A discussion of basic concepts from game theory, an incredibly useful lemma concerning auctions from mechanism design, and a discussion of TFNP, an interesting complexity class which captures search problems where an answer is guaranteed to exist, such as the problem of finding Nash equilibria in games
This document provides an introduction to game theory. It discusses what game theory is, its essential features, and some key concepts in game theory including Nash equilibrium, backward induction, extensive form games, normal form games, mixed strategies, coordination games, zero-sum games, the prisoner's dilemma, chicken games, and repeated games. It also provides examples of applying game theory concepts to real-world situations such as the rivalry between Airbus and Boeing.
Game theory seeks to analyze competing situations that arise from conflicts of interest. It examines scenarios of conflict to identify optimal strategies for decision makers. Game theory assumes importance from a managerial perspective, as businesses compete for market share. The theory can help determine rational behaviors in competitive situations where outcomes depend on interactions between decision makers and competitors. It provides insights to help businesses convert weaknesses and threats into opportunities and strengths to maximize profits.
This document provides an overview of game theory, including its founders John von Neumann and John Nash. Game theory is the study of strategic decision making among rational players where outcomes depend on the choices of all. It has applications in economics, politics, and biology. Key concepts discussed include Nash equilibrium, where no player benefits from changing strategies alone; the prisoner's dilemma game; and the tit-for-tat strategy of reciprocal cooperation and defection. The document outlines the assumptions, elements, and applications of game theory.
Game theory is the study of strategic decision making where outcomes depend on the choices of multiple players. It originated in the 1920s and was popularized by John von Neumann. Game theory analyzes cooperative and non-cooperative games with various properties like the number of players, information available, and whether choices are simultaneous or sequential. Important concepts in game theory include Nash equilibrium, where no player can benefit by changing strategy alone, and prisoner's dilemma, where defecting dominates but collective cooperation yields higher payoffs. Game theory is now used widely in economics, politics, biology, and other fields involving interdependent actors.
A brief introduction to game theory prisoners dilemma and nash equilibrumpravesh kumar
Game theory is the study of strategic decision making between players. A game has players, strategies or actions for each player, and payoffs for outcomes. The Prisoner's Dilemma is a classic game where two prisoners must choose to confess or deny a crime without communicating. If both deny, they get a short sentence, but each has an incentive to confess regardless of the other's action for a lesser sentence. Nash equilibrium is where each player's strategy is the best response to the other players' strategies.
The document discusses games with incomplete information and how they are modeled. It notes:
- Games in the real world often have incomplete information where players do not know each other's payoffs or strategies.
- Harsanyi's approach models incomplete information using random variables for each player's preferences that are privately observed but have a commonly known probability distribution. This transforms incomplete information into imperfect information.
- A Bayesian game formally represents this setting, where a player's utility depends on their type drawn from a probability distribution. A Bayesian Nash equilibrium requires optimal strategies given beliefs over other players' types.
GAME THEORY NOTES FOR ECONOMICS HONOURS FOR ALL UNIVERSITIES BY SOURAV SIR'S ...SOURAV DAS
This document provides an overview of game theory concepts including definitions of key terms like mixed strategy, Nash equilibrium, payoff, perfect information, player, rationality, strategic form, strategy, zero-sum game, cooperative games, non-cooperative games, representation of games as normal form, pure vs mixed strategies, two-person zero-sum games, examples of zero-sum games, and using linear programming to determine optimal strategies in a game.
This document provides a brief introduction to game theory concepts, including normal form games, dominant strategies, and Nash equilibrium. It uses examples like the Prisoner's Dilemma and Cournot duopoly to illustrate these concepts. Normal form games represent strategic interactions through payoff matrices. Dominant strategies provide unambiguous best responses. Nash equilibrium is a prediction of strategies where no player benefits by deviating unilaterally. Multiple equilibria can exist in some games.
This document provides an introduction to game theory. It discusses what game theory is, its essential features, and some key concepts in game theory including Nash equilibrium, backward induction, extensive form games, normal form games, mixed strategies, coordination games, zero-sum games, the prisoner's dilemma, chicken games, and repeated games. It also provides examples of applying game theory concepts to real-world situations such as the rivalry between Airbus and Boeing.
Game theory seeks to analyze competing situations that arise from conflicts of interest. It examines scenarios of conflict to identify optimal strategies for decision makers. Game theory assumes importance from a managerial perspective, as businesses compete for market share. The theory can help determine rational behaviors in competitive situations where outcomes depend on interactions between decision makers and competitors. It provides insights to help businesses convert weaknesses and threats into opportunities and strengths to maximize profits.
This document provides an overview of game theory, including its founders John von Neumann and John Nash. Game theory is the study of strategic decision making among rational players where outcomes depend on the choices of all. It has applications in economics, politics, and biology. Key concepts discussed include Nash equilibrium, where no player benefits from changing strategies alone; the prisoner's dilemma game; and the tit-for-tat strategy of reciprocal cooperation and defection. The document outlines the assumptions, elements, and applications of game theory.
Game theory is the study of strategic decision making where outcomes depend on the choices of multiple players. It originated in the 1920s and was popularized by John von Neumann. Game theory analyzes cooperative and non-cooperative games with various properties like the number of players, information available, and whether choices are simultaneous or sequential. Important concepts in game theory include Nash equilibrium, where no player can benefit by changing strategy alone, and prisoner's dilemma, where defecting dominates but collective cooperation yields higher payoffs. Game theory is now used widely in economics, politics, biology, and other fields involving interdependent actors.
A brief introduction to game theory prisoners dilemma and nash equilibrumpravesh kumar
Game theory is the study of strategic decision making between players. A game has players, strategies or actions for each player, and payoffs for outcomes. The Prisoner's Dilemma is a classic game where two prisoners must choose to confess or deny a crime without communicating. If both deny, they get a short sentence, but each has an incentive to confess regardless of the other's action for a lesser sentence. Nash equilibrium is where each player's strategy is the best response to the other players' strategies.
The document discusses games with incomplete information and how they are modeled. It notes:
- Games in the real world often have incomplete information where players do not know each other's payoffs or strategies.
- Harsanyi's approach models incomplete information using random variables for each player's preferences that are privately observed but have a commonly known probability distribution. This transforms incomplete information into imperfect information.
- A Bayesian game formally represents this setting, where a player's utility depends on their type drawn from a probability distribution. A Bayesian Nash equilibrium requires optimal strategies given beliefs over other players' types.
GAME THEORY NOTES FOR ECONOMICS HONOURS FOR ALL UNIVERSITIES BY SOURAV SIR'S ...SOURAV DAS
This document provides an overview of game theory concepts including definitions of key terms like mixed strategy, Nash equilibrium, payoff, perfect information, player, rationality, strategic form, strategy, zero-sum game, cooperative games, non-cooperative games, representation of games as normal form, pure vs mixed strategies, two-person zero-sum games, examples of zero-sum games, and using linear programming to determine optimal strategies in a game.
This document provides a brief introduction to game theory concepts, including normal form games, dominant strategies, and Nash equilibrium. It uses examples like the Prisoner's Dilemma and Cournot duopoly to illustrate these concepts. Normal form games represent strategic interactions through payoff matrices. Dominant strategies provide unambiguous best responses. Nash equilibrium is a prediction of strategies where no player benefits by deviating unilaterally. Multiple equilibria can exist in some games.
Game theory is the study of strategic decision making between rational actors. It was first developed by John von Neumann and Oskar Morgenstern in 1944 to analyze economic behavior. Important concepts include Nash equilibrium and prisoners' dilemma. Game theory has real-world applications such as predicting outcomes in economics, politics, and evolution. Famous examples analyzed using game theory include A Beautiful Mind and optimizing train ticket availability in India.
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
Game theory is the study of strategic decision making. It involves analyzing interactions between players where the outcome for each player depends on the actions of all players. Key concepts in game theory include Nash equilibrium, where each player's strategy is the best response to the other players' strategies, and Prisoner's Dilemma, where the non-cooperative equilibrium results in a worse outcome for both players than if they had cooperated. Game theory is applied in economics, political science, biology, and many other fields to model strategic interactions.
Applications of game theory on event management Sameer Dhurat
Game theory is "the study of mathematical models of conflict and cooperation between intelligent rational decision-makers." Game theory is mainly used in economics, political science, and psychology, as well as logic, computer science and biology.
In this presentation ,discussed regarding Application of game theory on Event Management with the help of Prisoner's Dilemma Game
This document provides an overview of game theory, including:
- Defining game theory as a way to study strategic decision-making involving multiple participants with conflicting goals.
- The major assumptions of game theory include players having different objectives, making decisions simultaneously, and knowing potential payoffs.
- Common types of games are cooperative/non-cooperative, zero-sum/non-zero-sum, and simultaneous/sequential games.
- Popular examples used in game theory include the Prisoner's Dilemma and Chicken games, which demonstrate outcomes like Nash equilibrium.
- Game theory has applications in economics, politics, biology, and other fields for modeling interactions and predicting outcomes.
The document discusses the prisoner's dilemma game theory concept where two individuals may choose to cooperate or betray each other, and explains how in the classic prisoner's dilemma scenario, pursuing individual self-interest results in a worse outcome for both rather than cooperation. It provides an example of two prisoners, Dave and Henry, who each must decide whether to plead guilty or not guilty and explores the incentives that lead both to plead guilty even though cooperating by pleading not guilty would result in a shorter total sentence for both of them.
This document provides an overview of game theory and two-person zero-sum games. It defines key concepts such as players, strategies, payoffs, and classifications of games. It also describes the assumptions and solutions for pure strategy and mixed strategy games. Pure strategy games have a saddle point solution found using minimax and maximin rules. Mixed strategy games do not have a saddle point and require determining the optimal probabilities that players select each strategy.
The document discusses using game theory to analyze video games. It provides background on game theory, explaining that it is the formal study of decision-making where players' choices affect each other. It outlines some key game theory concepts like the prisoner's dilemma, chicken game, and Nash equilibrium. It then discusses how different types of video games like cooperative, semi-cooperative, and competitive games can be modeled using concepts from game theory.
Game theory is used to model strategic decision-making between competitors. It originated in the 20th century and applies concepts like players, strategies, and payoffs. Players select strategies and receive payoffs based on the strategies of all players. The optimal strategy maximizes a player's payoff. Techniques like minimax, maximin, and solving dominance-reduced payoff matrices can help determine optimal strategies and the value of a game.
This document provides an introduction and overview of game theory. It describes key concepts in game theory including the elements of a game, complete and incomplete information, perfect and imperfect information, Nash equilibrium, simultaneous decisions, pure strategies and dominant strategies. It provides examples of classic games including the prisoner's dilemma, trade war, and battle of the sexes to illustrate these concepts. The prisoner's dilemma and trade war examples show how the games have dominant strategies that lead to a Nash equilibrium that is not optimal for either player.
This document provides an overview of game theory concepts including its development, assumptions, classification of games, elements, significance, limitations, and methods for solving different types of games. Some key points:
- Game theory was developed in 1928 by John Von Neumann and Oscar Morgenstern to analyze decision-making involving two or more rational opponents.
- Games can be classified as two-person, n-person, zero-sum, non-zero-sum, pure-strategy, or mixed-strategy.
- Elements include the payoff matrix, dominance rules, optimal strategies, and the value of the game.
- Methods for solving games include using pure strategies if a saddle point exists, or mixed
Game theory is a branch of applied mathematics that analyzes strategic interactions between agents. It includes concepts like Nash equilibrium, mixed strategies, and coordination games. Game theory is used in economics, political science, biology, and other social sciences to model how individuals make decisions in strategic situations where outcomes depend on the decisions of others.
Game theory is used to analyze strategic decision-making situations involving multiple players under conditions of conflict or competition. It can help determine the best strategy for a firm given competitors' expected countermoves. Key concepts include pure and mixed strategies, optimal strategies, the value of the game, zero-sum and non-zero-sum games, and using payoff matrices to represent two-person zero-sum games and determine if a saddle point exists. When there is no saddle point, mixed strategies involving probabilities of different actions can determine the value of the game.
This document provides an overview of game theory concepts including its assumptions, classifications, elements, significance, and limitations. It also describes methods for solving different types of games such as the prisoner's dilemma, 2-person zero-sum games, and pure strategy games. Game theory analyzes strategic decision making among interdependent parties and can provide insights into situations involving conflict or competition between rational opponents.
Game theory is a mathematical approach that analyzes strategic interactions between parties. It is used to understand situations where decision-makers are impacted by others' choices. A game has players, strategies, payoffs, and information. The Nash equilibrium predicts outcomes as the strategies where no player benefits by changing alone given others' choices. For example, in the Prisoner's Dilemma game about two suspects, confessing dominates remaining silent no matter what the other does, leading both to confess for a worse joint outcome than remaining silent.
This document provides an overview of game theory, which was developed in 1928 to analyze competitive situations. It describes various types of games, such as zero-sum, non-zero-sum, pure-strategy, and mixed-strategy games. Methods for solving different types of games are presented, including the saddle point method for 2x2 games, dominance method, graphical method, and algebraic method. Limitations of game theory in assuming perfect information and rational behavior are also noted.
Game Theory - Quantitative Analysis for Decision MakingIshita Bose
WHAT IS GAME THEORY?
HISTORY OF GAME THEORY
APPLICATIONS OF GAME THEORY
KEY ELEMENTS OF A GAME
TYPES OF GAME
NASH EQUILIBRIUM (NE)
PURE STRATEGIES AND MIXED STRATEGIES
2-PLAYERS ZERO-SUM GAMES
PRISONER’S DILEMMA
Introduction to the Strategy of Game TheoryJonathon Flegg
Game theory is a strategic approach to understanding interactive situations. It examines how individuals make decisions in contexts where outcomes depend on the decisions of others. Key components of game theory include players, rules, strategies, and payoffs. Static games analyze single-shot interactions, while repeated games consider how cooperation can emerge over multiple iterations if players use strategies like tit-for-tat that punish non-cooperation but also forgive. Sequential games incorporate timing of moves, and concepts like backward induction help analyze them. Real-world applications of game theory include how to establish cooperation between parties and the value of commitment devices for changing strategic incentives.
This is the first of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
This lecture introduces the concept of a game, and the branch of mathematics known as Game Theory.
This document discusses applications of game theory in computer science, specifically in networking and algorithm analysis. It introduces fundamental game theory concepts like the Nash equilibrium. It then explores how game theory can be used to model network security as a stochastic game between a hacker and security team, allowing analysis of optimal strategies. It also explains Yao's minimax principle, which uses game theory to relate the complexities of deterministic and randomized algorithms by modeling them as players in a zero-sum game. By representing problems in game theoretic terms, complex issues can be analyzed to find solutions.
This document discusses game theory and provides examples of different types of games. It introduces the prisoner's dilemma game, which involves two players who must choose whether to cooperate with or betray each other. It also discusses finding Nash equilibria, including examples of mixed strategy equilibria in the Battle of the Sexes and Matching Pennies games. The document provides information on concepts such as dominant strategies, Pareto optimality, best responses, and expected utility in game theory.
Game theory intro_and_questions_2009[1]evamstrauss
Here are the key steps:
Row
1. Check for dominant strategies - none exist
2. Find possible Nash equilibria by looking at best responses:
- Up, Left
- Down, Centre
3. Therefore, the two Nash equilibria are:
- (Up, Left)
- (Down, Centre)
The two Nash equilibria are (Up, Left) and (Down, Centre). There are no dominant strategies.
Game theory is the study of strategic decision making between rational actors. It was first developed by John von Neumann and Oskar Morgenstern in 1944 to analyze economic behavior. Important concepts include Nash equilibrium and prisoners' dilemma. Game theory has real-world applications such as predicting outcomes in economics, politics, and evolution. Famous examples analyzed using game theory include A Beautiful Mind and optimizing train ticket availability in India.
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
Game theory is the study of strategic decision making. It involves analyzing interactions between players where the outcome for each player depends on the actions of all players. Key concepts in game theory include Nash equilibrium, where each player's strategy is the best response to the other players' strategies, and Prisoner's Dilemma, where the non-cooperative equilibrium results in a worse outcome for both players than if they had cooperated. Game theory is applied in economics, political science, biology, and many other fields to model strategic interactions.
Applications of game theory on event management Sameer Dhurat
Game theory is "the study of mathematical models of conflict and cooperation between intelligent rational decision-makers." Game theory is mainly used in economics, political science, and psychology, as well as logic, computer science and biology.
In this presentation ,discussed regarding Application of game theory on Event Management with the help of Prisoner's Dilemma Game
This document provides an overview of game theory, including:
- Defining game theory as a way to study strategic decision-making involving multiple participants with conflicting goals.
- The major assumptions of game theory include players having different objectives, making decisions simultaneously, and knowing potential payoffs.
- Common types of games are cooperative/non-cooperative, zero-sum/non-zero-sum, and simultaneous/sequential games.
- Popular examples used in game theory include the Prisoner's Dilemma and Chicken games, which demonstrate outcomes like Nash equilibrium.
- Game theory has applications in economics, politics, biology, and other fields for modeling interactions and predicting outcomes.
The document discusses the prisoner's dilemma game theory concept where two individuals may choose to cooperate or betray each other, and explains how in the classic prisoner's dilemma scenario, pursuing individual self-interest results in a worse outcome for both rather than cooperation. It provides an example of two prisoners, Dave and Henry, who each must decide whether to plead guilty or not guilty and explores the incentives that lead both to plead guilty even though cooperating by pleading not guilty would result in a shorter total sentence for both of them.
This document provides an overview of game theory and two-person zero-sum games. It defines key concepts such as players, strategies, payoffs, and classifications of games. It also describes the assumptions and solutions for pure strategy and mixed strategy games. Pure strategy games have a saddle point solution found using minimax and maximin rules. Mixed strategy games do not have a saddle point and require determining the optimal probabilities that players select each strategy.
The document discusses using game theory to analyze video games. It provides background on game theory, explaining that it is the formal study of decision-making where players' choices affect each other. It outlines some key game theory concepts like the prisoner's dilemma, chicken game, and Nash equilibrium. It then discusses how different types of video games like cooperative, semi-cooperative, and competitive games can be modeled using concepts from game theory.
Game theory is used to model strategic decision-making between competitors. It originated in the 20th century and applies concepts like players, strategies, and payoffs. Players select strategies and receive payoffs based on the strategies of all players. The optimal strategy maximizes a player's payoff. Techniques like minimax, maximin, and solving dominance-reduced payoff matrices can help determine optimal strategies and the value of a game.
This document provides an introduction and overview of game theory. It describes key concepts in game theory including the elements of a game, complete and incomplete information, perfect and imperfect information, Nash equilibrium, simultaneous decisions, pure strategies and dominant strategies. It provides examples of classic games including the prisoner's dilemma, trade war, and battle of the sexes to illustrate these concepts. The prisoner's dilemma and trade war examples show how the games have dominant strategies that lead to a Nash equilibrium that is not optimal for either player.
This document provides an overview of game theory concepts including its development, assumptions, classification of games, elements, significance, limitations, and methods for solving different types of games. Some key points:
- Game theory was developed in 1928 by John Von Neumann and Oscar Morgenstern to analyze decision-making involving two or more rational opponents.
- Games can be classified as two-person, n-person, zero-sum, non-zero-sum, pure-strategy, or mixed-strategy.
- Elements include the payoff matrix, dominance rules, optimal strategies, and the value of the game.
- Methods for solving games include using pure strategies if a saddle point exists, or mixed
Game theory is a branch of applied mathematics that analyzes strategic interactions between agents. It includes concepts like Nash equilibrium, mixed strategies, and coordination games. Game theory is used in economics, political science, biology, and other social sciences to model how individuals make decisions in strategic situations where outcomes depend on the decisions of others.
Game theory is used to analyze strategic decision-making situations involving multiple players under conditions of conflict or competition. It can help determine the best strategy for a firm given competitors' expected countermoves. Key concepts include pure and mixed strategies, optimal strategies, the value of the game, zero-sum and non-zero-sum games, and using payoff matrices to represent two-person zero-sum games and determine if a saddle point exists. When there is no saddle point, mixed strategies involving probabilities of different actions can determine the value of the game.
This document provides an overview of game theory concepts including its assumptions, classifications, elements, significance, and limitations. It also describes methods for solving different types of games such as the prisoner's dilemma, 2-person zero-sum games, and pure strategy games. Game theory analyzes strategic decision making among interdependent parties and can provide insights into situations involving conflict or competition between rational opponents.
Game theory is a mathematical approach that analyzes strategic interactions between parties. It is used to understand situations where decision-makers are impacted by others' choices. A game has players, strategies, payoffs, and information. The Nash equilibrium predicts outcomes as the strategies where no player benefits by changing alone given others' choices. For example, in the Prisoner's Dilemma game about two suspects, confessing dominates remaining silent no matter what the other does, leading both to confess for a worse joint outcome than remaining silent.
This document provides an overview of game theory, which was developed in 1928 to analyze competitive situations. It describes various types of games, such as zero-sum, non-zero-sum, pure-strategy, and mixed-strategy games. Methods for solving different types of games are presented, including the saddle point method for 2x2 games, dominance method, graphical method, and algebraic method. Limitations of game theory in assuming perfect information and rational behavior are also noted.
Game Theory - Quantitative Analysis for Decision MakingIshita Bose
WHAT IS GAME THEORY?
HISTORY OF GAME THEORY
APPLICATIONS OF GAME THEORY
KEY ELEMENTS OF A GAME
TYPES OF GAME
NASH EQUILIBRIUM (NE)
PURE STRATEGIES AND MIXED STRATEGIES
2-PLAYERS ZERO-SUM GAMES
PRISONER’S DILEMMA
Introduction to the Strategy of Game TheoryJonathon Flegg
Game theory is a strategic approach to understanding interactive situations. It examines how individuals make decisions in contexts where outcomes depend on the decisions of others. Key components of game theory include players, rules, strategies, and payoffs. Static games analyze single-shot interactions, while repeated games consider how cooperation can emerge over multiple iterations if players use strategies like tit-for-tat that punish non-cooperation but also forgive. Sequential games incorporate timing of moves, and concepts like backward induction help analyze them. Real-world applications of game theory include how to establish cooperation between parties and the value of commitment devices for changing strategic incentives.
This is the first of an 8 lecture series that I presented at University of Strathclyde in 2011/2012 as part of the final year AI course.
This lecture introduces the concept of a game, and the branch of mathematics known as Game Theory.
This document discusses applications of game theory in computer science, specifically in networking and algorithm analysis. It introduces fundamental game theory concepts like the Nash equilibrium. It then explores how game theory can be used to model network security as a stochastic game between a hacker and security team, allowing analysis of optimal strategies. It also explains Yao's minimax principle, which uses game theory to relate the complexities of deterministic and randomized algorithms by modeling them as players in a zero-sum game. By representing problems in game theoretic terms, complex issues can be analyzed to find solutions.
This document discusses game theory and provides examples of different types of games. It introduces the prisoner's dilemma game, which involves two players who must choose whether to cooperate with or betray each other. It also discusses finding Nash equilibria, including examples of mixed strategy equilibria in the Battle of the Sexes and Matching Pennies games. The document provides information on concepts such as dominant strategies, Pareto optimality, best responses, and expected utility in game theory.
Game theory intro_and_questions_2009[1]evamstrauss
Here are the key steps:
Row
1. Check for dominant strategies - none exist
2. Find possible Nash equilibria by looking at best responses:
- Up, Left
- Down, Centre
3. Therefore, the two Nash equilibria are:
- (Up, Left)
- (Down, Centre)
The two Nash equilibria are (Up, Left) and (Down, Centre). There are no dominant strategies.
I provide a (very) brief introduction to game theory. I have developed these notes to
provide quick access to some of the basics of game theory; mainly as an aid for students
in courses in which I assumed familiarity with game theory but did not require it as a
prerequisite
Game theory is the study of how optimal strategies are formulated in conflict situations involving two or more rational opponents with competing interests. It considers how the strategies of one player will impact the outcomes for others. Game theory models classify games based on the number of players, whether the total payoff is zero-sum, and the types of strategies used. The minimax-maximin principle provides a way to determine optimal strategies without knowing the opponent's strategy by having each player maximize their minimum payoff or minimize their maximum loss. A saddle point exists when the maximin and minimax values are equal, indicating optimal strategies for both players.
A brief introduction to the basics of game theoryWladimir Augusto
This document provides a brief introduction to game theory concepts. It discusses normal form games and representations using payoff matrices. It introduces the concepts of dominant strategies and Nash equilibrium as solution concepts. It provides examples of games like the prisoner's dilemma and Cournot duopoly to illustrate these concepts. Dominant strategies make predictions easy, while Nash equilibrium is a stable prediction where no player wants to deviate given what others do. Some games have multiple Nash equilibria.
I am Irene M. I am a Computer Network Assignments Expert at computernetworkassignmenthelp.com. I hold a Master's in Computer Science from, the California University of Technology. I have been helping students with their assignments for the past 10 years. I solve assignments related to the Computer Network.
Visit computernetworkassignmenthelp.com or email support@computernetworkassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with the Computer Network Assignments.
Game theory is the study of strategic decision making between two or more players under conditions of conflict or competition. A game involves players following a set of rules and receiving payoffs depending on the strategies chosen. Strategies include pure strategies that always select a particular action and mixed strategies that randomly select among pure strategies. The optimal strategies are those that maximize the minimum payoff for one player and minimize the maximum payoff for the other player. When the maximin and minimax values are equal, there is a saddle point representing the optimal strategies for both players.
The document discusses game theory concepts like probability, expected value, and strategies for 2x2 zero-sum games. It introduces roulette as a probability experiment and defines related terms. Expected value is explained using roulette bets, showing the "house edge" gives casinos an advantage. Card counting strategies for blackjack are outlined to shift the edge to the player. Optimal strategies like counterstrategies and the maximin theorem are presented for 2x2 zero-sum games.
Does any player have a dominated strategy?
If there is a dominated strategy, eliminate it.
If there is no dominant or dominated strategy, the game is unsolved by dominance. We need to look at other solution concepts like Nash equilibrium.
This document summarizes the key findings of a research paper on the frequency of convergent games under best-response dynamics. The paper shows that:
1) The frequency of randomly generated games with a unique pure strategy Nash equilibrium goes to zero as the number of players or strategies increases.
2) Convergent games with fewer pure strategy Nash equilibria are more common than those with more equilibria.
3) For 2-player games with less than 10 strategies, games with a unique equilibrium are most common, but games with multiple equilibria are more likely for more than 10 strategies.
Game theory is the mathematical modeling of strategic decision making between rational actors. Standard game theory assumes all players are rational and have complete information. However, criticisms note real world games are complex and player rationality is not guaranteed. The Prisoner's Dilemma shows the Nash equilibrium is not necessarily Pareto optimal. In the Battle of the Sexes, there are multiple Nash equilibria but the outcome cannot be predicted with certainty as it is a one-shot, non-sequential game. Iterated elimination of strictly dominated strategies is one method to solve games by removing irrational strategies.
This document provides an overview of game theory. It defines game theory as the study of how people interact and make decisions in strategic situations, using mathematical models. It discusses the history and key concepts of game theory, including players, strategies, payoffs, assumptions of rationality and perfect information. It provides examples of zero-sum and non-zero-sum games like the Prisoner's Dilemma. The document also outlines the key elements of a game and different types of game theory, and discusses applications in economics, computer science, military strategy, biology and other fields.
This document provides an overview of game theory concepts. It defines key terms like games, strategies, payoffs, optimal strategies, and payoff matrices. It discusses different types of games including zero-sum games, positive-sum games, negative-sum games, games with dominant strategies, and Nash equilibria. Specific examples analyzed include the prisoners' dilemma, the battle of the sexes, and mixed strategy equilibria. Repeated games and how they can be used to enforce cartels are also covered. The document concludes with a discussion of sequential games and how they relate to entry deterrence strategies by incumbent firms.
This document provides an overview of game theory. It defines game theory as the study of how people interact and make decisions strategically, taking into account that each person's actions impact others. It discusses the history and key concepts of game theory, including players, strategies, payoffs, assumptions of rationality and perfect information. It provides examples of zero-sum and non-zero-sum games like the Prisoner's Dilemma. The document is intended to introduce game theory and its basic elements.
1. The document provides a brief introduction to the basics of game theory, covering fundamental concepts such as normal form games, dominant strategies, and Nash equilibrium.
2. It uses the prisoners' dilemma game and a Cournot duopoly game to illustrate the concept of a normal form game and dominant strategies. Both games have a unique Nash equilibrium where both players defect.
3. Nash equilibrium is defined as a profile of strategies where each player's strategy is a best response to the other players' strategies. An example advertising game is presented to illustrate the concept of Nash equilibrium.
1) The document introduces game theory and describes the key components of games, including players, options/moves, outcomes, and payoffs.
2) Games can be described verbally, through a matrix (table), or a decision tree diagram. Matrices are best for simultaneous games while trees are used for sequential games.
3) A dominant strategy is one that always leads to the highest payoff regardless of the opponent's choice. The Prisoner's Dilemma game is discussed as an example where both players have a dominant strategy.
The document discusses Nash equilibrium, which is a solution concept in game theory where each player is making the best response given the other players' strategies. It provides an example of a simple game between two players, Tom and Sam, where choosing strategy A is the Nash equilibrium since neither player has an incentive to deviate. Mixed strategies are introduced where players randomize between different actions. An example game is used to illustrate finding the Nash equilibrium using mixed strategies. The document also discusses properties of Nash equilibria, including that every matrix game has at least one Nash equilibrium.
I am Tim D. I am a Computer Network Assignment Expert at computernetworkassignmenthelp.com. I hold a Master's in Computer Science from, West Virginia University, USA. I have been helping students with their assignments for the past 15 years. I solve assignments related to the Computer Network.
Visit computernetworkassignmenthelp.com or email support@computernetworkassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with the Computer Network Assignment.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
2. What is Algorithmic Game Theory?
Algorithmic game theory (AGT) is essentially the study of
game theory augmented with problems, assumptions, and
techniques from theoretical computer science, particularly
algorithm design and complexity theory.
I assume we all know a bit about theoretical computer science,
but not about game theory, so we’ll do a quick review.
3. What is Game Theory?
Game theory in the way we understand it today, as a
mathematical object, was originally put forth as a candidate
for a rigorous theory of microeconomics by Von Neumann and
Morgenstern [2] in the early 20th century.
In this work, a description of the field and an argument for the
validity of the underlying assumptions is made.
The sort of games we will consider in this talk are called
simultaneous move games.
4. Simultaneous Move Games
A simultanous move game (from now on referred to just as a
game) needs the following ingredients:
A set P = {1, 2, ..., n} of players
For any player i ∈ P a set Si of strategies
For each i ∈ P, a way to assess your cost or utility given a
choice of strategy from every other player,
ui , ci : i∈P Si → R, ui = −ci .
For my sanity, we’ll say S = i∈P Si . To play such a game, each
player chooses si ∈ Si and thus we have some s ∈ S which
comprises each of these choices. Each i ∈ P then receive their
outcomes ui (s) and the game is over.
5. Representing Games
To represent a game we’ll usually think of the utility function
written out in tabular form or given succinctly. In most of the
games one wants to consider, the tabular form will be quite
prohibitive, and so doing analysis on the computational properties
of these games when they’re represented succinctly is quite
important. In the example in the next slide, we’ll see a game which
is much more naturally represented succinctly.
6. Example: Environmental Policy Game
We define a game where P is interpreted as a set of countries and
∀i ∈ P, Si = {L, N}. We will say that if a country chooses L they
will draft legislation to fight pollution, which will cost them $3,
and if they do not, they add $1 in cost to each other country,
including themselves. Then the cost for player i is
ci (s) = 3[si = L] + k∈P[sk = N], where [p] = 1 if p is true and
[p] = 0 if p is false. The first question we will ask is how we
believe these players will play this game, but first we need to
introduce our first solution concept.
7. Dominant Strategies and their Solutions
If you’re playing a game as I’ve described them, and you are
thinking between strategies si and si , and you know that for any
strategy vectors s, s , containing si , si respectively but otherwise
completely identical, ui (s) ≥ ui (s ), then you should certainly
choose the strategy si over si . We will say that si dominates si . A
dominant strategy solution is then one in which ∀i ∈ P, si
dominates every single other strategy si which i has. Let’s now
consider our example again in this light.
8. Example: Environmental Policy Game
I claim that for any player i, N dominates L. How can we see this?
Well, lets assume that the rest of the players’ strategies are fixed
as s−i . We have the cost of i, ci (s) = 3[si = L] + k∈P[sk = N]
and thus:
ci = 3[si = L] + [si = L] +
k∈P−{i}
[sk = N]
If si = L, we pay $2 more than otherwise, so clearly N dominates L
for every given player. This means that we have a dominant
strategy solution s where ∀i ∈ P, si = N. Its also easy to see that
it is unique. That being said, this also maximizes the collective
cost for |P| > 2, so maybe this game wasn’t the best one to use
this solution concept for.
9. Nash Equilibria
Though we’ve first discussed dominant strategy solutions, it should
be clear that not all games will have these. The next solution
concept we will discuss is called the Nash equilibrium. We will say
that a strategy s ∈ S is a Nash equilibrium if:
∀p ∈ P, sp ∈ Sp, up(sp, s−p) ≥ up(sp, s−p)
This means that, given that everybody else behaves as they have
chosen, there is nothing better that anyone can do.
10. Example: Coordination Game
The simplest game which lacks dominant strategies but has Nash
equilibria is called the coordination game. We imagine two friends,
Lucille and Markus, are trying to decide on their plans for the
night. Lucille wants to spend the night at the house, and we’ll say
her utility will be 1 in this case, as she is being fully satisfied,
whereas Markus wants to spend the night at a party and will
receive 1 utility in this case. If either of them end up at the place
they don’t like, then they will be quite sad and receive 1
2 utility,
but they will receive 0 if they don’t end up together, as they really
want to spend some time together. If we give this a little thought,
it’s clear that there are two Nash equilibria, one where they go to
the party and another where they spend the night at the house.
11. Mixed Nash Equilibria
Sadly, there will as well be games wherein there are no Nash
equilibria, and for this we need to allow the players to randomize
and instead of each choosing a strategy, they will sample a
strategy from some distribution of their choice, which will be what
we call their mixed strategy. We will say a vector of such solutions
m ∈ MS = p∈P Dist(Sp) is a mixed Nash equilibrium if the
expected cost of each player given their distribution is as good as it
could be given that the other players stay the same. Formally, we
have: ∀p ∈ P, mp ∈ Dist(Sp), Esp∼mp,s−p∼m−p up(sp, s−p) ≥
Esp∼mp,s−p∼m−p up(sp, s−p).
12. Example: Keeper and Shooter
Imagine that Shooter is running up to the net and can either shoot
in the left or right side of the goal, while Keeper is standing there
about to dive left or right. If Shooter shoots left and Keeper dives
left, then Shooter gets 1 utility and Keeper −1. If Shooter shoots
left and Keeper dives right, then Keeper will block the goal and
they will each receive 0 utility. We can represent this game as the
following bimatrix:
left right
left -1/1 0/0
right 0/0 -1/1
13. Example: Keeper and Shooter
Giving this a little bit of thought, there are no pure Nash equilibria.
On the other hand, in what scenarios will both of these players not
want to change? Well, I bet it is clear by the symmetry of this
situation that they should each choose their strategies totally
randomly, i.e. by choosing left with probability 1
2 as well as right.
Let’s do some thinking together to show how to prove that this is
the unique mixed Nash equilibrium. We will find that if Shooter is
working with this strategy, Keeper will be okay with doing
anything, and otherwise they will bias towards one side, and the
same will be true for Shooter. Thus, we know that the unique
mixed Nash equilibrium is when they each choose uniformly at
random.
14. Mechanism Design
Mechanism design is the art/science of manipulating the
mechanics of situations which actors are in so that we achieve
some desired outcome. Our first experiment in this domain will be
in designing auctions to meet various criteria.
15. Single Item Auction
The single item auction (in a single parameter environment) is a
slight variation on a simultaneous move game wherein the
auctioneer gets to see the bidders moves moves before they make
their own. This game can be described in the following way, given
a set of bidders B and an auctioneer a, and valuations for each
player i, vi ∈ R+.
P = {a} ∪ B
Si∈P = R+
Sa = (B, R+)
ui∈B(s) = (vi − snd(sa))[fst(sa) = i]
ua(s) = snd(sa)
16. Vickrey Auction
Given a vector of bids b such that ∀i ∈ B, bi = si , the strategy
that the auctioneer takes is to choose the highest bidder and
charge him the second highest bidder’s bid. I claim that this
auction maximizes what is called the social surplus for the bidders
if they play their dominant strategies, that in this situation it is a
dominant strategy to bid honestly, and that no honest player will
ever receive negative utility. First let’s show the latter.
17. Truth Telling Gives Nonnegative Utility
Assume that bi = vi and bj=i is set arbitrarily. Then if i loses, this
is clearly true, as the utility is 0. If i wins, then they will pay
maxj=i bj , which by definition must be less than or equal to vi , and
thus i’s utility will be positive or 0 if there was a tie.
18. Truth Telling in the Vickrey Auction is a Dominant
Strategy
We fix some unknown vector of bids b and assume that bi = vi for
some player i ∈ P. If bi = maxj∈Bbj , then the utility received by
player i is this valuation vi minus second highest bidder’s bid, let’s
say bk. Otherwise the utility received is 0. If the utility is 0, then
by decreasing their bid, their utility cannot increase because they
already are not the max. By increasing their bid to the point where
they win, their utility becomes negative, as the second price will
still be higher than their valuation as it will be the former first
price. Assuming that the utility is vi − bk, increasing bi will not do
anything for you, as this utility is independent of bi . Decreasing it,
on the other hand, will risk loss, as if you accidentally go below the
second highest bid suddenly they win and your utility goes from
some positive number to 0.
19. The Vickrey Auction Maximizes Social Surplus
The social surplus in such an auction is defined as i∈B vi xi ,
where xi is an indicator being 1 if i won and 0 if i lost. Clearly this
auction maximizes this quantity given that players play their
dominant strategies.
20. What Does the Vickrey Auction Give Us?
1. Dominant Strategy Incentive Compatibility (DSIC)
2. Maximizes the Social Surplus
3. Implementable in Polynomial Time
The question we will now touch upon is: when can we (as the
auctioneer) get these nice properties for other sorts of auctions?
21. Proposed Methodology
1. Create a polynomial time algorithm for maximizing the social
surplus, assuming truthful bidders
2. Given what we’ve done in step 1, how do we assign payments
to make our assumption true
We will flesh this methodology out for a set of situations called
single parameter environments.
22. Single Parameter Environments
We still have our set of bidders B and we’ll say that n = |B|. Each
player i ∈ B has a valuation vi ∈ R+ which is the only thing we
don’t know about the bidder as the auctioneer. We also have a set
of feasible allocations X which the auctioneer can choose, where
X ⊆ Rn.
23. Sealed Bid Auctions
1. Collect the bids b ∈ Rn
2. Choose x(b) ∈ X where xi (b) is allocated to bidder i
3. Choose p(b) ∈ Rn where pi (b) is what bidder i pays
ui (b) = vi xi (b) − pi (b)
24. Implementable Allocation Rules
Given a single parameter environment for a sealed bid auction, an
allocation rule x : Rn → X is called implementable if
∃p : Rn → Rn, a payment rule, such that x and p together make
our sealed bid auction DSIC. Clearly to use our methodology, we
can only use implementable allocation functions.
25. Monotone Allocation Rules
An allocation rule x is monotone if ∀i ∈ B, b−i , xi (bi , b−i ) is
monotone increasing in bi .
26. Myerson’s Lemma [7]
1. x monotone ⇔ x implementable
2. Assuming bi = 0 ⇒ pi (b) = 0, x monotone ⇒ ∃!p which is
explicit and makes x and p DSIC
27. Proof of Myerson’s Lemma [3]
Given some x, , we’ll try to understand what p must look like in
order for x, p to be a DSIC mechanism. We fix i, b−i . Assuming
that we have such a p, we will take two values 0 ≤ z ≤ y, and
imagine two scenarios:
1. z is i’s true value but they are scheming to bid y.
2. y is i’s true value but they are scheming to bid z.
28. x implementable ⇒ x monotone
I will invoke the DSIC constraint on each of these scenarios, writing
x(z) = xi (z, b−i ), p(z) = pi (z, b−i ) and see what I can retrieve.
1. zx(z) − p(z) ≥ zx(y) − p(y)
2. yx(y) − p(y) ≥ yx(z) − p(z)
From these two relationships, we can write
z(x(y) − x(z)) ≤ p(y) − p(z) ≤ y(x(y) − x(z))
If x not monotone, ∃0 ≤ z < y, x(z) ≥ x(y). We thus have that
−q = x(y) − x(z) < 0 and we have that −qz ≤ −qy ⇒ z ≥ y,
which is a contradiction. Thus we have shown that x
implementable ⇒ x monotone.
29. x monotone, piecewise constant ⇒ x implementable
Assuming x monotone, piecewise constant, we take the limit
y → z from above of our inequality and realize that, if there is a
jump of magnitude h at z, the left and the right hand sides both
tend to zh. This means that for any z, the increment of p at z is
equal to z times the jump in x at z. Assuming p(0) = 0, we can
see that we have an explicit payment function
pi (bi , b−i ) =
l−1
j=0
zj · jump(zj )
where zi∈[l] are the breakpoints of our piecewise constant function
xi (·, b−i ) in the range [0, bi ] and jump(zi ) are the associated
jumps.
30. x monotone, differentiable ⇒ x implementable
Assuming x is monotone and differentiable, we take that same
inequality and divide it by y − z:
z(x(y) − x(z))
y − z
≤
p(y) − p(z)
y − z
≤
y(x(y) − x(z))
y − z
. Taking the limit as y → z, it is clear by the definition of the
derivative that p (z) = zx (z). Using our assumption that
p(0) = 0, we have that:
pi (bi , b−i ) =
bi
0
z
d
dz
xi (z, b−i )dz
31. Vickrey Auction from Myerson
Given the former discussion, we can now derive the Vickrey auction
easily. We decide we want to allocate our item to the highest
bidder and the jump takes place at the second to highest bidder
and is of magnitude 1 so the payment is that of the second highest
bidder.
32. Nash’s Existence Theorem and a Detour
John Nash showed in his thesis that for any finite game, there exist
mixed Nash equilibria. The obvious question for us to tackle here
is: given such a game, can we compute a mixed Nash equilibrium
efficiently? If not, more generally, how can we capture the
complexity of this problem?
33. Capturing the Complexity of Problems like Nash
This question turns out to be very interesting and ends up inspiring
an approach to capturing the complexity of search problems like
Nash which are guaranteed to have a solution. Generally, when one
has an existence proof for some class of objects and one would like
to compute them, they might go to the proof and try to extract an
algorithm which will allow one to compute the existent object
efficiently. In the case of computing mixed Nash equilibria however,
it seems that the known proofs do not yield efficient algorithms.
34. Why Might These Problems Be Hard?
Normally in TCS, when one runs up against a seemingly hard
problem, they would like to understand why it is so. To do this, we
rely upon the paradigms of reductions and completeness. Let’s
say we were to try and reduce SAT to Nash. Well, we’d try to
produce a game such that there is a Nash equilibrium if and only if
the formula is satisfiable. On the other hand, Nash’s theorem tells
us that if such a reduction were possible, then every formula would
be satisfiable, which of course is not true. Clearly SAT does not
reduce to Nash at least in the way described.
35. FNP
We say that a relation R ⊂ (Σ∗)2 is polynomially balanced if
∃c ∈ N, ∀(x, y) ∈ R, |x|c ≥ |y|. We define FNP to be the class of
relations which are polynomially balanced and that are
recognizable in polynomial time.
36. TFNP
We define TFNP = {R ∈ FNP | ∀x∃y, xRy}. This class is clearly
extremely related to F(NP ∩ coNP), which is the class of search
problems which involve two relations R1, R2 which are polynomially
balanced such that ∀x, ∃y, (x, 1y) ∈ R1 ∨ (x, 2y) ∈ R2. If we have
R ∈ TFNP, clearly R1 = R, R2 = ∅ is in F(NP ∩ coNP) and if
R1, R2 are in F(NP ∩ coNP) then we have R = R1 ∪ R2 clearly in
TFNP [5].
37. TFNP Reductions
Say we have relations R, S ∈ TFNP and we want to reduce R ≤ S.
Given any x, our goal in the search problem defined by R is to find
y such that (x, y) ∈ R. To do this using S, we can map input x to
rx (x), find y such that (rx (x), y ) ∈ S, and then map this solution
back such that (x, ry (x, y )) ∈ R. Thus each reduction is witnessed
by two maps, one mapping the input space of R to the input space
of S, and the other mapping the output space of S to the output
space of R.
38. TFNP Reductions
Given one of these reductions (rx , ry ) : R ≤ S, if we did not know
prior to having this reduction that R ∈ TFNP, but we did know
that S ∈ TFNP, then this reduction completes a proof that
R ∈ TFNP. Thus, whatever proof methods we used to show that
S ∈ TFNP, as well as whatever was used to show the correctness
of the reduction, will be that which was used to show that
R ∈ TFNP.
39. Complete Problems in TFNP
As of yet, no complete problems have been found for TFNP,
essentially for the reason that there is no known recursive
enumeration of the machines which solve these problems. For this
reason, we are forced to construct subclasses of TFNP which do
have complete problems. About a week ago, I would have gone on
to tell you about PPAD, the class of relations which finding Nash
equilibria is complete for, but because of a new paper on April 6th,
I think there are more interesting avenues to go down. I’ve
included the PPA, PPAD slides but I will skip them.
40. PPA
All problems in PPA are defined in the following way, given some
polytime deterministic machine M: given an input x, for any string
in the configuration space c ∈ C(x) = Σ[p(|x|)], where p is some
polynomial, M outputs in time O(p(n)) a set of at most two
configurations. We say that c, c are neighbors ({c, c } ∈ G(x)) if
c ∈ M(x, c ) ∧ c ∈ M(x, c). This generates a symmetric graph of
degree at most 2. We define our machines M in such a way that
M(x, 0...0) = {1...1} and 0...0 ∈ M(x, 1...1), so 0...0 is always a
leaf which we will call the standard leaf. Our question is to find a
leaf which is nonstandard [6].
41. PPAD
We define PPAD in a very similar way, except that
(c, c ) ∈ G(x) ≡ M(x, c) = ( , c ) ∧ M(x, c ) = (c, ).
42. Towards a Unified Theory of Total Functions [4]
In a recent paper by Goldberg and Papadimitriou, there is a
unification of the known syntactic subclasses of TFNP into the
class PTFNP. The classes in particular which are contained inside
of it are listed below with the lemmas which embody them:
PPP: f : [n] → [n − 1] has at least one collision
PPAD: G with one unbalanced node has another
PPADS: same as PPAD, looking for oppositely unbalanced
node
PPA: G with one vertex of odd degree has another
PLS: every DAG G has a sink
The new class PTFNP embodies the fact that in a consistent
proof system, if you have a proof where the last two lines are A,
¬A, then there is some error somewhere in the proof.
43. A Connection with Model Theory
There is an interesting way that each of the lemmas which embody
these classes are connected: they are true in finite models but not
infinite. Given that we believe there are hard problems in each of
these classes, this makes sense, as by Herbrand’s theorem, any
existential sentence which is true in all models is a finite
disjunction of quantifier free formulas and thus the related search
problem will be in P.
44. PTFNP
We define the problem Wrong Proof to be one wherein we receive
an succinctly represented proof in a propositional proof system
similar to extended Frege but with the ability to define new
function symbols which, in the last two lines, proves A and ¬A.
Given that our theory is consistent, we then want to find where the
misstep in the proof takes place, which is clearly verifiable in
polynomial time but not clearly discoverable. The class PTFNP is
the set of problems which are reducible to Wrong Proof . In the
new paper, we find that:
PPAD, PPA, PPADS, PLS, PPP ⊆ PTFNP
45. Sources I
N. Nisan. T. Roughgarden. E. Tardos. V. Vazirani.
Algorithmic Game Theory
J. Von Neumann. O. Morgenstern.
Theory of Games and Economic Behavior
T. Roughgarden.
CS364A: Algorithmic Game Theory (Fall 2013)
P. Goldberg. C. Papadimitriou.
Towards a Unified Theory of Total Functions
C. Papadimitriou. N. Megiddo.
A Note on Total Functions, Existence Theorems, and
Computational Complexity
C. Papadimitriou.
On the Complexity of the Parity Argument and Other
Inefficient Proofs of Existence