1) Introduction
The Core Components
The Main Equation
Agents that Evolve and Learn
Signers, Interpreters, and Signals
Overview of Chapters
Relevant Literature
2) Symptoms and Sickness
Grounding the Scenario
Finding the Posteriors
The Value of the Interpretants
Calculating the Critical Price
The Value of a Sign
The Information in a Sign
The Complexity of a Universe
Better and Worse Grounds
3) Predators and Prey
The Grounds of Predation
From the Perspective of the Prey
Predation as Conversation
Two-Dimensional Dynamics
Landscape as Ally and Antagonist
From Hyperbolic Valley to Rolling Hills
4) Biosemiotic Agents
Evolution and the Difference Equation
The Relative Fitness of Biosemiotic Agents
Dynamic Organism, Fixed Environment
Dynamic Organism, Dynamic Environment
Semiosis and Symbiosis
Meaning in Biosemiotic Systems
5) Hawks, Doves, and Mutants
The Classic Scenario
Biosemiotic Mutations
Hawklike Mutants
Dovelike Mutants
The Cost of a Semiotic Capacity
The Fixed Points of Costly Agents
6) Reinforcement Learning
The Basic System
From Individual to Ensemble
Establishing a Convention
Motivating Conventions
Parasites
Niche Disruption
7) Machine Semiosis
Machine Learning
Neural Networks
Establishing a Code
The Time it Takes to Establish a Code
Machine Learning as Meta-Semiosis
Language Models
8) Possible World Semiotics
Introduction to Possible Worlds
Possible World Semantics
Probabilities of Possible Worlds
Bayesian Networks and Expected Value
Conversational Backgrounds
Worlds, Times, Scales
Cutting the University Down to Size
9) Meta-Semiotic Processes
Variations on the Main Equation
Fluid Grounds
Self-Updating Semiotic Agents
A Simple Example of Self-Updating
Modeling Another Agent's Model
Culture as Shared Interpretive Ground
Enemies, Parasites, and Infrastructure
10) Conclusion
Synopsis of Arguments
Revelation and Confrontation
Appendices
Energy and Entropy, Information and Value
Complexity, Organization, and Constraint
The Utility of a Sign