It’s not the winning but the taking part that counts: how the process of applying for competitive grants is of benefit to researchers

“The most important thing in the Olympic Games is not winning but taking part.” So goes the famous saying by Pierre de Coubertin, the father of modern Olympic Games. But does the same apply for competitive research grants? Charles Ayoubi, Michele Pezzoni and Fabiana Visentin report on their study which finds that simply taking part in an application process has a positive effect on researchers’ publication rates and on the average impact factor of the journals in which they publish. Participating in a competitive grant also allows applicants to enhance their learning, explore new trends of research, and extend their collaboration networks

A heterogeneous evolutionary stable population under assortative matching: Exploring the diversity of preferences

Recent studies have shown that a population acting not only upon self-interest but also exhibiting some morality preference has an evolutionary advantage. Specifically, in the setting of a symmetric fitness game, a resident population is evolutionary stable against all types of mutants if it has the utility function of Homo-moralis, with a morality equal to the assortativity. In this paper, we extend the scope of analysis allowing for the presence of a diversity of preferences in the population. Establishing a Payoff Equality condition, we prove the possibility of co-existence of two residents in the population. We then introduce a tripartite assortment function and study the conditions for the evolutionary stability of this diverse population. In the case of a constant assortment function, we show the existence of an evolutionary stable and heterogeneous resident population

Exploring the diversity of social preferences: Is a heterogeneous population evolutionarily stable under assortative matching?

Why do individuals make different decisions when confronted with similar choices? This paper investigates whether the answer lies in an evolutionary process. Our analysis builds on recent work in evolutionary game theory showing the superiority of a given type of preferences, homo moralis, in fitness games with assortative matching. We adapt the classical definition of evolutionary stability to the case where individuals with distinct preferences in a population coexist. This approach allows us to establish the characteristics of an evolutionarily stable population. Then, introducing an assortment matrix for assortatively matched interactions, we prove the existence of a heterogeneous evolutionarily stable population in 2x2 symmetric fitness games under constant assortment, and we identify the conditions for its existence. Conversely to the classical setting, we find that the favored preferences in a heterogeneous evolutionarily stable population are context-dependent. As an illustration, we discuss when and how an evolutionarily stable population made of both selfish and moral individuals exists in a prisoner's dilemma. These findings offer a theoretical foundation for the empirically observed diversity of preferences among individuals

Institutions and Incentives in Knowledge Production and Diffusion: From Science to Innovation

This thesis presents four essays providing novel empirical and theoretical insights on the incentives and institutional structures that favor knowledge production and diffusion. The first two studies analyze these processes in the realm of scientific research, while the last two essays evaluate broader applications with vast social welfare implications. The first essay (chapter 2) of this dissertation, in collaboration with Michele Pezzoni and Fabiana Visentin, exploits a dataset on all applicants to a prestigious Swiss grant to explore a central process in academic life: the application for funds. The results suggest that scientists applying to a grant significantly increase their publicationsâ quality and quantity, learn more, and extend their collaboration network. Beyond the effect of applying, receiving the research funds increases the probability of co-authoring with co-applicants, but it does not have any additional effect on other scientific outcomes. These results justified the title of the chapter since, as it is the case in the Olympics, in research grants, âthe important thing is not to win, it is to take part.â The second essay (chapter 3), also in collaboration with Michele Pezzoni and Fabiana Visentin, uses the same empirical context to explore the determinants of knowledge flows among collaborating scientists. The chapter proposes a novel methodology based on journal references to track knowledge flows among researchers working together. The results suggest that geographical distance does not significantly affect the knowledge flows between team members, but the cognitive distance separating two members does. More specifically, there is an inverted U-curve effect of cognitive distance on the learning among team members: the higher the distance between two scientists in terms of subjects studied, the more they exchange knowledge, up to the point when the distance becomes detrimental because they have too little common ground to communicate. The third essay (chapter 4), in collaboration with Boris Thurm, goes beyond the exploration of the determinants for scientistsâ knowledge production and diffusion to delve into the incentives of all individuals to exchange knowledge. The chapter has two major contributions. First, it acts as a literature review of the empirical evidence on non-financial incentives for knowledge diffusion, such as social recognition, career prospects, and moral considerations. Second, the chapter proposes a simple economic model with heterogeneous agents holding both selfish and moral motives to derive some novel policy implications. The last essay (chapter 5), in collaboration with Dominique Foray, delves into a specific case of knowledge diffusion, the integration of machine learning technologies in healthcare. The analysis suggests that machine learning has the potential for spurring innovation in healthcare but faces several institutional levers. Collecting quantitative data on patents and publications, and qualitative data on hospitals, the results show that machine learning affects healthcare in different ways than older information and communication technologies. The apparition of new business models encourages tech giants to enter the healthcare sector. These patterns have the potential to increase social welfare by reducing externalities in terms of innovation complementarities, but they pose new challenges such as competition policy and human capital formation

Novel researchers have a lower chance of winning funding

Swiss programme favours mainstream work, but unconventional applicants aren’t deterred, say Charles Ayoubi and colleague

Does it pay to do novel science? The selectivity patterns in science funding

Public funding of science aims to provide the necessary investment for the radical scientific discoveries of tomorrow. This paper brings evidence that the funding process is not always awarding the most novel scientists. Exploiting rich data on all applications to a leading Swiss research funding program, we find that novel scientists have a higher probability of applying for funds than non-novel scientists, but they get on average lower ratings by grant evaluators and have lower chances of being funded

Does it pay to do novel science? The selectivity patterns in science funding

Public funding of science aims to provide the necessary investment for the radical scientific discoveries of tomorrow. This paper brings evidence that the funding process is not always awarding the most novel scientists. Exploiting rich data on all applications to a leading Swiss research funding program, we find that novel scientists have a higher probability of applying for funds than non-novel scientists, but they get on average lower ratings by grant evaluators and have lower chances of being funded

At the origins of learning: Absorbing knowledge flows from within the team

International audienceEmpirical studies document a positive effect of collaboration on team productivity. However, little has been done to assess how knowledge flows among team members. Our study addresses this issue by exploring unique rich data on a Swiss funding program promoting research team collaboration. We find that being involved in an established collaboration and team size foster the probability of an individual learning from the other team members. We also find that team members with limited experience are more likely to learn from experienced peers. Moreover, there is an inverted U-shaped effect of cognitive distance on the probability of learning from other team members