IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T13:35:45ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2015-11-16T09:10:37Z2015-11-16T09:10:37Zhttp://eprints.imtlucca.it/id/eprint/2900This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/29002015-11-16T09:10:37ZMemory Kernel in the Expertise of Chess PlayersIn this work we investigate a mechanism for the emergence of long-range time correlations observed in a chronologically ordered database of chess games. We analyze a modified Yule-Simon preferential growth process proposed by Cattuto et al., which includes memory effects by means of a probabilistic kernel. According to the Hurst exponent of different constructed time series from the record of games, artificially generated databases from the model exhibit similar long-range correlations. In addition, the inter-event time frequency distribution is well reproduced by the model for realistic parameter values. In particular, we find the inter-event time distribution properties to be correlated with the expertise of the chess players through the memory kernel extension. Our work provides new information about the strategies implemented by players with different levels of expertise, showing an interesting example of how popularities and long-range correlations build together during a collective learning process.Ana L. Schaigorodskyalschaigorodsky@gmail.comJuan I. Perottijuanignacio.perotti@imtlucca.itOrlando V. Billonialschaigorodsky@gmail.com2014-12-04T10:56:07Z2014-12-04T10:56:07Zhttp://eprints.imtlucca.it/id/eprint/2402This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24022014-12-04T10:56:07ZInnovation and nested preferential growth in chess playing behaviorComplexity develops via the incorporation of innovative properties. Chess is one of the most complex strategy games, where expert contenders exercise decision making by imitating old games or introducing innovations. In this work, we study innovation in chess by analyzing how different move sequences are played at the population level. It is found that the probability of exploring a new or innovative move decreases as a power law with the frequency of the preceding move sequence. Chess players also exploit already known move sequences according to their frequencies, following a preferential growth mechanism. Furthermore, innovation in chess exhibits Heaps' law suggesting similarities with the process of vocabulary growth. We propose a robust generative mechanism based on nested Yule-Simon preferential growth processes that reproduces the empirical observations. These results, supporting the self-similar nature of innovations in chess are important in the context of decision making in a competitive scenario, and extend the scope of relevant findings recently discovered regarding the emergence of Zipf's law in chess.Juan I. Perottijuanignacio.perotti@imtlucca.itHang-Hyun JoAna L. SchaigorodskyOrlando V. Billoni2014-12-04T10:44:08Z2014-12-04T10:52:18Zhttp://eprints.imtlucca.it/id/eprint/2401This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24012014-12-04T10:44:08ZMemory and long-range correlations in chess games In this paper we report the existence of long-range memory in the opening moves of a chronologically ordered set of chess games using an extensive chess database. We used two mapping rules to build discrete time series and analyzed them using two methods for detecting long-range correlations; rescaled range analysis and detrended fluctuation analysis. We found that long-range memory is related to the level of the players. When the database is filtered according to player levels we found differences in the persistence of the different subsets. For high level players, correlations are stronger at long time scales; whereas in intermediate and low level players they reach the maximum value at shorter time scales. This can be interpreted as a signature of the different strategies used by players with different levels of expertise. These results are robust against the assignation rules and the method employed in the analysis of the time series. Ana L. SchaigorodskyJuan I. Perottijuanignacio.perotti@imtlucca.itOrlando V. Billoni2014-12-04T09:48:08Z2014-12-04T11:46:07Zhttp://eprints.imtlucca.it/id/eprint/2399This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/23992014-12-04T09:48:08ZSmart random walkers: the cost of knowing the pathIn this work we study the problem of targeting signals in networks using entropy information measurements to quantify the cost of targeting. We introduce a penalization rule that imposes a restriction on the long paths and therefore focuses the signal to the target. By this scheme we go continuously from fully random walkers to walkers biased to the target. We found that the optimal degree of penalization is mainly determined by the topology of the network. By analyzing several examples, we have found that a small amount of penalization reduces considerably the typical walk length, and from this we conclude that a network can be efficiently navigated with restricted information.Juan I. Perottijuanignacio.perotti@imtlucca.itOrlando V. Billoni2014-12-04T09:41:14Z2014-12-04T09:41:14Zhttp://eprints.imtlucca.it/id/eprint/2398This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/23982014-12-04T09:41:14ZStability as a natural selection mechanism on interacting networksBiological networks of interacting agents exhibit similar topological properties for a wide range of scales, from cellular to ecological levels, suggesting the existence of a common evolutionary origin. A general evolutionary mechanism based on global stability has been proposed recently [J I Perotti, et al., Phys. Rev. Lett. 103, 108701 (2009)]. This mechanism was incorporated into a model of a growing network of interacting agents in which each new agent's membership in the network is determined by the agent's effect on the network's global stability. In this work, we analyze different quantities that characterize the topology of the emerging networks, such as global connectivity, clustering and average nearest neighbors degree, showing that they reproduce scaling behaviors frequently observed in several biological systems. The influence of the stability selection mechanism on the dynamics associated to the resulting network, as well as the interplay between some topological and functional features are also analyzed.Sergio A. CannasJuan I. Perottijuanignacio.perotti@imtlucca.itOrlando V. BilloniFrancisco A. Tamarit2014-12-04T09:22:00Z2016-04-06T09:57:54Zhttp://eprints.imtlucca.it/id/eprint/2397This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/23972014-12-04T09:22:00ZEmergent self-organized complex network topology out of stability constraintsAlthough most networks in nature exhibit complex topologies, the origins of such complexity remain unclear. We propose a general evolutionary mechanism based on global stability. This mechanism is incorporated into a model of a growing network of interacting agents in which each new agent’s membership in the network is determined by the agent’s effect on the network’s global stability. It is shown that out of this stability constraint complex topological properties emerge in a self-organized manner, offering an explanation for their observed ubiquity in biological networks.Juan I. Perottijuanignacio.perotti@imtlucca.itOrlando V. BilloniFrancisco A. TamaritDante ChialvoSergio A. Cannas