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Palombi F.,ENEA | Toti S.,ISTAT Italian National Institute of Statistics
Physics Procedia

In the talk we reviewed universal aspects of voting behavior in proportional elections and universality breaking patterns, as established in the existing literature. Focus was made on the Brazilian elections, which are characterized by compulsory voting. We showed how agent-based models can qualitatively and/or quantitatively reproduce the observed empirical distributions. As an example, we discussed the multi-state voter model over a network based on interacting cliques and zealot candidates. © 2015 The Authors. Published by Elsevier B.V. Source

Palombi F.,ENEA | Toti S.,ISTAT Italian National Institute of Statistics
International Journal of Modern Physics C

Approximate weak solutions of the Fokker-Planck equation represent a useful tool to analyze the equilibrium fluctuations of birth-death systems, as they provide a quantitative knowledge lying in between numerical simulations and exact analytic arguments. In this paper, we adapt the general mathematical formalism known as the Ritz-Galerkin method for partial differential equations to the Fokker-Planck equation with time-independent polynomial drift and diffusion coefficients on the simplex. Then, we show how the method works in two examples, namely the binary and multi-state voter models with zealots. © 2015 World Scientific Publishing Company. Source

Zardetto D.,ISTAT Italian National Institute of Statistics | Scannapieco M.,ISTAT Italian National Institute of Statistics
Studies in Classification, Data Analysis, and Knowledge Organization

Record Linkage (RL) aims at identifying pairs of records coming from different sources and representing the same real-world entity. Probabilistic RL methods assume that the pairwise distances computed in the record-comparison process obey a well defined statistical model, and exploit the statistical inference machinery to draw conclusions on the unknown Match/Unmatch status of each pair. Once model parameters have been estimated, classical Decision Theory results (e.g. the MAP rule) can generally be used to obtain a probabilistic clustering of the pairs into Matches and Unmatches. Constrained RL tasks (arising whenever one knows in advance that either or both the data sets to be linked do not contain duplicates) represent a relevant exception. In this paper we propose an Evolutionary Algorithm to find optimal decision rules according to arbitrary objectives (e.g. Maximum complete-Likelihood) while fulfilling 1:1, 1:N and N:1 matching constraints. We also present some experiments on real-world constrained RL instances, showing the accuracy and efficiency of our approach. © Springer International Publishing Switzerland 2013. Source

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