By Christian Kammler1, Maarten Jensen1, Rajith Vidanaarachchi2 Cezara Păstrăv1
- Department of Computer Science, Umeå University, Sweden
Transport, Health, and Urban Design (THUD)
- Research Lab, The University of Melbourne, Australia
“Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live.” — John Woods
Agent-based modelling can be a valuable tool for gaining insight into crises , both, during and before to increase resilience. However, in the current state of the art, the models have to build up from scratch which is not well suitable for a crisis situation as it hinders quick responses. Consequently, the models do not play the central supportive role that they could. Not only is it hard to compare existing models (given the absence of existing standards) and asses their quality, but also the most widespread toolkits, such as Netlogo , MESA (Python) , Repast (Java) [1,5], or Agents.jl (Julia) , are specific for the modelling field and lack the platform support necessary to empower policy makers to use the model (see Figure 1).
Fig. 1. Platform in the middle as a connector between the code and the model and interaction point for the user. It must not require any expert knowledge.
While some of these issues are systemic within the field of ABM (Agent-Based Modelling) itself, we aim to alleviate some of them in this particular context by using a platform purpose-built for developing and using ABM in a crisis. To do so, we view the problem through a multi-dimensional space which is as follows (consisting of the dimensions A-F):
- A: Back-end to front-end interactivity
- B: User and stakeholder levels
– Social simulators to domain experts to policymakers
– Skills and expertise in coding, modelling and manipulating a model
- C: Crisis levels (Risk, Crisis, Resilience – also identified as – Pre Crisis, In Crisis, Post Crisis)
- D: Language specific to language independent
- E: Domain specific to domain-independent (e.g.: flooding, pandemic, climate change, )
- F: Required iteration level (Instant, rapid, slow)
A platform can now be viewed as a vector within this space. While all of these axes require in-depth research (for example in terms of correlation or where existing platforms fit), we chose to focus on the functionalities we believe would be the most relevant in ABM for crises.
2 Rapid Development
During a crisis, time is compressed, and fast iterations are necessary (mainly focusing on axes C and F), making instant and rapid/fast iterations necessary while slow iterations are not suitable. As the crisis develops, the model may need to be adjusted to quickly absorb new data, actors, events, and response strategies, leading to new scenarios that need to be modelled and simulated. In this environment, models need to be built with reusability and rapid versioning in mind from the beginning, otherwise every new change makes the model more unstable and less trustworthy.
While a suite of best practices exists in general Software Development, they are not widely used in the agent-based modelling community. The platform needs a coding environment that favors modular reusable code, easy storage and sharing of such modules in well-organized libraries and makes it easy to integrate existing modules with new code.
Having this modularity is not only helping with the right side of Figure 1, we can also use it to help with the left side of the Figure at the same time. Meaning that the conceptual model can be part of the respective module, allowing to quickly determine if a module is relevant and understanding what the module is doing. Furthermore, it can be used to create a top-level drag and drop like model building environment to allow for rapid changes without having to write code (given that we take of the interface properly).
Having the code and the conceptual model together would also lower the effort required to review these modules. The platform can further help with this task by keeping track of which modules have been reviewed, and with versioning of the modules, as they can be annotated accordingly. It has to be noted however,
that such as system does not guarantee a trustworthy model, even though it might be up to date in terms of versioning.
3 Model transparency
Another key factor we want to focus on is the stakeholder dimension (axis B). These people are not experts in terms of models, mainly the left side of Figure 1, and thus need extensive support to be empowered to use the simulation in a – for them – meaningful way. While for the visualization side (the how? ) we can use insights from Data Visualization, for the why side it is not that easy.
In a crisis, it is crucial to quickly determine why the model behaves in a certain way in order to interpret the results. Here, the platform can help by offering tools to build model narratives (at agent, group, or whole population level), to detect events and trends, and to compare model behavior between runs. We can take inspiration from the larger software development field for a few useful ideas on how to visually track model elements, log the behavior of model elements, or raise flags when certain conditions or events are detected. However, we also have to be careful here, as we easily move towards the technical solution side and away from the stakeholder and policy maker. Therefore, more research has to be done on what support policy makers actually need. An avenue here can be techniques from data story-telling.
4 The way forward
What this platform will look like depends on the approaches we take going forward. We think that the following two questions are central (also to prompt further research):
- What are relevant roles that can be identified for a platform?
- Given a role for the platform, where should it exist within the space de- scribed, and what attributes/characteristics should it have?
While these questions are key to identify whether or not existing platforms can be extended and shaped in the way we need them or if we need to build a sandbox from scratch, we strongly advocate or an open source approach. An open source approach can not only help to allow for the use of the range of expertise spread across the field, but also alleviate some of the trust challenges. One of the main challenges is that a trustworthy, well-curated model base with different modules does not yet exist. As such, the platform should aim first to aid in building this shared resource and add more related functionality as it becomes relevant. As for model tracking tools, we should aim for simple tools first and build more complex functionality on top of them later.
A starting point can be to build modules for existing crises, such as earth- quakes or floods where it is possible to pre-identify most of the modelling needs, the level of stakeholder engagement, the level of policymaker engagement, etc.
With this we can establish the process of open-source modelling and learn how to integrate new knowledge quickly, and be potentially better prepared for unknown crises in the future.
This piece is a result of discussions at the Lorentz workshop on “Agent Based Simulations for Societal Resilience in Crisis Situations” at Leiden, NL in earlier this year! We are grateful to the organisers of the workshop and to the Lorentz Center as funders and hosts for such a productive enterprise.
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- Wilensky, U.: Netlogo. http://ccl.northwestern.edu/netlogo/, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999), http://ccl.northwestern.edu/netlogo/
Kammler, C., Jensen, M., Vidanaarachchi, R. and Păstrăv, C. (2023) Towards an Agent-based Platform for Crisis Management. Review of Artificial Societies and Social Simulation, 10 May 2023. https://rofasss.org/2023/05/10/abm4cm
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