A flexible approach to model coupling through probabilistic pooling
2017
Rising, James A.
Model coupling is an important approach to studying the dynamics of complex systems, but by introducing new feedback loops, the dynamics of coupled models can be artificially distorted. This paper describes a new method of model coupling which addresses this problem through a dynamic form of regularization. The method allows the time series evolution of model variables to be mutually informed by multiple models, and models to influence each other in proportion to their degree of certainty. Uncoupled forms of the coupled models can act as dynamic priors on the trajectory of coupled variables, strengthening model stability and offering additional calibration of the coupling process. Finally, models that describe different spatial scales can be coupled into multi-scale models, so that, for example, spatially-distributed models can be coupled with aggregate models, and influence one another. We apply this technique to a coupled socio-ecological system of population growth and ecosystem harvesting.
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