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.
Complexity science is not a single consistent theory or approach, but spans sets of different tools and techniques from different research disciplines. It is a useful lens to describe real-world policy situations, where formal analytic modelling reaches limitations of tractability – that is, the maths are not workable. Conceptualising the situation as a complex system provides an alternative. The definition of what is ‘complexity science’ can be seen most clearly by looking at some common principles of the models used. However, it should be understood that the underlying phenomena – the real-world complexity – are much more difficult to define than the models would suggest.
Complex system models have characteristics which can make them suitable analogies of complex systems themselves, but these characteristics also make the models difficult to understand fully. The models:
- have many component parts and therefore many local variables (which give many possible system states).
- involve interactions among locally connected parts, and the interactions need to be understood just as much as the functions of the individual components. For example, the interactions can contribute to significant non-linearities and emergent properties. An example of a non-linearity is that how one gets from A to B is not the opposite of how one gets from B to A. An emergent property is a property at one level of a system that cannot be defined by its components, for example, water is a liquid, whereas its components (hydrogen and oxygen) are gases. To take a sociological example, ‘resilience’ of a community could be said to be based on the skills and resources of individuals interacting in their landscape.
- have macro-level properties that are not properties of any components of the system, are difficult to describe formally and – most relevant to policy research – can appear surprising, novel and unpredictable.
Complexity research tends to rely mainly on modelling and interaction theory, but also uses both qualitative as well as quantitative methods. Interaction theory, or network theory, is based on a model which considers the following four aspects: the entities or ‘actors’; the links between them; the attributes of the actors and links; and boundary conditions of the network determining inclusion and exclusion of actors. Qualitative methods – such as narrative inquiry – are needed to understand real-world complexity and its many nuances in diverse social contexts. On the other hand, our models of complexity also need to be supported by research. Qualitative, quantitative, and mixed-method approaches may be employed to help the design and validation of complex systems models.
To take a network theory example – a social network ‘map’ can be generated from a quantitative social survey. This can be validated using a different method, for instance through qualitative stakeholder interviews. This offers the opportunity for ‘ground-truthing’ whether the researcher’s current map or model of the system is broadly correct or as expected, and also for further learning and questioning. (For another example of ground-truthing, see Pete Barbrook-Johnson’s blog post on models as ‘interested amateurs’.)
Agent-based modelling
Agent-based modelling is one type of modelling useful for helping to understand complex systems. It helps in understanding relationships and thus possible causal mechanisms in complex systems, by generating models of them from the bottom up.
For example, agent-based modelling concentrates on describing a social system at the micro-level of the actors within it. This is usually done using a computer model (program). The description for each agent includes a set of instructions or “rules” governing the agent’s behaviour. Agents also have goals and other internal information (knowledge, beliefs, values, etc.) which uniquely shape their actions.
This bundling of data with instructions for agents allows them to be, in practice, coded as autonomous units representing different social entities. The agent descriptions are used as a template to create many copies and thereby populate a model (hence, agent-based models are sometimes also known as multi-agent systems or multi-agent models).
While there is a focus on the micro-behavioural level, models can include many or multiple types of agency at different levels of action, eg., households, firms or local authorities. There is also a focus on interactions with other agents and interaction with the environment: agent-based models have been used quite extensively to understand management and use of environmental resources, as well as adaptation processes under environmental change.
To include greater levels of detail and specificity, of course, brings new difficulties. Where traditional models reduce systems to easy-to-grasp components, agent-based models (and complex system models in general) may be difficult to interpret. It all comes down to how they might be applied, what you want to find out, and whether the difficulties will outweigh the usefulness.
Potential users or developers of agent-based models often ask the following questions which are addressed in the paper by Taylor and colleagues (2016):
- Do I need an agent-based model?
- Are there good examples of agent-based modelling in my problem domain?
- Is agent-based modelling a stakeholder engagement method?
- Can agent-based modelling be used in conjunction with other methods?
What other questions would you ask? What experience do you have in tackling complex policy problems? What methods have you used? What possible uses do you see for agent-based models in helping facilitate policy processes?
To find out more:
Taylor, R., Besa, M. C. and Forrester, J. (2016). Agent-based modelling: A tool for addressing the complexity of environment and development policy issues. Stockholm Environment Institute (SEI) Working Paper 2016-12: Oxford, United Kingdom. Online: https://www.sei-international.org/publications?pid=3053
Biography: Richard Taylor develops agent-based social simulation models that can be applied to the study of sustainability-related problems and adaptation, and is interested in participatory approaches to inform and improve relevance of models, and put them to wider use. He has expertise in mixed method research and integrated methodology design in applied research. He is a Senior Researcher at the Stockholm Environment Institute Oxford Centre.
Biography: John Forrester is a social anthropologist who uses maps and models to explore the complex relationships behind stakeholders’ understanding of environmental issues. He does this so that ‘situated knowledge’ may feed into both scientific and policy knowledge co-creation processes. John works with the Stockholm Environment Institute at York, where he has experience in multidisciplinary and transdisciplinary science communication for sustainable development; transport planning; upland ecology; flood risk management; coastal ecosystems; and community resilience.