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.
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Paper provided by Kiel Institute for the World Economy in its series Economics Discussion Papers with number
2008-16.
Find related papers by JEL classification: C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Models of Political Processes: Rent-seeking, Elections, Legislatures, and Voting Behavior E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
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