Symposium # 27: Advancing the Individual-Based Modeling Approach: New Tools and Concepts.

Organized by: S. Railsback, J. Anderson and R. Lamberson.
Thursday, August 10, 2000
8:00 AM to 12:00 PM
Ballroom III - Cliff Lodge

Individual-based models (IBMs) have great potential for both advancing and communicating ecology. IBMs are among the few tools ecologists have for studying and demonstrating natural complexities. We will make the following points: 1) Despite their promise, IBMs have made little contribution to basic or applied ecology, 2) One root cause of IBMs failure to advance ecology is their lack of an established conceptual foundation. Modelers are unaware of key conceptual flaws in their models. For example, the practice of forcing model individuals to reproduce behaviors observed in real populations is dangerous when the model is applied to previously unobserved situations. Failing to let individuals make decisions predictively makes realistic behavior unlikely. We lack even a list of IBM design considerations, 3) A second root cause has been the lack of appropriate software tools. Because IBMs use individual behavior to predict population responses, IBM software must allow individual behaviors to be observed; otherwise, the model is essentially untestable and unlikely to advance the science, 4) New software tools for individual-based simulations help eliminate the software concerns. Technologies like animation and probes allow individual behaviors to be observed, making models much easier test and improve, communicate, and believe, 5) The new field of Complex Adaptive Systems appears useful as a conceptual foundation for IBMs. Thinking about such issues as emergent vs. imposed behaviors, what kind of adaptation is appropriate, how individuals predict decision outcomes, and how fitness is evaluated can help modelers identify and address the subtle but important formulation decisions that determine model success, 6) Giving individuals simple fitness-maximizing decisions rules, and the information about their environment necessary to predict decision outcomes, can cause many realistic behaviors to emerge naturally from an IBM. This result makes us hopeful that IBMs can meet their promise for advancing ecology as a predictive science.