|
on Sociology of Economics |
| By: | Eldar Knar |
| Abstract: | An aggregated recursive K-index is proposed as a new scientometric indicator of added value and scientific research output of individual publications. This index can be used instead of or in addition to the H-index (J.E. Hirsch. An index to quantify an individual's scientific research output, arXiv:physics/0508025). In particular, it is proposed to switch from a pure strategy for assessing the quality and effectiveness of R&D using the H-index (Hirsch index) to a mixed strategy (in the context of publication activity as a combination of cooperative and noncooperative games) using the K-index on subnational and H-index on international or differentiated levels. In the context of a hybrid strategy of the scientist's payoff functions. This transition is correct and in demand for a number of national scientific systems with limited financial, material, infrastructural and linguistic (in terms of the English language) potential. Scientific systems with highly developed indigenous (autochthonous) characteristics are also needed in some scientific areas. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.04276&r=sog |
| By: | Xuanye Wang |
| Abstract: | We study the theoretical consequence of p-hacking on the accumulation of knowledge under the framework of mis-specified Bayesian learning. A sequence of researchers, in turn, choose projects that generate noisy information in a field. In choosing projects, researchers need to carefully balance as projects generates big information are less likely to succeed. In doing the project, a researcher p-hacks at intensity $\varepsilon$ so that the success probability of a chosen project increases (unduly) by a constant $\varepsilon$. In interpreting previous results, researcher behaves as if there is no p-hacking because the intensity $\varepsilon$ is unknown and presumably small. We show that over-incentivizing information provision leads to the failure of learning as long as $\varepsilon\neq 0$. If the incentives of information provision is properly provided, learning is correct almost surely as long as $\varepsilon$ is small. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.08984&r=sog |