‘Homo economicus’ as an intuitive statistician (2): Bayesian diagnostic learning

Reza Salehnejad

Chapter 4 in Rationality, bounded rationality and microfoundations, 2007, pp 106-164 from Palgrave Macmillan

Abstract: Abstract The bounded rationality programme views the economy as a society of intuitive statisticians. The key for the success of this programme is the existence of a ‘tight enough’ theory of statistical inference. We have so far shown that there is no entirely data-driven algorithm that receives a finite sample of data and yields the model that best approximates the process generating the data. Learning an interpretable model of a choice situation requires starting with a parametric probability model. To analyse the programme further, we now examine the possibility of a ‘tight enough’ theory of learning within the general framework of the Bayesian theory, which is primarily a theory of parametric inference.

Keywords: Predictive Distribution; Bounded Rationality; Prior Density; Empirical Adequacy; Distribution Family (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:palchp:978-0-230-62515-0_5

Ordering information: This item can be ordered from
http://www.palgrave.com/9780230625150

DOI: 10.1057/9780230625150_5

Access Statistics for this chapter

More chapters in Palgrave Macmillan Books from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().