Chapter 8

In many types of systems—biological, mechanical and organizational—having a diversity of agents can improve the chance of a response, at least when there is some way to unify the diversity. Each of several agents separately responds to a condition with a certain probability, a conditional-action probability. More specifically each agent separately detects a condition or responds with a certain probability to that condition. Here, agents autonomously implement one or more probabilistic condition-action rules. Agents that detect a condition, then implement an action. The action could be registering a “positive” cancer result, or actually responding to a condition, depending on what we wish to model. For example, if a condition exceeds a certain threshold, such as a certain prevalence of infections, then an agent detects the outbreak condition and signals an alert. In this chapter we model advantages and costs of diversity in increasing successful detection, prediction and response. With the password that comes with the book, you can access the interactive models.