Chapter 3 presents models in which artificial agents interact through games. It first introduces a typical mainstream economics model known as the prisoner’s dilemma, in which agents interact through a classic game theory game, and then contrast it with an artificial evolutionary game based on the same dilemma. In this evolutionary game, the dynamic evolution of a population of boundedly rational agents is represented and simulated using a genetic algorithm. It finally contrasts the assumptions of artificial economics against those of mainstream economics when modeling games.

There are various paradigms and programming methods suitable for different types of applications. The programming and simulation of an artificial economy can be carried out with standard methods, or through object-oriented programming, which is a very structured way of constructing computational objects, which are encapsulated in themselves, but can interact with other computational objects.

Chapter 8 presents the main positions in economics and in the social sciences regarding the agent/structure problem, and explores some contributions that can be made from artificial economics. First, it presents and discusses the individualist/reductionist, structuralist/holistic, and intermediate positions, regarding the agent/structure problem. Then presents simple artificial economics examples of the generation of endogenous preferences, agents' behavioral changes derived from their economic interaction, and of the demographic effects of the introduction of a market institution into an artificial economy.

Chapter 5 presents the subject matter of artificial intelligence, focusing on machine learning, where these machines are artificial agents. It presents simple examples of unsupervised learning, supervised learning, and reinforcement learning, and introduces notions of dynamic programming, Q-learning, and stochastic control. After that, it explores some links that can be established between artificial intelligence and the philosophy of mind, presenting and discussing the Turing test, the philosophical approaches of eliminativism and functionalism, and the problem of tacit knowledge.

Chapter 2 focuses on market interactions between artificial agents. First, it introduces two versions of the famous Sugarscape model, which is an excellent model for getting into artificial economics. In the first version, artificial agents are very simple and dedicated, in an environment in which there is only one resource called sugar, to collect and consume that resource to meet their metabolic needs. In the second version, the environment provides two resources, sugar and spice. And the artificial agents are more sophisticated, as they are not only dedicated to collecting and consuming such resources but also engage in market exchanges of them. Finally, it presents a static and a dynamic general equilibrium model of market economies, typical of mainstream economics, in a way that illustrates by contrast the assumptions of artificial economics versus the ones of mainstream economics when modeling markets.

Chapter 6 discusses the topic of artificial evolution. First, it introduces the concept of extrinsic adaptation and illustrates it through simple examples of genetic algorithms. It then introduces the concept of intrinsic adaptation and illustrates it through examples of evolution in artificial markets. Finally, introduces some basic concepts of the science of evolution related to genetic dynamic and natural selection, and presents and discusses the scope and limitations of the uses of evolutionary theory concepts and method in economics.

Chapter 4 focuses in the methodological and instrumental contrasts between artificial economics and mainstream economics. It discusses the mathematical methods of mainstream economics (centered around the use of optimization methods and systems of equations representations) versus the computational methods of artificial economics (characterized by the use of algorithms, software, and computer hardware). Presents basic notions on algorithms, recursion, and Turing machines. And discusses the methodological and instrumental differences between artificial economics and mainstream economics as derived from differences between classical mathematics and constructive mathematics.

Chapter 1 looks at how historically the concept of economic agent developed within mainstream economics, and how the concept of artificial agent emerged while cognitive science became the successor of behaviorism. Then, considering that artificial economics tries to build realistic models of artificial agents, it introduces the main models of mental architectures that derive from cognitive science, and some recent advances in neuroscience (specially within neuroeconomics, social neuroscience, and neurosociology) that relate directly to the economic and social behavior of individuals. Finally, it reviews some models and approaches that try to capture the cognitive, neurological, emotional, and social aspects of agents in an integrated way.

Chapter 7 introduces the subject matter of artificial complexity. First, it presents examples of artificial complexity by means of cellular automata. It presents one-dimensional cellular automata following Wolfram’s rules, and a two-dimensional cellular automaton in the form of a spatial evolutionary game.Then it introduces the concepts of complexity and emergence, as used in the science of complexity, and discusses some issues related to their definition and measurement. Finally, it discusses the scope and controversies around the application of the concepts and models of the science of complexity in economics.