Why model?

By Steven Lade

Steven Lade
Steven Lade (biography)

What do you think about mathematical modelling of ‘wicked’ or complex problems? Formal modelling, such as mathematical modelling or computational modelling, is sometimes seen as reductionist, prescriptive and misleading. Whether it actually is depends on why and how modelling is used.

Here I explore four main reasons for modelling, drawing on the work of Brugnach et al. (2008):

  • Prediction
  • Understanding
  • Exploration
  • Communication.

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Designing scenarios to guide robust decisions

Community member post by Bonnie McBain

Bonnie McBain (biography)

What makes scenarios useful to decision makers in effectively planning for the future? Here I discuss three aspects of scenarios:

  • goals;
  • design; and,
  • use and defensibility.

Goals of scenarios

Since predicting the future is not possible, it’s important to know that scenarios are not predictions. Instead, scenarios stimulate thinking and conversations about possible futures. Continue reading

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

Managing uncertainty in decision making: What can we learn from economics?

Community member post by Siobhan Bourke and Emily Lancsar

Siobhan Bourke (biography)

How can researchers interested in complex societal and environmental problems best understand and deal with uncertainty, which is an inherent part of the world in which we live? Accidents happen, governments change, technological innovation occurs making some products and services obsolete, markets boom and inevitably go bust. How can uncertainty be managed when all possible outcomes of an action or decision cannot be known? In particular, are there lessons from the discipline of economics which have broader applicability? Continue reading

Conceptual modelling of complex topics: ConML as an example / Modelado conceptual de temas complejos: ConML como ejemplo

Community member post by Cesar Gonzalez-Perez

cesar-gonzalez-perez
Cesar Gonzalez-Perez (biography)

A Spanish version of this post is available

What are conceptual models? How can conceptual modelling effectively represent complex topics and assist communication among people from different backgrounds and disciplines?

This blog post describes ConML, which stands for “Conceptual Modelling Language”. ConML is a specific modelling language that was designed to allow researchers who are not expert in information technologies to create and develop their own conceptual models. It is useful for the humanities, social sciences and experimental sciences. Continue reading

A checklist for documenting knowledge synthesis

Community member post by Gabriele Bammer

Gabriele Bammer (biography)

How do you write-up the methods section for research synthesizing knowledge from different disciplines and stakeholders to improve understanding about a complex societal or environmental problem?

In research on complex real-world problems, the methods section is often incomplete. An agreed protocol is needed to ensure systematic recording of what was undertaken. Here I use a checklist to provide a first pass at developing such a protocol specifically addressing how knowledge from a range of disciplines and stakeholders is brought together.

KNOWLEDGE SYNTHESIS CHECKLIST

1. What did the synthesis of disciplinary and stakeholder knowledge aim to achieve, which knowledge was included and how were decisions made? Continue reading