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.
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.
This paper provides a substantial reconceptualization of the serial clearing of the product market on the basis of structural axioms. The change of premises is required simply because from the accustomed premises only the accustomed conclusions can be derived and these are known to be inapplicable in the real world. This holds in particular for the still popular idea that the working of a market can be described in terms of the triad demand function–supply function–equilibrium. Structural axiomatization provides the complete and consistent picture of interrelated product market events.
We study a phenomenological model for the continuous double auction,
equivalent to two independent $M/M/1$ queues. The continuous double auction
defines a continuous-time random walk for trade prices. The conditions for
ergodicity of the auction are derived and, as a consequence, three possible
regimes in the behavior of prices and logarithmic returns are observed. In the
ergodic regime, prices are unstable and one can observe an intermittent
behavior in the logarithmic returns. On the contrary, non-ergodicity triggers
stability of prices, even if two different regimes can be seen.
We have analyzed the Indices of Industrial Production (Seasonal Adjustment
Index) for a long period of 240 months (January 1988 to December 2007) to
develop a deeper understanding of the economic shocks. The angular frequencies
estimated using the Hilbert transformation, are almost identical for the 16
industrial sectors. Moreover, the partial phase locking was observed for the 16
sectors. These are the direct evidence of the synchronization in the Japanese
business cycle. We also showed that the information of the economic shock is
carried by the phase time-series. The common shock and individual shocks are
separated using phase time-series. The former dominates the economic shock in
all of 1992, 1998 and 2001. The obtained results suggest that the business
cycle may be described as a dynamics of the coupled limit-cycle oscillators
exposed to the common shocks and random individual shocks.
We present and discuss a stochastic model of financial assets dynamics based
on the idea of an inverse renormalization group strategy. With this strategy we
construct the multivariate distributions of elementary returns based on the
scaling with time of the probability density of their aggregates. In its
simplest version the model is the product of an endogenous auto-regressive
component and a random rescaling factor embodying exogenous influences.
Mathematical properties like increments' stationarity and ergodicity can be
proven. Thanks to the relatively low number of parameters, model calibration
can be conveniently based on a method of moments, as exemplified in the case of
historical data of the S&P500 index. The calibrated model accounts very well
for many stylized facts, like volatility clustering, power law decay of the
volatility autocorrelation function, and multiscaling with time of the
aggregated return distribution. In agreement with empirical evidence in
finance, the dynamics is not invariant under time reversal and, with suitable
generalizations, skewness of the return distribution and leverage effects can
be included. The analytical tractability of the model opens interesting
perspectives for applications, for instance in terms of obtaining closed
formulas for derivative pricing. Further important features are: The
possibility of making contact, in certain limits, with auto-regressive models
widely used in finance; The possibility of partially resolving the endogenous
and exogenous components of the volatility, with consistent results when
applied to historical series.
This paper sets up a methodology for approximately solving optimal investment
problems using duality methods combined with Monte Carlo simulations. In
particular, we show how to tackle high dimensional problems in incomplete
markets, where traditional methods fail due to the curse of dimensionality.