Intro: My name is Aleksejus Kononovicius (lt. Kononovičius). I hold PhD in Physics and currently work as a senior researcher at Institute of Theoretical Physics and Astronomy (Faculty of Physics, Vilnius University). Besides conducting research in Econophysics and Sociophysics, which involves stochastic and agent-based modeling as well as a bit of Data Science. I blog about my research interests on Physics of Risk.
- Resemblance of the power-law scaling behavior of a non-Markovian and nonlinear point processes. Non-Markovian models have memory directly baked into the model, which means that any long-range memory effects are not emergent. On the other hand our group at Institute of Theoretical Physics and Astronomy has been developing various nonlinear Markovian models, which exhibit emergent long-range memory. Here we have shown that similar power-law scaling behavior can be obtained both from a point process driven by the fractional Gaussian noise, and from a nonlinear Markovian point process. This result prompts further investigation on if it is possible to differentiate between two different kinds of long-range memory models.
- Supportive interactions in the noisy voter model. Typically voter models include various implementations of recruitment. Yet, as theories in social sciences predict, there is another type of herding interaction. In real life people also predict and reinforce beliefs held by their peers. In this paper we have examined implications of these supportive interactions on the phenomenology of the noisy voter model. The observed phenomenology is quite similar to the aging and freezing phenomena observed in magnetic materials.
- Compartmental voter model. Most models in Sociophysics are observed over time, while the empirical data is quite often spatial. In this paper I have proposed a new kind of voter model, which evolves both in space and time. The proposed model is able to reproduce electoral and census data.
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