Advanced Search
MyIDEAS: Login to save this paper or follow this series

How to Classify a Government? Can a Neural Network do it?

Contents:

Author Info

  • António Caleiro

    ()
    (Department of Economics, University of Évora)

Abstract

An electoral cycle created by governments is a phenomenon that seems to characterise, at least in some particular occasions and/or circumstances, the democratic economies. As it is generally accepted, the short-run electorally-induced fluctuations prejudice the long-run welfare. Since the very first studies on the matter, some authors offered suggestions as to what should be done against this electorally-induced instability. A good alternative to the obvious proposal to increase the electoral period length is to consider that voters abandon a passive and naive behaviour and, instead, are willing to learn about government?s intentions. The electoral cycle literature has developed in two clearly distinct phases. The first one considered the existence of non-rational (naive) voters whereas the second one considered fully rational voters. It is our view that an intermediate approach is more appropriate, i.e. one that considers learning voters, which are boundedly rational. In this sense, one may consider neural networks as learning mechanisms used by voters to perform a classification of the incumbent in order to distinguish opportunistic (electorally motivated) from benevolent (non-electorally motivated) behaviour of the government. The paper explores precisely the problem of how to classify a government showing in which, if so, circumstances a neural network, namely a perceptron, can resolve that problem.

Download Info

If 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.
File URL: http://hdl.handle.net/10174/8428
Download Restriction: no

Bibliographic Info

Paper provided by University of Évora, Department of Economics (Portugal) in its series Economics Working Papers with number 9_2005.

as in new window
Length: 24 pages
Date of creation: 2005
Date of revision:
Handle: RePEc:evo:wpecon:9_2005

Contact details of provider:
Postal: Largo dos Colegiais 2, 7000 - 803ÉVORA
Phone: + 351 266 74 08 94
Fax: + 351 266 74 24 94
Email:
Web page: http://www.decon.uevora.pt
More information through EDIRC

Related research

Keywords: Classification; Elections; Government; Neural Networks; Output Persistence; Perceptions;

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:evo:wpecon:9_2005. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Maria Aurora Murcho Galego).

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.