Publications

The paper explores market selection in an Arrow-Debreu economy with complete markets where agents learn over misspecified models. Standard Bayesian learning loses formal justification, and biased learning processes may provide a selection advantage. The ecology of traders in the market affects selection dynamics and long-run asset valuation. Model misspecification makes it difficult to rank learning behaviors based on survival prospects. Prediction averaging has an advantage when the true data generating process belongs to the same family of models, but this advantage disappears when the true model belongs to a more general class. Rules guaranteeing survival exploit imitative mechanisms.

Evaluating the effects of some or all academic research funding is difficult because of the many different and overlapping sources, types, and scopes. It is therefore important to identify the key aspects of research funding so that funders and others assessing its value do not overlook them. This article outlines 18 dimensions through which funding varies substantially, as well as three funding records facets.

This reflection piece seeks to tackle this blurriness and advance the concept’s analytical precision by presenting and discussing transparency’s main dimensions, as well as its framing within the scholarly body of good governance, democracy, and development. To do so, the reflection paper presents a novel approach; to define transparency in the form of a function (which places transparency in relation to, and as a function) of its two constituting dimensions: a) information (timely and qualitative), and b) accountability mechanisms (namely, a free media environment and legitimate accountability channels).

Increasingly, researchers are expected to make their research data openly available. However, scientific fields differ in their research practices and norms for sharing research data. We provide quantitative evidence of differences in data practices and the public sharing of research data at a granularity of field-specificity that is rarely reported in open data surveys. Based on a survey of 8,822 researchers at German Universities, we find considerable variation, within and across disciplines, of data practices and rates of open data sharing. Our findings underline that in order to evaluate rates of public data sharing, a better understanding of the embedding of public data sharing into field-specific research practices is needed.