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We introduce a general framework for exploring the problem of selecting a committee of representatives with the aim of studying a networked voting rule based on a decentralized large-scale platform, which can assure a strong accountability of the elected. The results of our simulations suggest that this algorithm-based approach is able to obtain a high representativeness for relatively small committees, performing even better than a classical voting rule based on a closed list of candidates. We show that a general relation between committee size and representatives exists in the form of an inverse square root law and that the normalized committee size approximately scales with the inverse of the community size, allowing the scalability to very large populations. These findings are not strongly influenced by the different networks used to describe the individuals' interactions, except for the presence of few individuals with very high connectivity which can have a marginal negative effect in the committee selection process.

The vast amount of research devoted to public goods games has shown that contributions may be dramatically affected by varying framing conditions. This is particularly relevant in the context of donations to charities and non-governmental organizations. Here, we design a multiple public goods experiment by introducing five types of funds, each differing in the fraction of the contribution that is donated to a charity. We found that people contribute more to public goods when the associated social donations are presented as indirect rather than as direct donations. At the same time, the fraction of the donations devoted to charity is not affected by the framing. We have also found that, on average, women contribute to public goods and donate to charity significantly more than men. These findings are of potential interest to the design of social investment tools, in particular for charities to ask for better institutional designs from policy makers. ; This work was partially supported by MINECO (Spain) and FEDER funds through grant no. FIS2017-87519-P (Y.M.) and FJCI-2016-28276 (A.A.); by Ministerio de Ciencia, Innovación y Universidades/FEDER (Spain/UE) through grant no. PGC2018-098186-B-I00 (BASIC) (A.S. and J.C.); by Comunidad de Madrid under grant no. PRACTICO-CM and by Comunidad de Madrid/Universidad Carlos III de Madrid under grant no. CAVTIONS-CM-UC3M (A.S.); by Comunidad de Aragón (Spain) through grant no. E36-20R to FENOL (C.G.L. and Y.M.); by the EU through FET-Proactive Project MULTIPLEX (contract no. 317532, Y.M.) and FET-Proactive Project DOLFINS (contract no. 640772, C.G.L., Y.M. and A.S.), and by the Spanish State Research Agency and FEDER funds, through the María de Maeztu Program for Units of Excellence in R&D (MDM-2017-0711, S.M.) and under the PACSS grant no. (RTI2018-093732-B-C22, S.M.).