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Making Models FAIR: An educational initiative to build good ABM practices

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By Marco A. Janssen1, Kelly Claborn1, Bruce Edmonds2, Mohsen Shahbaznezhadfard1 and Manuela Vanegas-Ferro1

  1. Arizona State University, USA
  2. Manchester Metropolitan University, UK

Imagine a world where models are available to build upon. You do not have to build from scratch and painstakingly try to figure out how published papers are getting the published results. To achieve this utopian world, models have to be findable, accessible, interoperable, and reusable (FAIR). With the “Making Models FAIR” initiative, we seek to contribute to moving towards this world.

The initiative – Making Models FAIR – aims to provide capacity building opportunities to improve the skills, practices, and protocols to make computational models findable, accessible, interoperable and reusable (FAIR). You can find detailed information about the project on the website (tobefair.org), but here we will present the motivations behind the initiative and a brief outline of the activities.

There is increasing interest to make data and model code FAIR, and there is quite a lot of discussion on standards (https://www.openmodelingfoundation.org/ ). What is lacking are opportunities to gain skills for how to do this in practice. We have selected a list of highly cited publications from different domains and developed a protocol for making those models FAIR. The protocol may be adapted over time when we learn what works well.

This list of model publications provides opportunities to learn the skills needed to make models FAIR. The current list is a starting point, and you can suggest alternative model publications as desired. The main goal is to provide the modeling community a place to build capacity in making models FAIR. How do you use Github, code a model in a language or platform of your choice, and write good model documentation? These are necessary skills for collaboration and developing FAIR models. A suggested way of participating is for an instructor to have student groups participate in this activity, selecting a model publication that is of interest to their research.

To make a model FAIR, we focus on five activities:

  1. If the code is not available with the publication, find out whether the code is available (contact the authors) or replicate the model based on the model documentation. It might also happen that the code is available in programming language X, but you want to have it available in another language.
  2. If the code does not have a license, make sure an appropriate license is selected to make it available.
  3. Get a DOI, which is a permanent link to the model code and documentation. You could use comses.net or zenodo.org or similar services.
  4. Can you improve the model documentation? There is typically a form of documentation in a publication, in the article or an appendix, but is this detailed enough to understand how and why certain model choices have been made? Could you replicate the model from the information provided in the model documentation?
  5. What is the state of the model code? We know that most of us are not professional programmers and might be hesitant to share our code. Good practice is to provide comments on what different procedures are doing, defining variables, and not leave all kinds of wild ideas commented out left in the code base.

Most of the models listed do not have code available with the publication, which will require participants to contact the original others to obtain the code and/or to reproduce the code from the model documentation.

We are eager to learn what challenges people experience to make models FAIR. This could help to improve the protocols we provide. We also hope that those who made a model FAIR publish a contribution in RofASSS or relevant modeling journals. For publishing contributions in journals, it would be interesting to use a FAIR model to explore the robustness of the model results, especially for models that have been published many years ago and for which there were less computational resources available.

The tobefair.org website contains a lot of detailed information and educational opportunities. Below is a diagram from the site that aims to illustrate the road map of making models FAIR, so you can easily find the relevant information. Learn more by navigating to the About page and clicking through the diagram.

Making simulation models findable, accessible, interoperable and reusable is an important part of good scientific practice for simulation research. If important models fail to reach this standard, then this makes it hard for others to reproduce, check and extend them. If you want to be involved – to improve the listed models, or to learn the skills to make models FAIR – we hope you will participate in the project by going to tobefair.org and contributing.

Janssen, M.A., Claborn, K., Edmonds, B., Shahbaznezhadfard, M. and Vanegas-Ferro, M. (2023) Making Models FAIR: An educational initiative to build good ABM practices. Review of Artificial Societies and Social Simulation, 8 May 2023. https://rofasss.org/2023/05/11/fair/

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

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Towards an Agent-based Platform for Crisis Management

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By Christian Kammler1, Maarten Jensen1, Rajith Vidanaarachchi2 Cezara Păstrăv1

  1. Department of Computer Science, Umeå University, Sweden
    Transport, Health, and Urban Design (THUD)
  2. 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

1       Introduction

Agent-based modelling can be a valuable tool for gaining insight into crises [3], 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 [6], MESA (Python) [4], Repast (Java) [1,5], or Agents.jl (Julia) [2], 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):

  1. What are relevant roles that can be identified for a platform?
  2. 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.

Acknowledgements

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.

References

  1. Collier, N., North, M.: Parallel agent-based simulation with repast for high per- formance computing. SIMULATION 89(10), 1215–1235 (2013), https://doi.org/10. 1177/0037549712462620
  2. Datseris, G., Vahdati, A.R., DuBois, T.C.: Agents.jl: a performant and feature-full agent-based modeling software of minimal code complexity. SIMULATION 0(0), 003754972110688 (2022), https://doi.org/10.1177/00375497211068820
  3. Dignum, F. (ed.): Social Simulation for a Crisis: Results and Lessons from Simulating the COVID-19 Crisis. Springer International Publishing, Cham (2021)
  4. Kazil, J., Masad, D., Crooks, A.: Utilizing python for agent-based modeling: The mesa framework. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A., Hussain, M. (eds.) Social, Cultural, and Behavioral Modeling. pp. 308–317. Springer Interna- tional Publishing, Cham (2020)
  5. North, M.J., Collier, N.T., Ozik, J., Tatara, E.R., Macal, C.M., Bragen, M., Sydelko, P.: Complex adaptive systems modeling with Repast Simphony. Complex Adaptive Systems Modeling 1(1), 3 (March 2013), https://doi.org/10.1186/2194-3206-1-3
  6. 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

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)