Conclusion

We have presented here a simple, but workable, model with which to model the flow of an important opinion in a population of humans. It can be used with populations small or large, homogeneous or heterogeneous.

This work is the first that I know of in which a social network is the raw substrate for a dynamic simulation. As such it represents just the beginning of study. But even in its initial form we can use it to help create realistic game worlds.

Of course, this work represents just another step toward a Matrix type complete simulation of the world of humans. We can’t yet model accurately all people and their interactions yet, so we use a simplified model (connections, assessments, alignments, etc.) Placing our players in ever more realistic game worlds promises to make the games more intuitive and instantly engaging. This trend will only continue.

For Further Information

Source Code

Software to run these types of simulations is located at http://www.skipcole.com/modeling_opinion_flow. It is covered under the GNU public license, so it is free for you to use.

It is probable that you will need to modify it for your own particular purposes. Since the code is set up as interfaces, and one can overwrite the methods that create the connections and initial alignments, going to higher levels of detail should not be difficult.

Continuing Work

On the site listed above we will continue to improve this code and more features. Future refinements may include:

1. Allowing linkages between individuals to change over time. Linkages may even be formed as ‘like minded’ individuals come together.
2. Improving the terms that may affect the composition of the actors. For example:
   • Demographic data may be used to help refine the percentage of the population that are likely to change their minds on topics, or be susceptible to radicalization (as under employed young men tend to be.)
   • Population dependent things such as literacy rate, education and critical thinking skills may effect the influence of written literature and how well ideas permeate from those sources into the general population.
3. Handling the interrelatedness of ideas. If one is interested in changing opinion, sometimes it may be necessary to not tackle things, but first remove ideas that tend to support the idea that you are trying to change. This is common in argument. How to decompose ideas, at least as far as they are part of the common cultural psyche, is an interesting and potentially fruitful area of study.
4. Input from actual members from a population. If these simulations ever grow large enough and contain real world examples, it may be possible to allow the actual participants to plug in and indicate how they feel on a topic.

References

We have eclectically cobbled together ideas from many disciplines. The references below have been essential reading.

• The Tipping Point: How Little Things Can Make a Big Difference by Malcolm Gladwell
• “The True Believer: Thoughts on the Nature of Mass Movements” by Eric Hoffer
• “Artificial Intelligence: A Modern Approach” by Stuart J. Russell, Peter Norvig
• “AI Game Engine Programming” by Brian Schwab
• Boids Background and Update by Craig Reynolds
• “Social Network Analysis: Methods and Applications” by Stanley Wasserman, Katherine Faust, Dawn Iacobucci, and Mark Granovetter