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).
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.
Developing the game and model
Working with a local partner, CIRAD ran a series of workshops with Gnith villagers and other land use stakeholders. Together, the group developed a game that highlighted the key features of the conflict. CIRAD then created a computerised version of that game, an agent-based model.
In the first step, workshop participants decided that the important decision makers are (crop) farmers and cattle herders, and they became the two types of players in the game. The key influences on their land use decisions were identified as soil type, distance to water and time of year.
These factors were used to establish the game rules. A grid was overlaid on a map of the local area to create a playing board. Each grid location contained either water or one of six soil types and was scored by workshop participants for:
- suitability for two types of agriculture (rice and vegetables), based on soil type; and,
- suitability for cattle at different times of the year, based on access to water and fodder availability.
Gameplay consisted of each player selecting a location in turn, with a new selection each ‘month’ for twelve rounds (one year). Players were not permitted to play the same role as their real-life occupation. The objective of the game was to acquire enough points throughout the year to support a family by occupying high scoring locations.
In many ways, this game is already an agent-based model, as it has the following characteristics:
- The game represents a process, that of land use decisions.
- Actions are autonomous; each player makes their own decision without any central control.
- Differences are meaningful; plots of land change their characteristics over time, farmer and herder players make different decisions, and the same player will make different decisions at different times.
- There is interaction between players and between players and the environment; a player gains points by choosing a plot of land, that choice makes the land unavailable to other players and also changes the land’s characteristics, for example, when a farmer harvests a crop.
The capacity to simulate individuals with autonomy, heterogeneity and interaction is the defining characteristic of agent-based modelling, and what makes it the appropriate choice if those features are important drivers of the system being modelled. All of these features are essential in appropriately representing the decisions that Gnith land users make. The conflict is a feature of the system that is generated by the individual land use decisions.
At the end of the game, players explained the motives for their actions and the problems they faced during play. The researchers then developed a computer simulation to replicate the game, including the point system and decision processes of players. This programmed implementation of the game was an agent-based model.
Using the game and model for knowledge synthesis
The game and model captured a shared understanding of the most important features of the conflict situation and the preferences among the participants. This synthesis of diverse of knowledge held by different stakeholders is facilitated by the modelling process and is not unique to agent-based models.
The figure shows the common phases in projects that develop models with stakeholders (participatory modelling) and the relevant steps within those phases (Badham et al. 2019); knowledge synthesis occurs primarily in the planning and development phases.
The steps of problem definition (focus on land use conflict) and stakeholder planning contributed to the shared understanding by ensuring that the workshop participants covered the range of relevant knowledge. This knowledge was elicited through a process where stakeholders themselves decided what was essential to represent in the model. In establishing the game rules, required data about such factors as the soil quality were automatically collected. The game then became a very detailed conceptual model for the agent-based model.
While all participatory modelling processes support knowledge synthesis, the rules in agent-based models are relatively natural and accessible compared to some other modelling methods. The computer code takes the perspective of individuals taking actions based on their own characteristics and situation. This perspective facilitates engagement and participation, as the stakeholders are providing their own experience for translation into model rules.
Using the game and model for decision support
Players were able to try out different approaches and see the consequences of their actions with the game. However, game playing is time consuming, which limits the number of approaches that can be tried. The advantage of the computerised version is that it is much quicker and can therefore be used to extrapolate from many different ‘what if?’ proposals.
Furthermore, because participants played different roles than their own real-world role, they gained an understanding of the problems faced by others and were more willing to compromise and find proposals that worked for everybody. For example, farmers had previously been unwilling to discuss herder access to post harvest fodder but the game and model allowed this idea to explored.
The participants experimented with several proposals, including regulations about access to water and designating certain areas of land for livestock or to forbid livestock. The agent-based model was used to explore the likely outcomes of these proposals and then the participants were able to discuss these outcomes to form a consensus. The game and model facilitated productive discussions that led to much greater commitment to the agreed policy.
Conclusion
Much of the knowledge synthesis occurred during the planning and development phases where stakeholders made their decision making processes explicit. Using the model during the application phase allowed potential solutions to be explored and facilitated a community decision. The CIRAD approach of using a game to design the agent-based model promotes engagement as the game is very accessible. However, similar knowledge synthesis and decision support outcomes can be achieved without the intermediate step of the game, as described in a different land use agent-based model presented in the blog post Models as ‘interested amateurs’ by Peter Barbrook-Johnson.
For non-modellers, the unique perspective of agent-based modelling can be difficult to understand. If you are a reader who is not a modeller, does the intermediate game help to understand why agent-based modelling is effective when the system being modelled has heterogeneity and interaction?
I tend to use diagrams as the conceptual model when designing an agent-based model. For readers who are modellers, what do you use and what advantages have you found?
References:
Badham, J., Elsawah, S., Guillaume, J. H. A., Hamilton, S. H., Hunt, R. J., Jakeman, A. J., Pierce, S. A, Snow, V.O., Babbar-Sebens, M., Fu, B, Gober, P., Hill, M. C., Iwanaga, T., Loucks, D. P., Merritt, W. S., Peckham, S. D., Richmond, A. K., Zare, F., Ames, D. and Bammer, G. (2019). Effective Modeling for Integrated Water Resource Management: A Guide to Contextual Practices by Phases and Steps and Future Opportunities. Environmental Modelling and Software, 116: 40-56. (Online) (DOI): https://doi.org/10.1016/j.envsoft.2019.02.013
D’Aquino, P., Le Page, C., Bousquet, F. and Bah, A. (2003). Using Self-designed Role-playing Games and a Multi-agent System to Empower a Local Decision-making Process for Land Use Management: The SelfCormas Experiment in Senegal. Journal of Artificial Societies and Social Simulation, 6, 3: Article 5. (Online – Open Access): https://jasss.soc.surrey.ac.uk/6/3/5.html
Additional reading:
Badham, J. M., Chattoe-Brown, E., Gilbert, N., Chalabi, Z., Kee, F. and Hunter, R. F. (2018). Developing Agent-based Models of Complex Health Behaviour. Health and Place, 54: 170-177. (Online) (DOI): https://doi.org/10.1016/j.healthplace.2018.08.022
Biography: Jen Badham PhD is based at Queen’s University Belfast, Northern Ireland. She has worked as both a modeller and policy advisor and is currently interested in the way in which social networks influence changes in behaviour.