The document provides an introduction to game theory, covering its history, key concepts, and applications. It discusses how game theory analyzes strategic decision-making between interdependent actors. The document outlines different types of games including dominant games, Nash equilibriums, and multiple equilibrium games. It also presents examples like the prisoner's dilemma game to illustrate game theory concepts and strategies.
This presentation is an attempt to introduce Game Theory in one session. It's suitable for undergraduates. In practice, it's best used as a taster since only a portion of the material can be covered in an hour - topics can be chosen according to the interests of the class.
The main reference source used was 'Games, Theory and Applications' by L.C.Thomas. Further notes available at: http://bit.ly/nW6ULD
This presentation is an attempt to introduce Game Theory in one session. It's suitable for undergraduates. In practice, it's best used as a taster since only a portion of the material can be covered in an hour - topics can be chosen according to the interests of the class.
The main reference source used was 'Games, Theory and Applications' by L.C.Thomas. Further notes available at: http://bit.ly/nW6ULD
Put simply, economic models are simplified versions of reality. The situation in any given economy is very complex because there are several variables having great interdependence among them. Copy the link given below and paste it in new browser window to get more information on Economic Models:- www.transtutors.com/homework-help/economics/economic-models.aspx
Game theory is the study of mathematical models of strategic interaction between rational decision-makers.The mathematical theory of games was invented by John von Neumann and Oskar Morgenstern (1944). For reasons to be discussed later, limitations in their mathematical framework initially made the theory applicable only under special and limited conditions.Increasingly, companies are utilizing the science of Game Theory to help them make high risk/high reward strategic decisions in highly competitive markets and situations. ... Said another way, each decision maker is a player in the game of business.
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
Details at: http://dmml.asu.edu/smm/
Put simply, economic models are simplified versions of reality. The situation in any given economy is very complex because there are several variables having great interdependence among them. Copy the link given below and paste it in new browser window to get more information on Economic Models:- www.transtutors.com/homework-help/economics/economic-models.aspx
Game theory is the study of mathematical models of strategic interaction between rational decision-makers.The mathematical theory of games was invented by John von Neumann and Oskar Morgenstern (1944). For reasons to be discussed later, limitations in their mathematical framework initially made the theory applicable only under special and limited conditions.Increasingly, companies are utilizing the science of Game Theory to help them make high risk/high reward strategic decisions in highly competitive markets and situations. ... Said another way, each decision maker is a player in the game of business.
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
Details at: http://dmml.asu.edu/smm/
Abstract. In disasters such as the earthquake in Haiti and the tsunami in Japan, people used social media to ask for help or report injuries. The popularity, effciency, and ease of use of social media has led to its pervasive use during the disaster.
This creates a pool of timely reports about the disaster, injuries, and help requests.
This offers an alternative opportunity for first responders and disaster relief organizations to collect information about the disaster, victims, and their needs.
It also presents a challenge for these organizations to aggregate and process the requests from different social media.
Given the sheer volume of requests, it is necessary to filter reports and select those of high priority for decision making.
Little is known about how the two phases should be smoothly integrated.
In this paper we report the use of social media during a simulated crisis and crisis response process, the ASU Crisis Response Game.
Its main objective is to creat a training capability to understand how to use social media in crisis.
We report lessons learned from this exercise that may benefit first responders and NGOs who use social media to manage relief efforts during the disaster.
Real-World Behavior Analysis through a Social Media LensAli Abbasi
In this paper, using a large amount of data collected from Twitter, the blogosphere, social networks, and news sources, we perform preliminary research to investigate if human behavior in the real world can be understood by analyzing social media data. The goals of this research is twofold: (1) determining the relative effectiveness of a social media lens in analyzing and predicting real-world collective behavior, and (2) exploring the domains and situations under which social media can be a predictor for real-world's behavior. We develop a four-step model: community selection, data collection, online behavior analysis, and behavior prediction. The results of this study show that in most cases social media is a good tool for estimating attitudes and further research is needed for predicting social behavior.
Learning To Recognize Reliable Users And Content In Social Media With Coupled...Ali Abbasi
Learning to Recognize Reliable Users and Content in Social Media with Coupled Mutual Reinforcement, Mohammad Ali Abbasi,
Arizona State University
http://dmml.asu.edu
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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!
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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
Editor's Notes
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