IDEAS home Printed from https://ideas.repec.org/p/zbw/bamber/114.html
   My bibliography  Save this paper

Rational allocation of attention in decision-making

Author

Listed:
  • Schmitt, Stefanie Yvonne

Abstract

This paper proposes a model of attention allocation in decision-making. Attention has various definitions across the literature. Here, I understand attention as selecting information for costly processing. The paper investigates how an agent rationally allocates attention. The resulting attention allocation is context-dependent and influences choice quality. Next to inattention, two strategies of allocating attention prevail. These strategies share similarities with bottom-up and top-down attention - concepts reported in the psychological literature. Exploring firms' strategic considerations reveals an incentive for firms to produce high quality and highlight quality, if consumers expect low quality, and to exploit consumers by producing low quality and shrouding quality, if agents expect high quality.

Suggested Citation

  • Schmitt, Stefanie Yvonne, 2016. "Rational allocation of attention in decision-making," BERG Working Paper Series 114, Bamberg University, Bamberg Economic Research Group.
  • Handle: RePEc:zbw:bamber:114
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/144610/1/864415354.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xavier Gabaix, 2014. "A Sparsity-Based Model of Bounded Rationality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1661-1710.
    2. Paola Manzini & Marco Mariotti, 2014. "Stochastic Choice and Consideration Sets," Econometrica, Econometric Society, vol. 82(3), pages 1153-1176, May.
    3. Raj Chetty & Adam Looney & Kory Kroft, 2009. "Salience and Taxation: Theory and Evidence," American Economic Review, American Economic Association, vol. 99(4), pages 1145-1177, September.
    4. Paul Heidhues & Botond K?szegi & Takeshi Murooka, 2016. "Exploitative Innovation," American Economic Journal: Microeconomics, American Economic Association, vol. 8(1), pages 1-23, February.
    5. Xavier Gabaix & David Laibson, 2018. "Shrouded attributes, consumer myopia and information suppression in competitive markets," Chapters, in: Victor J. Tremblay & Elizabeth Schroeder & Carol Horton Tremblay (ed.), Handbook of Behavioral Industrial Organization, chapter 3, pages 40-74, Edward Elgar Publishing.
    6. Hossain Tanjim & Morgan John, 2006. "...Plus Shipping and Handling: Revenue (Non) Equivalence in Field Experiments on eBay," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 5(2), pages 1-30, January.
    7. Amy Finkelstein, 2009. "E-ztax: Tax Salience and Tax Rates," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(3), pages 969-1010.
    8. , & ,, 2011. "On the strategic use of attention grabbers," Theoretical Economics, Econometric Society, vol. 6(1), January.
    9. Daniel Kahneman, 2003. "Maps of Bounded Rationality: Psychology for Behavioral Economics," American Economic Review, American Economic Association, vol. 93(5), pages 1449-1475, December.
    10. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    11. Jennifer Brown & Tanjim Hossain & John Morgan, 2010. "Shrouded Attributes and Information Suppression: Evidence from the Field," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(2), pages 859-876.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mundt, Philipp & Alfarano, Simone & Milaković, Mishael, 2020. "Exploiting ergodicity in forecasts of corporate profitability," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xavier Gabaix, 2017. "Behavioral Inattention," NBER Working Papers 24096, National Bureau of Economic Research, Inc.
    2. Emmanuel Farhi & Xavier Gabaix, 2020. "Optimal Taxation with Behavioral Agents," American Economic Review, American Economic Association, vol. 110(1), pages 298-336, January.
    3. Hunt Allcott & Nathan Wozny, 2014. "Gasoline Prices, Fuel Economy, and the Energy Paradox," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 779-795, December.
    4. Saur, Marc P. & Schlatterer, Markus G. & Schmitt, Stefanie Y., 2022. "Limited perception and price discrimination in a model of horizontal product differentiation," Games and Economic Behavior, Elsevier, vol. 134(C), pages 151-168.
    5. Koichiro Ito, 2014. "Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing," American Economic Review, American Economic Association, vol. 104(2), pages 537-563, February.
    6. Johannes Abeler & Simon Jäger, 2013. "Complex Tax Incentives - An Experimental Investigation," CESifo Working Paper Series 4231, CESifo.
    7. Tipoe, Eileen, 2021. "Price inattention: A revealed preference characterisation," European Economic Review, Elsevier, vol. 134(C).
    8. George Loewenstein & Zachary Wojtowicz, 2023. "The Economics of Attention," CESifo Working Paper Series 10712, CESifo.
    9. Nicola Lacetera & Devin G. Pope & Justin R. Sydnor, 2012. "Heuristic Thinking and Limited Attention in the Car Market," American Economic Review, American Economic Association, vol. 102(5), pages 2206-2236, August.
    10. Zemin (Zachary) Zhong, 2022. "Chasing Diamonds and Crowns: Consumer Limited Attention and Seller Response," Management Science, INFORMS, vol. 68(6), pages 4380-4397, June.
    11. Raj Chetty & Adam Looney & Kory Kroft, 2009. "Salience and Taxation: Theory and Evidence," American Economic Review, American Economic Association, vol. 99(4), pages 1145-1177, September.
    12. Kenan Kalaycı & Marta Serra-Garcia, 2016. "Complexity and biases," Experimental Economics, Springer;Economic Science Association, vol. 19(1), pages 31-50, March.
    13. Dmitry Taubinsky & Alex Rees-Jones, 2018. "Attention Variation and Welfare: Theory and Evidence from a Tax Salience Experiment," Review of Economic Studies, Oxford University Press, vol. 85(4), pages 2462-2496.
    14. Liang, Hanchao & Yang, Chunpeng & Zhang, Rengui & Cai, Chuangqun, 2017. "Bounded rationality, anchoring-and-adjustment sentiment, and asset pricing," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 85-102.
    15. Tom Blake & Sarah Moshary & Kane Sweeney & Steve Tadelis, 2021. "Price Salience and Product Choice," Marketing Science, INFORMS, vol. 40(4), pages 619-636, July.
    16. Markus Dertwinkel-Kalt & Mats Köster & Matthias Sutter, 2019. "To Buy or not to Buy? Shrouding and Partitioning of Prices in an Online Shopping Field Experiment," CESifo Working Paper Series 7475, CESifo.
    17. Gabaix, Xavier, 2015. "Behavioral Macroeconomics Via Sparse Dynamic Programming," CEPR Discussion Papers 11026, C.E.P.R. Discussion Papers.
    18. Stefano DellaVigna, 2009. "Psychology and Economics: Evidence from the Field," Journal of Economic Literature, American Economic Association, vol. 47(2), pages 315-372, June.
    19. Dertwinkel-Kalt, Markus & Köster, Mats & Sutter, Matthias, 2020. "To buy or not to buy? Price salience in an online shopping field experiment," European Economic Review, Elsevier, vol. 130(C).
    20. Reto Foellmi & Stefan Legge & Lukas Schmid, 2016. "Do Professionals Get It Right? Limited Attention and Risk‐taking Behaviour," Economic Journal, Royal Economic Society, vol. 0(592), pages 724-755, May.

    More about this item

    Keywords

    rational attention; information-processing; decision-making; shrouding;
    All these keywords.

    JEL classification:

    • D10 - Microeconomics - - Household Behavior - - - General
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:bamber:114. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/bebamde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.