We are working on a project on developing methods for functional agent-based modelling. We have implemented a SIR model using Haskell and Yampa, a functional reactive programming library.
Is it possible to do some correctness proving / formal verification in agent based modelling? Can you guide us to the relevant literature that explains the process?
I would try to look at Web of Science with a filter applied to the topic you are looking for: https://login.webofknowledge.com/error/Error?PathInfo=%2F&Error=IPError
This paper may be helpful. It describes some guidelines about developing ABMs.
https://pdfs.semanticscholar.org/2869/5f97c7f5cedab143016720a42e67e5ea29a9.pdf
There are a lot of works on validation of ABMs but, as a summary, there is no a specific set of techniques. It depends on which features you want to validate. As a recommendation, normally, all the ABMs are composed of different "small pieces" (or modules). Try to see if the small parts make sense and they behave correctly. In ABMs, the weakest point is the agent's behavior. This paper illustrates another way to validate ABM based on agents' behavior.
http://strategicreasoning.org/wp-content/uploads/2016/01/transcript.pdf
I hope my answer will be useful. Regards.
I wrote a paper about calibration and validation an agent based simulation model
Conference Paper To Calibrate & Validate an Agent-based Simulation Model - An...
It is perhaps helpful for you
Hi, take a look at our chapter "Verifying and Validating Simulations"Chapter Verifying and Validating Simulations
oriented towards ABM.
More specifically, for verification of ABM model replications, check our paper "Model-independent comparison of simulation output"Article Model-independent comparison of simulation output
Good luck!
I agree with Ervin regarding the North and Macal text.
North, M. & Macal, C. (2007). Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling. New York: Oxford University Press.
You may also want to review:
Railsback, S. & Grimm, V. (2012). Agent-based and individual-based modeling: A practical introduction. Princeton University Press. New Jersey.
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