Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Acknowledgments
- 1 COMMUNICATION, INFLUENCE, AND THE CAPACITY OF CITIZENS TO DISAGREE
- 2 NEW INFORMATION, OLD INFORMATION, AND PERSISTENT DISAGREEMENT
- 3 DYADS, NETWORKS, AND AUTOREGRESSIVE INFLUENCE
- 4 DISAGREEMENT, HETEROGENEITY, AND THE EFFECTIVENESS OF POLITICAL COMMUNICATION
- 5 DISAGREEMENT, HETEROGENEITY, AND PERSUASION: HOW DOES DISAGREEMENT SURVIVE?
- 6 AGENT-BASED EXPLANATIONS, PATTERNS OF COMMUNICATION, AND THE INEVITABILITY OF HOMOGENEITY
- 7 AGENT-BASED EXPLANATIONS, AUTOREGRESSIVE INFLUENCE, AND THE SURVIVAL OF DISAGREEMENT
- 8 HETEROGENEOUS NETWORKS AND CITIZEN CAPACITY: DISAGREEMENT, AMBIVALENCE, AND ENGAGEMENT
- 9 SUMMARY, IMPLICATIONS, AND CONCLUSION
- APPENDIX A THE INDIANAPOLIS–ST. LOUIS STUDY
- APPENDIX B THE OPINION SIMULATION SOFTWARE
- References
- Index
APPENDIX B - THE OPINION SIMULATION SOFTWARE
Published online by Cambridge University Press: 03 December 2009
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Acknowledgments
- 1 COMMUNICATION, INFLUENCE, AND THE CAPACITY OF CITIZENS TO DISAGREE
- 2 NEW INFORMATION, OLD INFORMATION, AND PERSISTENT DISAGREEMENT
- 3 DYADS, NETWORKS, AND AUTOREGRESSIVE INFLUENCE
- 4 DISAGREEMENT, HETEROGENEITY, AND THE EFFECTIVENESS OF POLITICAL COMMUNICATION
- 5 DISAGREEMENT, HETEROGENEITY, AND PERSUASION: HOW DOES DISAGREEMENT SURVIVE?
- 6 AGENT-BASED EXPLANATIONS, PATTERNS OF COMMUNICATION, AND THE INEVITABILITY OF HOMOGENEITY
- 7 AGENT-BASED EXPLANATIONS, AUTOREGRESSIVE INFLUENCE, AND THE SURVIVAL OF DISAGREEMENT
- 8 HETEROGENEOUS NETWORKS AND CITIZEN CAPACITY: DISAGREEMENT, AMBIVALENCE, AND ENGAGEMENT
- 9 SUMMARY, IMPLICATIONS, AND CONCLUSION
- APPENDIX A THE INDIANAPOLIS–ST. LOUIS STUDY
- APPENDIX B THE OPINION SIMULATION SOFTWARE
- References
- Index
Summary
By referring to the Opinion Simulation as an agent-based model, we intend to convey the idea that the individuals are represented as separate, self-contained computer objects and that those objects carry out actions according to information that they collect and maintain on their own. An agent-based model generates “bottom-up” results because the agents are highly individualized, and their behavior is not dictated by a high level entity. Beyond the agents, where our analytical focus resides, other types of objects exist as well – objects that represent the environment and objects that collect data and show it on the screen. The environment in the Opinion model is rather like a series of chess boards, where agents are able to position themselves within any particular board or move from one board to another.
About the code
The Opinion model is written in Objective-C and makes liberal use of the Swarm Simulation libraries. Objective-C, a language invented by Brad Cox, was chosen for Swarm because it is a simple extension of the C language that incorporates concepts from object-oriented programming. Objective-C is not so well known as C++, another object-oriented extension of C, but it has many of the same advantages. Programs written in Objective-C are reasonably fast, and the language is quite easy to learn if one has experience in C or Java. Programs that access the Swarm libraries can be written in other languages as well. After Objective-C, the next most frequently used language is Java.
- Type
- Chapter
- Information
- Political DisagreementThe Survival of Diverse Opinions within Communication Networks, pp. 222 - 234Publisher: Cambridge University PressPrint publication year: 2004