1. Before we proceed - Where are we going? - Models - What stands in the way? - Getting on with it - Mathematical notation 2. Games and numbers - Beginnings - Checkersplaying and neural networks - Mysteries and deeper mysteries - Board games and rules - Numbers - Building blocks - Computer-based models 3. Maps, game theory, and computer-based modeling - Game theory - States - Tree of moves - Strateies - Emergnce-A first look - Dynamic models - Computer-based models-A closer look 4. Checkers - Why it's difficult to become a king - What samuel did about it - Valuing moves - From valuation to strategy - A learning procedure - Making the learning procedure work - Cautions - Emergent consequences of weight changing - Subgoals - Anticipating the opponent - Toward minimax - Bootstrapping - Lookahead - Summing up - Generated complexity - Learning without immediate reward - Credit stage-setting actions - Modeling other agents 5. Neural nets - Some facts about neurons - Modeling neurons - Networks of fixed threshold neirons - Differences and limitations - More facts about neurons - Time-varying thresholds - Fatigue - Changes in synapse weights - Networks with cycles - Indefinite memory - Looking at triangles-An example - Organization of the model - Emergent behavior - Summing Up - Comparisons - Onward 6. Toward a general setting - Agent-based models - Enter the computer - Emergence and nonlinearity - Requeriments for a genral setting 7. Constrained generating procedures - Mechanisms - Interactions and linkage - Cellular automata as cgp's - A simple cellular automaton - Gliders - Lessons about emergence 8. Samuel's checkersplayer and other models as cgps's - Samuel's checkersplayer - Signaling between agents - Standing back - Central nervous system models - Copycat 9. Variation - Cgp's with variable geometry - Tags - Standard descriptions - Generalizing the current-components list - Mechanisms as processors - Summing up - Examples - Making a cgp into a general-purpose computer - Making a cgp into a cellular automaton - Billiard balls and hot gases - Genetic algorithms and cgp-v - Lessons about emergence - Computational equivalence - Levels of description 10. Levels of description and reduction - Levels - How to tile a cellular automaton - Defining a tiled automaton - Reducing a automaton - Macrolaws derived from conway's automaton 11. Metaphor and innovation - Innovation and greation in the sciences - Metaphor-A first look - Relations between metaphor and model - Cultivating innovation - Practice - Selection of building blocks - Exploring larger patterns - In brief - Poetry and physics 12. Closing - Closing a summing up - Basic concepts - Recapitulation - Closing as a way station - Mathematical obstacles - Cognitive obstacles - A mental obstacle: "the end of science" - Synthesis - Emergence-next steps - Emergence-horizons - Models as guides - Control and prediction - Innovation and greation - Emergence-far horizons