Naiveté About Temptation and Self-Control: Foundations for Naive Quasi-Hyperbolic Discounting
Davis S. Ahn,
Ryota Iijima () and
Todd Sarver ()
Additional contact information
Davis S. Ahn: University of California, Berkeley
Ryota Iijima: Cowles Foundation, Yale University, http://economics.yale.edu/people/ryota-iijima
No 2099, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University
We introduce and characterize a recursive model of dynamic choice that accommodates naiveté about present bias. The model incorporates costly self-control in the sense of Gul and Pesendorfer (2001) to overcome the technical hurdles of the Strotz representation. The important novel condition is an axiom for naiveté. We first introduce appropriate definitions of absolute and comparative naiveté for a simple two-period model, and explore their implications for the costly self-control model. We then extend this definition for infinite-horizon environments, and discuss some of the subtleties involved with the extension. Incorporating the definition of absolute naiveté as an axiom, we characterize a recursive representation of naive quasi-hyperbolic discounting with self-control for an individual who is jointly overoptimistic about her present-bias factor and her ability to resist instant gratification. We study the implications of our proposed comparison of naiveté for the parameters of the recursive representation. Finally, we discuss the obstacles that preclude more general notions of naiveté, and illuminate the impossibility of a definition that simultaneously incorporates both random choice and costly self-control. devices.
Keywords: Naive; Sophisticated; Self-control; Quasi-hyperbolic discounting (search for similar items in EconPapers)
JEL-codes: D11 D15 D91 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cbe, nep-evo, nep-mic, nep-neu and nep-upt
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Working Paper: Naiveté About Temptation and Self-Control: Foundations for Naive Quasi-Hyperbolic Discounting (2018)
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