Cyclic motifs in the Sardex monetary network

From decentralized banking systems to digital community currencies, the way humans perceive and use money is changing1–3, thus creating novel opportunities for solving important economic and social problems. Here, we study Sardex, a fast-growing community currency in Sardinia (involving 1,477 businesses arrayed in a network with 48,170 transactions) using network analysis to shed light on its operation. Based on our experience with its day-to-day operations, we propose performance metrics tailored for Sardex but also to similar economic systems, introduce criteria for identifying prominent economic actors and investigate the interplay between network structure and economic robustness. Leveraging new methods for quantifying network ‘cyclic density’ and ‘k-cycle centrality,’ we show that geodesic transaction cycles, where money flows in a circle through the network, are prevalent and that certain nodes have a pivotal role in them. We analyse the transactions within cycles and find that the economic turnover of the involved firms is higher, and that excessive currency and debt accumulations are lower. We also measure a similar, but secondary, effect for nodes and edges that serve as intermediaries to many transactions. These metrics are strong indicators of the success of such mutual credit systems at individual and collective levels.Analyses of transactions in a new monetary system (Sardex community currency) reveal that transaction cycles increase in prevalence over time and that economic activity within these cycles is higher compared to linear transactions through the network.

[1]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[2]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[3]  G. Simmel The Philosophy of Money , 1979 .

[4]  J. Guyer Soft currencies, cash economies, new monies: Past and present , 2012, Proceedings of the National Academy of Sciences.

[5]  David G. Rand,et al.  Static network structure can stabilize human cooperation , 2014, Proceedings of the National Academy of Sciences.

[6]  Panayotis Antoniadis,et al.  From an idea of a scalable working model: merging economic benefits with social values in Sardex , 2014 .

[7]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[8]  N. Christakis,et al.  Intermediate Levels of Network Fluidity Amplify Economic Growth and Mitigate Economic Inequality in Experimental Social Networks , 2015 .

[9]  Avrim Blum,et al.  Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges , 2007, EC '07.

[10]  Noel Longhurst,et al.  Growing green money? Mapping community currencies for sustainable development , 2013 .

[11]  A. Pentland,et al.  Life in the network: The coming age of computational social science: Science , 2009 .

[12]  Vasco M. Carvalho,et al.  Supply Chain Disruptions: Evidence from the Great East Japan Earthquake , 2016, The Quarterly Journal of Economics.

[13]  Walter E. Beyeler,et al.  The topology of interbank payment flows , 2007 .

[14]  S. Nakamoto,et al.  Bitcoin: A Peer-to-Peer Electronic Cash System , 2008 .

[15]  B. Bollobás The evolution of random graphs , 1984 .

[16]  Donald B. Johnson,et al.  Finding All the Elementary Circuits of a Directed Graph , 1975, SIAM J. Comput..

[17]  Albert-László Barabási,et al.  Control Centrality and Hierarchical Structure in Complex Networks , 2012, PloS one.

[18]  Tore Opsahl,et al.  Prominence and control: the weighted rich-club effect. , 2008, Physical review letters.

[19]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[20]  G. Camera,et al.  Money and trust among strangers , 2013, Proceedings of the National Academy of Sciences.

[21]  Adilson E Motter,et al.  Local structure of directed networks. , 2007, Physical review letters.

[22]  John M. Carroll,et al.  What Is the Sharing Economy? , 2019, Hustle and Gig.

[23]  Arun G. Chandrasekhar,et al.  The Diffusion of Microfinance , 2012, Science.

[24]  William Jack,et al.  Documenting the birth of a financial economy , 2012, Proceedings of the National Academy of Sciences.

[25]  F. Caccioli,et al.  Pathways towards instability in financial networks , 2016, Nature Communications.

[26]  Nobuhiro Kiyotaki,et al.  Evil Is the Root of All Money , 2002 .

[27]  Giorgio Fagiolo,et al.  On the Topological Properties of the World Trade Web: A Weighted Network Analysis , 2007, 0708.4359.

[28]  A. Pentland,et al.  Computational Social Science , 2009, Science.

[29]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[30]  Ravi Iyengar,et al.  Ordered cyclic motifs contribute to dynamic stability in biological and engineered networks , 2008, Proceedings of the National Academy of Sciences.

[31]  David G. Rand,et al.  Inequality and visibility of wealth in experimental social networks , 2015, Nature.

[32]  C. Butts Cycle Census Statistics for Exponential Random Graph Models , 2006 .

[33]  Tom A. B. Snijders,et al.  The social relations model for family data: A multilevel approach , 1999 .

[34]  L. Freeman Centrality in social networks conceptual clarification , 1978 .