Why is there such a gap between economic theory and the reality of our lived experience? Mainstream economics often presumes that people make rational decisions based on perfect information. These fictional decision-makers inhabit static worlds, where variables remain constant and where simple algorithms can predict the rise and fall of financial markets. Yet we all know the real world presents a much more dynamic setting.
This panel will explore the possibilities for advancing our understanding of economics through the ideas and tools of complexity science. By viewing markets as complex adaptive systems, rather than a series of unrelated transactions, complexity scientists are gaining a better understanding of emergent behaviors and changing states that characterize real-world economies.
How can agent-based modeling, behavioral economics, and machine learning increase our understanding of economic systems? What are the implications of these insights for economic and investment practice? Join us to explore these questions and more. This panel discussion, co-hosted by the Santa Fe Institute and Thornburg Investment Management features:
Rob Axtell, who earned an interdisciplinary Ph.D. degree at Carnegie Mellon University, where he studied computing, social science, and public policy. His teaching and research involves computational and mathematical modeling of social and economic processes. Specifically, he works at the intersection of multi-agent systems computer science and the social sciences, building so-called agent-based models for a variety of market and non-market phenomena.