Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

An Introduction to MultiAgent Systems.

Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling systems composed of autonomous, interacting agents. Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.

/pdf/tutorial-on-agent-based-modelling-and-simulation-4xgz9pwney.pdf

Tutorial on agent-based modelling and simulation

A review of computer tools for analysing the integration of renewable energy into various energy systems

The Political Aesthetic of Yeats, Eliot, and Pound

If art and science have one thing in common, it's a hunger for the new - new ideas and innovations, new ways of seeing and depicting the world. But that desire for novelty carries with it a fundamental philosophical problem: If everything has to come from something, how can anything truly new emerge? Is novelty even possible? In Novelty, Michael North takes us on a dazzling tour of more than two millennia of thinking about the problem of the new, from the puzzles of the pre-Socratics all the way up to the art world of the 1960s and '70s. The terms of the debate, North shows, were established before Plato, and have changed very little since: novelty, philosophers argued, could only arise from either recurrence or recombination. The former, found in nature's cycles of renewal, and the latter, seen most clearly in the workings of language - between them we have accounted for nearly all the ways in which novelty has been conceived of in Western history, including reformation, renaissance, invention, revolution, and even evolution. As he pursues this idea through centuries and across disciplines, North exhibits astonishing range, drawing on figures as diverse as Charles Darwin and Robert Smithson, Thomas Kuhn and Ezra Pound, Norbert Wiener and Andy Warhol, all of whom offer different ways of grappling with the idea of originality. Novelty, North demonstrates, remains a central problem of contemporary science and literature - an ever-receding target that, in its complexity and evasiveness, continues to inspire and propel the modern. A heady, ambitious intellectual feast, Novelty is rich with insight, a masterpiece of perceptive synthesis.

Novelty: A History of the New

Agent-based simulation (ABS) is a relatively recent modeling technique that is being widely used to model complex adaptive systems by many disciplines. Few full length courses exist on agent-based modeling and a standard curriculum has not yet been established, but there is considerable demand to include ABS into simulation courses. Modelers often come to agent-based simulation by way of self-study or attendance at tutorials and short courses. Although there is substantial overlap, there are many aspects of ABS that differ from discrete-event simulation (DES) and System Dynamics (SD), including applicable problem domains, disciplines and backgrounds of students, and the underpinnings of its computational implementation. These factors make ABS difficult to include as an incremental add-on to existing simulation courses. This paper reports on some approaches to teaching the modeling of complex systems and agent-based simulation that the authors have used in a range of classes and workshops.

/pdf/toward-teaching-agent-based-simulation-2qvucbyz8d.pdf

Toward teaching agent-based simulation

The most notable development in public sculpture of the last thirty years has been the disappearance of the sculpture itself. Ever since Jean Tinguely's Homage to New York destroyed itself at the Museum of Modern Art in 1960, sculptors have tried to find new ways to make the sculptural object invisible, immaterial, or remote. Where the sculpture did have some material presence, it often took unexpected forms. As Rosalind Krauss says, "Rather surprising things have come to be called sculpture: narrow corridors with TV monitors at the ends; large photographs documenting country hikes; mirrors placed at strange angles in ordinary rooms; temporary lines cut into the floor of the desert."' However various these experiments may seem, they began with a single motive: to escape the constraints of the pedestal, the gallery, and finally of art itself. To prevent this new work from becoming just another commodity in the market, artists either produced works so intangible or remote they could not be bought and sold, or disseminated their ideas in so many reproducible forms they could not be

https://www.jstor.org/stable/info/1343772

The Public as Sculpture: From Heavenly City to Mass Ornament

Complex Adaptive Systems (CASs) can be applied to investigate complex infrastructures and infrastructure interdependencies. Agent-based modeling (ABM) is a new CAS-based approach to the construction of models. The CAS agents within the Spot Market Agent Research Tool (SMART) and Flexible Agent Simulation Toolkit (FAST) ABMs allow investigation of the electric power infrastructure, the natural gas infrastructure, and their interdependencies. The Swarm-based SMART models use sets of agents and interconnections to represent electric power and natural gas systems. A prototype virtual reality (VR) interface has also been constructed for a version of the SMART model. This tool is intended to explore the use of advanced interactive three-dimensional visualization in agent-based modeling. The Java-based FAST model is currently under construction. FAST is a complete redesign of the SMART models that includes improvements in the modeling environment, model detail, and representational fidelity. Developing ABMs is difficult but can be rewarding.

Michael North

Papers

The Political Aesthetic of Yeats, Eliot, and Pound

Novelty: A History of the New

Toward teaching agent-based simulation

The Public as Sculpture: From Heavenly City to Mass Ornament

Toward Strength and Stability Agent-Based Modeling of Infrastructure Markets