%0 Journal Article %1 Railsback:2001:em %A Railsback, Steven F. %D 2001 %J Ecological Modelling %K epidemes %N 1 %P 47--62 %R 10.1016/S0304-3800(01)00228-9 %T Concepts from complex adaptive systems as a framework for individual-based modelling %V 139 %X Abstract Individual-based models (IBMs) have long been proposed as a key tool for understanding and predicting ecosystem complexities, yet the contribution of this approach to basic or applied ecology has been less than anticipated. Fundamental reasons for the disappointing contribution of IBMs have been, in the current absence of a theoretical foundation for IBMs, conceptual flaws in model formulation and the failure to address critical computer implementation issues. Researchers in the new field of Complex Adaptive Systems (CAS) study how complex behaviors emerge in systems of relatively simple interacting individuals. Research on CAS, while still new and informal, has identified key concepts for making individual-based systems realistic. I propose that explicit consideration of the following concepts from CAS should make the design of IBMs less ad hoc and more likely to produce models of value for basic and applied ecology: (1) Emergence: what behaviors and population dynamics should emerge from the model's mechanistic representation of key processes vs. being imposed on the model as empirical relations? How should individual traits be modeled so that realistic population responses emerge?; (2) Adaptation: given the model's temporal and spatial scales, what adaptive processes of individuals should be modeled? What mechanisms do individuals use to adapt in response to what environmental forces?; (3) Fitness and strategy: what measures of fitness are appropriate to use as the basis for modelling decision making? Should fitness measures change with life history state?; (4) State-based responses: how should decision processes depend on an individual's state?; (5) Prediction: anticipating decision outcomes appears essential for modelling many behaviors; what are realistic assumptions about how organisms predict the consequences of decisions?; (6) Computer implementation: what user interfaces are necessary to make the model, and especially individual behaviors, observable and testable? How will the model's full design and computer implementation be documented and tested so results are reproducible and valid?

@article{Railsback:2001:em, abstract = {Abstract Individual-based models (IBMs) have long been proposed as a key tool for understanding and predicting ecosystem complexities, yet the contribution of this approach to basic or applied ecology has been less than anticipated. Fundamental reasons for the disappointing contribution of IBMs have been, in the current absence of a theoretical foundation for IBMs, conceptual flaws in model formulation and the failure to address critical computer implementation issues. Researchers in the new field of Complex Adaptive Systems (CAS) study how complex behaviors emerge in systems of relatively simple interacting individuals. Research on CAS, while still new and informal, has identified key concepts for making individual-based systems realistic. I propose that explicit consideration of the following concepts from CAS should make the design of IBMs less ad hoc and more likely to produce models of value for basic and applied ecology: (1) Emergence: what behaviors and population dynamics should emerge from the model's mechanistic representation of key processes vs. being imposed on the model as empirical relations? How should individual traits be modeled so that realistic population responses emerge?; (2) Adaptation: given the model's temporal and spatial scales, what adaptive processes of individuals should be modeled? What mechanisms do individuals use to adapt in response to what environmental forces?; (3) Fitness and strategy: what measures of fitness are appropriate to use as the basis for modelling decision making? Should fitness measures change with life history state?; (4) State-based responses: how should decision processes depend on an individual's state?; (5) Prediction: anticipating decision outcomes appears essential for modelling many behaviors; what are realistic assumptions about how organisms predict the consequences of decisions?; (6) Computer implementation: what user interfaces are necessary to make the model, and especially individual behaviors, observable and testable? How will the model's full design and computer implementation be documented and tested so results are reproducible and valid?}, added-at = {2017-12-16T10:57:55.000+0100}, author = {Railsback, Steven F.}, biburl = {https://www.bibsonomy.org/bibtex/23c75e2a324aee5a245615898cf55fb91/krevelen}, doi = {10.1016/S0304-3800(01)00228-9}, interhash = {1afc24b956e24421335e4352a1510dad}, intrahash = {3c75e2a324aee5a245615898cf55fb91}, issn = {0304-3800}, journal = {Ecological Modelling}, keywords = {epidemes}, number = 1, pages = {47--62}, timestamp = {2017-12-16T10:57:55.000+0100}, title = {Concepts from complex adaptive systems as a framework for individual-based modelling}, volume = 139, year = 2001 }