Agent-based modelling – Integration and Implementation Insights

Agent-based modelling for knowledge synthesis and decision support

April 15, 2022June 25, 2019

By Jen Badham

The most familiar models are predictive, such as those used to forecast the weather or plan the economy. However, models have many different uses and different modelling techniques are more or less suitable for specific purposes.

Here I present an example of how a game and a computerised agent-based model have been used for knowledge synthesis and decision support.

The game and model were developed by a team from the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), a French agricultural research organisation with an international development focus. The issue of interest was land use conflict between crop and cattle farming in the Gnith community in Senegal (D’Aquino et al. 2003).

Complexity and agent-based modelling

May 3, 2021March 7, 2017

By Richard Taylor and John Forrester

Policy problems are complex and – while sometimes simple solutions can work – complexity tools and complexity thinking have a major part to play in planning effective policy responses. What is ‘complexity’ and what does ‘complexity science’ do? How can agent-based modelling help address the complexity of environment and development policy issues?

Complexity

At the most obvious level, one can take complexity to mean all systems that are not simple, by which we mean that they can be influenced but not controlled. Complexity can be examined through complexity science and complex system models.

ICTAM: Bringing mental models to numerical models

August 24, 2022September 22, 2016

By Sondoss Elsawah

How can we capture the highly qualitative, subjective and rich nature of people’s thinking – their mental models – and translate it into formal quantitative data to be used in numerical models?

This cannot be addressed by a single method or software tool. We need multi-method approaches that have the capacity to take us through the learning journey of eliciting and representing people’s mental models, analysing them, and generating algorithms that can be incorporated into numerical models.

More importantly, this methodology should allow us to see in a transparent way the progression on this learning journey.

La modélisation participative, un lieu privilégié pour l’interdisciplinarité? / Participatory modeling: An ideal place for interdisciplinarity?

August 24, 2022April 7, 2016

By Pierre Bommel

An English version of this post is available

La modélisation participative cherche à impliquer un groupe de personnes dans la conception et la révision d’un modèle. L’objectif à terme consiste à mieux caractériser les problèmes actuels et imaginer collectivement comment tenter de les résoudre. Dans le domaine de l’environnement en particulier, il apparaît nécessaire que les acteurs concernés se sentent impliqués dans la démarche de modélisation, afin qu’ils puissent exprimer leurs propres points de vue, mais aussi pour mieux s’engager dans des décisions collectives. De ce fait, pour aborder la gestion intégrée des ressources, il est nécessaire de mettre les acteurs au centre des préoccupations de recherche, à la fois lors de la phase la conception du modèle mais aussi pour l’exploration de ces scénarios.