on 08-Jul-2022 (Fri)

Augustijn et al. [55] categorized the applications of ML in ABM according to three different stages in the modeling process: preprocessing, agent-behavior prediction, decision-making, and postprocessing of decision analysis. An ML algorithm can be trained to model in the pre-processing state as input for the subsequent ABM to replace the rule-based module; further, the ML algorithm is restored for agent behavior prediction to provide the agent actions in the decision-making stage, which can be done using a pre-trained ML algorithm or can include training via the RL. For the postprocessing, the data are used and mapped back to a trained ML algorithm to calibrate or validate the model after running the ABM [55]. Since the ML can be used in different stages of ABM, the functionalities of ML vary. ML mainly serves two functions: 1) to improve the agents’ situational awareness, which is related to prediction or pattern recognition and 2) to reinforce agents’ behaviors, which is related to behavioral interventions

Developing Physically | i Cognitive Neuroscience The Biology of the Mind MICHAEL S. GAZZANIGA University of California, Santa Barbara RICHARD B.

Augustijn et al. [55] categorized the applications of ML in ABM according to three different stages in the modeling process: preprocessing, agent-behavior prediction, decision-making, and postprocessing of decision analysis.

Developers of ABMs have consistently encountered the difficulty of deriving informed rules for behaviors at the scale of individual agents. This gives them little choice but to employ abstract proxy representation of agent behavior, which is not suitable for quantitative analysis.

4. Conclusion

We showed that by using the presented framework it is possible to implement an agent-based model without the need to manually find rules or equations for agent behaviour, which is the most challenging step for most agent-based models.