Challenges in Complex Systems Science

TL;DR

This paper discusses the main challenges in complex systems science, especially in social sciences, focusing on data-driven modeling, prediction, and understanding emergent phenomena in interconnected, multi-scale systems.

Contribution

It outlines the key scientific and methodological challenges in complex systems science within the FuturICT initiative, emphasizing the integration of data, models, and prediction in social and technological systems.

Findings

Identifies challenges in data collection and management for complex systems.

Highlights the need for new modeling approaches beyond correlation.

Emphasizes importance of understanding cause-effect relationships and multilevel dynamics.

Abstract

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda.