
    Equilibrium

                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   28
Unbalanced Coordination Game



                                                                                Your Partner
                                                                            Power Point Keynote

                                                   Power
                                                                                       1, 1              0, 0
                                                    Point
  You
                                             Keynote                                   0, 0              2, 2


                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   29
Battle of the Sexes



                                                                                       Wife
                                                                                Romantic    Action


                                            Romantic                                   1, 2              0, 0
  Husba
  nd
                                                   Action                              0, 0              2, 1


                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   30
Stag Hunt Game



                                                                                                Hunter 2
                                                                                              Stag      Hare


                                                                  Stag                        4, 4              0, 3
  Hunter 1
                                                                 Hare                         3, 0              3, 3


                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell       An Introduction to Game Theory   31
Hawk- Dove game



                                                                                                Animal 2
                                                                                              Dove     Hawk


                                                               Dove                           3, 3              1, 5
  Animal 1
                                                              Hawk                            5, 1              0, 0


                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell       An Introduction to Game Theory   32
Mixed Strategies- Matching Pennies

  Zero-sum
  Game                                                                                           Player 2
                                                                                               Head       Tail


                                                               Head                           -1, +1           +1, -1
  Player 1
                                                                      Tail                    +1, -1           -1, +1


                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell         An Introduction to Game Theory   33
Be ready for a Game!




              Arizona State University
Data Mining and Machine Learning Lab
                                         Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   34
play a real game!

  • Select a random number between 0 and 100
  • The winner is the one how, his number is closest
    to 0.75 of the average.
         – If average is AVG, closest number to AVG * 0.75 is
           winner
  • Score distribution:
         – 1st : 100
         – 2nd : 50
         – Others: 0
  • Talk about your selection

                 Arizona State University
   Data Mining and Machine Learning Lab
                                            Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   35
Mohammad-Ali Abbasi (Ali),
                                         Ali, is a Ph.D student at Data Mining
                                         and Machine Learning Lab, Arizona
                                         State University.
                                         His research interests include Data
                                         Mining, Machine Learning, Social
                                         Computing, and Social Media Behavior
                                         Analysis.

                                         http://www.public.asu.edu/~mabbasi2/

              Arizona State University
Data Mining and Machine Learning Lab
                                          Data Mining and Machine Learning- in a nutshell   An Introduction to Game Theory   36

Mais conteúdo relacionado

Mais procurados

Intro to game theory
Intro to game theory Intro to game theory
Intro to game theory Nadav Carmel
 
Game Theory.Pptx
Game Theory.PptxGame Theory.Pptx
Game Theory.Pptxferrisea
 
Prisoner's Dilemma
Prisoner's DilemmaPrisoner's Dilemma
Prisoner's DilemmaAcquate
 
Game Theory - Quantitative Analysis for Decision Making
Game Theory - Quantitative Analysis for Decision MakingGame Theory - Quantitative Analysis for Decision Making
Game Theory - Quantitative Analysis for Decision MakingIshita Bose
 
Game theory project
Game theory projectGame theory project
Game theory projectAagam Shah
 
Game theory application
Game theory applicationGame theory application
Game theory applicationshakebaumar
 
Using Game Theory in Your Economics Exams
Using Game Theory in Your Economics ExamsUsing Game Theory in Your Economics Exams
Using Game Theory in Your Economics Examstutor2u
 
An introduction to Game Theory
An introduction to Game TheoryAn introduction to Game Theory
An introduction to Game TheoryPaul Trafford
 
Game theory and its applications
Game theory and its applicationsGame theory and its applications
Game theory and its applicationsEranga Weerasekara
 
Game theory
Game theoryGame theory
Game theorygtush24
 
Introduction to Game Theory
Introduction to Game TheoryIntroduction to Game Theory
Introduction to Game TheoryCesar Sobrino
 

Mais procurados (20)

Game theory
Game theory Game theory
Game theory
 
Game Theory
Game TheoryGame Theory
Game Theory
 
Game theory
Game theoryGame theory
Game theory
 
Intro to game theory
Intro to game theory Intro to game theory
Intro to game theory
 
Game Theory.Pptx
Game Theory.PptxGame Theory.Pptx
Game Theory.Pptx
 
Game theory
Game theoryGame theory
Game theory
 
Prisoner's Dilemma
Prisoner's DilemmaPrisoner's Dilemma
Prisoner's Dilemma
 
Game theory
Game theoryGame theory
Game theory
 
Game theory
Game theoryGame theory
Game theory
 
Game theory
Game theoryGame theory
Game theory
 
Game Theory - Quantitative Analysis for Decision Making
Game Theory - Quantitative Analysis for Decision MakingGame Theory - Quantitative Analysis for Decision Making
Game Theory - Quantitative Analysis for Decision Making
 
Game theory
Game theoryGame theory
Game theory
 
Game theory project
Game theory projectGame theory project
Game theory project
 
Game theory application
Game theory applicationGame theory application
Game theory application
 
Using Game Theory in Your Economics Exams
Using Game Theory in Your Economics ExamsUsing Game Theory in Your Economics Exams
Using Game Theory in Your Economics Exams
 
An introduction to Game Theory
An introduction to Game TheoryAn introduction to Game Theory
An introduction to Game Theory
 
Game theory and its applications
Game theory and its applicationsGame theory and its applications
Game theory and its applications
 
Game theory
Game theoryGame theory
Game theory
 
Game theory
Game theoryGame theory
Game theory
 
Introduction to Game Theory
Introduction to Game TheoryIntroduction to Game Theory
Introduction to Game Theory
 

Mais de Ali Abbasi

Social Media Mining: An Introduction
Social Media Mining: An IntroductionSocial Media Mining: An Introduction
Social Media Mining: An IntroductionAli Abbasi
 
Data Mining: an Introduction
Data Mining: an IntroductionData Mining: an Introduction
Data Mining: an IntroductionAli Abbasi
 
Active learning
Active learningActive learning
Active learningAli Abbasi
 
Disaster Relief Using Social Media Data
Disaster Relief Using Social Media DataDisaster Relief Using Social Media Data
Disaster Relief Using Social Media DataAli Abbasi
 
Real-World Behavior Analysis through a Social Media Lens
Real-World Behavior Analysis through a Social Media LensReal-World Behavior Analysis through a Social Media Lens
Real-World Behavior Analysis through a Social Media LensAli Abbasi
 
Collective Intelligence, part II
Collective Intelligence, part IICollective Intelligence, part II
Collective Intelligence, part IIAli Abbasi
 
Collective Inteligence Part I
Collective Inteligence Part ICollective Inteligence Part I
Collective Inteligence Part IAli Abbasi
 
Learning To Recognize Reliable Users And Content In Social Media With Coupled...
Learning To Recognize Reliable Users And Content In Social Media With Coupled...Learning To Recognize Reliable Users And Content In Social Media With Coupled...
Learning To Recognize Reliable Users And Content In Social Media With Coupled...Ali Abbasi
 

Mais de Ali Abbasi (8)

Social Media Mining: An Introduction
Social Media Mining: An IntroductionSocial Media Mining: An Introduction
Social Media Mining: An Introduction
 
Data Mining: an Introduction
Data Mining: an IntroductionData Mining: an Introduction
Data Mining: an Introduction
 
Active learning
Active learningActive learning
Active learning
 
Disaster Relief Using Social Media Data
Disaster Relief Using Social Media DataDisaster Relief Using Social Media Data
Disaster Relief Using Social Media Data
 
Real-World Behavior Analysis through a Social Media Lens
Real-World Behavior Analysis through a Social Media LensReal-World Behavior Analysis through a Social Media Lens
Real-World Behavior Analysis through a Social Media Lens
 
Collective Intelligence, part II
Collective Intelligence, part IICollective Intelligence, part II
Collective Intelligence, part II
 
Collective Inteligence Part I
Collective Inteligence Part ICollective Inteligence Part I
Collective Inteligence Part I
 
Learning To Recognize Reliable Users And Content In Social Media With Coupled...
Learning To Recognize Reliable Users And Content In Social Media With Coupled...Learning To Recognize Reliable Users And Content In Social Media With Coupled...
Learning To Recognize Reliable Users And Content In Social Media With Coupled...
 

Último

Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfchloefrazer622
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactdawncurless
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxSayali Powar
 
The byproduct of sericulture in different industries.pptx
The byproduct of sericulture in different industries.pptxThe byproduct of sericulture in different industries.pptx
The byproduct of sericulture in different industries.pptxShobhayan Kirtania
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptxVS Mahajan Coaching Centre
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3JemimahLaneBuaron
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13Steve Thomason
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104misteraugie
 
Disha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfDisha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfchloefrazer622
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Krashi Coaching
 
Russian Call Girls in Andheri Airport Mumbai WhatsApp 9167673311 💞 Full Nigh...
Russian Call Girls in Andheri Airport Mumbai WhatsApp  9167673311 💞 Full Nigh...Russian Call Girls in Andheri Airport Mumbai WhatsApp  9167673311 💞 Full Nigh...
Russian Call Girls in Andheri Airport Mumbai WhatsApp 9167673311 💞 Full Nigh...Pooja Nehwal
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfSoniaTolstoy
 
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionMastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionSafetyChain Software
 
Web & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfWeb & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfJayanti Pande
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityGeoBlogs
 
Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..Disha Kariya
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxGaneshChakor2
 

Último (20)

Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdf
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impact
 
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
 
Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1
 
The byproduct of sericulture in different industries.pptx
The byproduct of sericulture in different industries.pptxThe byproduct of sericulture in different industries.pptx
The byproduct of sericulture in different industries.pptx
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
Disha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfDisha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdf
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
 
Russian Call Girls in Andheri Airport Mumbai WhatsApp 9167673311 💞 Full Nigh...
Russian Call Girls in Andheri Airport Mumbai WhatsApp  9167673311 💞 Full Nigh...Russian Call Girls in Andheri Airport Mumbai WhatsApp  9167673311 💞 Full Nigh...
Russian Call Girls in Andheri Airport Mumbai WhatsApp 9167673311 💞 Full Nigh...
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
 
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionMastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory Inspection
 
Web & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfWeb & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdf
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activity
 
Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptx
 

Game Theory: an Introduction

  • 1. DATA MINING AND MACHINE LEARNING IN A NUTSHELL GAME THEORY, AN INTRODUCTION Mohammad-Ali Abbasi http://www.public.asu.edu/~mabbasi2/ SCHOOL OF COMPUTING, INFORMATICS, AND DECISION SYSTEMS ENGINEERING ARIZONA STATE UNIVERSITY Arizona State University http://dmml.asu.edu/ Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 1
  • 2. Agenda • History • Introduction to Game Theory • Type of Games – Dominant Games – Nash Equilibrium – Multiple Equilibrium • Game Time Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 2
  • 3. History • Interdisciplinary (Economic and Mathematic) approach to the study of human behavior • Founded in the 1920s by John von Neumann • 1994 Nobel prize in Economics awarded to three researchers • “Games” are a metaphor for wide range of human interactions Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 3
  • 4. What is a Game • Game theory is concerned with situations in which decision-makers interact with one another, • and in which the happiness of each participant with the outcome depends not just on his or her own decisions but on the decisions made by everyone. Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 4 4
  • 5. A Game! • Ten of you go to a restaurant • If each of you pays for your own meal… – This is a decision problem • If you all agree to split the bill... – Now, this is a game Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 5
  • 6. Restaurant Decision-Making • Bill splitting policy changes incentives. May I recommend that with the Bleu Cheese for ten dollars more? Sure! It is only a dollar more for me! Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 6
  • 7. Decision theory vs. Game theory • Decision Theory – You are self-interested and selfish • Game Theory – So is everyone else Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 7 7
  • 8. Applications • Market: – pricing of a new product when other firms have similar new products – deciding how to bid in an auction • Networking: – choosing a route on the Internet or through a transportation networks • Politic: – Deciding whether to adopt an aggressive or a passive stance in international relations • Sport: – choosing how to target a soccer penalty kick and choosing how to defend against – Choosing whether to use performance-enhancing drugs in a professional sport Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 8 8
  • 9. Introduction to Game Theory • Review a Game • Characteristics • Rules • Assumptions Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 9
  • 10. The Prisoner’s Dilemma • Two burglars, Jack and Tom, are captured and separated by the police • Each has to choose whether or not to confess and implicate the other • If neither confesses, they both serve one year for carrying a concealed weapon • If each confesses and implicates the other, they both get 4 years • If one confesses and the other does not, the confessor goes free, and the other gets 8 years Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 10
  • 11. Prisoners dilemma • Introduction Tom Not Confess Confess Not Confess -1, -1 -8, 0 Jack Confess 0, -8 -4, -4 Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 11
  • 12. Jack’s Decision Tree If Tom Confesses If Tom Does Not Confess Jack Jack Confess Not Confess Confess Not Confess 4 Years in 8 Years in 1 Years in Free Prison Prison Prison Best Best Strategy Strategy Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 12
  • 13. Basic elements of a Game • Players – Everyone who has an effect on your earnings • Strategies – Actions available to each player – Define a plan of action for every contingency • Payoffs – Numbers associated with each outcome – Reflect the interests of the players Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 13
  • 14. Assumptions in the Game Theory • Player – We assume that each player knows everything about the structure of the game – Player don’t know about another’s decision – Each player knows the rules of the game – Players are rational and expert • Strategy – Each player has two or more well-specified choices – Each player chooses a strategy to maximize his own payoff – Every possible combination of strategies available to the players leads to a well-defined end-state (win, loss, draw) that terminates the game • Payoff – everything that a player cares about is summarized in the player's payoffs Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 14
  • 15. Basic Games • games with only two players – We can apply it on any number of players • simple, one-shot games – Simultaneously, Independent and only once – Not dynamic Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 16
  • 16. Types of Games • Dominant Games • Nash Equilibrium • Multiple Equilibrium Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 17
  • 17. Prisoner’s Dilemma If Tom Confesses If Tom Does Not Confess Jack Jack Confess Not Confess Confess Not Confess 4 Years in 8 Years in 1 Years in Free Prison Prison Prison Best Best Strategy Strategy Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 18
  • 18. Dominant strategy • A players has a dominant strategy if that player's best strategy does not depend on what other players do. P1(S,T) >= P1 (S’, T) • Strict Dominant strategy P1(S,T) > P1 (S’, T) • Games with dominant strategies are easy to play – No need for “what if …” thinking Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 19
  • 19. Prisoner's Dilemma • Strategies must be undertaken without the full knowledge of what other players will do. • Players adopt dominant strategies, • BUT they don't necessarily lead to the best outcome. Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 20
  • 20. If only one player has Strictly dominant Strategy • Players: Firm A and Firm B – Produce a new product • Options: Low Price and Upscale • 60% of people would prefer low price and 40% high price • Firm A is dominant and can gets 80% of market Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 21
  • 21. Marketing Strategy • Dominant Games Firm B Low Price Upscale Low .48, .12 .6, .4 Price Firm A Upscale .4, .6 .32, .08 Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 22
  • 22. A three client Game • Two Firms: Firm 1 and Firm 2 • Three Clients: Client A, B and C • Conditions: – If two firms apply for same client can get half of its business – Firm 1 is too small to attract a business -> payoff = 0 – If firm 2 approaches to B or C on its own, it will take all their business (their business is worth 2) – A is larger client and its business is worth 8. they can work with it if both of them target it. Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 23
  • 23. Marketing Strategy • Nash Equilibrium Firm 2 A B C A 4, 4 0, 2 0, 2 Firm 1 B 0, 0 1, 1 0, 2 C 0, 0 0, 2 1, 1 Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 24
  • 24. Nash Equilibrium • A Nash equilibrium is a situation in which none of them have dominant Strategy and each player makes his or her best response – (S, T) is Nash equilibrium if S is the best strategy to T and T is the best strategy to S • John Nash shared the 1994 Nobel prize in Economic for developing this idea! Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 25
  • 25. Multiple Equilibriums • Coordination Game • The Hawk-Dove Game Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 26
  • 26. Coordination Game Your Partner Power Point Keynote Power 1, 1 0, 0 Point You Keynote 0, 0 1, 1 Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 27
  • 27. Other samples of Coordination Game • Using Metric units of measurement of English Units • Two people trying to find each other in a crowded mall with two entrance • … • These games has more than one Nash Equilibrium Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 28
  • 28. Unbalanced Coordination Game Your Partner Power Point Keynote Power 1, 1 0, 0 Point You Keynote 0, 0 2, 2 Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 29
  • 29. Battle of the Sexes Wife Romantic Action Romantic 1, 2 0, 0 Husba nd Action 0, 0 2, 1 Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 30
  • 30. Stag Hunt Game Hunter 2 Stag Hare Stag 4, 4 0, 3 Hunter 1 Hare 3, 0 3, 3 Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 31
  • 31. Hawk- Dove game Animal 2 Dove Hawk Dove 3, 3 1, 5 Animal 1 Hawk 5, 1 0, 0 Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 32
  • 32. Mixed Strategies- Matching Pennies Zero-sum Game Player 2 Head Tail Head -1, +1 +1, -1 Player 1 Tail +1, -1 -1, +1 Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 33
  • 33. Be ready for a Game! Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 34
  • 34. play a real game! • Select a random number between 0 and 100 • The winner is the one how, his number is closest to 0.75 of the average. – If average is AVG, closest number to AVG * 0.75 is winner • Score distribution: – 1st : 100 – 2nd : 50 – Others: 0 • Talk about your selection Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 35
  • 35. Mohammad-Ali Abbasi (Ali), Ali, is a Ph.D student at Data Mining and Machine Learning Lab, Arizona State University. His research interests include Data Mining, Machine Learning, Social Computing, and Social Media Behavior Analysis. http://www.public.asu.edu/~mabbasi2/ Arizona State University Data Mining and Machine Learning Lab Data Mining and Machine Learning- in a nutshell An Introduction to Game Theory 36

Notas do Editor

  1. There is a set of participants, whom we call the players. In our example, you and yourpartner are the two players.(ii) Each player has a set of options for how to behave; we will refer to these as the player'spossible strategies. In the example, you and your partner each have two possiblestrategies: to prepare for the presentation, or to study for the exam.(iii) For each choice of strategies, each player receives a payo that can depend on thestrategies selected by everyone. The payos will generally be numbers, with eachplayer preferring larger payos to smaller payos. In our current example, the payoto each player is the average grade he or she gets on the exam and the presentation