The Complexity of New Data Worlds: Policy, Security, Environment, and Inference
Over the last decade the quantity, diversity, and quality of research data have undergone a transformation. The benefits of this data are readily apparent and have generated discussions both substantive and superficial around “big data” and machine learning.
Of significant interest to complexity science is how several data sources can be combined and then best analyzed to reach new forms of consensus in matters of global importance. In particular, we should like to understand how the increase in data availability is often coupled to significant skepticism over scientific theories (evolution, climate change, financial instability), can present new challenges to privacy, generate large-scale violations of security, and suggest new forms of prediction no longer based on the understanding of fundamental mechanisms.
The expertise of the SFI research network in matters of machine inference, environmental science, research policy, financial markets, and complexity education, suggests that SFI could make important contributions to the understanding these New Data Worlds. By convening thoughtful data scientists with complexity scientists spanning a range of disciplines from ecology to the economy, we aim to foster conversations and research efforts that can better integrate their approaches.