Publikationen

In this work, we investigate Markov aggregation for agent-based models (ABMs). Specifically, if the ABM models agent movements on a graph, if its ruleset satisfies certain assumptions, and if the aim is to simulate aggregate statistics such as vertex populations, then the ABM can be replaced by a Markov chain on a comparably small state space. This equivalence between a function of the ABM and a smaller Markov chain allows to reduce the computational complexity of the agent-based simulation from being linear in the number of agents, to being constant in the number of agents and polynomial in the number of locations.We instantiate our theory for a recent ABM for forced migration (Flee). We show that, even though the rulesets of Flee violate some of our necessary assumptions, the aggregated Markov chain-based model, MarkovFlee, achieves comparable accuracy at substantially reduced computational cost. Thus, MarkovFlee can help NGOs and policy makers forecast forced migration in certain conflict scenarios in a cost-effective manner, contributing to fast and efficient delivery of humanitarian relief.

In the context of humanitarian support for forcibly displaced persons, camps play an important role in protecting people and ensuring their survival and health. A challenge in this regard is to find optimal locations for establishing a new asylum-seeker/unrecognized refugee or IDPs (internally displaced persons) camp. In this paper we formulate this problem as an instantiation of the well-known facility location problem (FLP) with three objectives to be optimized. In particular, we show that AI techniques and migration simulations can be used to provide decision support on camp placement.

Accurate digital twinning of the global challenges (GC) leadsto computationally expensive coupled simulations. These simulationsbring together not only different models, but also various sources of mas-sive static and streaming data sets. In this paper, we explore ways tobridge the gap between traditional high performance computing (HPC)and data-centric computation in order to provide efficient technologicalsolutions for accurate policy-making in the domain of GC. GC simula-tions in HPC environments give rise to a number of technical challengesrelated to coupling. Being intended to reflect current and upcoming situ-ation for policy-making, GC simulations extensively use recent streamingdata coming from external data sources, which requires changing tradi-tional HPC systems operation. Another common challenge stems fromthe necessity to couple simulations and exchange data across data centersin GC scenarios. By introducing a generalized GC simulation workflow,this paper shows commonality of the technical challenges for various GCand reflects on the approaches to tackle these technical challenges in theHiDALGO project