Rules and Consequences

Emergence can be described as the result of a system that has properties greater than the sum of its parts. The result and individual actions are not traceable through each step of the process, but instead emerge from the behavior of all agents. Examples of emergence can be found in nature, such as birds flying in strange patterns and ants foraging for food. Understanding these systems is hard to for the human intuition, but since computer science enables techniques to examine emergence by introducing simulation tools we can see the results and get a deeper understanding of the system and its rules. This thesis investigates what happens when the programmer does not micromanage how each component in the system interacts with other components. When multiple parts work together using simple rules, we sometimes get phenomena that we could not predict. This is what we call rules and consequences; when the rules for the components of the system give rise to emergent phenomena. We have produced 25 different models and analyzed them using a guiding principle we refer to as disciplined exploration, meaning two things. First, results are not discarded even if they do not show what was intended or expected. Second, we systematically map out the scope for the parameters, as opposed to randomly choosing, by defining a “parameter space”. The thesis also provides insights about modeling systems this way using NetLogo. While it is a simple and easy tool to use, we also show that it does have drawbacks. Its internal scheduling and time complexity issues are problematic. Lastly, we provide conclusions to why emergent behavior is interesting and surprising. Emergence can be used to explain how complex system domains could be in reality and is always of interest due to our limited intuition of what will come out of a multi-agent system.