Multimedialne seminarium z ekono- i socjofizyki
2009/2010 | 2010/2011 | 2011/2012 | 2012/2013 | 2013/2014 | 2014/2015 | 2015/2016 | 2016/2017 | 2017/2018 | 2018/2019 | 2019/2020 | 2021/2022 | 2022/2023 | 2023/2024
2020-01-21 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:30 
Robert Paluch (Warsaw University of Technology)Locating the source of spreading in complex networks with limited observations
The phenomena of spreading are essential part of social complex networks. Regardless of whether the virus, rumor or news is spread, very often the first question is where it comes from? In their seminal work, Pinto et al.[1] introduced gaussian maximum likelihood estimator for localization of single source, based on sparse observers placed in the network. During this lecture we will explore this method, learn its limitation and find out how to optimize it in terms of computing time[2] and precision[3].[1] Pinto, P. C., Thiran, P., & Vetterli, M. (2012). Locating the source of diffusion in large-scale networks. Physical Review Letters, 109(6), 1–5.[2] Paluch, R., Lu, X., Suchecki, K., Szymański, B. K., & Hołyst, J. A. (2018). Fast and accurate detection of spread source in large complex networks. Scientific Reports, 8(1), 2508.[3] Gajewski, Ł. G., Suchecki, K., & Hołyst, J. A. (2019). Multiple propagation paths enhance locating the source of diffusion in complex networks. Physica A, 519, 34–41.
2020-01-16 (Czwartek)
Zapraszamy do sali B0.14, ul. Pasteura 5 o godzinie 11:15
Zbigniew R. Struzik (The University of Tokyo, Wydział Fizyki UW)(ZIP) Excibition/Inhibition
Patrz Seminarium Fizyki Biomedycznej https://zfbweb.zfb.fuw.edu.pl/index.php/events/ oraz https://www.fuw.edu.pl/tl_files/studia/materialy/ef/ef_zip_Seminar_E-I_reducedencrypted.pdf (hasło: complexus)
2019-12-03 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:30
Michał Chorowski (Wydział Fizyki UW)Irrational fractional Brownian motion model approach to financial markets modeling
During the seminar I will present a new approach to modelling returns distributions for financial market indices described in [1]. This approach uses an extra stochastic function over the traditional Geometric Brownian Motion, with only two parameters to be estimated. This modification is an attempt to incorporate the irrationality of the agents in financial market modeling. Earlier publications suggest it allows for obtaining a better fit to historical returns distributions [2]. I will also present a methodology which allows for forecasting kurtosis of the asset returns [1]. Finally, I will show and discuss some simulation results of my own.[1] Dhesi, G., Shakeel, B., & Ausloos, M. (2019). Modelling and forecasting the kurtosis and returns distributions of financial markets: irrational fractional Brownian motion model approach. Annals of Operations Research, 1-14.[2] Dhesi, G., Shakeel, M. B., & Xiao, L. (2016). Modified Brownian motion approach to modeling returns distribution. Wilmott, 2016(82), 74-77.
2019-11-26 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:30
Mgr Tomasz Raducha (Wydział Fizyki UW)The voter model - from ants behavior to US presidential election part 2
The voter model's history spans back to 1973 when it was formulated forthe first time, however under the name of 'invasion process'. Since thendozens various modifications and extensions have been proposed. As thenumber of its versions, the number of its applications is alsoimpressive - from ants behavior when faced two food sources,monomer-monomer catalytic reactions, investors behavior on a stockmarket, opinion dynamics, to voting processes (from where the usual namecomes). The model was studied under many different names. Therefore, atthe seminar I will give the classification rules and discuss standardversions of the voter model, together with a few results of my own.
2019-11-19 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:30
Mgr Tomasz Raducha (Wydział Fizyki UW)The voter model - from ants behavior to US presidential election
The voter model's history spans back to 1973 when it was formulated forthe first time, however under the name of 'invasion process'. Since thendozens various modifications and extensions have been proposed. As thenumber of its versions, the number of its applications is alsoimpressive - from ants behavior when faced two food sources,monomer-monomer catalytic reactions, investors behavior on a stockmarket, opinion dynamics, to voting processes (from where the usual namecomes). The model was studied under many different names. Therefore, atthe seminar I will give the classification rules and discuss standardversions of the voter model, together with a few results of my own.
2019-11-05 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:30
dr hab. inż Krzysztof Malarz (AGH University of Science and Technology Faculty of Physics and Applied Computer Science)Cellular automata motivated model of vehicle traffic in contemporary city center
In this lecture the necessary improvements in Chowdhury–Schadschneider model [1] allowing for modelling bi-directional cars traffic will be presented. The modified model is applied for simulating traffic in real topology of streets in Kraków [2]. Also possible further model modifications [3] allowing for taking into account railway vehicles (trams) will be presented.[1] D. Chowdhury, A. Schadschneider, Self-organization of traffic jams in cities: Effects of stochastic dynamics and signal periods, Phys. Rev. E 59 (1999) R1311[2] R. Socha, M.Sc. Thesis, AGH University of Science and Technology, Kraków, (2017)[3] K. Nowak, M.Sc. Thesis, AGH University of Science and Technology, Kraków, (2017)
2019-10-22 (Wtorek)
Zapraszamy do sali 1.03, ul. Pasteura 5 o godzinie 18:30
Dr Mateusz Wiliński (Wydział Fizyki UW)Skalowalne uczenie się modelu `Independent Cascade' na podstawie cząstkowych obserwacji
Scalable learning of Independent Cascade model from partial observations
Modelling spreading processes and diffusion on networks is one of the most popular subjects among researchers dealing with complex systems. The reason for that may be a growing number of phenomenon, which can be described with such models. Epidemics, fake news or cascading failures in power grids, to name only a few. In general, using these models in empirical setting is difficult because the spreading or transmission probabilities are not known. One way to estimate them is to use actual cascades and reverse engineer their values by maximising their likelihood. Unfortunately, in reality we are often able to observe only a fraction of the network, which makes this task computationally inefficient. We propose a novel efficient algorithm as a solution to this problem. Our approach is based on dynamic message passing and it allows for scalable computations, suited for large real-world networks. We present application of our method to the Independent Cascade model, but it can easily be generalised to other models.