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This chapter begins with a brief survey of the history of logical thought, and of the basics of the interplay of natural processes that have led to the evolution of computational intelligence, both biological and artificial. This is followed by a compact introduction to Darwin's theory of biological evolution. The relevance and importance of Lamarckism in artificial evolution is emphasized. The notion of self-organizing systems and the evolution of distributed intelligence in such systems is illustrated by considering the example of a beehive. Emergent behaviour, pattern formation, and perpetual novelty in complex open systems are described. The various topics coming under the umbrella term ‘computational intelligence’ are introduced. Not only software, but even hardware can evolve, and some examples are described. The last section of this chapter is about some of the design principles discovered by scientists in biological networks.

The closing chapter summarizes the book’s statements about the reality, illusion, and manipulation of objectivity. Whether we like it or not, ranking is with us and will remain with us for the foreseeable future. Computer scientists design ranking algorithms, and modern computers can process huge datasets with these algorithms. We are not always happy with the results, so we might ask whether, when, and how the results of a ranking algorithm should be controlled by content curators. Recent public debates about the use and misuse of data reinforce the message of the book that we need the combination of human and computational intelligence.

This book is about the integration of agent-based modeling and network science. The leading chapter gives a brief historical review as a background and motivation of the book. The historical review begins with the network origin of agent-based models, the rising of autonomous agents and network sciences, the game-theoretic formulation of networks, and the agent-based formulation of networks, and ends with networks as an alternative manifestation of markets. Along this series of development, we see how network work becomes an indispensable element of agent-based models, and how agents-based models can be a powerful tool for modeling the dynamics on and of networks. With this large picture, the book is divided into three parts: foundations, primary dynamics on and of networks, and applications.