Agent-based models, network analysis, evolutionary economics, and machine learning
Santa Fe Institute • 2025
David Krakaeur (head of Santa Fe Institute) and Melanie Mitchell (the author of Complexity: A Guided Tour) examine claims that Large Language Models exhibit emergent capabilities, reviewing several approaches to quantifying emergence, and secondly ask whether LLMs possess emergent intelligence.
Materials:
Annual Review of Statistics and Its Application • 2019
Approximate Bayesian Computation (ABC) is a family of computational methods rooted in Bayesian statistics that are particularly useful for calibrating models of complex systems where traditional likelihood-based methods are infeasible. This paper provides an overview of ABC techniques and their applications.
Santa Fe Institute • 2009
Powerlaw distributions are a common feature in empirical data from complex systems. Aaron Clauset et al. present a principled statistical framework for discerning and quantifying power-law behavior in empirical data.
