Game Balance Example Game: Rock-Paper-Scissors
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A History of Von Neumann and Morgenstern's Theory of Games, 12Pt Escolha Sob Incerteza Ax
Marcos Thiago Graciani Axiomatic choice under uncertainty: a history of von Neumann and Morgenstern’s Theory of Games Escolha sob incerteza axiomática: uma história do Theory of Games de von Neumann e Morgenstern São Paulo 2019 Prof. Dr. Vahan Agopyan Reitor da Universidade de São Paulo Prof. Dr. Fábio Frezatti Diretor da Faculdade de Economia, Administração e Contabilidade Prof. Dr. José Carlos de Souza Santos Chefe do Departamento de Economia Prof. Dr. Ariaster Baumgratz Chimeli Coordenador do Programa de Pós-Graduação em Economia Marcos Thiago Graciani Axiomatic choice under uncertainty: a history of von Neumann and Morgenstern’s Theory of Games Escolha sob incerteza axiomática: uma história do Theory of Games de von Neumann e Morgenstern Dissertação de Mestrado apresentada ao Departamento de Economia da Faculdade de Economia, Administração e Contabilidade da Universidade de São Paulo (FEA-USP) como requisito parcial à obtenção do título de Mestre em Ciências. Área de Concentração: Teoria Econômica. Universidade de São Paulo — USP Faculdade de Economia, Administração e Contabilidade Programa de Pós-Graduação Orientador: Pedro Garcia Duarte Versão Corrigida (A versão original está disponível na biblioteca da Faculdade de Economia, Administração e Contabilidade.) São Paulo 2019 FICHA CATALOGRÁFICA Elaborada pela Seção de Processamento Técnico do SBD/FEA com dados inseridos pelo autor. Graciani, Marcos Thiago. Axiomatic choice under uncertainty: a history of von Neumann and Morgenstern’s Theory of Games / Marcos Thiago Graciani. – São Paulo, 2019. 133p. Dissertação (Mestrado) – Universidade de São Paulo, 2019. Orientador: Pedro Garcia Duarte. 1. História da teoria dos jogos 2. Axiomática 3. Incerteza 4. Von Neumann e Morgenstern I. -
Game Theory Lecture Notes
Game Theory: Penn State Math 486 Lecture Notes Version 2.1.1 Christopher Griffin « 2010-2021 Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License With Major Contributions By: James Fan George Kesidis and Other Contributions By: Arlan Stutler Sarthak Shah Contents List of Figuresv Preface xi 1. Using These Notes xi 2. An Overview of Game Theory xi Chapter 1. Probability Theory and Games Against the House1 1. Probability1 2. Random Variables and Expected Values6 3. Conditional Probability8 4. The Monty Hall Problem 11 Chapter 2. Game Trees and Extensive Form 15 1. Graphs and Trees 15 2. Game Trees with Complete Information and No Chance 18 3. Game Trees with Incomplete Information 22 4. Games of Chance 24 5. Pay-off Functions and Equilibria 26 Chapter 3. Normal and Strategic Form Games and Matrices 37 1. Normal and Strategic Form 37 2. Strategic Form Games 38 3. Review of Basic Matrix Properties 40 4. Special Matrices and Vectors 42 5. Strategy Vectors and Matrix Games 43 Chapter 4. Saddle Points, Mixed Strategies and the Minimax Theorem 45 1. Saddle Points 45 2. Zero-Sum Games without Saddle Points 48 3. Mixed Strategies 50 4. Mixed Strategies in Matrix Games 53 5. Dominated Strategies and Nash Equilibria 54 6. The Minimax Theorem 59 7. Finding Nash Equilibria in Simple Games 64 8. A Note on Nash Equilibria in General 66 Chapter 5. An Introduction to Optimization and the Karush-Kuhn-Tucker Conditions 69 1. A General Maximization Formulation 70 2. Some Geometry for Optimization 72 3. -
Flexible Games by Which I Mean Digital Game Systems That Can Accommodate Rule-Changing and Rule-Bending
Let’s Play Our Way: Designing Flexibility into Card Game Systems Gifford Cheung A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2013 Reading Committee: David Hendry, Chair David McDonald Nicolas Ducheneaut Jennifer Turns Program Authorized to Offer Degree: Information School ©Copyright 2013 Gifford Cheung 2 University of Washington Abstract Let’s Play Our Way: Designing Flexibility into Card Game Systems Gifford Cheung Chair of the Supervisory Committee: Associate Professor David Hendry Information School In this dissertation, I explore the idea of designing “flexible game systems”. A flexible game system allows players (not software designers) to decide on what rules to enforce, who enforces them, and when. I explore this in the context of digital card games and introduce two design strategies for promoting flexibility. The first strategy is “robustness”. When players want to change the rules of a game, a robust system is able to resist extreme breakdowns that the new rule would provoke. The second is “versatility”. A versatile system can accommodate multiple use-scenarios and can support them very well. To investigate these concepts, first, I engage in reflective design inquiry through the design and implementation of Card Board, a highly flexible digital card game system. Second, via a user study of Card Board, I analyze how players negotiate the rules of play, take ownership of the game experience, and communicate in the course of play. Through a thematic and grounded qualitative analysis, I derive rich descriptions of negotiation, play, and communication. I offer contributions that include criteria for flexibility with sub-principles of robustness and versatility, design recommendations for flexible systems, 3 novel dimensions of design for gameplay and communications, and rich description of game play and rule-negotiation over flexible systems. -
CS599: Algorithm Design in Strategic Settings Fall 2012 Lecture 2: Game Theory Preliminaries
CS599: Algorithm Design in Strategic Settings Fall 2012 Lecture 2: Game Theory Preliminaries Instructor: Shaddin Dughmi Administrivia Website: http://www-bcf.usc.edu/~shaddin/cs599fa12 Or go to www.cs.usc.edu/people/shaddin and follow link Emails? Registration Outline 1 Games of Complete Information 2 Games of Incomplete Information Prior-free Games Bayesian Games Outline 1 Games of Complete Information 2 Games of Incomplete Information Prior-free Games Bayesian Games Example: Rock, Paper, Scissors Figure: Rock, Paper, Scissors Games of Complete Information 2/23 Rock, Paper, Scissors is an example of the most basic type of game. Simultaneous move, complete information games Players act simultaneously Each player incurs a utility, determined only by the players’ (joint) actions. Equivalently, player actions determine “state of the world” or “outcome of the game”. The payoff structure of the game, i.e. the map from action vectors to utility vectors, is common knowledge Games of Complete Information 3/23 Typically thought of as an n-dimensional matrix, indexed by a 2 A, with entry (u1(a); : : : ; un(a)). Also useful for representing more general games, like sequential and incomplete information games, but is less natural there. Figure: Generic Normal Form Matrix Standard mathematical representation of such games: Normal Form A game in normal form is a tuple (N; A; u), where N is a finite set of players. Denote n = jNj and N = f1; : : : ; ng. A = A1 × :::An, where Ai is the set of actions of player i. Each ~a = (a1; : : : ; an) 2 A is called an action profile. u = (u1; : : : un), where ui : A ! R is the utility function of player i. -
Introduction to Probability
Massachusetts Institute of Technology Course Notes 10 6.042J/18.062J, Fall ’02: Mathematics for Computer Science November 4 Professor Albert Meyer and Dr. Radhika Nagpal revised November 6, 2002, 572 minutes Introduction to Probability 1 Probability Probability will be the topic for the rest of the term. Probability is one of the most important sub- jects in Mathematics and Computer Science. Most upper level Computer Science courses require probability in some form, especially in analysis of algorithms and data structures, but also in infor- mation theory, cryptography, control and systems theory, network design, artificial intelligence, and game theory. Probability also plays a key role in fields such as Physics, Biology, Economics and Medicine. There is a close relationship between Counting/Combinatorics and Probability. In many cases, the probability of an event is simply the fraction of possible outcomes that make up the event. So many of the rules we developed for finding the cardinality of finite sets carry over to Proba- bility Theory. For example, we’ll apply an Inclusion-Exclusion principle for probabilities in some examples below. In principle, probability boils down to a few simple rules, but it remains a tricky subject because these rules often lead unintuitive conclusions. Using “common sense” reasoning about probabilis- tic questions is notoriously unreliable, as we’ll illustrate with many real-life examples. This reading is longer than usual . To keep things in bounds, several sections with illustrative examples that do not introduce new concepts are marked “[Optional].” You should read these sections selectively, choosing those where you’re unsure about some idea and think another ex- ample would be helpful. -
Game Design 2 Game Balance
CE810 - Game Design 2 Game Balance Joseph Walton-Rivers & Piers Williams Friday, 18 May 2018 University of Essex 1 What is Balance? Game Balance Question What is balance? 2 Game Balance “All players have an equal chance of winning” – Richard Bartle Richard covered a combat example in the first part of the module. 3 On Strategies Game Balance • What about higher level strategies? • Zerg rush? • Dominant strategies • Metagaming 4 Metagaming - Rock Paper Scissors • A beats B, B beats C, C beats A • If there are lots of A players, people will play C • Then there are a lot of C players, so people play B • and so on... 5 Metagaming - Dominant Strategies • What if A is significantly stronger? • No one will use the other two strategies • We want to encourage variety in play 6 Can we detect this? • Can we detect strategies which are overpowered? • Try to punish strategies we don’t want to see • We did this earlier in the week with rotate and shoot! • Can we measure this? 7 Automated Game Tuning • Academics seem to think so... • Ryan Leigh et al (2008) - Co-evolution for game balancing • Alexander Jaffe et al (2012) - Restricted-Play balance framework • Mihail Morosan - GAs for tuning parameters 8 Game Curves First Move Advantage First Move Advantage • Typically affects turn based games • Going first in tac tac toe means either a win or adraw • White has > 50% win rate over all games • Worse effects if you have resources • We need a way of dealing with this 9 First Move Advantage Magic Second player gets an extra card Go Second player gets 7.5 bonus -
Cgcopyright 2013 Alexander Jaffe
c Copyright 2013 Alexander Jaffe Understanding Game Balance with Quantitative Methods Alexander Jaffe A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2013 Reading Committee: James R. Lee, Chair Zoran Popovi´c,Chair Anna Karlin Program Authorized to Offer Degree: UW Computer Science & Engineering University of Washington Abstract Understanding Game Balance with Quantitative Methods Alexander Jaffe Co-Chairs of the Supervisory Committee: Professor James R. Lee CSE Professor Zoran Popovi´c CSE Game balancing is the fine-tuning phase in which a functioning game is adjusted to be deep, fair, and interesting. Balancing is difficult and time-consuming, as designers must repeatedly tweak parameters and run lengthy playtests to evaluate the effects of these changes. Only recently has computer science played a role in balancing, through quantitative balance analysis. Such methods take two forms: analytics for repositories of real gameplay, and the study of simulated players. In this work I rectify a deficiency of prior work: largely ignoring the players themselves. I argue that variety among players is the main source of depth in many games, and that analysis should be contextualized by the behavioral properties of players. Concretely, I present a formalization of diverse forms of game balance. This formulation, called `restricted play', reveals the connection between balancing concerns, by effectively reducing them to the fairness of games with restricted players. Using restricted play as a foundation, I contribute four novel methods of quantitative balance analysis. I first show how game balance be estimated without players, using sim- ulated agents under algorithmic restrictions. -
Game Balancing
Game Balancing CS 2501 – Intro to Game Programming and Design Credit: Some slide material courtesy Walker White (Cornell) CS 2501 Dungeons and Dragons • D&D is a fantasy roll playing system • Dungeon Masters run (and someCmes create) campaigns for players to experience • These campaigns have several aspects – Roll playing – Skill challenges – Encounters • We will look at some simple balancing of these aspects 2 CS 2501 ProbabiliCes of D&D • Skill Challenge • Example: The player characters (PCs) have come upon a long wall that encompasses a compound they are trying to enter • The wall is 20 feet tall and 1 foot thick • What are some ways to overcome the wall? • How hard should it be for the PCs to overcome the wall? 3 CS 2501 ProbabiliCes of D&D • How hard should it be for the PCs to overcome the wall? • We approximate this in the game world using a Difficulty Class (DC) Level Easy Moderate Hard 7 11 16 23 8 12 16 23 9 12 17 25 10 13 18 26 11 13 19 27 4 CS 2501 ProbabiliCes of D&D • Easy – not trivial, but simple; reasonable challenge for untrained character • Medium – requires training, ability, or luck • Hard – designed to test characters focused on a skill Level Easy Moderate Hard 7 11 16 23 8 12 16 23 9 12 17 25 10 13 18 26 11 13 19 27 5 CS 2501 Balancing • Balancing a game is can be quite the black art • A typical player playing a game involves intuiCon, fantasy, and luck – it’s qualitave • A game designer playing a game… it’s quanCtave – They see the systems behind the game and this can actually “ruin” the game a bit 6 CS 2501 Building Balance • General advice – Build a game for creavity’s sake first – Build a game for parCcular mechanics – Build a game for parCcular aestheCcs • Then, aer all that… – Then balance – Complexity can be added and removed if needed – Other levers can be pulled • Complexity vs. -
Chapter 16 Oligopoly and Game Theory Oligopoly Oligopoly
Chapter 16 “Game theory is the study of how people Oligopoly behave in strategic situations. By ‘strategic’ we mean a situation in which each person, when deciding what actions to take, must and consider how others might respond to that action.” Game Theory Oligopoly Oligopoly • “Oligopoly is a market structure in which only a few • “Figuring out the environment” when there are sellers offer similar or identical products.” rival firms in your market, means guessing (or • As we saw last time, oligopoly differs from the two ‘ideal’ inferring) what the rivals are doing and then cases, perfect competition and monopoly. choosing a “best response” • In the ‘ideal’ cases, the firm just has to figure out the environment (prices for the perfectly competitive firm, • This means that firms in oligopoly markets are demand curve for the monopolist) and select output to playing a ‘game’ against each other. maximize profits • To understand how they might act, we need to • An oligopolist, on the other hand, also has to figure out the understand how players play games. environment before computing the best output. • This is the role of Game Theory. Some Concepts We Will Use Strategies • Strategies • Strategies are the choices that a player is allowed • Payoffs to make. • Sequential Games •Examples: • Simultaneous Games – In game trees (sequential games), the players choose paths or branches from roots or nodes. • Best Responses – In matrix games players choose rows or columns • Equilibrium – In market games, players choose prices, or quantities, • Dominated strategies or R and D levels. • Dominant Strategies. – In Blackjack, players choose whether to stay or draw. -
Turing Learning with Nash Memory
Turing Learning with Nash Memory Machine Learning, Game Theory and Robotics Shuai Wang Master of Science Thesis University of Amsterdam Local Supervisor: Dr. Leen Torenvliet (University of Amsterdam, NL) Co-Supervisor: Dr. Frans Oliehoek (University of Liverpool, UK) External Advisor: Dr. Roderich Groß (University of Sheffield, UK) ABSTRACT Turing Learning is a method for the reverse engineering of agent behaviors. This approach was inspired by the Turing test where a machine can pass if its behaviour is indistinguishable from that of a human. Nash memory is a memory mechanism for coevolution. It guarantees monotonicity in convergence. This thesis explores the integration of such memory mechanism with Turing Learning for faster learning of agent behaviors. We employ the Enki robot simulation platform and learns the aggregation behavior of epuck robots. Our experiments indicate that using Nash memory can reduce the computation time by 35.4% and results in faster convergence for the aggregation game. In addition, we present TuringLearner, the first Turing Learning platform. Keywords: Turing Learning, Nash Memory, Game Theory, Multi-agent System. COMMITTEE MEMBER: DR.KATRIN SCHULZ (CHAIR) PROF. DR.FRANK VAN HARMELEN DR.PETER VAN EMDE BOAS DR.PETER BLOEM YFKE DULEK MSC MASTER THESIS,UNIVERSITY OF AMSTERDAM Amsterdam, August 2017 Contents 1 Introduction ....................................................7 2 Background ....................................................9 2.1 Introduction to Game Theory9 2.1.1 Players and Games................................................9 2.1.2 Strategies and Equilibria........................................... 10 2.2 Computing Nash Equilibria 11 2.2.1 Introduction to Linear Programming.................................. 11 2.2.2 Game Solving with LP............................................. 12 2.2.3 Asymmetric Games............................................... 13 2.2.4 Bimatrix Games................................................. -
The Making of a Conceptual Design for a Balancing Tool
Södertörns högskola | Institutionen för Institutionen för naturvetenskap, miljö och teknik Kandidatuppsats 15 hp | Medieteknik | höstterminen 2014 The Making of a Conceptual Design for a Balancing Tool Av: Jonas Eriksson Handledare: Inger Ekman Abstract Balancing is usually done in the later phases of creating a game to make sure everything comes together to an enjoyable experience. Most of the time balancing is done with a series of playthroughs by the designers or by outsourced play testers and the imbalances found are corrected followed by more playthroughs. This method occupies a lot of time and might therefore not find everything. In this study I use information gathered from interviews with experienced designers and designer texts along with features from methods frequently used for aiding the designers to make a conceptual design of a tool that is aimed towards simplifying the process of balancing and reducing the amount of work hours having to be spent on this phase. Keywords Balancing, Interviews, Conceptual Design, Game Development, External Tool 2 Sammanfattning Balansering görs framförallt i de senare faserna när man skapar ett spel för att se till att alla delar tillsammans skapar en bra upplevelse. För det mesta utförs balanseringen i form av upprepade speltester genomförda av antingen utvecklaren eller inhyrda testpersoner. Obalanserade saker som upptäcks korrigeras och följs sedan av ytterligare tester. Denna metod tar väldigt lång tid att utföra och på grund av detta är det inte säkert att alla fel upptäcks innan spelet lanseras. I den här studien använder jag information som insamlats från intervjuer med erfarna designers och designtexter sida vid sida med funktioner från metoder som ofta används för att underlätta för utvecklarna. -
Metagame Balance
Metagame Balance Alex Jaffe @blinkity Data Scientist, Designer, Programmer Spry Fox 1 Today I’ll be doing a deep dive into one aspect of game balance. It’s the 25-minute lightning talk version of the 800-minute ring cycle talk. I’ll be developing one big idea, so hold on tight! Balance In the fall of 2012, I worked as a technical designer, using data to help balance PlayStation All-Stars Battle Royale, Superbot and Sony Santa Monica’s four- player brawler featuring PS characters from throughout the years. Btw, the game is great, and surprisingly unique, despite its well-known resemblance to that other four-player mascot brawler, Shrek Super Slam. My job was to make sense of the telemetry we gathered and use it to help the designers balance every aspect of the game. What were the impacts of skill, stage, move selection, etc. and what could that tell us about what the game feels like to play? Balance In the fall of 2012, I worked as a technical designer, using data to help balance PlayStation All-Stars Battle Royale, Superbot and Sony Santa Monica’s four- player brawler featuring PS characters from throughout the years. Btw, the game is great, and surprisingly unique, despite its well-known resemblance to that other four-player mascot brawler, Shrek Super Slam. My job was to make sense of the telemetry we gathered and use it to help the designers balance every aspect of the game. What were the impacts of skill, stage, move selection, etc. and what could that tell us about what the game feels like to play? Character Balance There were a lot of big questions about what it’s like to play the game, but at least one pretty quantitative question was paramount: are these characters balanced with respect to one another? I.e.