**Evolutionary Games on Networks**

Development of mathematical models, grounded on evolutionary game equations, where players are located at the nodes of a network of connections. The models describe decision mechanisms of a finite set of players under replication, selection pressure and social influence. An extended version of the model describes the continuous dynamics of individuals or groups interacting each other under a baseline prisoner's dilemma game appropriately modified with the aim of accounting for a self-regulative inertial factor which favours the onset of cooperation. The extended model well explains propensity to cooperation in European Countries under the assumption that individuals are driven by homophily, endogamy and civicness and shows formally that strong cooperation within familistic cliques induces a decrease of cooperation at global level.

The model has been also applied to investigate the formation of networks among the members of a microbial population as a response to hostile environments. A further application to neuroscience showed a good performance of the evolutionary game equations on networks in reproducing spontaneous brain behavior.

Madeo D., Mocenni C., "How cooperation helps Epidemic Containment", *Games*, submitted 2021

Madeo D., Salvatore S., Mannarini T., Mocenni C., "Modeling pluralism and self-regulation explains the emergence of cooperation in networked societies", *Sci. Rep.* 2021

Madeo D., Mocenni C., "Consensus towards Partially Cooperative Strategies in Self-Regulated Evolutionary Games on Networks", *Games* 2021

Madeo D., Mocenni C., "Self-regulation versus social influence for promoting cooperation on networks", *Sci. Rep.* 2020

Madeo D., Talarico A.,Pascual-Leone A., Mocenni C., Santarnecchi E., *"*An evolutionary game theory model of spontaneous brain functioning", *Sci. Rep.* 2017

Iacobelli G., Madeo, D., Mocenni C., "Lumping evolutionary game dynamics on networks", *J. Theor. Biol. *2016

Madeo D. & Mocenni C., "Game Interactions and Dynamics on Networked Populations" *IEEE Trans. Aut. Control*, 2015

**Biophysical Systems Analysis and Behavior**

In the field of monitoring and analysing the environmental status of water systems, we have developed tools for decision support and real-time water monitoring based on low cost unmanned surface vehicles and mathematical modeling.

Madeo D., Pozzebon A., Mocenni, C., & Bertoni D., "A Low-Cost Unmanned Surface Vehicle for Pervasive Water Quality Monitoring", *IEEE Trans. Instr. and Meas.*, 2020

Casini M., Mocenni C., Paoletti S., & Pranzo M., "Decision support system development for integrated management of European coastal lagoons", *Env. Mod. & Soft.*, 2015

A physical model for the characterization of magnetic hydrogels subject to external magnetic fields has been introduced and validated.

Madeo D., Bevilacqua G., Biancalana V., Dancheva Y., Mocenni, C., "A physical model for the characterization of magnetic hydrogels subject to external magnetic fields", *J. Magn. & Magn. Mat.*, 2020

A bounded rationality model of spontaneous remission from addctive behavioral patterns has been designed. We showed that natural recovery from addiction without external committments is feasible by assuming enough high level of awareness of individuals.

Mocenni C., Tiezzi S., Montefrancesco G., "A Model of Spontaneous Remission from Addiction", *J. Appl. Behav. Econ.*, 2019

**Nonlinear time series analysis**

Nonlinear time series analysis has been applied to the study of brain patterns with particular attention to the level of hypnotizability of subjects. The research has been conducted by analyzing the EEG time series recorded in several regions of the scalpo of resting state, not hypnotized subjects.

Madeo D., Caastellani E., Mocenni C., Santarcangelo E., "Pain perception and EEG dynamics: does hypnotizability account for the efficacy of the suggestions of analgesia?", *Physiol. & Behav.*, 2015

Application of nonlinear time series analysis to the reconstruction of embedded state spaces and detection of critical regimes in complex systems. The methodology allowed the identification of structurally different regimes, e.g. stable and unstable spiral waves, in the Complex Ginzburg-Landau equation and in biochemical reaction-diffusion systems undergoing Turing instabilities and in presence of roughness in the boundaries.

Mocenni C., Sparacino E., & J. P. Z., "Effective rough boundary parametrization for reaction-diffusion systems", Appl. An. Discr. Math., 2014

Facchini A., & Mocenni C., "Recurrence methods for the identification of morphogenetic patterns", *Plos ONE*, 2013

Mocenni C., Facchini A., & Vicino A., "Identifying the dynamics of complex spatio-temporal systems by spatial recurrence properties", *PNAS*, 2010