(Dynamic) adaptation of level of detail – Computationally modeling a system at fine-grained levels of detail can often lead to performance problems.Thus, one needs to be able to couple diverse models such that the needs and ontology of each model are respected.

Furthermore, the models must be able to have significantly different topologies, components, and types of interactions. The timescale for dynamics within an ecological model might be measured in hours or days, whereas in a climate model it could be in years, decades, or centuries. One example of such a coupling that we will discuss throughout the paper is a 2-model system in which we couple a climate change model with a model showing an ecological system. Furthermore, the forms that the models of the interacting systems take may differ wildly.

Coupling of heterogeneous models – Most systems do not exist in isolation and, thus, there is sometimes a need to simulate multiple interacting systems.

In this survey, Morvan describes three types of problems that ML-ABM languages or packages are typically used to address: Morvan ( 2012) provides a survey of applications, methods, and approaches for the construction of ML-ABMs. Recently, there has been an increasing interest in Multi-Level Agent-Based Modeling, or ML-ABM. Finally, we present six dimensions for describing different kinds of inter-model interactions that LevelSpace facilitates and discuss how these six dimensions help us better describe NetLogo models built with LevelSpace. We provide examples of how modelers can use LevelSpace to elaborate on processes within existing models, or to connect phenomena, by letting theoretically infinitely large systems of models communicate and interact with each other. In this paper, we present LevelSpace, a NetLogo extension that provides a simple set of NetLogo primitives for connecting models and building multi-level or multi-model models. However, having only these two levels of simulated processes cuts out things that lie outside of the model, like the impact that segregation might have on neighborhood schools, or things that are inside the model but at a more detailed level like the decision process “within” each agent of where to move, and why. While the good reasons for restricting models to two levels are manifold, this comes at a cost: by reducing a phenomenon to two levels, we necessarily either exclude processes and entities that do not fit at these levels, or we are forced to abstract them into proto-agents or other low-fidelity simulations.įor instance, in the classic Segregation ( Schelling 1969) model, we are restricted to two levels: houses, represented by space, and individuals, who make decisions about whether to move or not.

This approach has been useful across many disciplines and domains, but has shown very useful in the social sciences where the micro-level often represents the states and behaviors of social organizations or individual people (e.g. Bar-Yam 1997 Epstein & Axtell 1996 Mitchell 2009 Wilensky & Rand 2015).

They are modeled as the interactions between two levels: a micro- (or agent-) and a macro- (aggregate-) level (e.g. Agent-based models are typically conceptualized and coded as self-contained, separate units, each affording an in-depth ‘thinking and analysis space’ of one particular phenomenon.

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