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• W1519524618 abstract "Agent-based modelling offers a way to break from the crudeassumptions of mean-field type models, which ignore spacecorrelations between elements of the system and replace localinteractions with uniform long-range ones. Multi-Agent Systems(MAS) explicitly model spatially distributed individuals; howeverthe richness of such a model can also be a liability due to thesensitive dependence of such high-dimensional systems. This hasimplications for choice of MAS architecture, programming of rules,confidence in predictions, and calibration of model parameters.Delay-embedding, also known as geometry from a timeseries, provides a deep theoretical foundation for the analysisof time series generated by nonlinear deterministic dynamicalsystems. The profound insight of embedding is that an accessiblevariable can explicitly retrieve unseen internal degrees offreedom.In the domain of complex systems modelling, however, theretypically exist an abundance of observables, in which casereconstructing hidden degrees of freedom may be problematic oreven nonsensical. Also, many observables often implies highdimensionality, which generally precludes a dynamical systemsapproach in the first instance. Un-cautious use ofdelay-embedding, from which it is easy to get a resultregardless of physical justification, has in the past led to adegree of negative press for this idea.However, the recent extensions of Takens' delay-embedding theorem todeterministically and stochastically forced systemsprovide a rigorous framework in which to reconstruct using multiple observables. Thisholds great significance for pattern discovery in complex dataseries, which we define to be more than one series - spatial,temporal or a mixture - of an underlying complex system. Inparticular, the concept of a bundle embedding highlights away to usefully employ the 'surplus' observables in the embeddingprocess. More generally, forced embeddings provide a methodologyto breakdown complex system data sets in a modular fashion, whilestill retaining nonlinear relationships.Cluster-Weighted Modelling is a sophisticated approach to densityestimation that, when applied to the output of a delay-embeddingprocess, is able to obtain a statistical representation of thedynamics. These two concepts - forced embeddings and densityestimation - provide a promising theory and a practicalprobabilistic interpretation respectively to the 'inverse problem'of system identification.Expert-knowledge based MAS construction and density-estimation ofdelay-embedded data can therefore be thought of as twocomplementary approaches to the goal of bottom-up, complex systemsmodelling. The original contribution of this paper is to present the latteras a highly data-driven approach to MAS construction in its own right,and, perhaps more importantly, as an aid to constructing and calibratingthe more expert-knowledge rule-driven approach. The emphasis is on asolid theoretical and conceptual foundation.To illustrate the feasibility of our approach, preliminary implementation results for anecological modelling scenario are presented and discussed." @default.
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• W1519524618 date "2005-01-01" @default.
• W1519524618 modified "2023-09-27" @default.
• W1519524618 title "Delay-Embedding Approach to Multi-Agent System Construction and Calibration" @default.
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