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Gopalakrishnan S, Gullans MJ. Entanglement and Purification Transitions in Non-Hermitian Quantum Mechanics. PHYSICAL REVIEW LETTERS 2021; 126:170503. [PMID: 33988446 PMCID: PMC9707733 DOI: 10.1103/physrevlett.126.170503] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 04/02/2021] [Indexed: 06/12/2023]
Abstract
A quantum system subject to continuous measurement and postselection evolves according to a non-Hermitian Hamiltonian. We show that, as one increases the strength of postselection, this non-Hermitian Hamiltonian can undergo a spectral phase transition. On one side of this phase transition (for weak postselection), an initially mixed density matrix remains mixed at all times, and an initially unentangled state develops volume-law entanglement; on the other side, an arbitrary initial state approaches a unique pure state with low entanglement. We identify this transition with an exceptional point in the spectrum of the non-Hermitian Hamiltonian, at which PT symmetry is spontaneously broken. We characterize the transition as well as the nontrivial steady state that emerges at late times in the mixed phase using exact diagonalization and an approximate, analytically tractable mean-field theory; these methods yield consistent conclusions.
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Affiliation(s)
- Sarang Gopalakrishnan
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Department of Physics and Astronomy, CUNY College of Staten Island, Staten Island, New York 10314, and Physics Program and Initiative for the Theoretical Sciences, The Graduate Center, CUNY, New York, New York 10016, USA
| | - Michael J Gullans
- Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, Maryland 20742 USA
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2
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Lee HA, Alves LGA, Nunes Amaral LA. Spreader events and the limitations of projected networks for capturing dynamics on multipartite networks. Phys Rev E 2021; 103:022320. [PMID: 33736087 DOI: 10.1103/physreve.103.022320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 01/26/2021] [Indexed: 11/07/2022]
Abstract
Many systems of scientific interest can be conceptualized as multipartite networks. Examples include the spread of sexually transmitted infections, scientific collaborations, human friendships, product recommendation systems, and metabolic networks. In practice, these systems are often studied after projection onto a single class of nodes, losing crucial information. Here, we address a significant knowledge gap by comparing transmission dynamics on temporal multipartite networks and on their time-aggregated unipartite projections to determine the impact of the lost information on our ability to predict the systems' dynamics. We show that the dynamics of transmission models can be dramatically dissimilar on multipartite networks and on their projections at three levels: final outcome, the magnitude of the variability from realization to realization, and overall shape of the temporal trajectory. We find that the ratio of the number of nodes to the number of active edges over the time-aggregation scale determines the ability of projected networks to capture the dynamics on the multipartite network. Finally, we explore which properties of a multipartite network are crucial in generating synthetic networks that better reproduce the dynamical behavior observed in real multipartite networks.
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Affiliation(s)
- Hyojun A Lee
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
| | - Luiz G A Alves
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
| | - Luís A Nunes Amaral
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA.,Department of Physics and Astronomy, Northwestern University, Evanston, Illinois 60208-3112, USA.,Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois 60208-4057, USA
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3
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Vucetich JA, Macdonald EA, Burnham D, Bruskotter JT, Johnson DDP, Macdonald DW. Finding Purpose in the Conservation of Biodiversity by the Commingling of Science and Ethics. Animals (Basel) 2021; 11:837. [PMID: 33809534 PMCID: PMC7998897 DOI: 10.3390/ani11030837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 03/12/2021] [Indexed: 11/24/2022] Open
Abstract
Averting the biodiversity crisis requires closing a gap between how humans tend to behave, individually and collectively, and how we ought to behave-"ought to" in the sense of behaviors required to avert the biodiversity crisis. Closing that gap requires synthesizing insight from ethics with insights from social and behavioral sciences. This article contributes to that synthesis, which presents in several provocative hypotheses: (i) Lessening the biodiversity crisis requires promoting pro-conservation behavior among humans. Doing so requires better scientific understanding of how one's sense of purpose in life affects conservation-relevant behaviors. Psychology and virtue-focused ethics indicate that behavior is importantly influenced by one's purpose. However, conservation psychology has neglected inquiries on (a) the influence of one's purpose (both the content and strength of one's purpose) on conservation-related behaviors and (b) how to foster pro-conservation purposes; (ii) lessening the biodiversity crisis requires governance-the regulation of behavior by governments, markets or other organization through various means, including laws, norms, and power-to explicitly take conservation as one of its fundamental purposes and to do so across scales of human behaviors, from local communities to nations and corporations; (iii) lessening the biodiversity crisis requires intervention via governance to nudge human behavior in line with the purpose of conservation without undue infringement on other basic values. Aligning human behavior with conservation is inhibited by the underlying purpose of conservation being underspecified. Adequate specification of conservation's purpose will require additional interdisciplinary research involving insights from ethics, social and behavioral sciences, and conservation biology.
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Affiliation(s)
- John A. Vucetich
- College of Forest Resources and Environmental Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Ewan A. Macdonald
- Wildlife Conservation Research Unit, Department of Zoology, Recanati-Kaplan Centre, University of Oxford, Oxfordshire OX13 5QL, UK; (E.A.M.); (D.B.); (D.W.M.)
- Saïd Business School, University of Oxford, Oxford OX1 1HP, UK
| | - Dawn Burnham
- Wildlife Conservation Research Unit, Department of Zoology, Recanati-Kaplan Centre, University of Oxford, Oxfordshire OX13 5QL, UK; (E.A.M.); (D.B.); (D.W.M.)
| | - Jeremy T. Bruskotter
- Terrestrial Wildlife Ecology Lab, School Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA;
| | - Dominic D. P. Johnson
- Department of Politics and International Relations, University of Oxford, Oxford OX1 3UQ, UK;
| | - David W. Macdonald
- Wildlife Conservation Research Unit, Department of Zoology, Recanati-Kaplan Centre, University of Oxford, Oxfordshire OX13 5QL, UK; (E.A.M.); (D.B.); (D.W.M.)
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Chen M. Collective variable-based enhanced sampling and machine learning. THE EUROPEAN PHYSICAL JOURNAL. B 2021; 94:211. [PMID: 34697536 PMCID: PMC8527828 DOI: 10.1140/epjb/s10051-021-00220-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 10/03/2021] [Indexed: 05/14/2023]
Abstract
ABSTRACT Collective variable-based enhanced sampling methods have been widely used to study thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced sampling methods are affected by two factors: constructing appropriate collective variables for enhanced sampling and generating accurate free energy surfaces. Recently, many machine learning techniques have been developed to improve the quality of collective variables and the accuracy of free energy surfaces. Although machine learning has achieved great successes in improving enhanced sampling methods, there are still many challenges and open questions. In this perspective, we shall review recent developments on integrating machine learning techniques and collective variable-based enhanced sampling approaches. We also discuss challenges and future research directions including generating kinetic information, exploring high-dimensional free energy surfaces, and efficiently sampling all-atom configurations.
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Affiliation(s)
- Ming Chen
- Department of Chemistry, Purdue University, West Lafayette, IN 47907 USA
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5
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Ran Y, Deng X, Wang X, Jia T. A generalized linear threshold model for an improved description of the spreading dynamics. CHAOS (WOODBURY, N.Y.) 2020; 30:083127. [PMID: 32872812 DOI: 10.1063/5.0011658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 07/27/2020] [Indexed: 06/11/2023]
Abstract
Many spreading processes in our real-life can be considered as a complex contagion, and the linear threshold (LT) model is often applied as a very representative model for this mechanism. Despite its intensive usage, the LT model suffers several limitations in describing the time evolution of the spreading. First, the discrete-time step that captures the speed of the spreading is vaguely defined. Second, the synchronous updating rule makes the nodes infected in batches, which cannot take individual differences into account. Finally, the LT model is incompatible with existing models for the simple contagion. Here, we consider a generalized linear threshold (GLT) model for the continuous-time stochastic complex contagion process that can be efficiently implemented by the Gillespie algorithm. The time in this model has a clear mathematical definition, and the updating order is rigidly defined. We find that the traditional LT model systematically underestimates the spreading speed and the randomness in the spreading sequence order. We also show that the GLT model works seamlessly with the susceptible-infected or susceptible-infected-recovered model. One can easily combine them to model a hybrid spreading process in which simple contagion accumulates the critical mass for the complex contagion that leads to the global cascades. Overall, the GLT model we proposed can be a useful tool to study complex contagion, especially when studying the time evolution of the spreading.
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Affiliation(s)
- Yijun Ran
- College of Computer and Information Science, Southwest University, Beibei, Chongqing 400715, People's Republic of China
| | - Xiaomin Deng
- College of Computer and Information Science, Southwest University, Beibei, Chongqing 400715, People's Republic of China
| | - Xiaomeng Wang
- College of Computer and Information Science, Southwest University, Beibei, Chongqing 400715, People's Republic of China
| | - Tao Jia
- College of Computer and Information Science, Southwest University, Beibei, Chongqing 400715, People's Republic of China
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6
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Abstract
Predicting innovation is a peculiar problem in data science. Following its definition, an innovation is always a never-seen-before event, leaving no room for traditional supervised learning approaches. Here we propose a strategy to address the problem in the context of innovative patents, by defining innovations as never-seen-before associations of technologies and exploiting self-supervised learning techniques. We think of technological codes present in patents as a vocabulary and the whole technological corpus as written in a specific, evolving language. We leverage such structure with techniques borrowed from Natural Language Processing by embedding technologies in a high dimensional euclidean space where relative positions are representative of learned semantics. Proximity in this space is an effective predictor of specific innovation events, that outperforms a wide range of standard link-prediction metrics. The success of patented innovations follows a complex dynamics characterized by different patterns which we analyze in details with specific examples. The methods proposed in this paper provide a completely new way of understanding and forecasting innovation, by tackling it from a revealing perspective and opening interesting scenarios for a number of applications and further analytic approaches.
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Affiliation(s)
- Andrea Tacchella
- European Commission, Joint Research Centre (JRC), Seville, Spain
- Institute for Complex Systems, CNR, Rome, Italy
| | - Andrea Napoletano
- Institute for Complex Systems, CNR, Rome, Italy
- Sapienza, University of Rome, Rome, Italy
| | - Luciano Pietronero
- Sapienza, University of Rome, Rome, Italy
- Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Compendio del Viminale, Rome, Italy
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7
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Wang W, Ma Y, Wu T, Dai Y, Chen X, Braunstein LA. Containing misinformation spreading in temporal social networks. CHAOS (WOODBURY, N.Y.) 2019; 29:123131. [PMID: 31893637 DOI: 10.1063/1.5114853] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 12/06/2019] [Indexed: 06/10/2023]
Abstract
Many researchers from a variety of fields, including computer science, network science, and mathematics, have focused on how to contain the outbreaks of Internet misinformation that threaten social systems and undermine societal health. Most research on this topic treats the connections among individuals as static, but these connections change in time, and thus social networks are also temporal networks. Currently, there is no theoretical approach to the problem of containing misinformation outbreaks in temporal networks. We thus propose a misinformation spreading model for temporal networks and describe it using a new theoretical approach. We propose a heuristic-containing (HC) strategy based on optimizing the final outbreak size that outperforms simplified strategies such as those that are random-containing and targeted-containing. We verify the effectiveness of our HC strategy on both artificial and real-world networks by performing extensive numerical simulations and theoretical analyses. We find that the HC strategy dramatically increases the outbreak threshold and decreases the final outbreak threshold.
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Affiliation(s)
- Wei Wang
- Cybersecurity Research Institute, Sichuan University, Chengdu 610065, China
| | - Yuanhui Ma
- School of Mathematics, Southwest Jiaotong University, Chengdu 610031, China
| | - Tao Wu
- School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Yang Dai
- School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China
| | - Xingshu Chen
- Cybersecurity Research Institute, Sichuan University, Chengdu 610065, China
| | - Lidia A Braunstein
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata-CONICET, Funes 3350, 7600 Mar del Plata, Argentina
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8
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Wang W, Liu QH, Liang J, Hu Y, Zhou T. Coevolution spreading in complex networks. PHYSICS REPORTS 2019; 820:1-51. [PMID: 32308252 PMCID: PMC7154519 DOI: 10.1016/j.physrep.2019.07.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 06/27/2019] [Accepted: 07/18/2019] [Indexed: 05/03/2023]
Abstract
The propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.
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Affiliation(s)
- Wei Wang
- Cybersecurity Research Institute, Sichuan University, Chengdu 610065, China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Quan-Hui Liu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Compleχ Lab, University of Electronic Science and Technology of China, Chengdu 610054, China
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Junhao Liang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Yanqing Hu
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
- Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, 519082, China
| | - Tao Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Compleχ Lab, University of Electronic Science and Technology of China, Chengdu 610054, China
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9
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Close and ordinary social contacts: How important are they in promoting large-scale contagion? Phys Rev E 2018; 98:052311. [PMCID: PMC7217557 DOI: 10.1103/physreve.98.052311] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
An outstanding problem of interdisciplinary interest is to understand quantitatively the role of social contacts in contagion dynamics. In general, there are two types of contacts: close ones among friends, colleagues and family members, etc., and ordinary contacts from encounters with strangers. Typically, social reinforcement occurs for close contacts. Taking into account both types of contacts, we develop a contact-based model for social contagion. We find that, associated with the spreading dynamics, for random networks there is coexistence of continuous and discontinuous phase transitions, but for heterogeneous networks the transition is continuous. We also find that ordinary contacts play a crucial role in promoting large-scale spreading, and the number of close contacts determines not only the nature of the phase transitions but also the value of the outbreak threshold in random networks. For heterogeneous networks from the real world, the abundance of close contacts affects the epidemic threshold, while its role in facilitating the spreading depends on the adoption threshold assigned to it. We uncover two striking phenomena. First, a strong interplay between ordinary and close contacts is necessary for generating prevalent spreading. In fact, only when there are propagation paths of reasonable length which involve both close and ordinary contacts are large-scale outbreaks of social contagions possible. Second, abundant close contacts in heterogeneous networks promote both outbreak and spreading of the contagion through the transmission channels among the hubs, when both values of the threshold and transmission rate among ordinary contacts are small. We develop a theoretical framework to obtain an analytic understanding of the main findings on random networks, with support from extensive numerical computations. Overall, ordinary contacts facilitate spreading over the entire network, while close contacts determine the way by which outbreaks occur, i.e., through a second- or first-order phase transition. These results provide quantitative insights into how certain social behaviors can emerge and become prevalent, which has potential implications not only to social science but also to economics and political science.
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10
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Shu P, Liu QH, Wang S, Wang W. Social contagions on interconnected networks of heterogeneous populations. CHAOS (WOODBURY, N.Y.) 2018; 28:113114. [PMID: 30501222 DOI: 10.1063/1.5042677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Accepted: 10/26/2018] [Indexed: 06/09/2023]
Abstract
Recently, the dynamics of social contagions ranging from the adoption of a new product to the diffusion of a rumor have attracted more and more attention from researchers. However, the combined effects of individual's heterogenous adoption behavior and the interconnected structure on the social contagions processes have yet to be understood deeply. In this paper, we study theoretically and numerically the social contagions with heterogeneous adoption threshold in interconnected networks. We first develop a generalized edge-based compartmental approach to predict the evolution of social contagion dynamics on interconnected networks. Both the theoretical predictions and numerical results show that the growth of the final recovered fraction with the intralayer propagation rate displays double transitions. When increasing the initial adopted proportion or the adopted threshold, the first transition remains continuous within different dynamic parameters, but the second transition gradually vanishes. When decreasing the interlayer propagation rate, the change in the double transitions mentioned above is also observed. The heterogeneity of degree distribution does not affect the type of first transition, but increasing the heterogeneity of degree distribution results in the type change of the second transition from discontinuous to continuous. The consistency between the theoretical predictions and numerical results confirms the validity of our proposed analytical approach.
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Affiliation(s)
- Panpan Shu
- Xi'an University of Technology, Xi'an 710054, China
| | - Quan-Hui Liu
- Big Data Research Center,University of Electronic Science and Technology of China, Chengdu 610054, China
| | | | - Wei Wang
- Cybersecurity Research Institute, Sichuan University, Chengdu 610065, China
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Napoletano A, Tacchella A, Pietronero L. A Context Similarity-Based Analysis of Countries' Technological Performance. ENTROPY 2018; 20:e20110833. [PMID: 33266558 PMCID: PMC7512395 DOI: 10.3390/e20110833] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 10/10/2018] [Accepted: 10/17/2018] [Indexed: 11/16/2022]
Abstract
This work contributes to the literature in the field of innovation by proposing a quantitative approach for the prediction of the timing and location of patenting activity. In a recent work, it was shown that focusing on couples of technological codes allows for the formation of testable predictions of innovation events, defined as the first time two codes appear together in a patent. In particular, the construction of the vector space of codes and the introduction of the context similarity metric allows for a quantitative analysis of technological progress. Here, we move from that result and we show that, through context similarity, it is possible to assign to countries a score which measures the probability of being the first to patent a potential innovation. In other words, we show that we can not only estimate the likelihood that a potential innovation will be patented in the imminent future, but also forecast where it will be patented.
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Affiliation(s)
- Andrea Napoletano
- Institute for Complex Systems—CNR, Via dei Taurini 19, 00185 Rome, Italy
- Correspondence: ; Tel.: +39-06-4991-3450
| | - Andrea Tacchella
- Institute for Complex Systems—CNR, Via dei Taurini 19, 00185 Rome, Italy
- International Finance Corporation—World Bank Group, Washington, DC 20433, USA
| | - Luciano Pietronero
- Institute for Complex Systems—CNR, Via dei Taurini 19, 00185 Rome, Italy
- International Finance Corporation—World Bank Group, Washington, DC 20433, USA
- Dipartimento di Fisica, Sapienza University of Rome, Piazzale Aldo Moro, 00185 Rome, Italy
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12
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Who is left out: exploring social boundaries in entrepreneurial ecosystems. JOURNAL OF TECHNOLOGY TRANSFER 2018. [DOI: 10.1007/s10961-018-9694-0] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Abstract
Innovation is to organizations what evolution is to organisms: it is how organizations adapt to environmental change and improve. Yet despite advances in our understanding of evolution, what drives innovation remains elusive. On the one hand, organizations invest heavily in systematic strategies to accelerate innovation. On the other, historical analysis and individual experience suggest that serendipity plays a significant role. To unify these perspectives, we analysed the mathematics of innovation as a search for designs across a universe of component building blocks. We tested our insights using data from language, gastronomy and technology. By measuring the number of makeable designs as we acquire components, we observed that the relative usefulness of different components can cross over time. When these crossovers are unanticipated, they appear to be the result of serendipity. But when we can predict crossovers in advance, they offer opportunities to strategically increase the growth of the product space. Organizations can take different approaches to innovation: they can either follow a strategic process or a serendipitous perspective. Here Fink et al. develop a statistical model to analyse how components combine to obtain a product and thus explain the mechanism behind the two approaches.
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Weiss CH, Krishnan JA, Au DH, Bender BG, Carson SS, Cattamanchi A, Cloutier MM, Cooke CR, Erickson K, George M, Gerald JK, Gerald LB, Goss CH, Gould MK, Hyzy R, Kahn JM, Mittman BS, Mosesón EM, Mularski RA, Parthasarathy S, Patel SR, Rand CS, Redeker NS, Reiss TF, Riekert KA, Rubenfeld GD, Tate JA, Wilson KC, Thomson CC. An Official American Thoracic Society Research Statement: Implementation Science in Pulmonary, Critical Care, and Sleep Medicine. Am J Respir Crit Care Med 2017; 194:1015-1025. [PMID: 27739895 PMCID: PMC5441016 DOI: 10.1164/rccm.201608-1690st] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Many advances in health care fail to reach patients. Implementation science is the study of novel approaches to mitigate this evidence-to-practice gap. METHODS The American Thoracic Society (ATS) created a multidisciplinary ad hoc committee to develop a research statement on implementation science in pulmonary, critical care, and sleep medicine. The committee used an iterative consensus process to define implementation science and review the use of conceptual frameworks to guide implementation science for the pulmonary, critical care, and sleep community and to explore how professional medical societies such as the ATS can promote implementation science. RESULTS The committee defined implementation science as the study of the mechanisms by which effective health care interventions are either adopted or not adopted in clinical and community settings. The committee also distinguished implementation science from the act of implementation. Ideally, implementation science should include early and continuous stakeholder involvement and the use of conceptual frameworks (i.e., models to systematize the conduct of studies and standardize the communication of findings). Multiple conceptual frameworks are available, and we suggest the selection of one or more frameworks on the basis of the specific research question and setting. Professional medical societies such as the ATS can have an important role in promoting implementation science. Recommendations for professional societies to consider include: unifying implementation science activities through a single organizational structure, linking front-line clinicians with implementation scientists, seeking collaborations to prioritize and conduct implementation science studies, supporting implementation science projects through funding opportunities, working with research funding bodies to set the research agenda in the field, collaborating with external bodies responsible for health care delivery, disseminating results of implementation science through scientific journals and conferences, and teaching the next generation about implementation science through courses and other media. CONCLUSIONS Implementation science plays an increasingly important role in health care. Through support of implementation science, the ATS and other professional medical societies can work with other stakeholders to lead this effort.
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15
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Armano G, Javarone MA. The Beneficial Role of Mobility for the Emergence of Innovation. Sci Rep 2017; 7:1781. [PMID: 28496113 PMCID: PMC5431937 DOI: 10.1038/s41598-017-01955-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 04/05/2017] [Indexed: 11/29/2022] Open
Abstract
Innovation is a key ingredient for the evolution of several systems, including social and biological ones. Focused investigations and lateral thinking may lead to innovation, as well as serendipity and other random discovery processes. Some individuals are talented at proposing innovation (say innovators), while others at deeply exploring proposed novelties, at getting further insights on a theory, or at developing products, services, and so on (say developers). This separation in terms of innovators and developers raises an issue of paramount importance: under which conditions a system is able to maintain innovators? According to a simple model, this work investigates the evolutionary dynamics that characterize the emergence of innovation. In particular, we consider a population of innovators and developers, in which agents form small groups whose composition is crucial for their payoff. The latter depends on the heterogeneity of the formed groups, on the amount of innovators they include, and on an award-factor that represents the policy of the system for promoting innovation. Under the hypothesis that a "mobility" effect may support the emergence of innovation, we compare the equilibria reached by our population in different cases. Results confirm the beneficial role of "mobility", and the emergence of further interesting phenomena.
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Affiliation(s)
- Giuliano Armano
- Department of Electronics and Computer Engineering, University of Cagliari, Cagliari, 09123, Italy
| | - Marco Alberto Javarone
- Department of Mathematics and Computer Science, University of Cagliari, Cagliari, 09123, Italy.
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16
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Brown CH, Curran G, Palinkas LA, Aarons GA, Wells KB, Jones L, Collins LM, Duan N, Mittman BS, Wallace A, Tabak RG, Ducharme L, Chambers DA, Neta G, Wiley T, Landsverk J, Cheung K, Cruden G. An Overview of Research and Evaluation Designs for Dissemination and Implementation. Annu Rev Public Health 2017; 38:1-22. [PMID: 28384085 PMCID: PMC5384265 DOI: 10.1146/annurev-publhealth-031816-044215] [Citation(s) in RCA: 282] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The wide variety of dissemination and implementation designs now being used to evaluate and improve health systems and outcomes warrants review of the scope, features, and limitations of these designs. This article is one product of a design workgroup that was formed in 2013 by the National Institutes of Health to address dissemination and implementation research, and whose members represented diverse methodologic backgrounds, content focus areas, and health sectors. These experts integrated their collective knowledge on dissemination and implementation designs with searches of published evaluations strategies. This article emphasizes randomized and nonrandomized designs for the traditional translational research continuum or pipeline, which builds on existing efficacy and effectiveness trials to examine how one or more evidence-based clinical/prevention interventions are adopted, scaled up, and sustained in community or service delivery systems. We also mention other designs, including hybrid designs that combine effectiveness and implementation research, quality improvement designs for local knowledge, and designs that use simulation modeling.
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Affiliation(s)
- C Hendricks Brown
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611;
| | - Geoffrey Curran
- Division of Health Services Research, Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72205;
| | - Lawrence A Palinkas
- Department of Children, Youth and Families, School of Social Work, University of Southern California, Los Angeles, California 90089;
| | - Gregory A Aarons
- Department of Psychiatry, University of California, San Diego, School of Medicine, La Jolla, California 92093;
| | - Kenneth B Wells
- Center for Health Services and Society, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, California 90024;
| | - Loretta Jones
- Healthy African American Families, Los Angeles, California 90008;
| | - Linda M Collins
- The Methodology Center and Department of Human Development & Family Studies, Pennsylvania State University, University Park, Pennsylvania 16802;
| | - Naihua Duan
- Department of Psychiatry, Columbia University Medical Center, Columbia University, New York, NY 10027;
| | - Brian S Mittman
- VA Center for Implementation Practice and Research Support, Virginia Greater Los Angeles Healthcare System, North Hills, California 91343;
| | - Andrea Wallace
- College of Nursing, The University of Iowa, Iowa City, Iowa 52242;
| | - Rachel G Tabak
- Prevention Research Center, George Warren Brown School, Washington University, St. Louis, Missouri 63105;
| | - Lori Ducharme
- National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland 20814;
| | - David A Chambers
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland 20850; ,
| | - Gila Neta
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland 20850; ,
| | - Tisha Wiley
- National Institute on Drug Abuse, National Institutes of Health, Bethesda, Maryland 20814;
| | | | - Ken Cheung
- Mailman School of Public Health, Columbia University, New York, NY 10032;
| | - Gracelyn Cruden
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611;
- Department of Health Policy and Management, University of North Carolina, Chapel Hill, North Carolina 27514;
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17
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Shu P, Gao L, Zhao P, Wang W, Stanley HE. Social contagions on interdependent lattice networks. Sci Rep 2017; 7:44669. [PMID: 28300198 PMCID: PMC5353708 DOI: 10.1038/srep44669] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Accepted: 02/13/2017] [Indexed: 11/15/2022] Open
Abstract
Although an increasing amount of research is being done on the dynamical processes on interdependent spatial networks, knowledge of how interdependent spatial networks influence the dynamics of social contagion in them is sparse. Here we present a novel non-Markovian social contagion model on interdependent spatial networks composed of two identical two-dimensional lattices. We compare the dynamics of social contagion on networks with different fractions of dependency links and find that the density of final recovered nodes increases as the number of dependency links is increased. We use a finite-size analysis method to identify the type of phase transition in the giant connected components (GCC) of the final adopted nodes and find that as we increase the fraction of dependency links, the phase transition switches from second-order to first-order. In strong interdependent spatial networks with abundant dependency links, increasing the fraction of initial adopted nodes can induce the switch from a first-order to second-order phase transition associated with social contagion dynamics. In networks with a small number of dependency links, the phase transition remains second-order. In addition, both the second-order and first-order phase transition points can be decreased by increasing the fraction of dependency links or the number of initially-adopted nodes.
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Affiliation(s)
- Panpan Shu
- School of Sciences, Xi’an University of Technology, Xi’an, 710054, China
| | - Lei Gao
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Pengcheng Zhao
- School of Physics and Optoelectronic Engineering, Xidian University, Xi’an, 710071, China
| | - Wei Wang
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, 610054, China
- Big data research center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts, 02215, USA
| | - H. Eugene Stanley
- Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts, 02215, USA
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18
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Liu QH, Wang W, Tang M, Zhang HF. Impacts of complex behavioral responses on asymmetric interacting spreading dynamics in multiplex networks. Sci Rep 2016; 6:25617. [PMID: 27156574 PMCID: PMC4860576 DOI: 10.1038/srep25617] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 04/20/2016] [Indexed: 11/29/2022] Open
Abstract
Information diffusion and disease spreading in communication-contact layered network are typically asymmetrically coupled with each other, in which disease spreading can be significantly affected by the way an individual being aware of disease responds to the disease. Many recent studies have demonstrated that human behavioral adoption is a complex and non-Markovian process, where the probability of behavior adoption is dependent on the cumulative times of information received and the social reinforcement effect of the cumulative information. In this paper, the impacts of such a non-Markovian vaccination adoption behavior on the epidemic dynamics and the control effects are explored. It is found that this complex adoption behavior in the communication layer can significantly enhance the epidemic threshold and reduce the final infection rate. By defining the social cost as the total cost of vaccination and treatment, it can be seen that there exists an optimal social reinforcement effect and optimal information transmission rate allowing the minimal social cost. Moreover, a mean-field theory is developed to verify the correctness of simulation results.
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Affiliation(s)
- Quan-Hui Liu
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wei Wang
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ming Tang
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hai-Feng Zhang
- School of Mathematical Science, Anhui University, Hefei 230039, China
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19
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Abstract
Money is central in US politics, and most campaign contributions stem from a tiny, wealthy elite. Like other political acts, campaign donations are known to be socially contagious. We study how campaign donations diffuse through a network of more than 50000 elites and examine how connectivity among previous donors reinforces contagion. We find that the diffusion of donations is driven by independent reinforcement contagion: people are more likely to donate when exposed to donors from different social groups than when they are exposed to equally many donors from the same group. Counter-intuitively, being exposed to one side may increase donations to the other side. Although the effect is weak, simultaneous cross-cutting exposure makes donation somewhat less likely. Finally, the independence of donors in the beginning of a campaign predicts the amount of money that is raised throughout a campaign. We theorize that people infer population-wide estimates from their local observations, with elites assessing the viability of candidates, possibly opposing candidates in response to local support. Our findings suggest that theories of complex contagions need refinement and that political campaigns should target multiple communities.
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20
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Wang W, Tang M, Zhang HF, Lai YC. Dynamics of social contagions with memory of nonredundant information. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 92:012820. [PMID: 26274238 DOI: 10.1103/physreve.92.012820] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Indexed: 05/20/2023]
Abstract
A key ingredient in social contagion dynamics is reinforcement, as adopting a certain social behavior requires verification of its credibility and legitimacy. Memory of nonredundant information plays an important role in reinforcement, which so far has eluded theoretical analysis. We first propose a general social contagion model with reinforcement derived from nonredundant information memory. Then, we develop a unified edge-based compartmental theory to analyze this model, and a remarkable agreement with numerics is obtained on some specific models. We use a spreading threshold model as a specific example to understand the memory effect, in which each individual adopts a social behavior only when the cumulative pieces of information that the individual received from his or her neighbors exceeds an adoption threshold. Through analysis and numerical simulations, we find that the memory characteristic markedly affects the dynamics as quantified by the final adoption size. Strikingly, we uncover a transition phenomenon in which the dependence of the final adoption size on some key parameters, such as the transmission probability, can change from being discontinuous to being continuous. The transition can be triggered by proper parameters and structural perturbations to the system, such as decreasing individuals' adoption threshold, increasing initial seed size, or enhancing the network heterogeneity.
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Affiliation(s)
- Wei Wang
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ming Tang
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Hai-Feng Zhang
- School of Mathematical Science, Anhui University, Hefei 230039, China
| | - Ying-Cheng Lai
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA
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21
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Sáenz-Royo C, Gracia-Lázaro C, Moreno Y. The role of the organization structure in the diffusion of innovations. PLoS One 2015; 10:e0126076. [PMID: 25978360 PMCID: PMC4433189 DOI: 10.1371/journal.pone.0126076] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2015] [Accepted: 03/28/2015] [Indexed: 11/19/2022] Open
Abstract
Diffusion and adoption of innovations is a topic of increasing interest in economics, market research, and sociology. In this paper we investigate, through an agent based model, the dynamics of adoption of innovative proposals in different kinds of structures. We show that community structure plays an important role on the innovation diffusion, so that proposals are more likely to be accepted in homogeneous organizations. In addition, we show that the learning process of innovative technologies enhances their diffusion, thus resulting in an important ingredient when heterogeneous networks are considered. We also show that social pressure blocks the adoption process whatever the structure of the organization. These results may help to understand how different factors influence the diffusion and acceptance of innovative proposals in different communities and organizations.
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Affiliation(s)
| | - Carlos Gracia-Lázaro
- Institute for Biocomputation and Physics of Complex Systems. University of Zaragoza, 50009 Zaragoza, Spain
- * E-mail:
| | - Yamir Moreno
- Institute for Biocomputation and Physics of Complex Systems. University of Zaragoza, 50009 Zaragoza, Spain
- Departamento de Física Teórica. University of Zaragoza, 50009 Zaragoza, Spain
- Complex Networks and Systems Lagrange Lab, Institute for Scientific Interchange, Turin 10126, Italy
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