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Data Engineering for Cognitive Economics

Author

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  • 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.

Suggested Citation

  • Andrew Caplin, 2021. "Data Engineering for Cognitive Economics," NBER Working Papers 29378, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:29378
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    Cited by:

    1. Michele Giannola, 2022. "Parental investments and intra-household inequality in child human capital: evidence from a survey experiment," IFS Working Papers W22/54, Institute for Fiscal Studies.
    2. Jose Apesteguia & Miguel A. Ballester, 2023. "The rationalizability of survey responses," Economics Working Papers 1863, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Ashesh Rambachan, 2022. "Identifying Prediction Mistakes in Observational Data," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    4. Jose Apesteguia & Miguel Ángel Ballester, 2023. "The Rationalizability of Survey Responses," Working Papers 1393, Barcelona School of Economics.
    5. Erin T. Bronchetti & Judd B. Kessler & Ellen B. Magenheim & Dmitry Taubinsky & Eric Zwick, 2023. "Is Attention Produced Optimally? Theory and Evidence From Experiments With Bandwidth Enhancements," Econometrica, Econometric Society, vol. 91(2), pages 669-707, March.

    More about this item

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • C0 - Mathematical and Quantitative Methods - - General
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D15 - Microeconomics - - Household Behavior - - - Intertemporal Household Choice; Life Cycle Models and Saving
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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