Thriving in a Turbulent World: Harnessing Complexity for New Possibilities

Abstract

Complexity science has become increasingly important as the world has undergone a series of major transitions, including rapid urban densification, increased physical and digital connection, the rise of global challenges (e.g., climate, migration, and disease) and the growing importance of compute infrastructure and AI. Systems of networked adaptive agents (complex systems) have multiplied from clusters of human communities to large-scale systems of production and exchange. These transitions have coincided with a rise in regional conflict, increasing competition over scarce  resources, and the breakdown in traditional systems of trust and legitimacy. We are entering an era where processes of adaptation (with adaptive agents operating at all scales -- firms, governments, workers, investors) – are increasingly central to performance and resilience. 

Over the last 40 years, significant strides have been made in the development of complexity science. Complexity science takes an interdisciplinary approach to uncovering and illuminating the universal underlying generative mechanisms that animate complex adaptive systems. Understanding these mechanisms provides practitioners with mental, mathematical, and computational models that are better suited to a turbulent world. These mechanisms also help analysts build better statistical models capable of leveraging high-dimensional data sets. The insights provided by this more connected way of thinking is especially relevant in areas like climate variability, digital transformation, economic transitions, and the integration of AI into human decision-making and scientific discovery.

This meeting introduced underlying mechanisms of complexity science in several domains, with a detailed exploration of:

1. Intelligence — Intelligence exists at both the individual and collective levels, encompassing human cognition as well as increasingly artificial forms of intelligence. New technologies, and in particular advances in artificial intelligence, are reshaping intelligence at all levels by augmenting, automating, and in some cases substituting human decision-making. These changes have wide-ranging implications for markets, political processes, organizations, and other social and economic systems.
2. Agent Based Models (ABMs) — The pioneering work at SFI on ABMs seeks to incorporate adaptive processes directly into economic theory and models. These methods are required to deal with problems whose dominant determinants are human decision making under uncertainty. 
3. Emergent Engineering — Humans are very good at engineering tasks when the components are non-adaptive. How should we approach engineering projects when agents can adapt? This includes mechanisms within adaptive agents and their collective behavior in a variety of settings from firms to cites.

Overview Agenda

Thursday, February 5th
9AM to 4PM Gulf Standard Time

The Complexity of Natural Intelligence, Artificial Capability, and the Uses of Emergence with David Krakauer

Agent-Based Modeling in Complexity Economy & Climate Change with J. Doyne Farmer

Experiment in a Complex Setting with Guido Imbens

Hidden Laws of Life, Growth, Death, and Innovation—from Organisms and Companies to Cities and the Anthroposphere with Geoffrey West

Complexity & Policy: A Case Study with Will Tracy