This paper proposes a novel cognitive architecture and game-theoretic model for resource sharing among netizens, thus improving their quality of experience (QoE) in multi-layer social sensing environments. The underlying approach is to quantify micro-rewards and inequalities derived from social multi-layer interactions. Specifically, we model our society as a social multi-layer network of individuals or groups of individuals (nodes), where the layers represent multiple channels of interactions (on various services). The weighted edges correspond to the multiple social relationships between nodes participating in different services, reflecting the importance assigned to each of these edges and are defined based on the concepts of awareness and homophily. Heterogeneity, both interactions-wise on the multiple layers and related to homophily between individuals, on each node and layer of a weighted multiplex network produces a complex multi-scale interplay between nodes in the multi-layer structure. Applying game theory, we quantify the impact of heterogeneity on the evolutionary dynamics of social sensing through a data driven approach based on the propagation of individual-level micro-affirmations and micro-inequalities. The micro-packets of energy continuously exchanged between nodes may impact positively or negatively on their social behaviors, producing peaks of extreme dissatisfaction and in some cases a form of distress. Quantifying the evolutionary dynamics of human behaviors enables the detection of such peaks in the population and enable us design a targeted control mechanism, where social rewards and self-healing help improve the QoE of the netizens.

Improving QoE in Multi-layer Social Sensing: A Cognitive Architecture and Game Theoretic Model

Alessandro Di Stefano;Marialisa Scatà;Aurelio La Corte;
2019

Abstract

This paper proposes a novel cognitive architecture and game-theoretic model for resource sharing among netizens, thus improving their quality of experience (QoE) in multi-layer social sensing environments. The underlying approach is to quantify micro-rewards and inequalities derived from social multi-layer interactions. Specifically, we model our society as a social multi-layer network of individuals or groups of individuals (nodes), where the layers represent multiple channels of interactions (on various services). The weighted edges correspond to the multiple social relationships between nodes participating in different services, reflecting the importance assigned to each of these edges and are defined based on the concepts of awareness and homophily. Heterogeneity, both interactions-wise on the multiple layers and related to homophily between individuals, on each node and layer of a weighted multiplex network produces a complex multi-scale interplay between nodes in the multi-layer structure. Applying game theory, we quantify the impact of heterogeneity on the evolutionary dynamics of social sensing through a data driven approach based on the propagation of individual-level micro-affirmations and micro-inequalities. The micro-packets of energy continuously exchanged between nodes may impact positively or negatively on their social behaviors, producing peaks of extreme dissatisfaction and in some cases a form of distress. Quantifying the evolutionary dynamics of human behaviors enables the detection of such peaks in the population and enable us design a targeted control mechanism, where social rewards and self-healing help improve the QoE of the netizens.
978-1-4503-6706-6
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/363544
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