V
Vito Latora
Researcher at Queen Mary University of London
Publications - 360
Citations - 41121
Vito Latora is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Complex network & Centrality. The author has an hindex of 78, co-authored 332 publications receiving 35697 citations. Previous affiliations of Vito Latora include University of Catania & University of Paris.
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Evolutionary game model of risk propensity in group decision making.
TL;DR: In this paper, the authors introduce an evolutionary game on hypergraphs in which decisions between a risky alternative and a safe one are taken in social groups of different sizes, and the model naturally reproduces choice shifts, namely the differences between the preference of individual decision makers and the consensual choice of a group.
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The Ultimatum Game in Complex Networks
Roberta Sinatra,Roberta Sinatra,Jaime Iranzo,Jaime Iranzo,Jesús Gómez-Gardeñes,Jesús Gómez-Gardeñes,Jesús Gómez-Gardeñes,Luis Mario Floría,Vito Latora,Vito Latora,Yamir Moreno +10 more
TL;DR: In this article, an ultimatum game with three types of players (empathetic, pragmatic, and independent agents) is considered, and the emergence of fairness in different settings and network topologies is discussed.
A Methodological Approach To Assess Seismic Resilience Of City Ecosystems Through The Complex Networks Theory
TL;DR: In this article, a multi-scale approach is proposed to measure urban efficiency and systemic structural damage through the assessment of specific engineering measures, and two diverse recovery strategies are modelled and simulated to study the efficiency recovery and progress with a step-by-step procedure.
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Olami-Feder-Christensen Model on different Networks
TL;DR: In this article, numerically the self-organized criticality properties of the dissipative Olami-Feder-Christensen model on small-world and scale-free networks were investigated.
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Memory order decomposition of symbolic sequences.
TL;DR: In this paper, a general method for the study of memory in symbolic sequences based on higher-order Markov analysis is introduced. But this method is not suitable for the analysis of real data and can be used to extract relevant stochastic properties.