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We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the “S&P 500 1987” bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs., PLoS ONE, 11 (11), ISSN:1932-6203

In a variety of open source software projects, we document a superlinear growth of production intensity (R ~ c(β)) as a function of the number of active developers c, with a median value of the exponent β ≃ 4/3, with large dispersions of β from slightly less than 1 up to 3. For a typical project in this class, doubling of the group size multiplies typically the output by a factor 2(β) = 2.5, explaining the title. This superlinear law is found to hold for group sizes ranging from 5 to a few hundred developers. We propose two classes of mechanisms, interaction-based and large deviation, along with a cascade model of productive activity, which unifies them. In this common framework, superlinear productivity requires that the involved social groups function at or close to criticality, or in a "superradiance" mode, in the sense of the appearance of a cooperative process and order involving a collective mode of developers defined by the build up of correlation between the contributions of developers. In addition, we report the first empirical test of the renormalization of the exponent of the distribution of the sizes of first generation events into the renormalized exponent of the distribution of clusters resulting from the cascade of triggering over all generation in a critical branching process in the non-meanfield regime. Finally, we document a size effect in the strength and variability of the superlinear effect, with smaller groups exhibiting widely distributed superlinear exponents, some of them characterizing highly productive teams. In contrast, large groups tend to have a smaller superlinearity and less variability.

We study the co-evolutionary emergence of fairness preferences in the form of other-regarding behavior and its effect on the origination of costly punishment behavior in public good games. Our approach closely combines empirical results from three experiments with an evolutionary simulation model. In this way, we try to fill a gap between the evolutionary theoretical literature on cooperation and punishment on the one hand and the empirical findings from experimental economics on the other hand. As a principal result, we show that the evolution among interacting agents inevitably favors a sense for fairness in the form of “disadvantageous inequity aversion”. The evolutionary dominance and stability of disadvantageous inequity aversion is demonstrated by enabling agents to co-evolve with different self- and other-regarding preferences in a competitive environment with limited resources. Disadvantageous inequity aversion leads to the emergence of costly (“altruistic”) punishment behavior and quantitatively explains the level of punishment observed in contemporary lab experiments performed on subjects with a western culture. Our findings corroborate, complement, and interlink the experimental and theoretical literature that has shown the importance of other-regarding behavior in various decision settings., PLoS ONE, 8 (3), ISSN:1932-6203