Complexity Science and AI
Understand complex adaptive systems and use AI to model, predict, decide and transform them responsibly.
- For
- Engineers and researchers taking AI into real, reactive systems
- You will be able to
- Explain what makes a system complex — interaction, feedback and emergence
- Model complex systems with agent-based, dynamical and network models
- Apply AI-driven search and algorithm discovery to open problems
- Analyse self-improving agents as complex adaptive systems — stability, runaway and evaluator gaming
- Anticipate the second-order effects of deploying AI into real systems
- Build and study one system end to end
AI that searches, improves itself and acts inside complex systems — understood through complexity science, and taken from benchmarks into real, reactive settings.
From the work
- Complex Systems, Not Integrations13 Sep 2026 · why AI and forward-deployed engineering need systems thinking
Questions about this course, or a case that argues with it? Write to mepc3197@columbia.edu · the title goes in the subject line
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