Frontiers in Physics | |
For whom will the Bayesian agents vote? | |
Jonatas eCesar1  Nestor eCaticha1  Renato eVicente2  | |
[1] University of Sao Paulo;University of São Paulo; | |
关键词: agent-based models; Bayesian learning; Sociophysics; opinion dynamics; moral foundations; | |
DOI : 10.3389/fphy.2015.00025 | |
来源: DOAJ |
【 摘 要 】
Within an agent-basedmodel where moral classifications are socially learned, we ask if a population of agents behaves ina way that may be compared withconservative or liberal positions in the real political spectrum. We assume that agents firstexperienceaformative period,in which they adjust their learning style acting assupervised Bayesian adaptive learners. The formative phaseisfollowed bya period of social influence by reinforcement learning. By comparing data generated by the agents with data from a sample of 15000 Moral Foundation questionnaires we found the following. 1. The number of information exchanges in the formative phase correlates positively with statistics identifying liberals in the social influence phase. This is consistent with recent evidence that connectsthe dopamine receptor D4-7R gene, political orientation and early age social clique size. 2. The learning algorithms that result from theformative phase vary in the way they treat novelty and corroborative information with more conservative-like agents treating it more equally than liberal-like agents. This is consistent with the correlation betweenpolitical affiliation and the Openness personality trait reported in the literature.3. Under the increase of a model parameter interpreted as an external pressure, the statistics of liberal agents resemble more those of conservative agents,consistent with reports on the consequences of external threatson measures of conservatism. We also show that in the social influence phase liberal-like agents readapt much faster than conservative-like agentswhensubjected to changes on the relevant set of moral issues. This suggests a verifiable dynamical criterium for attaching liberal or conservative labels to groups.
【 授权许可】
Unknown