Specifying, detecting and analysing emergent behaviours in multi-level agent-based simulations

We introduce a method for analysing emergent behaviours in multi-agent simulations using complex events. Complex events are composed of interrelated events, and they can be defined at any level of spatio-temporal abstraction (equal to or above the lowest level of abstraction given by the model). Minimal types of complex events define sets, which are equated with particular emergent behaviours and can be detected in simulation. Since complex events are derived from the agent-based model itself, they provide significant benefits when compared with traditional state-aggregation methods. First, they provide a method of specifying emergent behaviour, so that such behaviour can be monitored. Second, they provide a mechanism that retains the underlying structure of that behaviour. This latter property supports analysis of the mechanisms at lower levels that give rise to emergent behaviours, and identification of patterns between levels. In other words, multi-agent simulations become less 'opaque' [1]

Context sensitivity in individual-based modeling

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Computing Highly Correlated Positions Using Mutual Information and Graph Theory for G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are a superfamily of seven transmembrane-spanning proteins involved in a wide array of physiological functions and are the most common targets of pharmaceuticals. This study aims to identify a cohort or clique of positions that share high mutual information. Using a multiple sequence alignment of the transmembrane (TM) domains, we calculated the mutual information between all inter-TM pairs of aligned positions and ranked the pairs by mutual information. A mutual information graph was constructed with vertices that corresponded to TM positions and edges between vertices were drawn if the mutual information exceeded a threshold of statistical significance. Positions with high degree (i.e. had significant mutual information with a large number of other positions) were found to line a well defined inter-TM ligand binding cavity for class A as well as class C GPCRs. Although the natural ligands of class C receptors bind to their extracellular N-terminal domains, the possibility of modulating their activity through ligands that bind to their helical bundle has been reported. Such positions were not found for class B GPCRs, in agreement with the observation that there are not known ligands that bind within their TM helical bundle. All identified key positions formed a clique within the MI graph of interest. For a subset of class A receptors we also considered the alignment of a portion of the second extracellular loop, and found that the two positions adjacent to the conserved Cys that bridges the loop with the TM3 qualified as key positions. Our algorithm may be useful for localizing topologically conserved regions in other protein families

A method for validating and discovering associations between multi-level emergent behaviours in agent-based simulations

Agent-based models (ABM) and their simulations have been used to study complex systems with interacting entities and to model multi-agent systems. Simulations are used to explore the dynamic consequences of these models. In many cases, the behaviours that are of interest are emergent ones that arise as a result of interactions between agents rather than the actions of any individual agent. In this paper, we propose a formalism for describing emergent behaviours at any level of abstraction based on the idea that event types can be defined that characterise sets of behavioural 'motifs'. This provides the basis for a method for studying the associations between multi-level behaviours in simulations. There are two categories of hypotheses that we seek to address with respect to an ABM and its simulations: Hypotheses concerned with associations between emergent behaviours defined at various levels of abstraction. Hypotheses concerned with the links between parameter sensitivity / initial conditions and emergent behaviours e.g. the ABM is sensitive to a parameter x because x predisposes the system or part of the system to exhibit a particular (emergent) behaviour. © 2008 Springer-Verlag Berlin Heidelberg