Abstract
Models of emergent phenomena are designed to provide an explanation to global-scale phenomena from local-scale processes. Model validation is commonly done by verifying that the model is able to reproduce the patterns to be explained. We argue that robust validation must not only be based on corroboration, but also on attempting to falsify the model, i.e. making sure that the model behaves soundly for any reasonable input and parameter values. We propose an open-ended evolutionary method based on Novelty Search to look for the diverse patterns a model can produce. The Pattern Space Exploration method was tested on a model of collective motion and compared to three common a priori sampling experiment designs. The method successfully discovered all known qualitatively different kinds of collective motion, and performed much better than the a priori sampling methods. The method was then applied to a case study of city system dynamics to explore the model’s predicted values of city hierarchisation and population growth. This case study showed that the method can provide insights on potential predictive scenarios as well as falsifiers of the model when the simulated dynamics are highly unrealistic.
Highlights
Writing models to explain emergent phenomena is trying to figure out what mechanisms at the local scale create the patterns observed at the global scale
Often computerbased and requiring simulation, are keys to the scientific inquiry as soon as the system’s description involves a local and a global scale and it is not trivial to explain the global patterns from the local dynamics
The method we propose here, called Pattern Space Exploration (PSE), is novelty search adapted to the exploration of agent-based and more generally computer-based models of complex systems
Summary
Writing models to explain emergent phenomena is trying to figure out what mechanisms at the local scale create the patterns observed at the global scale Such models, often computerbased and requiring simulation, are keys to the scientific inquiry as soon as the system’s description involves a local and a global scale and it is not trivial to explain the global patterns from the local dynamics. Often computerbased and requiring simulation, are keys to the scientific inquiry as soon as the system’s description involves a local and a global scale and it is not trivial to explain the global patterns from the local dynamics They are increasingly developed in a wide range of disciplines from physics to biology, ecology, and social sciences. A long list of examples includes species evolution [1], problem solving in social insects [2], the dynamics of neural networks [3], social segregation [4], population dynamics [5], collective motion [6], and many more.
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