Citations are commonly held to represent scientific impact. To date, however, there is no empirical evidence in support of this postulate that is central to research assessment exercises and Science of Science studies. Here, we report on the first empirical verification of the degree to which citation numbers represent scientific impact as it is actually perceived by experts in their respective field. We run a large-scale survey of about 2000 corresponding authors who performed a pairwise impact assessment task across more than 20000 scientific articles. Results of the survey show that citation data and perceived impact do not align well, unless one properly accounts for strong psychological biases that affect the opinions of experts with respect to their own papers vs. those of others. First, researchers tend to largely prefer their own publications to the most cited papers in their field of research. Second, there is only a mild positive correlation between the number of citations of top-cited papers in given research areas and expert preference in pairwise comparisons. This also applies to pairs of papers with several orders of magnitude differences in their total number of accumulated citations. However, when researchers were asked to choose among pairs of their own papers, thus eliminating the bias favouring one’s own papers over those of others, they did systematically prefer the most cited article. We conclude that, when scientists have full information and are making unbiased choices, expert opinion on impact is congruent with citation numbers.
Quantifying perceived impact of scientific publications
Filippo Radicchi, Alexander Weissman, Johan Bollen
The introduction of ICT in techno-socio-economic systems, such as Smart Grids, traffic management, food supply chains and others, transforms the role of simulation as a scientific method for studying these complex systems. The scientific focus and challenge in simulations move from understanding system complexity to actually prototyping online and distributed regulatory mechanisms for supporting system operations. Existing simulation tools are not designed to address the challenges of this new reality, however, simulation is all about capturing reality at an adequate level of detail. This paper fills this gap by introducing a Java-based distributed simulation framework for inter-connected and inter-dependent techno-socio-economic system: SFINA, the Simulation Framework for Intelligent Network Adaptations. Three layers outline the design approach of SFINA: (i) integration of domain knowledge and dynamics that govern various techno-socio-economic systems, (ii) system modeling with dynamic flow networks represented by temporal directed weighted graphs and (iii) simulation of generic regulation models, policies and mechanisms applicable in several domains. SFINA aims at minimizing the fragmentation and discrepancies between different simulation communities by allowing the interoperability of SFINA with several other existing domain backends. The coupling of three such backends with SFINA is illustrated in the domain of Smart Grids and disaster mitigation. It is shown that the same model of cascading failures in Smart Grids is developed once and evaluated with both MATPOWER and InterPSS backends without changing a single line of application code. Similarly, application code developed in SFINA is reused for the evaluation of mitigation strategies in a backend that simulates the flows of a disaster spread. Results provide a proof-of-concept for the high modularity and reconfigurability of SFINA and puts the foundations of a new generation of simulation tools that prototype and validate online decentralized regulation in techno-socio-economic systems.
SFINA – Simulation Framework for Intelligent Network Adaptations
Evangelos Pournaras, Ben-Elias Brandt, Manish Thapa, Dinesh Acharya, Jose Espejo-Uribe, Mark Ballandies, Dirk Helbing
Simulation Modelling Practice and Theory
Volume 72, March 2017, Pages 34–50
Any physical system can be viewed from the perspective that information is implicitly represented in its state. However, the quantification of this information when it comes to complex networks has remained largely elusive. In this work, we use techniques inspired by quantum statistical mechanics to define an entropy measure for complex networks and to develop a set of information-theoretic tools, based on network spectral properties, such as Rényi q entropy, generalized Kullback-Leibler and Jensen-Shannon divergences, the latter allowing us to define a natural distance measure between complex networks. First, we show that by minimizing the Kullback-Leibler divergence between an observed network and a parametric network model, inference of model parameter(s) by means of maximum-likelihood estimation can be achieved and model selection can be performed with appropriate information criteria. Second, we show that the information-theoretic metric quantifies the distance between pairs of networks and we can use it, for instance, to cluster the layers of a multilayer system. By applying this framework to networks corresponding to sites of the human microbiome, we perform hierarchical cluster analysis and recover with high accuracy existing community-based associations. Our results imply that spectral-based statistical inference in complex networks results in demonstrably superior performance as well as a conceptual backbone, filling a gap towards a network information theory.
Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison
Manlio De Domenico and Jacob Biamonte
Phys. Rev. X 6, 041062 – Published 21 December 2016
How communication technologies shape our collective memory.
See Also: http://pantheon.media.mit.edu
This book is about the modern corporation. It is a tale of complexity, morality, efficiency, and freedom. Our culture is imbued with three myths stemming from our somewhat contradictory beliefs in the invisible hand and optimization by design. The first myth would have us believe that the modern corporation, based on property rights and enforceable contracts, is maximizing wealth and efficiency. The second myth would have us believe that profits and morality are disconnected from each other because they are subject to different constraints. The third myth would have us believe that private property rights over knowledge will deliver unabated economic growth, just as it happened during the industrial revolution. This book presents a bold vision of the modern corporation, one that some might find unsettling, for it calls into question the real implications of human agency, and the very notion of economic efficiency.
The Nature of the Corporation: A Tale of Economic Complexity