Neighborhood Approximations for Non-Linear Voter Models
Non-linear voter models assume that the opinion of an agent depends on the opinions of its neighbors in a non-linear manner. This allows for voting rules different from majority voting. While the linear voter model is known to reach consensus, non-linear voter models can result in the coexistence of...
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MDPI AG
2015-11-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/17/11/7658 |
| _version_ | 1811298220998918144 |
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| author | Frank Schweitzer Laxmidhar Behera |
| author_facet | Frank Schweitzer Laxmidhar Behera |
| author_sort | Frank Schweitzer |
| collection | DOAJ |
| description | Non-linear voter models assume that the opinion of an agent depends on the opinions of its neighbors in a non-linear manner. This allows for voting rules different from majority voting. While the linear voter model is known to reach consensus, non-linear voter models can result in the coexistence of opposite opinions. Our aim is to derive approximations to correctly predict the time dependent dynamics, or at least the asymptotic outcome, of such local interactions. Emphasis is on a probabilistic approach to decompose the opinion distribution in a second-order neighborhood into lower-order probability distributions. This is compared with an analytic pair approximation for the expected value of the global fraction of opinions and a mean-field approximation. Our reference case is averaged stochastic simulations of a one-dimensional cellular automaton. We find that the probabilistic second-order approach captures the dynamics of the reference case very well for different non-linearities, i.e., for both majority and minority voting rules, which only partly holds for the first-order pair approximation and not at all for the mean-field approximation. We further discuss the interesting phenomenon of a correlated coexistence, characterized by the formation of large domains of opinions that dominate for some time, but slowly change. |
| first_indexed | 2024-04-13T06:16:17Z |
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| id | doaj.art-76c5e12939594357bf1ffe1b16d6c05d |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T06:16:17Z |
| publishDate | 2015-11-01 |
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| spelling | doaj.art-76c5e12939594357bf1ffe1b16d6c05d2022-12-22T02:58:48ZengMDPI AGEntropy1099-43002015-11-0117117658767910.3390/e17117658e17117658Neighborhood Approximations for Non-Linear Voter ModelsFrank Schweitzer0Laxmidhar Behera1Chair of Systems Design, ETH Zürich, Weinbergstrasse 58, 8092 Zürich, SwitzerlandDepartment of Electrical Engineering, Indian Institute of Technology, 208016 Kanpur, IndiaNon-linear voter models assume that the opinion of an agent depends on the opinions of its neighbors in a non-linear manner. This allows for voting rules different from majority voting. While the linear voter model is known to reach consensus, non-linear voter models can result in the coexistence of opposite opinions. Our aim is to derive approximations to correctly predict the time dependent dynamics, or at least the asymptotic outcome, of such local interactions. Emphasis is on a probabilistic approach to decompose the opinion distribution in a second-order neighborhood into lower-order probability distributions. This is compared with an analytic pair approximation for the expected value of the global fraction of opinions and a mean-field approximation. Our reference case is averaged stochastic simulations of a one-dimensional cellular automaton. We find that the probabilistic second-order approach captures the dynamics of the reference case very well for different non-linearities, i.e., for both majority and minority voting rules, which only partly holds for the first-order pair approximation and not at all for the mean-field approximation. We further discuss the interesting phenomenon of a correlated coexistence, characterized by the formation of large domains of opinions that dominate for some time, but slowly change.http://www.mdpi.com/1099-4300/17/11/7658opinion dynamicsvoter modelpair approximationhigher-order probability distributioncellular automata |
| spellingShingle | Frank Schweitzer Laxmidhar Behera Neighborhood Approximations for Non-Linear Voter Models Entropy opinion dynamics voter model pair approximation higher-order probability distribution cellular automata |
| title | Neighborhood Approximations for Non-Linear Voter Models |
| title_full | Neighborhood Approximations for Non-Linear Voter Models |
| title_fullStr | Neighborhood Approximations for Non-Linear Voter Models |
| title_full_unstemmed | Neighborhood Approximations for Non-Linear Voter Models |
| title_short | Neighborhood Approximations for Non-Linear Voter Models |
| title_sort | neighborhood approximations for non linear voter models |
| topic | opinion dynamics voter model pair approximation higher-order probability distribution cellular automata |
| url | http://www.mdpi.com/1099-4300/17/11/7658 |
| work_keys_str_mv | AT frankschweitzer neighborhoodapproximationsfornonlinearvotermodels AT laxmidharbehera neighborhoodapproximationsfornonlinearvotermodels |