Modeling the world requires an understanding of its dynamic nature and the emergence of equilibrium as a phenomenon rather than a certainty. While a model may suggest a path from one state to another, it emphasizes the importance of the journey and dynamics involved, rather than just the end point. Individual behaviors can deviate significantly from ideal market conditions, but this doesn't negate the fundamental constraints of supply and demand that shape the market. People are not perfectly rational in their decisions; instead, they learn from the system's constraints, which highlights the necessity of integrating learning into models to accurately reflect human behavior.
Physicist J. Doyne Farmer wants a new kind of economics that takes account of what we've learned from chaos theory and that builds more accurate models of how humans actually behave. Listen as he makes the case for complexity economics with EconTalk's Russ Roberts. Farmer argues that complexity economics makes better predictions than standard economic theory and does a better job dealing with the biggest problems in today's society.