By Robin Faber
I am currently doing my master thesis in Computer Science at TU Delft on Data Mining (DM) in Agent-Based Simulation. The goal of this thesis is to provide model designers and analysts with DM tools to make the evaluation of models easier.
The main idea is to create a tool in Python that connects with NetLogo to run models, design experiments and obtain and present the output with visualisations. Because Python has many data analytic libraries, it provides tools that NetLogo lacks in terms of data analytics for the output of ABMs. From my understanding, there are some tools in NetLogo such as BehaviorSpace to run experiments, but this is quite basic and produces a text file which still has to be analysed elsewhere. What I would like to do is develop a library in Python that streamlines this whole process of “run model → get output → analyse output”, with a focus on the usability and ease of use to also make it available for people that are not experienced programmers. However, because my background is in Computer Science, I obviously lack some knowledge of what is needed in order for the tool to be useful and usable for an actual model designer.
The three main questions I would like to ask the RofASSS readers are these:
- Which requirements would you define to make the tool easy to use for non-programmers? (e.g. documentation, GUI, lines of code, data structures)
- What type of information is important to obtain from a simulation? (e.g variables, locations, agent counts)
- How should the information obtained from the model output/experiments be presented? (e.g, types of graphs/tables/visualisation)
If you have any questions, comments or would like to schedule a call to discuss this topic, please contact me using the comments facility at the bottom of this post (for comments) or emailing me at: email@example.com.
Faber, R.J. (2021) Query: How could we make Data Mining tools more useful for Agent-Based Modelling. Review of Artificial Societies and Social Simulation, 5th February 2021. https://rofasss.org/2021/02/3/dm4abm/