Technological novelty profile and invention’s future impact

Kim, Daniel; Cerigo, Daniel Burkhardt; Jeong, Hawoong; Youn, Hyejin

doi:10.1140/epjds/s13688-016-0069-1

Cited by 45 publications

(39 citation statements)

References 46 publications

(57 reference statements)

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“…The highest-impact science is primarily grounded in conventional combinations of prior work, yet it simultaneously features unusual combinations (25–27). Papers of this type are twice as likely to receive high citations (26).…”

Section: Noveltymentioning

confidence: 99%

Science of science

Fortunato

Bergstrom

Börner

et al. 2018

Science

969

709

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BACKGROUND The increasing availability of digital data on scholarly inputs and outputs—from research funding, productivity, and collaboration to paper citations and scientist mobility—offers unprecedented opportunities to explore the structure and evolution of science. The science of science (SciSci) offers a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales: It provides insights into the conditions underlying creativity and the genesis of scientific discovery, with the ultimate goal of developing tools and policies that have the potential to accelerate science. In the past decade, SciSci has benefited from an influx of natural, computational, and social scientists who together have developed big data–based capabilities for empirical analysis and generative modeling that capture the unfolding of science, its institutions, and its workforce. The value proposition of SciSci is that with a deeper understanding of the factors that drive successful science, we can more effectively address environmental, societal, and technological problems. ADVANCES Science can be described as a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas. This representation has unveiled patterns characterizing the emergence of new scientific fields through the study of collaboration networks and the path of impactful discoveries through the study of citation networks. Microscopic models have traced the dynamics of citation accumulation, allowing us to predict the future impact of individual papers. SciSci has revealed choices and trade-offs that scientists face as they advance both their own careers and the scientific horizon. For example, measurements indicate that scholars are risk-averse, preferring to study topics related to their current expertise, which constrains the potential of future discoveries. Those willing to break this pattern engage in riskier careers but become more likely to make major breakthroughs. Overall, the highest-impact science is grounded in conventional combinations of prior work but features unusual combinations. Last, as the locus of research is shifting into teams, SciSci is increasingly focused on the impact of team research, finding that small teams tend to disrupt science and technology with new ideas drawing on older and less prevalent ones. In contrast, large teams tend to develop recent, popular ideas, obtaining high, but often short-lived, impact. OUTLOOK SciSci offers a deep quantitative understanding of the relational structure between scientists, institutions, and ideas because it facilitates the identification of fundamental mechanisms responsible for scientific discovery. These interdisciplinary data-driven efforts complement contributions from related fields such as sciento-metrics and the economics and sociology of science. Although SciSci seeks long-standing universal laws and mechanisms that apply across various fields of science, a fundamental challenge going forward is accounting for un...

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Section: Noveltymentioning

confidence: 99%

Science of science

Fortunato

Bergstrom

Börner

et al. 2018

Science

969

709

View full text Add to dashboard Cite

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“…In order to compute the novelty of a creative work, we first consider the fact that any new creative work-be it a scientific paper, a technological patent, or a musical compositioncontains the familiar, 'conventional' elements that can be found in known older works, and the unfamiliar, 'novel' elements that have not [4,7,8]. Intuitively then a work that features a larger novel-to-conventional ratio of elements could be considered more novel, and vice versa.…”

Section: Model and Formalismmentioning

confidence: 99%

“…found their innate preference for new stimuli [5,6]; and second, data-driven studies on citation networks and impacts of scientific papers and patents have shown that novelty is often a key feature in influential scientific knowledge and technological systems [7,8]. This is also true in cultural creations such as music, where continual experimentations of musical elements (e.g.…”

mentioning

confidence: 99%

Novelty and influence of creative works, and quantifying patterns of advances based on probabilistic references networks

2020

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Recent advances in the quantitative, computational methodology for the modeling and analysis of heterogeneous large-scale data are leading to new opportunities for understanding human behaviors and faculties, including creativity that drives creative enterprises such as science. While innovation is crucial for novel and influential achievements, quantifying these qualities in creative works remains a challenge. Here we present an information-theoretic framework for computing the novelty and influence of creative works based on their generation probabilities reflecting the degree of uniqueness of their elements in comparison with other works. Applying the formalism to a high-quality, large-scale data set of classical piano compositions-works of significant scientific and intellectual value-spanning several centuries of musical history, represented as symbolic progressions of chords, we find that the enterprise's developmental history can be characterised as a dynamic process composed of the emergence of dominant, paradigmatic creative styles that define distinct historical periods. These findings can offer a new understanding of the evolution of creative enterprises based on principled measures of novelty and influence.

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“…The authors find that the rate of new technological capabilities is slowing down but a huge number of combinations allows for a "practically infinite space of technological configurations". By considering technology as a combinatorial process, [25] uses USPTO data to investigate the extent of novelty in patents. They propose a likelihood model for assessing the novelty of combinations of patent codes.…”

Section: Modularity and Hierarchymentioning

confidence: 99%

Emergence and Evolution of Hierarchical Structure in Complex Systems

Siyari

Dilkina

Dovrolis

2019

Dynamics on and of Complex Networks III

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It is well known that many complex systems, both in technology and nature, exhibit hierarchical modularity: smaller modules, each of them providing a certain function, are used within larger modules that perform more complex functions. What is not well understood however is how this hierarchical structure (which is fundamentally a network property) emerges, and how it evolves over time. We propose a modeling framework, referred to as Evo-Lexis, that provides insight to some fundamental questions about evolving hierarchical systems. Evo-Lexis models the most elementary modules of the system as symbols ("sources") and the modules at the highest level of the hierarchy as sequences of those symbols ("targets"). Evo-Lexis computes the optimized adjustment of a given hierarchy when the set of targets changes over time by additions and removals (a process referred to as "incremental design"). In this paper we use computation modeling to show that:-Low-cost and deep hierarchies emerge when the population of target sequences evolves through tinkering and mutation. -Strong selection on the cost of new candidate targets results in reuse of more complex (longer) nodes in an optimized hierarchy. -The bias towards reuse of complex nodes results in an "hourglass architecture" (i.e., few intermediate nodes that cover almost all source-target paths). -With such bias, the core nodes are conserved for relatively long time periods although still being vulnerable to major transitions and punctuated equilibria. -Finally, we analyze the differences in terms of cost and structure between incrementally designed hierarchies and the corresponding "clean-slate" hierarchies which result when the system is designed from scratch after a change.arXiv:1805.04924v2 [cs.NE] 4 Aug 20182. Under what conditions do the emergent hierarchies exhibit the so called "hourglass effect"? Why are few intermediate modules reused much more than others? 3. Do intermediate modules persist during the evolution of hierarchies? Or are there "punctuated equilibria" where the highly reused modules change significantly? 4. Which are the differences in terms of cost and structure between the incrementally designed and the corresponding clean-slate designed hierarchies?The structure of the paper is as follows: In Section 2, we present an overview of Lexis, the static optimization framework that serves as the main building block in Evo-Lexis. 1 In Section 3, we present the components of the Evo-Lexis framework, along with the metrics that we use for the analysis of evolving hierarchies. In Section 4, we evaluate the evolution of hierarchies under different target generation models. Sections 5 and 6 present further analysis regarding the evolvability and major transitions in hierarchies produced using the most full-fledged (MRS) target generation model. Finally, Section 7 focuses on the comparison between clean-slate and incremental design in terms of cost and structure. In Section 8, we review related work in the context of Evo-Lexis. Section 9 discusses the results and p...

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Technological novelty profile and invention’s future impact

Cited by 45 publications

References 46 publications

Science of science

Science of science

Novelty and influence of creative works, and quantifying patterns of advances based on probabilistic references networks

Emergence and Evolution of Hierarchical Structure in Complex Systems

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