From conceptual model to implementation model Piloting a multi-level case study in Cameroon.

Kameni, Eric; Weide, Th.P. van der; Groot, W.T. de

doi:10.46298/arima.3822

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PiC allows to present the behavior of actors in several levels, and shows the links between actors, their activities, facts/norms, and the impacts of those ones on their environment. The example of the use of the framework is demonstrated in [9] to describe an environmental problem well detailed in [13].…”

Section: The Land Use Change Problemmentioning

confidence: 99%

An Actor-Oriented and Architecture-Driven Approach for Spatially Explicit Agent-Based Modeling

Fotsing,

Kakeu,

Kameni

et al. 2023

CSIMQ

View full text Add to dashboard Cite

Nowadays, there is an increasing need to rapidly build more realistic models to solve environmental problems in an interdisciplinary context. In particular, agent-based and spatial modeling have proven to be useful for understanding land use and land cover change processes. Both approaches include simulation platforms often used in several research domains to develop models explaining and analyzing complex phenomena. Domain experts generally use an ad hoc approach for model development, which relies on a code-and-fix life cycle, going from a prototype model through progressive refinement. This adaptive approach does not capture systematically actors’ knowledge and their interactions with the environment. The development and maintenance of resulting models become cumbersome and time-consuming. In this article, we propose an actor and architecture-driven approach that relies on relevant existing methods and satisfies the needs of spatially explicit agent-based modeling and implementation. We have designed an Agent Global Experiment framework incorporating a meta-model built from actor, agent architecture, and spatial concepts to produce an initial model from specifications provided by domain experts and system analysts. An engine is built as a tool to support model transformation. Domain knowledge including spatial specifications is summarized in a class diagram which is later transformed into the agent-based model. Finally, the XML file representing the model produced is used as input in the transformation process leading to code. This approach is illustrated on a hunting and population dynamic model to generate a running code for GAMA, an agent-based and spatially explicit simulation platform.

show abstract

Section: The Land Use Change Problemmentioning

confidence: 99%