Investigation of socio-economic models faces three levels of difficulty: the agents definition, the choice of their interactions and the derivation of the associated macroscopic evolution from the chosen microscopic dynamics. Within such a framework, agent-based modeling combined with complex networks theory have opened the field of complex adaptive systems, where both the system's and the agents' structure co-evolve in a continuous interplay. Agents are thus allowed to locally interact with each other via a network of connections allowing both the agents' state (e.g., people's opinion in a social system or banks' liquidity in a financial system) and the agents' interactions (number and intensity) to eventually change in response to the neighbors' output, giving origin to a (time-discrete or time-continuous) dynamical process. As a result, some statistical regularities emerge, not derivable from the singular, microscopic behavior only: Sociophysics and Econophysics provide many examples, as shared opinions, cultures, languages, economic crises, bubbles of commodity prices, etc. Keeping up with statistical physics leads to interpret the emergent properties in terms of phase transitions (the ordered phase representing consensus and agreement, described by a low social temperature), diffusive processes (like the evolution of the price of stocks or options), turbulent phenomena (as the fluctuations in exchange rates between foreign currencies), magnetization (as the difference between the density of the agents having a different opinion) and so on. Such a modeling of socio-economic systems contrasts substantially with the traditional approach resting upon the axiomatic representative agent paradigm, according to which all agents in the systems are assumed to be isolated and to act in the same way, i.e. trying to optimize an utility function appropriately defined. On the contrary, modern agents-based models, often embedded into a complex network of interactions, allow to describe the dynamics of systems composed by strongly heterogeneous agents, characterized by particular behaviors (e.g. conformist, non-conformist, committed, etc.). Remarkably, agent-based models can be also embedded into multiple networks (multi-layer and multiplexes), with the aim of representing more realistic scenarios, e.g., interacting people on different online and offline social networks, interacting countries exchanging different kinds of commodities and more. Since the vast majority of the work behind agent-based modeling is feasible only via massive computer simulations, a deeper understanding of the possibilities of the latter is needed. The most promising road seems to have been traced by statistical physics and its rich contributions to the comprehension of large systems composed by many sub-parts. For this reason, SocInfo represents the ideal occasion to let the researchers from sociology, computer science and physics discuss together and strengthen the foundations of this relatively new field of science.
Scholars and researchers will be invited to submit contributions mainly (but not exclusively) on the following topics:

Agent-based modeling for socio-economic complex systems;
Spreading phenomena on socio-economic networks;
Statistical Physics models for socio-economic complex systems;
Multi-level socio-economic complex systems;
Big-data analysis of socio-economic systems;
Opinion and consensus dynamics;
Emergent phenomena in language dynamics;
Social networks and language evolution;
Systemic risk estimation for economic and financial networks.