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It utilizes software engineering methods to provide a structured approach to identify agent interactions, and design simulation architecture and agent behaviour. The framework uses the Unified Modelling Language (UML) as a standard specification language, and includes a simulation development lifecycle, a step-by-step development guideline, and a short guide for modelling agent behaviour with statecharts.
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We construct a development framework with incorporated software engineering techniques, then tailored it to ABMS of PGG. This thesis aims to leverage the potential of ABMS of PGG, focusing on the development methodology of ABMS and the modelling of agent behaviour. We believe that this has contributed to ABMS of PGG not having been used to its full potential. However, there is a lack of guidelines regarding the detailed development process and the modelling of agent behaviour for agent-based models of PGGs.
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Research findings from these laboratory studies have inspired studies using computational agents and vice versa. Social scientists can conduct laboratory experiments of PGGs to study human behaviours in strategic situations. In the PGG, participants secretly choose how many of their private money units to put into a public pot. One of the popular strategic situations is the Public Goods Game (PGG). In Economics, ABMS has been used to model many strategic situations. Because ABMS offers a methodology to create an artificial society in which actors with their behaviour can be designed and results of their interaction can be observed, it has gained attention in social sciences such as Economics, Ecology, Social Psychology, and Sociology. The interaction of the individual behaviours of the agents results in the global behaviour of the system. These charts showcased metrics like backlog, orders processed by value ($) and volume, aging buckets, team utilization, cost of operations, etc.In Agent-based Modelling and Simulation (ABMS), a system is modelled as a collection of agents, which are autonomous decision-making units with diverse characteristics. This view was used to display the impact of an AI engine on the process without shooting too many numbers at the viewer. Then we surrounded the high-level process view images with panes containing charts to provide an excellent visual interface. Then, we created motion paths to let the orders move over the images of sub-processes to give them a progressive motion feel. We then saved these slides as images to be used in the model. We created slides of as-is and to-be scenarios, each containing only the essential sub-processes. We again used this logic to hide and unhide in creating a high-level overview of the overall process. E.g., When the model starts, one will see the toggle button image in the off state, but when you click on the image, the off-state image is set to invisible, and the on-state image will become visible and vice versa. We used programming to hide and unhide the images. Instead, we used images of the two states of a toggle button, i.e., on and off. While creating the switch, we didn’t use a button control as it looked very basic. At this juncture, the work we did to make the switch to toggle between as-is and to-be scenarios and view areas, that I learned in the AnyLogic in 3 days book, was to become the base of our presentation layer. We took a step back and decided to create a separate presentation layer over what we had built.