Utilizing refined models of human behavior enhances economic predictions, a notion broadly supported by economists. However, a discrepancy exists between the principles of behavioral economics, which detail actual human behaviors in economic contexts, and the macroeconomic models employed by institutions like the Federal Reserve and US Treasury. This cognitive dissonance highlights the need for integration of behavioral insights into macroeconomic frameworks to improve policy outcomes.
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.