Adaptive submodel selection in hybrid models - Gray, Randall; Wotherspoon, Simon (Frontiers in Environmental Science, 2015)
Hybrid modelling seeks to address problems associated with the representation of complex systems using ``single-paradigm'' models: where traditional models may represent an entire system as a cellular automaton, for example, component parts within a hybrid model may mix representations as diverse as individual-based models of organisms, Markov chain models, fluid dynamics models of regional ocean currents, and coupled population dynamics models. In this context, hybrid modellers try to choose the best representations for each component submodel in order to maximize the utility of the model as a whole. Even with the flexibility afforded by the hybrid approach, component models and the dynamics associated with interacting models may be most efficient only in parts of the global state space of the system. The immediate consequence of this possibility is that we should consider adaptive hybrid models whose components may change their representation based on their own state and the states of the other submodels within the system. This paper uses a simple example model of an artificial ecosystem to explore a hybrid model which may change the form of its component sub\-models in response to their local conditions and internal state relative to some putative optimization choices. The example demonstrates the assessment and actions of a ``monitor'' agent which adjusts the mix of sub\-models as the model run progresses. A simple mathematical structure is also described and used as the basis for a sub\-model selection strategy, and alternative approaches are briefly discussed.