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.

I start with mental models – the informal representations of the world that we all use as we go about both our personal and professional lives – and then move on to formal models.

Mental models

We are all modellers! We all use mental models every day for a variety of different purposes:

  • To make quantitative predictions about the future. For example, if I throw the ball this fast, where will it land? How much money would my house sell for?
  • To understand things that happened. For example, why did the cake I baked not turn out like expected? Why was Donald Trump elected as president in the USA, against many expectations?
  • To explore alternative versions of our worlds. For example, what if I added a room to my house? What is life like for someone living in another country?
  • To communicate. Communication is nothing more than the construction and sharing of mental models via language, and we use it every day. For example, when we talk about love, the weather, justice, our garden, or tax, we use representations of these concepts that are at least partially shared among those involved in the conversation.

Formal models

All these purposes can also be fulfilled by formal models.

Prediction is the model purpose most commonly associated with formal modelling, though in wicked problems prediction should be treated cautiously and with full understanding of the model’s assumptions.

Understanding is the model purpose most commonly used in traditional science, to test hypotheses against observations.

The remaining two purposes, exploration and communication, are of the most relevance for wicked problems, yet are arguably the most underappreciated.

Exploration using formal models is nothing more than a reasoning tool to support our own mental modelling capacity for exploration. The effects of complex system dynamics features such as multiple interacting feedbacks can be difficult to anticipate and may even be counter-intuitive: that’s why they’re considered ‘complex’.

An example can be seen in research on how different poverty-environment relationships affect which poverty alleviation strategies are likely to be effective (Lade et al., 2017). We showed that in situations where poor people degrade their environment—usually because they have no choice—asset inputs may help break that cycle of poverty. But in situations where poor people maintain their environment, and agricultural intensification leads to increased environmental degradation, asset inputs may be counterproductive and even reinforce poverty, requiring other strategies.

Finally, sometimes the process of constructing the formal model can be just as valuable as the model itself. Participatory model construction encourages communication of each participant’s mental models, thereby developing awareness of others’ perspectives and possibly challenging one’s own mental model. In an earlier blog post, Jen Badham and Gabriele Bammer described how jointly designing formal models can help stakeholders draw out differences in their mental models of a complex system. For example, a modelling process could help draw out the different understandings that farmers and government policy-makers have of an agricultural system and the different challenges that they face when interacting with this complex system.

In summary, mathematical models have a valuable place even in complex systems with wicked problems, especially when used for exploration and communication. As with any tool, the key is to be aware of why you’re using them.

Why do you model? Do you have other modelling purposes to share? Or additional examples of the reasons for modelling described above?

References:
Brugnach, M., Pahl-Wostl, C., Lindenschmidt, K. E., Janssen, J. A. E. B., Filatova, T., Mouton, A., Holtz, G., van der Keur, P. and Gaber N. (2008). Complexity and Uncertainty: Rethinking The Modelling Activity. U.S. Environmental Protection Agency Papers, 72. (Online): http://digitalcommons.unl.edu/usepapapers/72

Lade, S. J., Haider, L. J.,  Engström, G. and Schlüter, M. (2017). Resilience offers escape from trapped thinking on poverty alleviation. Science Advances, 3, 5: e1603043. (Online) (DOI): https://doi.org/10.1126/sciadv.1603043

Biography: Steve Lade is a researcher at the Stockholm Resilience Centre, Stockholm University, Sweden and an Honorary Senior Lecturer at the Fenner School of Environment and Society, Australian National University in Canberra, Australia. He uses complex systems tools to study the resilience and sustainability of human and natural systems including fisheries, poverty traps and the Earth system. He is currently funded by a young researcher mobility grant from the Swedish Research Council Formas.

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