IRIS and SimTech

Project Description

As new technologies sweep the world, they are poised to bring extensive changes that will affect all aspects of our society and life, as well as see organizations increasingly adopt artificial intelligence (AI) to optimize various aspects of their operations. While AI offers new opportunities, it also presents unintended challenges that societal and organizational leaders must carefully navigate. Numerous predictions have been made about the forthcoming AI revolution, ranging from optimistic ones that predict a science fiction utopia driven by nanotechnology and AI, to pessimistic ones that see AI endangering the very survival of human beings, to pragmatic ones that believe AI technologies can be controlled through effective regulation, and finally the doubting ones stating that the AI will never have human-like capabilities and will never become a threat.

This project aims to develop a deeper understanding of systemic structures that govern dynamics of AI adoption within society and organizations, which will help us identify potential risks and opportunities, as well as propose actionable policies that will work for the betterment of our socio-economic systems. We aim to investigate this topic through conceptual analysis supported by systems thinking, system dynamics modeling and computer simulation, and laboratory experiments.

Duration

09/2022 - 08/2024

Funding

The project is funded by the German Research Foundation within the Cluster of Excellence "Data-Integrated Simulation Science" (EXC 2075).

Project focus

The transmission of infectious diseases depends on human behavior and their relations. However, current epidemiological models consider social structures only at a highly abstract level. To increase the predictive capability and explanatory power, models of human behavior incorporating social complexity are therefore urgently needed. We address this gap by developing an agent-based approach that utilizes comprehensive micro-level data of complete households. This allows us to create artificial societies that are representative for underlying social structures and contact networks. Based on comprehensive COVID-19 data, we then utilize Bayesian model calibration to estimate unknown parameters and quantify their uncertainty. Conducting various simulation experiments will then allow us to identify super-spreaders and assess the efficiency of interventions. Thus, the project makes not only a substantial contribution to a holistic ”Digital Human Model”, but is also a methodological response to the increasing demand for empirically-calibrated simulation models. However, computational models always bear the risk of incorporating biases. We will tackle this challenge, which is enhanced by potential stigmatization of super-spreaders, by incorporating sensitivity analyses and so pave the way for the development of systematic ”methods of reflection”.

Duration

07/2022 – 12/2025

Involved IRIS Members

• Jun.-Prof. Dr. Raphael Heiberger

• Marius Kaffai

Funding

The project is funded by the German Research Foundation within the Cluster of Excellence "Data-Integrated Simulation Science" (EXC 2075).