Scientific collaboration networks: how little differences can matter a lot

Empirical studies such as Goyal, van der Leij and Moraga (2006) or Newman (2004) show that scientific collaboration networks present a highly unequal and hierarchical distribution of links. This implies that some researchers can be much more active and productive than others and, consequently, they can enjoy a much better scientific eputation. One may think that big intrinsical differences among researchers can constitute the main driving force behind these huge inequalities. We propose a model that show how almost identical individuals self-organize themselves in a very unequal and hierarchical structure as is observed in the real-world co-authorship networks. In consequence, this model provides an incentives-based explanation of that empirical evidence.network formation game, scientific collaboration, co-authroship networks, inequality

Structural holes and densely connected communities

It has been empirically shown that structural holes in social networks enable potential large benefits to those individuals who bridge them (Burt, 2004). The pioneering paper Goyal and Vega-Redondo (2007) offers a new incentives based explanation of this phenomenon. But the main equilibrium network of their model does not display a basic empirical regularity: the architecture of social networks is characterized by the existence of densely linked communities loosely connected to one another (Granovetter, 1983). This paper analyzes the conditions under which agents who benefit from bridging structural holes can be sustained in equilibrium networks constituted by densely linked groups.network formation, personal income distribution, structural holes, communities

Evidence functions: a compositional approach to information

The discrete case of Bayes’ formula is considered the paradigm of information acquisition. Prior and posterior probability functions, as well as likelihood functions, called evidence functions, are compositions following the Aitchison geometry of the simplex, and have thus vector character. Bayes’ formula becomes a vector addition. The Aitchison norm of an evidence function is introduced as a scalar measurement of information. A fictitious fire scenario serves as illustration. Two different inspections of affected houses are considered. Two questions are addressed: (a) which is the information provided by the outcomes of inspections, and (b) which is the most informative inspection.Peer ReviewedPostprint (author's final draft