On the Computational Complexity of Consumer Decision Rules
AbstractA consumer entering a new bookstore can face more than 250,000 alternatives. The efficiency of compensatory and noncompensatory decision rules for finding a preferred item depends on the efficiency of their associated information operators. At best, item-by-item information operators lead to linear computational complexity; set information operators, on the other hand, can lead to constant complexity. We perform an experiment demonstrating that subjects are approximately rational in selecting between sublinear and linear rules. Many markets are organized by attributes that enable consumers to employ a set-selection-by-aspect rule using set information operations. In cyberspace decision rules are encoded as decision aids.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 23 (2004)
Issue (Month): 2 (03)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Peter Earl & Jason Potts, 2013. "The creative instability hypothesis," Journal of Cultural Economics, Springer, vol. 37(2), pages 153-173, May.
- Earl, Peter E. & Wakeley, Tim, 2010. "Economic perspectives on the development of complex products for increasingly demanding customers," Research Policy, Elsevier, vol. 39(8), pages 1122-1132, October.
- repec:ebl:ecbull:v:6:y:2008:i:30:p:1-12 is not listed on IDEAS
- A. Norman & M. Aberty & K. Brehm & M. Drake & S. Gour & C. Govil & B. Gu & J. Hart & G. Kadiri & J. Ke & S. Keyburn & M. Kulkarni & N. Mehta & A. Robertson & J. Sanghai & V. Shah & J. Schieck & Y. Siv, 2008. "Can Consumer Software Selection Code for Digital Cameras Improve Consumer Performance?," Computational Economics, Society for Computational Economics, vol. 31(4), pages 363-380, May.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn) or (Christopher F. Baum).
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.