Human achievements are often preceded by repeated attempts that fail, but little is known about the mechanisms that govern the dynamics of failure. Here, building on previous research relating to innovation1,2,3,4,5,6,7, human dynamics8,9,10,11 and learning12,13,14,15,16,17, we develop a simple one-parameter model that mimics how successful future attempts build on past efforts. Solving this model analytically suggests that a phase transition separates the dynamics of failure into regions of progression or stagnation and predicts that, near the critical threshold, agents who share similar characteristics and learning strategies may experience fundamentally different outcomes following failures. Above the critical point, agents exploit incremental refinements to systematically advance towards success, whereas below it, they explore disjoint opportunities without a pattern of improvement. The model makes several empirically testable predictions, demonstrating that those who eventually succeed and those who do not may initially appear similar, but can be characterized by fundamentally distinct failure dynamics in terms of the efficiency and quality associated with each subsequent attempt. We collected large-scale data from three disparate domains and traced repeated attempts by investigators to obtain National Institutes of Health (NIH) grants to fund their research, innovators to successfully exit their startup ventures, and terrorist organizations to claim casualties in violent attacks. We find broadly consistent empirical support across all three domains, which systematically verifies each prediction of our model. Together, our findings unveil detectable yet previously unknown early signals that enable us to identify failure dynamics that will lead to ultimate success or failure. Given the ubiquitous nature of failure and the paucity of quantitative approaches to understand it, these results represent an initial step towards the deeper understanding of the complex dynamics underlying failure.
Quantifying the dynamics of failure across science, startups and security
Yian Yin, Yang Wang, James A. Evans & Dashun Wang
Nature volume 575, pages190–194(2019)
How knowledge informs and alters disciplines is itself an enlightening, and vibrant field1. This type of meta research into new findings, insights, conceptual frameworks and techniques is important, among other things, for policymakers who fund research in the hope of tackling society’s most pressing challenges, which inevitably span disciplines.
Since its founding in 1869, Nature has offered a venue for publishing major advances from many fields. To mark its anniversary, we track here how papers cite and are cited across disciplines, using data on tens of millions of scientific articles indexed in Clarivate Analytics’ Web of Science (WoS), a bibliometric database that encompasses many thousands of research journals starting from 1900. We pay particular attention to articles that appeared in Nature. In our view, this snapshot, for all its idiosyncrasies, reveals how scientific work is ever more becoming a mixture of disciplines.
Nature’s reach: narrow work has broad impact
A scientific paper today is inspired by more disciplines than ever before, shows a new analysis marking the journal’s 150th anniversary.
Alexander J. Gates, Qing Ke, Onur Varol & Albert-László Barabási
Standard ethical frameworks struggle to deal with transhumanism, ecological issues and the rising technodiversity because they are focused on guiding and evaluating human behavior. Ethics needs its Copernican revolution to be able to deal with all moral agents, including not only humans, but also artificial intelligent agents, robots or organizations of all sizes. We argue that embracing the complexity worldview is the first step towards this revolution, and that standard ethical frameworks are still entrenched in the Newtonian worldview. We first spell out the foundational assumptions of the Newtonian worldview, where all change is reduced to material particles following predetermined trajectories governed by the laws of nature. However, modern physical theories such as relativity, quantum mechanics, chaos theory and thermodynamics have drawn a much more confusing and uncertain picture, and inspired indecisive, subjectivist, relativist, nihilist or postmodern worldviews. Based on cybernetics, systems theory and the new sciences of complexity, we introduce the complexity worldview that sees the world as interactions and their emergent organizations. We use this complexity worldview to show the limitations of standard ethical frameworks such as deontology, theology, consequentialism, virtue ethics, evolutionary ethics and pragmatism. Keywords: Complexity, philosophy, ethics, cybernetics, transhumanism, universal ethics, systems ethics.
Ethics and Complexity: Why standard ethical frameworks cannot cope with socio-technological change
Clément Vidal & Francis Heylighen
While great emphasis has been placed on the role of social interactions as driver of innovation growth, very few empirical studies have explicitly investigated the impact of social network structures on the innovation performance of cities. Past research has mostly explored scaling laws of socio-economic outputs of cities as determined by, for example, the single predictor of population. Here, by drawing on a publicly available dataset of the startup ecosystem, we build the first Workforce Mobility Network among US metropolitan areas. We found that node centrality computed on this network accounts for most of the variability observed in cities’ innovation performance and significantly outperforms other predictors such as population size or density, suggesting that policies and initiatives aiming at sustaining innovation processes might benefit from fostering professional networks alongside other economic or systemic incentives. As opposed to previous approaches powered by census data, our model can be updated in real-time upon open databases, opening up new opportunities both for researchers in a variety of disciplines to study urban economies in new ways, and for practitioners to design tools for monitoring such economies in real-time.
Predicting Urban Innovation from the Workforce Mobility Network in US
Moreno Bonaventura, Luca Maria Aiello, Daniele Quercia, Vito Latora
Information spreading on multiplex networks has been investigated widely. For multiplex networks, the relations of each layer possess different extents of intimacy, which can be described as weighted multiplex networks. Nevertheless, the effect of weighted multiplex network structures on information spreading has not been analyzed comprehensively. We herein propose an information spreading model on a weighted multiplex network. Then, we develop an edge-weight-based compartmental theory to describe the spreading dynamics. We discover that under any adoption threshold of two subnetworks, reducing weight distribution heterogeneity does not alter the growth pattern of the final adoption size versus information transmission probability while accelerating information spreading. For fixed weight distribution, the growth pattern changes with the heterogeneous of degree distribution. There is a critical initial seed size, below which no global information outbreak can occur. Extensive numerical simulations affirm that the theoretical predictions agree well with the numerical results.
Information Spreading on Weighted Multiplex Social Network
Xuzhen Zhu, Jinming Ma, Xin Su, Hui Tian, Wei Wang, and Shimin Cai
Volume 2019, Article ID 5920187, 15 pages