Discrete Dynamics Lab
The DDLab Manual - Preface and Contents - July 2001

The new illustrated DDLab manual (421 pages, 209 figures) corresponds exactly to DDLab Version 23, released in July 2001. This page gives the Contents (chapter headings only), Preface, and Acknowledgments, with links to versions of the first 4 chapters (without figures).

The complete new manual is available in both PostScript and pdf format at...

Click HERE for the new manual in pdf format.

Contents

    Preface
    Acknowledgments
    Contents
    List of Figures
    List of Tables
  1. Introduction
  2. Summary of DDLab functions
  3. Accessing DDLab
  4. Quick Start Examples
  5. Starting DDLab
  6. The first prompt in DDLab
  7. Network size, 1d
  8. Neighborhood, k, or mixed k
  9. The local neighborhood, and network geometry
  10. Setting the wiring, quick settings
  11. Setting special wiring
  12. Rules
  13. Rule-mix options
  14. Specify a subset of rules
  15. Setting Canalization in a random rule-mix
  16. Setting a singe rule
  17. Reviewing network architecture
  18. Transforming network rules
  19. Save/load/print network architecture
  20. The network graph, and attractor meta-graph
  21. The Seed or initial state
  22. The Derrida plot
  23. Graphic conventions for attractor basins
  24. Output parameters for attractor basins
  25. Layout of attractor basins
  26. Display of attractor basins
  27. Pausing attractor basins, and data
  28. Mutation of attractor basins
  29. Final options for attractor basins
  30. Drawing attractor basins, and changes on-the-fly
  31. Output parameters for space-time patterns
  32. Drawing space-time patterns, and changes on-the-fly
  33. Classifying rule space
  34. Learning, forgetting, and highlighting
  35. Filing
    Bibliography
    Index

Preface

Networks of sparsely inter-connected elements having discrete values and updating in discrete time (in parallel or sequentially) are central to a wide range of natural and artificial phenomena drawn from many areas of science; from physics to biology to cognition; to social and economic organization; to parallel computation and artificial life; to complex systems in general.

Such ``decision making'' networks are increasingly applied as idealized models, especially in the study of complexity and emergence, and in the behavior of networks in general, including neural and gene networks. In addition, the networks themselves have intrinsic interest as mathematical/physical systems with a large body of literature devoted to their study. Because the dynamics is difficult to describe by classical mathematics, computer simulation is required, and there is a need for simulation software for non-experts in programming to model networks in their particular fields.

DDLab is an interactive graphics program aimed at addressing these issues, widely used in both research and education. The dynamics of a wide range of finite binary networks may be investigated, from Cellular Automata to Random Boolean Networks and beyond.

As well as generating space-time patterns in one, two or three dimensions, DDLab constructs attractor basins, graphs that link network states according to their transitions, representing ``global'' dynamics, analogous to the phase portrait in continuous dynamical systems. The graphs, consisting of trees rooted on attractor cycles, reveal the network's ``memory'', how the network hierarchically categorizes state space. Learning and ``inverse problem'' algorithms are included that seek to reconstruct a network that satisfies a given set of transitions.

DDLab is an applications program, it does not require writing code. Network parameters and the graphics presentation can be flexibly set, reviewed and altered, including changes "on the fly". A wide variety of measures, data, analysis and statistics are available. This manual provides a comprehensive guide.

Acknowledgments

Many people have influenced DDLab by contributing ideas, suggesting new features, providing encouragement, criticism, and helping with programming. I reserve all the blame for its shortcomings. I would like to thank Mike Lesser, Grant Warrel, Crayton Walker, Chris Langton, Stuart Kauffman, Wentian Li, Pedro de Oliviera, Inman Harvey, Phil Husbands, Guillaume Barreau, Josh Smith, Raja Das, Christian Reidys, Brosl Hasslacher, Steve Harris, Simon Frazer, Burt Voorhees, John Myers, Roland Somogyi, Andrew Adamatzky, Mark Tilden, Rodney Douglas, Terry Bossomaier, Ed Coxon, Oskar Itzinger, Pietro diFenizio, Pau Fernandez, Ricard Sole, and many other friends and colleagues (to whom I apologize for not listing). Also DDLab users, and researchers and staff at the Santa Fe Institute since the spring of 1990, when I first turned up there summoned by Stuart Kauffman.

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Last modified: July 2003