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On sampling and modeling complex systems
KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
2013 (English)In: Journal of Statistical Mechanics: Theory and Experiment, ISSN 1742-5468, Vol. 2013, no 9Article in journal (Refereed) Published
Abstract [en]

The study of complex systems is limited by the fact that only a few variables are accessible for modeling and sampling, which are not necessarily the most relevant ones to explain the system behavior. In addition, empirical data typically undersample the space of possible states. We study a generic framework where a complex system is seen as a system of many interacting degrees of freedom, which are known only in part, that optimize a given function. We show that the underlying distribution with respect to the known variables has the Boltzmann form, with a temperature that depends on the number of unknown variables. In particular, when the influence of the unknown degrees of freedom on the known variables is not too irregular, the temperature decreases as the number of variables increases. This suggests that models can be predictable only when the number of relevant variables is less than a critical threshold. Concerning sampling, we argue that the information that a sample contains on the behavior of the system is quantified by the entropy of the frequency with which different states occur. This allows us to characterize the properties of maximally informative samples: within a simple approximation, the most informative frequency size distributions have power law behavior and Zipf's law emerges at the crossover between the under sampled regime and the regime where the sample contains enough statistics to make inferences on the behavior of the system. These ideas are illustrated in some applications, showing that they can be used to identify relevant variables or to select the most informative representations of data, e.g. in data clustering.

Place, publisher, year, edition, pages
2013. Vol. 2013, no 9
Keyword [en]
critical phenomena of socio-economic systems, protein function and design (theory), clustering techniques, statistical inference
National Category
Other Physics Topics
URN: urn:nbn:se:kth:diva-133546DOI: 10.1088/1742-5468/2013/09/P09003ISI: 000325154900003ScopusID: 2-s2.0-84885095940OAI: diva2:662428

QC 20131107

Available from: 2013-11-07 Created: 2013-11-06 Last updated: 2013-12-05Bibliographically approved

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Roudi, Yasser
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