Dynamics on Expanding Spaces: Modeling the Emergence of Novelties

Loreto, Vittorio; Servedio, Vito D. P.; Strogatz, Steven H.; Tria, Francesca

doi:10.1007/978-3-319-24403-7_5

Cited by 64 publications

(63 citation statements)

References 32 publications

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“…Recent computational and theoretical modelling work has addressed numerous themes relating to innovation, including patterns generated by the process of technological evolution [22], distinguishing novelty [23], quantifying the magnitude of innovation [24], measuring the time to attain an innovation [25], detecting innovation as revealed by power-law distributions of activities as proxies for inventions [26], and predicting innovations from the adjacent possible [27]. In the context of macroevolution, several interrelated approaches to assessing innovations have been proposed (reviewed in [28]; for adaptive radiations, see reviews in e.g.…”

Section: Box 2 Statistical Detection Of Innovation?mentioning

confidence: 99%

Innovation: an emerging focus from cells to societies

Hochberg

Marquet

Boyd

et al. 2017

Phil. Trans. R. Soc. B

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Innovations are generally unexpected, often spectacular changes in phenotypes and ecological functions. The contributions to this theme issue are the latest conceptual, theoretical and experimental developments, addressing how ecology, environment, ontogeny and evolution are central to understanding the complexity of the processes underlying innovations. Here, we set the stage by introducing and defining key terms relating to innovation and discuss their relevance to biological, cultural and technological change. Discovering how the generation and transmission of novel biological information, environmental interactions and selective evolutionary processes contribute to innovation as an ecosystem will shed light on how the dominant features across life come to be, generalize to social, cultural and technological evolution, and have applications in the health sciences and sustainability. This article is part of the theme issue ‘Process and pattern in innovations from cells to societies’.

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Section: Box 2 Statistical Detection Of Innovation?mentioning

confidence: 99%

Innovation: an emerging focus from cells to societies

Hochberg

Marquet

Boyd

et al. 2017

Phil. Trans. R. Soc. B

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“…Urn model and its variants are among the canonical models in social physics as well as innovation process 72 . This model family is closely related to the famous Heaps' law 73 , originally predicting that the number of distinct words S in a paragraph of length n scales as…”

Section: S26 Urn Modelsmentioning

confidence: 99%

Quantifying the dynamics of failure across science, startups and security

Yin

Wang

Evans

et al. 2019

Nature

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Human achievements are often preceded by repeated attempts that initially fail, yet little is known about the mechanisms governing the dynamics of failure. Here, building on the rich literature on innovation 1-10 , human dynamics 11-17 and learning 18-25 , we develop a simple one-parameter model that mimics how successful future attempts build on those past. Analytically solving this model reveals a phase transition that separates dynamics of failure into regions of stagnation or progression, predicting that near the critical threshold, agents who share similar characteristics and learning strategies may experience fundamentally different outcomes following failures. Below the critical point, we see those who explore disjoint opportunities without a pattern of improvement, and above it, those who exploit incremental refinements to systematically advance toward success. The model makes several empirically testable predictions, demonstrating that those who eventually succeed and those who do not may be initially similar, yet are characterized by fundamentally distinct failure dynamics in 1 arXiv:1903.07562v1 [physics.soc-ph]

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“…Interestingly, Pólya's urn model has found several applications in social science, e.g. to innovation [44].…”

Section: Introductionmentioning

confidence: 99%

Power accretion in social systems

et al. 2019

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We consider a model of power distribution in a social system where a set of agents play a simple game on a graph: the probability of winning each round is proportional to the agent's current power, and the winner gets more power as a result. We show that, when the agents are distributed on simple 1D and 2D networks, inequality grows naturally up to a certain stationary value characterized by a clear division between a higher and a lower class of agents. High class agents are separated by one or several lower class agents which serve as a geometrical barrier preventing further flow of power between them. Moreover, we consider the effect of redistributive mechanisms, such as proportional (non-progressive) taxation. Sufficient taxation will induce a sharp transition towards a more equal society, and we argue that the critical taxation level is uniquely determined by the system geometry. Interestingly, we find that the roughness and Shannon entropy of the power distributions are a very useful complement to the standard measures of inequality, such as the Gini index and the Lorenz curve.

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Dynamics on Expanding Spaces: Modeling the Emergence of Novelties

Cited by 64 publications

References 32 publications

Innovation: an emerging focus from cells to societies

Innovation: an emerging focus from cells to societies

Quantifying the dynamics of failure across science, startups and security

Power accretion in social systems

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