Forecasting Election Polls with Spin Systems
| Autor: | Ibarrondo, Ruben, Sanz, Mikel, Orus, Roman |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | SN COMPUT. SCI. 3, 44 (2022) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/s42979-021-00942-9 |
| Popis: | We show that the problem of political forecasting, i.e, predicting the result of elections and referendums, can be mapped to finding the ground state configuration of a classical spin system. Depending on the required prediction, this spin system can be a combination of XY, Ising and vector Potts models, always with two-spin interactions, magnetic fields, and on arbitrary graphs. By reduction to the Ising model our result shows that political forecasting is formally an NP-Hard problem. Moreover, we show that the ground state search can be recasted as Higher-order and Quadratic Unconstrained Binary Optimization (HUBO / QUBO) Problems, which are the standard input of classical and quantum combinatorial optimization techniques. We prove the validity of our approach by performing a numerical experiment based on data gathered from Twitter for a network of 10 people, finding good agreement between results from a poll and those predicted by our model. In general terms, our method can also be understood as a trend detection algorithm, particularly useful in the contexts of sentiment analysis and identification of fake news. Comment: 8 pages, 2 figures, 1 table, revised version |
| Databáze: | arXiv |
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