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).

Crafting Future Scenarios with the Help of AI - Roland M. Mueller, Katja Thoring at al.

Developing future research poses some problems, including the fact that you don't have the users for manymuch the technology you want to produce. Research questions: Goals: provide AI assistance to future scenario development; AI assistance with scenario rating; AI assisatance with Qualitative Feedback, AI assistance with scenario iterations etc. - Can we democratize access to collective expert knoweldge through Generative AI? - Can we expand the established - Can we build human twins to Project called: Delphi Study Experiment 1: They developed 23 future scenarios using a panel of experts: people from different non-AI fields, such as science fiction authors, business people. And they conmpared that with the ideas of the people in the AI research field. E.g. of solution of the painel of experts: Digital Detox Zone (a place in the office which is not digitally supported) Experiment 2: compare the Human expers and AI experts with a Digital Twin. In short, it does not work yet. Paper to read: Designing the Future With the “Delphi Design Sprint”: Introducing a Novel Method for Design Science Research - https://www.researchgate.net/publication/357746370_Designing_the_Future_With_the_Delphi_Design_Sprint_Introducing_a_Novel_Method_for_Design_Science_Research Discussion about the use of Digital Twins in these scenarios: - Good potential for triangulation with field experts and AI people. - Good inspiration for future works in this area Ethical concerns: - GenAI hallucinations are not asuch aproblem for scenairo development compared to factual quesitons - Tranparency of AI involvement - Specific requirement and charactiristic of AI scneaqrio Crafting Future Scnarios with the Help of AI: Potentials of a Hybrid Delphi Expert Panel. HICSS Mind th eFuturee Gap: Introducting the FOD Framework for Future Oriented Design. HICSS

Digital Everything: From Twins to Circular Economy - Barbara Dinter

Digital Everything: From Twins to Circular Economy Barbara Dinter Barbara is one of the IS chairs, focusing on Business Intelligence in TU Chemnitz This presentation is about some german-funded projects. Project 1 - Co-Twin - Vision of a collaboration digital tiwn (DT) in value chain networks. - Whole life cycle - she applies Business Models They transfered the ARIS idea of views (BP view, Data view etc.) to Digital Twins. They have: component view, data view, visualization view, network view... and others. - For all stakeholders in a value chain. The DT is used on the planning phase Results: demonstrator prottoype, 3 use cases, taxanmy, reference architecture, design guidelines and conceptualizations. Project 2 - The circular economy - Part 1 - integration with Co-Twin project Goals: sustainability, enrionmental protection and increased efficiency. Key aspects: Reduce, reuse, repair and recycle; sustainable business models, systemic approach, design for longevity and integration of digital technologies. - Part 2 - The circular economy Digital ecosytem for circular economy in the automotive industry (DIONA) collaboration with other academic partners: TU Dortmun and Fraunshofer ISST. She also mentioned 12 projects with industry. DIONA Focus areas - Transfer and networking: coordination of 12 MobilKreis projects, oraganization of physical and digital meetings, knwoeldge tranfer research activities. - Cyberphysical Lab for SMEs to open experimental space for simulations and test and vailidate scnearios without disrupting live processes. - Digital Hub Research topics: 1) conceptualization of use caes in Circular economy 2) BPM in Circular Economy (adaptation of capabilities, models and technologies for that)