40 research outputs found
Promoting cooperation by preventing exploitation: The role of network structure
A growing body of empirical evidence indicates that social and cooperative
behavior can be affected by cognitive and neurological factors, suggesting the
existence of state-based decision-making mechanisms that may have emerged by
evolution. Motivated by these observations, we propose a simple mechanism of
anonymous network interactions identified as a form of generalized reciprocity
- a concept organized around the premise "help anyone if helped by someone",
and study its dynamics on random graphs. In the presence of such mechanism, the
evolution of cooperation is related to the dynamics of the levels of
investments (i.e. probabilities of cooperation) of the individual nodes
engaging in interactions. We demonstrate that the propensity for cooperation is
determined by a network centrality measure here referred to as neighborhood
importance index and discuss relevant implications to natural and artificial
systems. To address the robustness of the state-based strategies to an invasion
of defectors, we additionally provide an analysis which redefines the results
for the case when a fraction of the nodes behave as unconditional defectors.Comment: 11 pages, 5 figure
Correlation Patterns in Foreign Exchange Markets
The value of an asset in a financial market is given in terms of another
asset known as numeraire. The dynamics of the value is non-stationary and
hence, to quantify the relationships between different assets, one requires
convenient measures such as the means and covariances of the respective log
returns. Here, we develop transformation equations for these means and
covariances when one changes the numeraire. The results are verified by a
thorough empirical analysis capturing the dynamics of numerous assets in a
foreign exchange market. We show that the partial correlations between pairs of
assets are invariant under the change of the numeraire. This observable
quantifies the relationship between two assets, while the influence of the rest
is removed. As such the partial correlations uncover intriguing observations
which may not be easily noticed in the ordinary correlation analysis
Central Limit Behavior in the Kuramoto model at the 'Edge of Chaos'
We study the relationship between chaotic behavior and the Central Limit
Theorem (CLT) in the Kuramoto model. We calculate sums of angles at equidistant
times along deterministic trajectories of single oscillators and we show that,
when chaos is sufficiently strong, the Pdfs of the sums tend to a Gaussian,
consistently with the standard CLT. On the other hand, when the system is at
the "edge of chaos" (i.e. in a regime with vanishing Lyapunov exponents),
robust -Gaussian-like attractors naturally emerge, consistently with
recently proved generalizations of the CLT.Comment: 15 pages, 8 figure
Empirical correction techniques: analysis and applications to chaotically driven low-order atmospheric models
Contemporary tools for reducing model error in weather and climate forecasting models include empirical correction techniques. In this paper we explore the use of such techniques on low-order atmospheric models. We first present an iterative linear regression method for model correction that works efficiently when the reference truth is sampled at large time intervals, which is typical for real world applications. Furthermore we investigate two recently proposed empirical correction techniques on Lorenz models with constant forcing while the reference truth is given by a Lorenz system driven with chaotic forcing. Both methods indicate that the largest increase in predictability comes from correction terms that are close to the average value of the chaotic forcing