Methodological Steps and Issues When Deriving Individual Based-Models from Equation-Based Models: A Case Study in Population Dynamics

Front. Appl. Math. Stat.

2022

Agent-based modeling (ABM) has been successfully used, since its emergence in the 1990s, to model and simulate the dynamics at work in complex socio-environmental systems, in many domains and applications where interactions between people and their environments give rise to emergent phenomena that are difficult to study otherwise (urban planning, land-use change, adaptation to environmental changes, biodiversity protection in socio-ecosystems, environmental pollution control, etc.). The inclusion of multiple levels of analysis, abstraction, and representation in these models, however, is much more recent and is still the subject of many proposals and discussions within a relatively informal field, Multilevel Agent-Based Modeling (ML-ABM), which is most often presented as an approach that extends the classical ABM paradigm to include multilevel concepts. Over the past decade, ML-ABM has been increasingly adopted and explored by researchers as an effective paradigm for framing and defining the mechanisms underlying multilevel dynamics. However, due to the youth of the field, no single definition, methodology, or tool unifies studies in this rapidly expanding area. This review will begin with an introduction to socio-environmental systems (SES) and the challenges that modeling approaches face in representing them properly, especially regarding the complexity of human behaviors and organizations. ABM presents opportunities for modeling SESs with respect to these challenges, including the simulation of individual and social behavior and their ability to provide a descriptive and generative representation of the simulated system. However, ABM is limited in its ability to represent levels and scales, as these concepts are absent from the classical ABM metamodel. A complete review of the ML-ABM literature will be carried out, structured around a continuum that emerged during the review: that of the distribution of behaviors (and thus, from a software engineering perspective, of control) across the levels, from approaches that allow only one level to be active at a time, to approaches that rely on simultaneous activity and feedback loops between several levels. Different design choices will, thus, be presented to meet the different needs of multi-level representation, focusing on the interest on modelers and the strengths and limitations of each. In particular, we will highlight a limitation shared by all the reviewed approaches, namely their inability to represent several parallel hierarchies of levels and their interactions, a capability that appears more and more crucial to finely represent social behaviors in SES. A new perspective on the interest that the AGR approach could represent to allow this representation of hierarchies allows us to conclude on the research perspectives are still open.

Section: Literature Reviewmentioning

confidence: 99%

Handling multiple levels in agent-based models of complex socio-environmental systems: A comprehensive review

Brugière

Nguyen-Ngoc

Front. Appl. Math. Stat.

2022

“…There is a need, then, to provide a form of translation (Moreira et al, 2009 calls it a "coupler") that takes into account the peculiarities of each model, often linked to the formalism in which it is described (e.g., agent-based modeling, discrete event, continuous equations). Numerous works have therefore addressed the problem of combining or coupling models described using different paradigms, like for example Rochette et al (2012) on the coupling of hydrodynamic continuous models and individual-based models, Quesnel (2005) on the coupling of physical and social models, Rousseaux et al (2012) on the coupling of continuous and discrete formalisms in ecological modeling or Nguyen et al (2008) on the coupling between agent-based models and equation-based models through the use of intermediate graph-based representations.…”

Section: Models Coupling In Socio-environmental Modelingmentioning

confidence: 99%

Coupling Environmental, Social and Economic Models to Understand Land-Use Change Dynamics in the Mekong Delta

Huynh

Truong

2016

Front. Environ. Sci.

Self Cite

The Vietnamese Mekong Delta has undergone in recent years a considerable transformation in agricultural land-use, fueled by a boom of the exportation, an increase of population, a focus on intensive crops, but also environmental factors like sea level rise or the progression of soil salinity. These transformations have been, however, largely misestimated by the 10-year agricultural plans designed at the provincial levels, on the predictions of which, though, most of the large-scale investments (irrigation infrastructures, protection against flooding or salinity intrusion, and so on) are normally planned. This situation raises the question of how to explain the divergence between the predictions used as a basis for these plans and the actual situation. Answering it could, as a matter of fact, offer some insights on the dynamics at play and hopefully allow designing them more accurately. The dynamics of land-use change at a scale of a region results from the interactions between heterogeneous actors and factors at different scales, among them institutional policies, individual farming choices, land-cover and environmental changes, economic conditions, social dynamics, just to name a few. Understanding its evolution, for example, in this case, to better support agricultural planning, therefore requires the use of models that can represent the individual contributions of each actor or factor, and of course their interactions. We address this question through the design of an integrated hybrid model of land-use change in a specific and carefully chosen case study, which relies on the central hypothesis that the main force driving land-use change is actually the individual choices made by farmers at their local level. Farmers are the actors who decide (or not) to switch from one culture to another and the shifts observed at more global levels (village, district, province, region) are considered, in this model, as a consequence of the aggregation of these individual decisions. The central component of our hybrid model is then an agent-based model of farmers, provided with a sophisticated mechanism of decision-making that is influenced, at different degrees, by their perception of the contexts in which they act or interact with other actors. The economic context, accessible by them through the market prices of crops, plays a role, as well as the changes observed or forecasted in their physical context (land-cover changes, salinity rise) or the decisions made by others in their social Coupling Environmental and Socio-Economic Models context (neighbors, family members, opinion leaders). The model of farmers is coupled, through this decision-making mechanism, with other independent sub-models, each of them carrying out a realistic description of one of these contexts. Since the dynamics depicted in these sub-models obey to different logics, operate at different scales and rely on different data, they are represented using appropriate modeling techniques: the spatial model is based on GIS information on parcels, soils, and ...

“…Each of these two models has its own strengths and weakness ( [6], [12]) depending on the purpose of study. EBMs, on one hand, play as compartment models and operate on global laws generally, defined by the equations that apply to all members of the compartments.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the process can be completely top-down: by distributing global parameters of a given EBM to obtain local parameters of a related ABM. We refer to our previous contribution [12] for this way. It can also be bottom-up, by extracting local parameters of a given ABM to obtain global parameters of inferred EBM.…”

Section: Introductionmentioning

confidence: 99%

Inferring Equation-Based Models from Agent-Based Models: A Case Study in Competition Dynamics

Nguyen

Taillandier

Principles and Practice of Multi-Agent Systems

et al. 2012

Self Cite

Two types of model, equation-based models (EBMs) and agentbased models (ABMs) are now widely used in modeling ecological complex systems and seem not to be reconciled. While ABMs can help in exploring and explaining the local causes of global phenomena, EBMs are useful for predicting their long-term evolution without having to explore them through simulated experiments. In this paper, we show that it is possible to use an ABM to infer an EBM. Base on the case study, a dynamics of two competing species, we illustrate our methodology through the presentation of two models: an ABM and an EBM. We also show that the two models give the same results on coexistence of the two competing species.