It has long been known that the repeated or collective application of very simple rules can produce surprisingly complex organized behavior. In recent years several compelling examples have caught the public's eye, including chaos, fractals, cellular automata, self-organizing systems, and swarm intelligence. These kinds of approaches and models have been applied to phenomena in fields as diverse as immunology, neuroscience, cardiology, social insect behavior, and economics. The interdisciplinary study of how such complex behavior arises has developed into a new scientific field called "complex systems." The complex systems that most challenge our understanding are those whose behavior involves learning or adaptation; these have been named "complex adaptive systems." Examples of complex adaptive behavior include the brain's ability, through the collective actions of large numbers of neurons, to alter the strength of its own connections in response to experiences in an environment; the immune system's continual and dynamic protection against an onslaught of ever-changing invaders; the ability of evolving species to produce, maintain, and reshape traits useful to their survival, even as environments change; and the power of economic systems to reflect, in the form of prices, supplies, and other market characteristics, the collective preferences and desires of millions of distributed, independent individuals engaged in buying and selling. What is similar in these diverse examples is that global behavior arises from the semi-independent actions of many players obeying relatively simple rules, with little or no central control. Moreover, this global behavior exhibits learning or adaptation in some form, which allows individual agents or the system as a whole to maintain or improve the ability to make predictions about the future and act in accordance with these predictions. Traditional methods of science and mathematics have had limited success explaining (and predicting) such phenomena, and an increasingly common view in the scientific community is that novel approaches are needed, particularly those involving computer simulation. Understanding complex adaptive systems is difficult for several reasons. One reason is that in such systems the lowest level components (often called agents) not only change their behavior in response to the environment, but, through learning, they can also change the underlying rules used to generate their behavior.
FFCAS: A Flexible Agent-Based Simulation Framework for Complex Adaptive Systems
