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    <dc:publisher>Economics: The Open-Access, Open Assessment E-Journal</dc:publisher>
    <dc:publisher>http://www.economics-ejournal.org</dc:publisher>
    <dc:language>en</dc:language>

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<dc:creator>António Caleiro</dc:creator>
<dc:title>How Can Voters Classify an Incumbent under Output Persistence</dc:title>
<dc:date>2008-04-11</dc:date>
<dc: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.</dc:description>
<dc:identifier>http://www.economics-ejournal.org/economics/discussionpapers/2008-16</dc:identifier>
<dc:subject>JEL C45</dc:subject>
<dc:subject>JEL D72</dc:subject>
<dc:subject>JEL E32</dc:subject>


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