Elephants, Donkeys, and Colonel Blotto

Ivan P. Yamshchikov, Sharwin Rezagholi

2018

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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