Reinforcement learning (RL) has recently emerged as a core methodology for modelling and analysing complex adaptive systems. Ensuring that learning agents behave safely, robustly, and transparently is ...

Do we always need to interview human beings in the flesh, or can we get reliable information by observing thousands of 'I-individuals' who are pre-programmed to follow the same utility function as ...

In this paper, we develop an agent-based model to study the impact of a broad range of regulation policies on the banking system. The model builds on an iterated version of the Diamond and Dybvig ...

"The promise of human behavioral simulation—general-purpose computational agents that replicate human behavior across domains—could enable broad applications in policymaking and social science." ...

The agent-based simulation model AMIRIS has been developed by the Energy Systems Analysis department and offers an innovative approach for the analysis and evaluation of energy policy instruments and ...

Advancements in Immersive Technologies and XR Integration This year’s Winter Simulation Conference really highlighted how far ...

Simulations in education are teaching strategies that mimic real-life scenarios of events or processes. They aid in a clear understanding of concepts and have been an integral part of science ...