# Papers

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Despite all our great advances in science, technology and financial innovations, many societies today are struggling with a financial, economic and public spending crisis, over-regulation, and mass unemployment, as well as lack of sustainability and innovation. Can we still rely on conventional economic thinking or do we need a new approach? <br />I argue that, as the complexity of socio-economic systems increases, networked decision-making and bottom-up self-regulation will be more and more important features. It will be explained why, besides the "homo economicus" with strictly self-regarding preferences, natural selection has also created a "homo socialis" with other-regarding preferences. While the "homo economicus" optimizes the own prospects in separation, the decisions of the "homo socialis" are self-determined, but interconnected, a fact that may be characterized by the term "networked minds". Notably, the "homo socialis" manages to earn higher payoffs than the "homo socialis". <br />I show that the "homo economicus" and the "homo socialis" imply a different kind of dynamics and distinct aggregate outcomes. Therefore, next to the traditional economics for the "homo economicus" ("economics 1.0"), a complementary theory must be developed for the "homo socialis". This economic theory might be called "economics 2.0" or "socionomics". The names are justified, because the Web 2.0 is currently promoting a transition to a new market organization, which benefits from social media platforms and could be characterized as "participatory market society". To thrive, the "homo socialis" requires suitable institutional settings such a particular kinds of reputation systems, which will be sketched in this paper. I also propose a new kind of money, so-called "qualified money", which may overcome some of the problems of our current financial system.
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Heavy-tailed distributions of meme popularity occur naturally in a model of meme diffusion on social networks. Competition between multiple memes for the limited resource of user attention is identified as the mechanism that poises the system at criticality. The popularity growth of each meme is described by a critical branching process, and asymptotic analysis predicts power-law distributions of popularity with very heavy tails (exponent $\alpha<2$, unlike preferential-attachment models), similar to those seen in empirical data.