Appendices
A-2. Models implemented in this book
In Part I. Introduction
I-3. Introduction to agent-based modeling
I-5. The fundamentals of NetLogo
In Part II. Our first agent-based evolutionary model
II-1. Our very first model
II-2. Extension to any number of strategies
II-3. Noise and initial conditions
nxn-imitate-if-better-noise.nlogo
II-4. Interactivity and efficiency
nxn-imitate-if-better-noise-interactive-profiler.nlogo
nxn-imitate-if-better-noise-efficient-but-more-than-once-profiler.nlogo
nxn-imitate-if-better-noise-efficient-played-profiler.nlogo
nxn-imitate-if-better-noise-efficient-tick-I-played-last-profiler.nlogo
nxn-imitate-if-better-noise-efficient-tick-I-played-last-and-other-players-profiler.nlogo
nxn-imitate-if-better-noise-efficient.nlogo (after exercise 5)
In Part III. Spatial interactions on a grid
III-1. Spatial chaos in the Prisoner’s Dilemma
III-2. Robustness and fragility
2×2-imitate-best-nbr-extended.nlogo
III-3. Extension to any number of strategies
III-4. Other types of neighborhoods and other decision rules
nxn-imitate-best-nbr-extended.nlogo
In Part IV. Games on networks
IV-1. The nxn game on a random network
nxn-imitate-if-better-rd-nw.nlogo
IV-2. Different types of network
nxn-imitate-if-better-networks.nlogo
IV-3. Implementing network metrics
nxn-imitate-if-better-networks-metrics.nlogo
IV-4. Other ways of computing payoffs and other decision rules
In Part V. Agent-based models vs ODE models
V-2. A rather general model for games played in well-mixed populations
nxn-imitate-if-better-noise-payoff-to-use.nlogo
nxn-games-in-well-mixed-populations.nlogo
