TY - JOUR AB - A unifying setup for opinion models originating in statistical physics and stochastic opinion dynamics are developed and used to analyze election data. The results are interpreted in the light of political theory. We investigate the connection between Potts (Curie-Weiss) models and stochastic opinion models in the view of the Boltzmann distribution and stochastic Glauber dynamics. We particularly find that the q-voter model can be considered as a natural extension of the Zealot model, which is adapted by Lagrangian parameters. We also discuss weak and strong effects (also called extensive and nonextensive) continuum limits for the models. The results are used to compare the Curie-Weiss model, two q-voter models (weak and strong effects), and a reinforcement model (weak effects) in explaining electoral outcomes in four western democracies (United States, Great Britain, France, and Germany). We find that particularly the weak effects models are able to fit the data (Kolmogorov-Smirnov test) where the weak effects reinforcement model performs best (AIC). Additionally, we show how the institutional structure shapes the process of opinion formation. By focusing on the dynamics of opinion formation preceding the act of voting, the models discussed in this paper give insights both into the empirical explanation of elections as such, as well as important aspects of the theory of democracy. Therefore, this paper shows the usefulness of an interdisciplinary approach in studying real world political outcomes by using mathematical models. AU - Gsänger, M.* AU - Hösel, V.* AU - Mohamad-Klotzbach, C.* AU - Müller, J. C1 - 70380 C2 - 55548 CY - St Alban-anlage 66, Ch-4052 Basel, Switzerland TI - Opinion models, election data, and political theory. JO - Entropy VL - 26 IS - 3 PB - Mdpi PY - 2024 ER - TY - JOUR AB - Causal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause-effect estimation and the identification of efficient and safe interventions. However, learning causal representations remains a major challenge, due to the complexity of many real-world systems. Previous works on causal representation learning have mostly focused on Variational Auto-Encoders (VAEs). These methods only provide representations from a point estimate, and they are less effective at handling high dimensions. To overcome these problems, we propose a Diffusion-based Causal Representation Learning (DCRL) framework which uses diffusion-based representations for causal discovery in the latent space. DCRL provides access to both single-dimensional and infinite-dimensional latent codes, which encode different levels of information. In a first proof of principle, we investigate the use of DCRL for causal representation learning in a weakly supervised setting. We further demonstrate experimentally that this approach performs comparably well in identifying the latent causal structure and causal variables. AU - Karimi Mamaghan, A.M.* AU - Dittadi, A. AU - Bauer, S. AU - Johansson, K.H.* AU - Quinzan, F.* C1 - 71325 C2 - 56077 CY - St Alban-anlage 66, Ch-4052 Basel, Switzerland TI - Diffusion-based causal representation learning. JO - Entropy VL - 26 IS - 7 PB - Mdpi PY - 2024 ER - TY - JOUR AB - We study selection bias in meta-analyses by assuming the presence of researchers (meta-analysts) who intentionally or unintentionally cherry-pick a subset of studies by defining arbitrary inclusion and/or exclusion criteria that will lead to their desired results. When the number of studies is sufficiently large, we theoretically show that a meta-analysts might falsely obtain (non)significant overall treatment effects, regardless of the actual effectiveness of a treatment. We analyze all theoretical findings based on extensive simulation experiments and practical clinical examples. Numerical evaluations demonstrate that the standard method for meta-analyses has the potential to be cherry-picked. AU - Yoneoka, D.* AU - Rieck, B. C1 - 67929 C2 - 54407 TI - A note on cherry-picking in meta-analyses. JO - Entropy VL - 25 IS - 4 PY - 2023 ER - TY - JOUR AB - The study proposes the contemporaneous assessment of conditional entropy (CE) and self-entropy (sE), being the two terms of the Shannon entropy (ShE) decomposition, as a function of the time scale via refined multiscale CE (RMSCE) and sE (RMSsE) with the aim at gaining insight into cardiac control in long QT syndrome type 1 (LQT1) patients featuring the KCNQ1-A341V mutation. CE was estimated via the corrected CE (CCE) and sE as the difference between the ShE and CCE. RMSCE and RMSsE were computed over the beat-to-beat series of heart period (HP) and QT interval derived from 24-hour Holter electrocardiographic recordings during daytime (DAY) and nighttime (NIGHT). LQT1 patients were subdivided into asymptomatic and symptomatic mutation carriers (AMCs and SMCs) according to the severity of symptoms and contrasted with non-mutation carriers (NMCs). We found that RMSCE and RMSsE carry non-redundant information, separate experimental conditions (i.e., DAY and NIGHT) within a given group and distinguish groups (i.e., NMC, AMC and SMC) assigned the experimental condition. Findings stress the importance of the joint evaluation of RMSCE and RMSsE over HP and QT variabilities to typify the state of the autonomic function and contribute to clarify differences between AMCs and SMCs. AU - Bari, V.* AU - Girardengo, G.* AU - Marchi, A.* AU - de Maria, B.* AU - Brink, P.A.* AU - Crotti, L. AU - Schwartz, P.J.* AU - Porta, A.* C1 - 47585 C2 - 40637 SP - 7768-7785 TI - A refined multiscale self-entropy approach for the assessment of cardiac control complexity: Application to long QT syndrome type 1 patients. JO - Entropy VL - 17 IS - 11 PY - 2015 ER - TY - JOUR AB - Entropy-based complexity of cardiovascular variability at short time scales is largely dependent on the noise and/or action of neural circuits operating at high frequencies. This study proposes a technique for canceling fast variations from cardiovascular variability, thus limiting the effect of these overwhelming influences on entropy-based complexity. The low-pass filtering approach is based on the computation of the fastest intrinsic mode function via empirical mode decomposition (EMD) and its subtraction from the original variability. Sample entropy was exploited to estimate complexity. The procedure was applied to heart period (HP) and QT (interval from Q-wave onset to T-wave end) variability derived from 24-hour Holter recordings in 14 non-mutation carriers (NMCs) and 34 mutation carriers (MCs) subdivided into 11 asymptomatic MCs (AMCs) and 23 symptomatic MCs (SMCs). All individuals belonged to the same family developing long QT syndrome type 1 (LQT1) via KCNQ1-A341V mutation. We found that complexity indexes computed over EMD-filtered QT variability differentiated AMCs from NMCs and detected the effect of beta-blocker therapy, while complexity indexes calculated over EMD-filtered HP variability separated AMCs from SMCs. The EMD-based filtering method enhanced features of the cardiovascular control that otherwise would have remained hidden by the dominant presence of noise and/or fast physiological variations, thus improving classification in LQT1. AU - Bari, V.* AU - Marchi, A.* AU - de Maria, B.* AU - Girardengo, G.* AU - George, A.L.* AU - Brink, P.A.* AU - Cerutti, S.* AU - Crotti, L. AU - Schwartz, P.J.* AU - Porta, A.* C1 - 32666 C2 - 35215 SP - 4839-4854 TI - Low-pass filtering approach via empirical mode decomposition improves short-scale entropy-based complexity estimation of QT interval variability in long QT syndrome type 1 patients. JO - Entropy VL - 16 IS - 9 PY - 2014 ER - TY - JOUR AB - Cellular automata (CA) are a remarkably efficient tool for exploring general properties of complex systems and spatiotemporal patterns arising from local rules. Totalistic cellular automata, where the update rules depend only on the density of neighboring states, are at the same time a versatile tool for exploring dynamical processes on graphs. Here we briefly review our previous results on cellular automata on graphs, emphasizing some systematic relationships between network architecture and dynamics identified in this way. We then extend the investigation towards graphs obtained in a simulated-evolution procedure, starting from Erdos-Renyi (ER) graphs and selecting for low entropies of the CA dynamics. Our key result is a strong association of low Shannon entropies with a broadening of the graph's degree distribution. AU - Marr, C. AU - Hütt, M.-T.* C1 - 7762 C2 - 29877 SP - 993-1010 TI - Cellular automata on graphs: Topological properties of ER graphs evolved towards low-entropy dynamics. JO - Entropy VL - 14 IS - 6 PB - MDPI AG PY - 2012 ER -