Outline of the Book

The book divides into ten chapters. Chapter 1 defines its key terms — life, intelligence, mind — and stakes out the territory the argument will occupy. Chapter 2 builds the conceptual foundation: mental representation, treated through semiotics and connectionism, and the computational models that formalize how humans encode and manipulate knowledge to solve problems.

Chapter 3 shifts scale from individual cognition to populations. Evolution, the argument runs, produces intelligence not only within organisms but between them, through the connective dynamics of swarms. This is the book's central claim: intelligence emerges from interaction. Individual agents, linked by simple local rules, generate collective behavior no single agent could produce. Human sociality is one instance; ant colonies and bird flocks are others. Computer science borrows this principle to build swarm algorithms that solve optimization problems without centralized control.

Chapter 5 grounds these ideas in social psychology (social learning theory, social influence, and the mathematical models that describe how beliefs propagate through groups and reshape both individual behavior and collective states). Chapter 6 extends this line through Axelrod's model of cultural dissemination (a formal framework for studying how local interactions produce large-scale cultural convergence or fragmentation) and reinterprets prior experimental results through a psychological lens.

Chapters 4 and 7–10 treat the computational machinery directly: genetic algorithms, particle swarm optimization, and related evolutionary computing methods. These chapters are more narrowly technical and fall outside this report's scope.

Next: Chapter 1: What is the Mind? What is Randomness?

Prev: Introduction