What are the Ontological Foundations of Simulation Modeling - Gerd Wagner - SCS Weekly Meeting

What are the Ontological Foundations of Simulation Modeling In simulation modeling, you care about modeling objects and events, since we want to simulate the real world, and these are the most important types of ontological categories in the world. Modeling and simulation (M$S) is concerned with modeling dynamical systems which considt of ojbect that are subject to state changes over tim. This happens when one or more of its attribute values are changed. These attributes that change are called state variables. Attribute values may be continuous (smooth) or discrete (in jumps), leading to continuous or discrete processes. In ISs we are typically more concerned with discrete processes. Sometimes, a mix of them: discrete events (e.g. a car bumping into another in traffic) but also continuous events (movement of different objects in traffic). Discrete Systems: - Example: predator-prey ecosystem such as an area populated by wolves and sheep, where births, death and predator-prey encounters are events. - A discrete dynamical system (such as the one in the example above) can be captured either more abstractly with the help of a continous simulation as in System Dynamics, or iwth the help of a Discrete Event Simulation model. Discrete Evnet Simulation (DES) Paradigms: - Event-based simulation with SIMSCRIPT (1962), Event Graphs (1983) - Process Network simulation with GPSS (1961), Arena (1992), AnyLogic etc. It is based on more high-level concepts w.r.t events, which help you to capture concepts of different domains (e.g. manufacturing, traffic etc.) - Coroutine-based Process Interaction simulation with Simla (1967), SimPy, etc. Coroutines are asynchronous programming process stations, which may start, be interrupted and then reestablish processing. - Simulation based on Petri Nets (from the 60s) Object Event M&S Based on the ontological principles: - objects participate in events - events cause state changes of participating objects and follow-up events according to causal regularities. The sturcture of objects and events is described in the form of a UMLclass mode defining object types and event types. The system's dynamics is described in the forms of DPMN (similar to BPMN) process model defining a set of rules. - which caputre causal regularities (as event rules) - and correspond to transition functions of an Abstract State Machine. Causal Regularity Simple Model:

Example of Object Event Model about Phishing We may see an OE Class Model to model the information and a BPMN/DPMN model to model the events

Agent-based M&S - ontologically speaking, agents are special objets that interact with each other and with their environment. - agents interact with their envionment via a perception-action cycel that is modeled in OEM&S in the form of perception events and action events. - Agents interact with each other by sending and receiving messages. In OEM%S, sending a message is an out-message action event and reeiving a message is an in-message event. Example of a basic BPMN Model of Phishing

Example of a Conceptual Information Model about Phishing

Besides the regular relationships (composition, specialization, and general associations), in these kind of Information Models, there are special kinds of associations and multiplicity restrictions: - the association between the entities mean the participation of agents/objects in events. - the multiplicity can indicate snapshot or historical multiplicity restrictions (you may need both kinds of multiplicity in one model).