Agent-based modelling for knowledge synthesis and decision support

Community member post by Jen Badham

Jen Badham (biography)

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).

Agent-based modelling is particularly effective where understanding is more important than prediction. This is because agent-based models can represent the real world in a very natural way, making them more accessible than some other types of models. Continue reading

Is it legitimate for transdisciplinary research to set out to change society?

Community member post by Antonietta Di Giulio and Rico Defila

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Antonietta Di Giulio (biography)
rico-defila
Rico Defila (biography)

An unspoken and unchallenged assumption underpinning much discourse about transdisciplinary research is that it must change society.

The assumption goes beyond whether research should contribute to change, or whether research impacts developments in society, or whether research should investigate societal problems and provide solutions, or anything similar – it is that research should actively and intentionally be transformative. This generally goes hand-in-hand with a deep conviction that researchers are entitled to actually change society according to what they believe to be right. For many this conviction allows researchers to impose their interventions and solutions on other societal actors by, if necessary, being manipulative. Continue reading

Using the concept of risk for transdisciplinary assessment

Community member post by Greg Schreiner

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Greg Schreiner (biography)

Global development aspirations, such as those endorsed within the Sustainable Development Goals, are complex. Sometimes the science is contested, the values are divergent, and the solutions are unclear. How can researchers help stakeholders and policy-makers use credible knowledge for decision-making, which accounts for the full range of trade-off implications?

‘Assessments’ are now commonly used. Following their formal adoption by the Intergovernmental Panel for Climate Change (IPCC) in the early 1990s, they have been used at the science-society-policy interface to tackle global questions relating to biodiversity and ecosystems services, human well-being, ozone depletion, water management, agricultural production, and many more. Continue reading

A new boundary object to promote researcher engagement with policy makers / Un nuevo objeto frontera para promover la colaboración de los investigadores con los tomadores de decisiones

Community member post by María D. López Rodríguez

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María D. López Rodríguez (biography)

A Spanish version of this post is available

Can boundary objects be designed to help researchers and decision makers to interact more effectively? How can the socio-political setting – which will affect decisions made – be reflected in the boundary objects?

Here I describe a new context-specific boundary object to promote decision making based on scientific evidence. But first I provide a brief introduction to boundary objects.

What is a ‘boundary object’?

In transdisciplinary research, employing a ‘boundary object’ is a widely used method to facilitate communication and understanding among stakeholder groups with different epistemologies. Boundary objects are abstract tools adaptable to different perspectives and across knowledge domains to serve as a means of symbolic communication. Continue reading

Managing deep uncertainty: Exploratory modeling, adaptive plans and joint sense making

Community member post by Jan Kwakkel

jan-kwakkel
Jan Kwakkel (biography)

How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources:

  1. Intrinsic limits to predictability in complex systems.
  2. A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
  3. Complex systems are generally subject to dynamic change, and can never be completely understood.

Deep uncertainty means that the various parties to a decision do not know or cannot agree on how the system works, how likely various possible future states of the world are, and how important the various outcomes of interest are. Continue reading

Sharing integrated modelling practices – Part 2: How to use “patterns”?

Community member post by Sondoss Elsawah and Joseph Guillaume

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Sondoss Elsawah (biography)

In part 1 of our blog posts on why use patterns, we argued for making unstated, tacit knowledge about integrated modelling practices explicit by identifying patterns, which link solutions to specific problems and their context. We emphasised the importance of differentiating the underlying concept of a pattern and a pattern artefact – the specific form in which the pattern is explicitly described. Continue reading