The presentation discusses the complementarities between AI and behavioral economics, with examples from the speaker's experiences as Chief Economist at Uber, Lyft, and Walmart. The speaker emphasizes the importance of effect identification, architectural nudges, and detection of heterogeneity in using AI to improve the bottom line and make the world better.
- The speaker shares examples of using AI and behavioral economics to improve products and scaling
- Effect identification, architectural nudges, and detection of heterogeneity are important tools in using AI to improve the bottom line and make the world better
- Examples include the economics of apologies at Uber, pricing and wait times at Lyft, and the voltage effect on scaling
- The speaker emphasizes the importance of combining economic theory, field experiments, and AI to identify causal effects and correlations