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.
We introduce the concept of self-healing in the field of complex networks.
Obvious applications range from infrastructural to technological networks. By
exploiting the presence of redundant links in recovering the connectivity of
the system, we introduce self-healing capabilities through the application of
distributed communication protocols granting the "smartness" of the system. We
analyze the interplay between redundancies and smart reconfiguration protocols
in improving the resilience of networked infrastructures to multiple failures;
in particular, we measure the fraction of nodes still served for increasing
levels of network damages. We study the effects of different connectivity
patterns (planar square-grids, small-world, scale-free networks) on the healing
performances. The study of small-world topologies shows us that the
introduction of some long-range connections in the planar grids greatly
enhances the resilience to multiple failures giving results comparable to the
most resilient (but less realistic) scale-free structures.
Simulation with agent-based models is increasingly used in the study of
complex socio-technical systems and in social simulation in general. This
paradigm offers a number of attractive features, namely the possibility of
modeling emergent phenomena within large populations. As a consequence, often
the quantity in need of calibration may be a distribution over the population
whose relation with the parameters of the model is analytically intractable.
Nevertheless, we can simulate. In this paper we present a simulation-based
framework for the calibration of agent-based models with distributional output
based on indirect inference. We illustrate our method step by step on a model
of norm emergence in an online community of peer production, using data from
three large Wikipedia communities. Model fit and diagnostics are discussed.
Pablo Piedrahíta, Javier Borge-Holthoefer, Yamir Moreno, Alex Arenas
posted by Matúš Medo
(21 May 2013)
The ability to understand and eventually predict the emergence of information
and activation cascades in social networks is core to complex socio-technical
systems research. However, the complexity of social interactions makes this a
challenging enterprise. Previous works on cascade models assume that the
emergence of this collective phenomenon is related to the activity observed in
the local neighborhood of individuals, but do not consider what determines the
willingness to spread information in a time-varying process. Here we present a
mechanistic model that accounts for the temporal evolution of the individual
state in a simplified setup. We model the activity of the individuals as a
complex network of interacting integrate-and-fire oscillators. The model
reproduces the statistical characteristics of the cascades in real systems, and
provides a framework to study time-evolution of cascades in a state-dependent
James P. Gleeson, Jonathan A. Ward, Kevin P. O'Sullivan, William T. Lee
posted by Matúš Medo
(21 May 2013)
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.