Working Paper: NBER ID: w29378
Authors: Andrew Caplin
Abstract: Cognitive economics studies imperfect information and decision-making mistakes. A central scientific challenge is that these can’t be identified in standard choice data. Overcoming this challenge calls for data engineering, in which new data forms are introduced to separately identify preferences, beliefs, and other model constructs. I present applications to traditional areas of economic research, such as wealth accumulation, earnings, and consumer spending. I also present less traditional applications to assessment of decision-making skills, and to human-AI interactions. Methods apply both to individual and to collective decisions. I make the case for broader application of data engineering beyond cognitive economics. It allows symbiotic advances in modeling and measurement. It cuts across existing boundaries between disciplines and styles of research.
Keywords: No keywords provided
JEL Codes: A12; D01; D15
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| state-dependent stochastic choice data (SDSC) (C25) | better identification of decision-making processes (utilities and beliefs) (D91) |
| information treatments (C22) | influence decision-making outcomes (D91) |