How Can Voters Classify an Incumbent under Output Persistence
AbstractThe 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. --
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Bibliographic InfoPaper provided by Kiel Institute for the World Economy in its series Economics Discussion Papers with number 2008-16.
Date of creation: 2008
Date of revision:
Classification; elections; incumbent; neural networks; output; persistence; perceptrons;
Find related papers by JEL classification:
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-05-10 (All new papers)
- NEP-CDM-2008-05-10 (Collective Decision-Making)
- NEP-MAC-2008-05-10 (Macroeconomics)
- NEP-POL-2008-05-10 (Positive Political Economics)
- NEP-SOC-2008-05-10 (Social Norms & Social Capital)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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