How Can Voters Classify an Incumbent under Output Persistence

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10419/17988

Title:	How Can Voters Classify an Incumbent under Output Persistence
Keywords:	E32 D72 C45 ddc:330 Classification elections incumbent neural networks output persistence perceptrons Politischer Konjunkturzyklus Wahlverhalten Lernprozess Neuronale Netze Theorie
Issue Date:	16-Oct-2013
Publisher:	Kiel Institute for the World Economy (IfW) Kiel
Description:	The literature on electoral cycles has developed in two distinct phases. The first one considered the existence of non-rational (naive) voters whereas the second one considered fully rational voters. In our perspective, an intermediate approach is more interesting, i.e. one that considers learning voters, which are boundedly rational. In this sense, neural networks may be considered 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. The paper shows in which circumstances a neural network, namely a perceptron, can resolve that problem of classification. This is done by considering a model allowing for output persistence, which is a feature of aggregate supply that, indeed, may make it impossible to correctly classify the incumbent.
URI:	http://koha.mediu.edu.my:8181/xmlui/handle/10419/17988
Other Identifiers:	http://hdl.handle.net/10419/17988 ppn:561923051 RePEc:zbw:ifwedp:7258
Appears in Collections:	EconStor

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