Innovations are disproportionately likely in the periphery of a scientific network (original) (raw)
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The diffusion of scientific innovations: A role typology
Studies in History and Philosophy of Science Part A, 2018
How do scientific innovations spread within and across scientific communities? In this paper, we propose a general account of the diffusion of scientific innovations. This account acknowledges that novel ideas must be elaborated on and conceptually translated before they can be adopted and applied to field-specific problems. We motivate our account by examining an exemplary case of knowledge diffusion, namely, the early spread of theories of rational decision-making. These theories were grounded in a set of novel mathematical tools and concepts that originated in John von Neumann and Oskar Morgenstern's Theory of Games and Economic Behavior (1944, 1947) and subsequently spread widely across the social and behavioral sciences. Introducing a network-based diffusion measure, we trace the spread of those tools and concepts into distinct research areas. We furthermore present an analytically tractable typology for classifying publications according to their roles in the diffusion process. The proposed framework allows for a systematic examination of the conditions under which scientific innovations spread within and across both preexisting and newly emerging scientific communities.
The Effects of Diversity and Network Ties on Innovations: The Emergence of a New Scientific Field
2014
This study examines the influence of different types of diversity, both observable and unobservable, on the creation of innovative ideas. Our framework draws on theory and research on information processing, social categorization, coordination, and homophily to posit the influence of cognitive, gender, and country diversity on innovation. Our longitudinal model is based on a unique data set of 1,354 researchers who helped create the new scientific field of oncofertility, by collaborating on 469 publications over a 4-year period. We capture the differences among researchers along cognitive, country, and gender dimensions, as well as examine how the resulting diversity or homophily influences the formation of collaborative innovation networks. We find that innovation, operationalized as publishing in a new scientific discipline, benefits from both homophily and diversity. Homophily in country of residence and working with prior collaborators help reduce uncertainty in the interactions associated with innovation, while diversity in knowledge enables the recombinant knowledge required for innovation.
The dynamics of innovations and citations
Economics Letters, 2015
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The Underlying Social Dynamics of Paradigm Shifts
PLOS ONE, 2015
We develop here a multi-agent model of the creation of knowledge (scientific progress or technological evolution) within a community of researchers devoted to such endeavors. In the proposed model, agents learn in a physical-technological landscape, and weight is attached to both individual search and social influence. We find that the combination of these two forces together with random experimentation can account for both i) marginal change, that is, periods of normal science or refinements on the performance of a given technology (and in which the community stays in the neighborhood of the current paradigm); and ii) radical change, which takes the form of scientific paradigm shifts (or discontinuities in the structure of performance of a technology) that is observed as a swift migration of the knowledge community towards the new and superior paradigm. The efficiency of the search process is heavily dependent on the weight that agents posit on social influence. The occurrence of a paradigm shift becomes more likely when each member of the community attaches a small but positive weight to the experience of his/her peers. For this parameter region, nevertheless, a conservative force is exerted by the representatives of the current paradigm. However, social influence is not strong enough to seriously hamper individual discovery, and can act so as to empower successful individual pioneers who have conquered the new and superior paradigm.
A wealth of empirical studies examine the diffusion of novel scientific ideas. While those studies typically focus on the low level of individual adoption or the top level of aggregate patterns, we examine how communities at the meso level mediate diffusion. As a case study, we analyze the diffusion of a specific scientific idea, namely the ’Strength of Weak Ties’ hypothesis, introduced by Granovetter in his 1973 paper. Using Web of Science data, we construct a network of scholars who referenced Granovetter’s paper. By combining topic modeling, network analysis and close reading, we show that the diffusion network features communities of scholars who interpret and use Granovetter’s hypothesis in distinct ways. Such communities collaboratively interpret Granovetter’s hypothesis to amend it to their specific perspectives and interests. Our analysis further shows that communities are clustered around figureheads, i.e., scholars who are central within their communities and perform pivotal roles in translating the general hypothesis into their specific field. The larger implication of our study is that scientific ideas change as they spread. We argue that the methodology presented in this paper has potential beyond the scientific domain, particularly in the study of the diffusion of opinions, symbols, and ideas.
Scientists' Response to Innovative Research: An Empirical Demonstration
Experienced social psychologists were asked to imagine being a reviewer asked to consider an empirical paper that was either very novel, or modestly novel, and was either very strong or modestly strong. The "reviewers" showed a pro-novelty bias for the weaker data, but a significant ANTI-novelty bias when considering the stronger research. These data suggest that, for top journals, there may be resistance to genuinely innovative research.
Sources of Scientific Innovation: A Meta-Analytic Approach (Commentary on Simonton, 2009)
Perspectives on Psychological Science, 2009
Innovations in science can be divided into at least four major types: radical revolutions (such as Copernican and Darwinian theory), technical revolutions (led by scientists such as Newton, Lavoisier, and Einstein), controversial innovations (for example, Semmelweis's theory of puerperal fever), and conservative innovations (eugenics and various vitalistic doctrines). Biographical predictors of support for scientific innovations are distinctly different depending on the type of innovation, as are the predictors of who initially engineers such innovations. A meta-analytic approach assessing each new scientific theory according to its salient features (including epistemological, ideological, and technical attributes) is required to make sense out of the varied predisposing factors associated with the origins of these innovations. These predisposing factors are not neatly classifiable in terms of Simonton's (2009, this issue) hierarchical model of domain-specific dispositions, ...
Rewiring the network. What helps an innovation to diffuse?
Journal of Statistical Mechanics: Theory and Experiment, 2014
A fundamental question related to innovation diffusion is how the social network structure influences the process. Empirical evidence regarding real-world influence networks is very limited. On the other hand, agent-based modeling literature reports different and at times seemingly contradictory results. In this paper we study innovation diffusion processes for a range of Watts-Strogatz networks in an attempt to shed more light on this problem. Using the so-called Sznajd model as the backbone of opinion dynamics, we find that the published results are in fact consistent and allow to predict the role of network topology in various situations. In particular, the diffusion of innovation is easier on more regular graphs, i.e. with a higher clustering coefficient. Moreover, in the case of uncertainty -which is particularly high for innovations connected to public health programs or ecological campaigns -a more clustered network will help the diffusion. On the other hand, when social influence is less important (i.e. in the case of perfect information), a shorter path will help the innovation to spread in the society and -as a result -the diffusion will be easiest on a random graph.
Unexpected Findings as a Source of Innovation 2
2016
On the basis of beliefs on open innovation, online social networks and Web 2.0, we propose a new type of approach based on people-to-people interaction to support national innovation activities. With the aim of generating new ideas, our National Open Innovation System (NOIS) combines two rival innovation sources: (1) technology and social foresight research, and (2) customer needs and experiences (i.e. customer orientation strategy). By integrating content recommendation tools with NOIS, we increase the dynamics of the individual's creativity and create an online environment where conventional habits are easily exceeded. Combined, the approaches of collaborative content production and intelligent content recommendation will significantly boost the possibilities of unexpected findings, which have been identified as a major innovation source.