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Authors: Ivan P. Yamshchikov and Sharwin Rezagholi

Affiliation: Max Planck Institute for Mathematics in the Sciences, Germany

ISBN: 978-989-758-297-4

Keyword(s): Electoral Competition, Classification of Political Issues, Dynamic Stochastic Blotto Game, Adaptive Learning.

Abstract: This paper employs a novel method for the empirical analysis of political discourse and develops a model that demonstrates dynamics comparable with the empirical data. Applying a set of binary text classifiers based on convolutional neural networks, we label statements in the political programs of the Democratic and the Republican Party in the United States. Extending the framework of the Colonel Blotto game by a stochastic activation structure, we show that, under a simple learning rule, the simulated game exhibits dynamics that resemble the empirical data.

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Paper citation in several formats:
Yamshchikov I. and Rezagholi S. (2018). Elephants, Donkeys, and Colonel Blotto.In Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS, ISBN 978-989-758-297-4, pages 113-119. DOI: 10.5220/0006761601130119

@conference{complexis18,
author={Ivan P. Yamshchikov and Sharwin Rezagholi},
title={Elephants, Donkeys, and Colonel Blotto},
booktitle={Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,},
year={2018},
pages={113-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006761601130119},
isbn={978-989-758-297-4},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,
TI - Elephants, Donkeys, and Colonel Blotto
SN - 978-989-758-297-4
AU - Yamshchikov I.
AU - Rezagholi S.
PY - 2018
SP - 113
EP - 119
DO - 10.5220/0006761601130119

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