Enhanced activation of interleukin-10, heme oxygenase-1, and AKT in C5aR2-deficient mice is associated with protection from ischemia reperfusion injury-induced inflammation and fibrosis

Nephrolog

Adaptive trust and co-operation: an agent-based simulation approach

Inter-firm relations have increasingly been analyzed by means of transaction cost economics (TCE). However, as has been widely acknowledged, TCE does not include dynamics of learning, adaptation or innovation, and it does not include trust. It assumes that efficient outcomes arise, while that may be in doubt, due to complexity and path-dependency of interactions between multiple agents that make, continue and break relations in unpredictable ways. We use the methodology of Agent-Based Computational Economics (ACE) to model how co-operation, trust and loyalty emerge and shift adaptively as relations evolve in a context of multiple, interacting agents. Agents adapt their trust on the basis of perceived loyalty. They adapt the weight they attach to trust relative to potential profit and they adapt their own loyalty, both as a function of realized profits. This allows us to explore when trust and loyalty increase and when they decrease, and what the effects are on (cumulative) profit. © Springer-Verlag Berlin Heidelberg 2001

Overconfidence and trading volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors. Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models—the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data—seems to be a promising way to better understand which psychological phenomena actually drive economic behavior. Copyright The Geneva Association 2007Overconfidence, Differences of opinion, Trading volume, Individual investors, Investor behavior, Correlation of economic and psychological variables, Combination of psychometric measures of judgment biases and field data, D8, G1,

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants