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Social alliances improve rank and fitness in convention-based societies

Social alliances improve rank and fitness in convention-based societies | Papers | Scoop.it

What forces produce and maintain social inequality, and why do society members tolerate this inequality? The “One Percent” clearly benefit from having high status, but low-status individuals have strong incentive to challenge the established pecking order and try to improve their position. This conundrum is particularly striking in the societies of many primates and spotted hyenas, where females who are born to low-status mothers rarely manage to improve their position. Here we find that females who are strongly allied with their group-mates are more likely to improve their status, and that upward social mobility is often achieved with support from their closest allies. This suggests that, much like some animals compete physically for status, these species compete through social alliances.

 

Social alliances improve rank and fitness in convention-based societies

Eli D. Strauss and Kay E. Holekamp
PNAS April 30, 2019 116 (18) 8919-8924

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Ecosystem antifragility: Beyond integrity and resilience 

We review the concept of ecosystem resilience in its relation to ecosystem integrity from an information theory approach. We summarize the literature on the subject identifying three main narratives: ecosystem properties that enable them to be more resilient; ecosystem response to perturbations; and complexity. We also include original ideas with theoretical and quantitative developments with application examples. The main contribution is a new way to rethink resilience, that is mathematically formal and easy to evaluate heuristically in real-world applications: ecosystem antifragility. An ecosystem is antifragile if it benefits from environmental variability. Antifragility therefore goes beyond robustness or resilience because while resilient/robust systems are merely perturbation-resistant, antifragile structures not only withstand stress but also benefit from it.

 

Equihua Zamora M, Espinosa M, Gershenson C, López-Corona O, Munguia M, Pérez-Maqueo O, Ramírez-Carrillo E. 2019. Ecosystem antifragility: Beyond integrity and resilience. PeerJ Preprints 7:e27813v1

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What happened to cognitive science?

What happened to cognitive science? | Papers | Scoop.it

More than a half-century ago, the ‘cognitive revolution’, with the influential tenet ‘cognition is computation’, launched the investigation of the mind through a multidisciplinary endeavour called cognitive science. Despite significant diversity of views regarding its definition and intended scope, this new science, explicitly named in the singular, was meant to have a cohesive subject matter, complementary methods and integrated theories. Multiple signs, however, suggest that over time the prospect of an integrated cohesive science has not materialized. Here we investigate the status of the field in a data-informed manner, focusing on four indicators, two bibliometric and two socio-institutional. These indicators consistently show that the devised multi-disciplinary program failed to transition to a mature inter-disciplinary coherent field. Bibliometrically, the field has been largely subsumed by (cognitive) psychology, and educationally, it exhibits a striking lack of curricular consensus, raising questions about the future of the cognitive science enterprise.

 

What happened to cognitive science?
Rafael Núñez, Michael Allen, Richard Gao, Carson Miller Rigoli, Josephine Relaford-Doyle & Arturs Semenuks
Nature Human Behaviour (2019)

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Turing patterns mediated by network topology in homogeneous active systems

Mechanisms of pattern formation—of which the Turing instability is an archetype—constitute an important class of dynamical processes occurring in biological, ecological, and chemical systems. Recently, it has been shown that the Turing instability can induce pattern formation in discrete media such as complex networks, opening up the intriguing possibility of exploring it as a generative mechanism in a plethora of socioeconomic contexts. Yet much remains to be understood in terms of the precise connection between network topology and its role in inducing the patterns. Here we present a general mathematical description of a two-species reaction-diffusion process occurring on different flavors of network topology. The dynamical equations are of the predator-prey class that, while traditionally used to model species population, has also been used to model competition between antagonistic features in social contexts. We demonstrate that the Turing instability can be induced in any network topology by tuning the diffusion of the competing species or by altering network connectivity. The extent to which the emergent patterns reflect topological properties is determined by a complex interplay between the diffusion coefficients and the localization properties of the eigenvectors of the graph Laplacian. We find that networks with large degree fluctuations tend to have stable patterns over the space of initial perturbations, whereas patterns in more homogenous networks are purely stochastic.

 

Turing patterns mediated by network topology in homogeneous active systems
Sayat Mimar, Mariamo Mussa Juane, Juyong Park, Alberto P. Muñuzuri, and Gourab Ghoshal
Phys. Rev. E 99, 062303

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Localist plasticity identified by mutual information

The issue of memory is difficult for standard neural network models. Ubiquitous synaptic plasticity introduces the problem of interference, which limits pattern recall and introduces conflation errors. We present a lognormal recurrent neural network, load patterns into it (MNIST), and test the resulting neural representation for information content by an output classifier. We identify neurons, which ‘compress’ the pattern information into their own adjacency network, and by stimulating these achieve recall. Learning is limited to intrinsic plasticity and output synapses of these pattern neurons (localist plasticity), which prevents interference.

Our first experiments show that this form of storage and recall is possible, with the caveat of a ‘lossy’ recall similar to human memory. Comparing our results with a standard Gaussian network model, we notice that this effect breaks down for the Gaussian model.

 

Localist plasticity identified by mutual information

Gabriele Scheler, Johann Schumann

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Quantifying the sensing power of vehicle fleets

Attaching sensors to crowd-sourced vehicles could provide a cheap and accurate way to monitor air pollution, road quality, and other aspects of a city’s health. But in order for so-called drive-by sensing to be practically useful, the sensor-equipped vehicle fleet needs to have large “sensing power”—that is, it needs to cover a large fraction of a city’s area during a given reference period. Here, we provide an analytic description of the sensing power of taxi fleets, which agrees with empirical data from nine major cities. Our results show taxis’ sensing power is unexpectedly large—in Manhattan; just 10 random taxis cover one-third of street segments daily, which certifies that drive-by sensing can be readily implemented in the real world.

 

Quantifying the sensing power of vehicle fleets
Kevin P. O’Keeffe, Amin Anjomshoaa, Steven H. Strogatz, Paolo Santi, and Carlo Ratti
PNAS

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Complex Methods Applied to Data Analysis, Processing, and Visualisation

The amount of data available every day is not only enormous but growing at an exponential rate. Over the last ten years there has been an increasing interest in using complex methods to analyse and visualise massive datasets, gathered from very different sources and including many different features: social networks, surveillance systems, smart cities, medical diagnosis systems, business information, cyberphysical systems, and digital media data. Nowadays, there are a large number of researchers working in complex methods to process, analyse, and visualise all this information, which can be applied to a wide variety of open problems in different domains. This special issue presents a collection of research papers addressing theoretical, methodological, and practical aspects of data processing, focusing on algorithms that use complex methods (e.g., chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory) in a variety of domains (e.g., software engineering, digital media data, bioinformatics, health care, imaging and video, social networks, and natural language processing). A total of 27 papers were received from different research fields, but sharing a common feature: they presented complex systems that process, analyse, and visualise large amounts of data. After the review process, 8 papers were accepted for publication (around 30% of acceptance ratio).

 

Complexity
Volume 2019, Article ID 9316123, 2 pages
https://doi.org/10.1155/2019/9316123
Editorial
Complex Methods Applied to Data Analysis, Processing, and Visualisation
Jose Garcia-Rodriguez, Anastasia Angelopoulou, David Tomás, and Andrew Lewis

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On complexity of branching droplets in electrical field

Decanol droplets in a thin layer of sodium decanoate with sodium chloride exhibit bifurcation branching growth due to interplay between osmotic pressure, diffusion and surface tension. We aimed to evaluate if morphology of the branching droplets changes when the droplets are subject to electrical potential difference. We analysed graph-theoretic structure of the droplets and applied several complexity measures. We found that, in overall, the current increases complexity of the branching droplets in terms of number of connected components and nodes in their graph presentations, morphological complexity and compressibility.

 

On complexity of branching droplets in electrical field
Mohammad Mahdi Dehshibi, Jitka Cejkova, Dominik Svara, Andrew Adamatzky

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Gender-specific preference in online dating

In this paper, to reveal the differences of gender-specific preference and the factors affecting potential mate choice in online dating, we analyze the users’ behavioral data of a large online dating site in China. We find that for women, network measures of popularity and activity of the men they contact are significantly positively associated with their messaging behaviors, while for men only the network measures of popularity of the women they contact are significantly positively associated with their messaging behaviors. Secondly, when women send messages to men, they pay attention to not only whether men’s attributes meet their own requirements for mate choice, but also whether their own attributes meet men’s requirements, while when men send messages to women, they only pay attention to whether women’s attributes meet their own requirements. Thirdly, compared with men, women attach great importance to the socio-economic status of potential partners and their own socio-economic status will affect their enthusiasm for interaction with potential mates. Further, we use the ensemble learning classification methods to rank the importance of factors predicting messaging behaviors, and find that the centrality indices of users are the most important factors. Finally, by correlation analysis we find that men and women show different strategic behaviors when sending messages. Compared with men, for women sending messages, there is a stronger positive correlation between the centrality indices of women and men, and more women tend to send messages to people more popular than themselves. These results have implications for understanding gender-specific preference in online dating further and designing better recommendation engines for potential dates. The research also suggests new avenues for data-driven research on stable matching and strategic behavior combined with game theory.

 

Gender-specific preference in online dating
Xixian Su and Haibo Hu
EPJ Data Science 2019 8:12

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Interacting contagions are indistinguishable from social reinforcement

From fake news to innovative technologies, many contagions spread via a process of social reinforcement, where multiple exposures are distinct from prolonged exposure to a single source. Contrarily, biological agents such as Ebola or measles are typically thought to spread as simple contagions. Here, we demonstrate that interacting simple contagions are indistinguishable from complex contagions. In the social context, our results highlight the challenge of identifying and quantifying mechanisms, such as social reinforcement, in a world where an innumerable amount of ideas, memes and behaviors interact. In the biological context, this parallel allows the use of complex contagions to effectively quantify the non-trivial interactions of infectious diseases.

 

Interacting contagions are indistinguishable from social reinforcement

Laurent Hébert-Dufresne, Samuel V. Scarpino, Jean-Gabriel Young

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An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.

 

An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue
Norman Packard, Mark A. Bedau, Alastair Channon, Takashi Ikegami,
Artificial Life
Volume 25 | Issue 2 | Spring 2019 p.93-103

Complexity Digest's insight:

Technology seems open-ended, and it is not living... or is it?

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Ancient DNA Yields Clues to Past Biodiversity

Ancient DNA Yields Clues to Past Biodiversity | Papers | Scoop.it
Surviving fragments of genetic material preserved in sediments allow metagenomics researchers to see the full diversity of past life — even microbes.
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A Quantum Router for the Entangled Web

Qubit transmission protocols are presently point-to-point, and thus restrictive in their functionality. A quantum router is necessary for the quantum Internet to become a reality. We present a quantum router design based on teleportation, as well as mechanisms for entangled pair management. The prototype was validated using a quantum simulator.

 

A Quantum Router for the Entangled Web
Bernardo A. Huberman, Bob Lund

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Reconciling cooperation, biodiversity and stability in complex ecological communities

Reconciling cooperation, biodiversity and stability in complex ecological communities | Papers | Scoop.it
Empirical evidences show that ecosystems with high biodiversity can persist in time even in the presence of few types of resources and are more stable than low biodiverse communities. This evidence is contrasted by the conventional mathematical modeling, which predicts that the presence of many species and/or cooperative interactions are detrimental for ecological stability and persistence. Here we propose a modelling framework for population dynamics, which also include indirect cooperative interactions mediated by other species (e.g. habitat modification). We show that in the large system size limit, any number of species can coexist and stability increases as the number of species grows, if mediated cooperation is present, even in presence of exploitative or harmful interactions (e.g. antibiotics). Our theoretical approach thus shows that appropriate models of mediated cooperation naturally lead to a solution of the long-standing question about complexity-stability paradox and on how highly biodiverse communities can coexist.

Via Samir
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Neurath’s boat and the Sally-Anne test: Life, Cognition, Matter and Stuff

Making sense of the world around us is likened to the task of staying afloat on a stormy sea while rebuilding our craft of ideas and concepts as we go. This metaphor is pursued through successive stages of cognitive development, and more sophisticated appreciation of multiple perspectives; from pre-theoretical to folk science to the theoretical, from individual to social to inter-subjective agreement. This inescapably generates reflections on the relationships between embodied and situated Life and Cognition.

 

Neurath’s boat and the Sally-Anne test: Life, Cognition, Matter and Stuff
Inman Harvey
Adaptive Behavior

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The physics of governance networks: critical transitions in contagion dynamics on multilayer adaptive networks with application to the sustainable use of renewable resources

Adaptive networks are a versatile approach to model phenomena such as contagion and spreading dynamics, critical transitions and structure formation that emerge from the dynamic coevolution of complex network structure and node states. Here, we study critical transitions in contagion dynamics on multilayer adaptive networks with dynamic node states and present an application to the governance of sustainable resource use. We focus on a three layer adaptive network model, where a polycentric governance network interacts with a social network of resource users which in turn interacts with an ecological network of renewable resources. We uncover that sustainability is favored for slow interaction timescales, large homophilic network adaptation rate (as long it is below the fragmentation threshold) and high taxation rates. Interestingly, we also observe a trade-off between an eco-dictatorship (reduced model with a single governance actor that always taxes unsustainable resource use) and the polycentric governance network of multiple actors. In the latter setup, sustainability is enhanced for low but hindered for high tax rates compared to the eco-dictatorship case. These results highlight mechanisms generating emergent critical transitions in contagion dynamics on multilayer adaptive network and show how these can be understood and approximated analytically, relevant for understanding complex adaptive systems from various disciplines ranging from physics and epidemiology to sociology and global sustainability science. The paper also provides insights into potential critical intervention points for policy in the form of taxes in the governance of sustainable renewable resource use that can inform more process-detailed social-ecological modeling.

 

The physics of governance networks: critical transitions in contagion dynamics on multilayer adaptive networks with application to the sustainable use of renewable resources
Fabian Geier, Wolfram Barfuss, Marc Wiedermann, Jürgen Kurths, Jonathan F. Donges

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Average Fitness Differences on NK Landscapes

Average Fitness Differences on NK Landscapes | Papers | Scoop.it

The average fitness difference between adjacent sites in a fitness landscape is an important descriptor that impacts in particular the dynamics of selection/mutation processes on the landscape. Of particular interest is its connection to the error threshold phenomenon. We show here that this parameter is intimately tied to the ruggedness through the landscape’s amplitude spectrum. For the NK model, a surprisingly simple analytical estimate explains simulation data with high precision.

 

Average Fitness Differences on NK Landscapes
Wim Hordijk, Stuart A. Kauffman, Peter F. Stadler

Theory in Biosciences

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Exploration of the chemical space and its three historical regimes

We found that the number of new chemical compounds has grown exponentially with a 4.4% annual production rate from 1800 to 2015 not even affected by World Wars. There are three distinct growth regimes: proto-organic, organic, and organometallic, with decreasing variability in the production of compounds over time. Contrary to the belief that organic synthesis developed only after 1828, synthesis had been a key provider of new compounds already at the beginning of the 19th century. By 1900, it became the established tool to report new compounds. We found that chemists are conservative when selecting starting materials and that despite the growing production of new compounds, most of them belong to a restricted set of chemical compositions.

 

Exploration of the chemical space and its three historical regimes

Eugenio J. Llanos, Wilmer Leal, Duc H. Luu, Jürgen Jost, Peter F. Stadler, and Guillermo Restrepo
PNAS

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Advances in Complex Systems and Their Applications to Cybersecurity

Cybersecurity is one of the fastest growing and largest technology sectors and is increasingly being recognized as one of the major issues in many industries, so companies are increasing their security budgets in order to guarantee the security of their processes. Successful menaces to the security of information systems could lead to safety, environmental, production, and quality problems.

One of the most harmful issues of attacks and intrusions is the ever-changing nature of attack technologies and strategies, which increases the difficulty of protecting computer systems. As a result, advanced systems are required to deal with the ever-increasing complexity of attacks in order to protect systems and information.

This special issue received several contributions, 5 of which have been accepted for publication.

 

Complexity
Volume 2019, Article ID 3261453, 2 pages
https://doi.org/10.1155/2019/3261453
Editorial
Advances in Complex Systems and Their Applications to Cybersecurity
Fernando Sánchez Lasheras, Danilo Comminiello, and Alicja Krzemień

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Complexity in Forecasting and Predictive Models

The challenge of this special issue has been to know the state of the problem related to forecasting modeling and the creation of a model to forecast the future behavior that supports decision making by supporting real-world applications.

This issue has been highlighted by the quality of its research work on the critical importance of advanced analytical methods, such as neural networks, soft computing, evolutionary algorithms, chaotic models, cellular automata, agent-based models, and finite mixture minimum squares (FIMIX-PLS)

Mainly, all the papers are focused on triggering a substantive discussion on how the model predictions can face the challenges around the complexity field that lie ahead. These works help to better understand the new trends in computing and statistical techniques that allow us to make better forecasts. Complexity plays a prominent role in these trends, given the increasing variety and changing data flows, forcing academics to adopt innovative and hybrid methods.

 

Complexity
Volume 2019, Article ID 8160659, 3 pages
https://doi.org/10.1155/2019/8160659
Editorial
Complexity in Forecasting and Predictive Models
Jose L. Salmeron, Marisol B. Correia, and Pedro R. Palos-Sanchez

 

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A simple contagion process describes spreading of traffic jams in urban networks

The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two novel macroscopic characteristics of network traffic, namely congestion propagation rate \b{eta} and congestion dissipation rate {\mu}. We describe the dynamics of congestion propagation and dissipation using these new parameters, \b{eta}, and {\mu}, embedded within a system of ordinary differential equations, analogous to the well-known Susceptible-Infected-Recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.

 

A simple contagion process describes spreading of traffic jams in urban networks
Meead Saberi, Mudabber Ashfaq, Homayoun Hamedmoghadam, Seyed Amir Hosseini, Ziyuan Gu, Sajjad Shafiei, Divya J. Nair, Vinayak Dixit, Lauren Gardner, S. Travis Waller, Marta C. González

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Human information processing in complex networks

Humans communicate using systems of interconnected stimuli or concepts -- from language and music to literature and science -- yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient and biased ways that humans process this information. Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system's network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules -- the two defining features of hierarchical organization. Together, these results suggest that many real networks are constrained by the pressures of information transmission, and that these pressures select for specific structural features.

 

Human information processing in complex networks

Christopher W. Lynn, Lia Papadopoulos, Ari E. Kahn, Danielle S. Bassett

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Simplicial models of social contagion

Simplicial models of social contagion | Papers | Scoop.it

Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterize social contagion processes such as opinion formation or the adoption of novelties, where complex mechanisms of influence and reinforcement are at work. Here we introduce a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes. Numerical simulations of the model on both empirical and synthetic simplicial complexes highlight the emergence of novel phenomena such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding of higher-order interactions in complex systems.

 

Simplicial models of social contagion
Iacopo Iacopini, Giovanni Petri, Alain Barrat & Vito Latora
Nature Communications 10, Article number: 2485 (2019)

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What is the Entropy of a Social Organization?

We quantify a social organization's potentiality, that is its ability to attain different configurations. The organization is represented as a network in which nodes correspond to individuals and (multi-)edges to their multiple interactions. Attainable configurations are treated as realizations from a network ensemble. To encode interaction preferences between individuals, we choose the generalized hypergeometric ensemble of random graphs, which is described by a closed-form probability distribution. From this distribution we calculate Shannon entropy as a measure of potentiality. This allows us to compare different organizations as well different stages in the development of a given organization. The feasibility of the approach is demonstrated using data from 3 empirical and 2 synthetic systems.

 

What is the Entropy of a Social Organization?
Christian Zingg, Giona Casiraghi, Giacomo Vaccario, Frank Schweitzer

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Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity

Consistent confirmations obtained independently of each other lend credibility to a scientific result. We refer to results satisfying this consistency as reproducible and assume that reproducibility is a desirable property of scientific discovery. Yet seemingly science also progresses despite irreproducible results, indicating that the relationship between reproducibility and other desirable properties of scientific discovery is not well understood. These properties include early discovery of truth, persistence on truth once it is discovered, and time spent on truth in a long-term scientific inquiry. We build a mathematical model of scientific discovery that presents a viable framework to study its desirable properties including reproducibility. In this framework, we assume that scientists adopt a model-centric approach to discover the true model generating data in a stochastic process of scientific discovery. We analyze the properties of this process using Markov chain theory, Monte Carlo methods, and agent-based modeling. We show that the scientific process may not converge to truth even if scientific results are reproducible and that irreproducible results do not necessarily imply untrue results. The proportion of different research strategies represented in the scientific population, scientists’ choice of methodology, the complexity of truth, and the strength of signal contribute to this counter-intuitive finding. Important insights include that innovative research speeds up the discovery of scientific truth by facilitating the exploration of model space and epistemic diversity optimizes across desirable properties of scientific discovery.

 

Devezer B, Nardin LG, Baumgaertner B, Buzbas EO (2019) Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity. PLoS ONE 14(5): e0216125. https://doi.org/10.1371/journal.pone.0216125

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Scale-free networks revealed from finite-size scaling

Networks play a vital role in the development of predictive models of physical, biological, and social collective phenomena. A quite remarkable feature of many of these networks is that they are believed to be approximately scale free: the fraction of nodes with k incident links (the degree) follows a power law p(k)∝k−λ for sufficiently large degree k. The value of the exponent λ as well as deviations from power law scaling provide invaluable information on the mechanisms underlying the formation of the network such as small degree saturation, variations in the local fitness to compete for links, and high degree cutoffs owing to the finite size of the network. Indeed real networks are not infinitely large and the largest degree of any network cannot be larger than the number of nodes. Finite size scaling is a useful tool for analyzing deviations from power law behavior in the vicinity of a critical point in a physical system arising due to a finite correlation length. Here we show that despite the essential differences between networks and critical phenomena, finite size scaling provides a powerful framework for analyzing self-similarity and the scale free nature of empirical networks. We analyze about two hundred naturally occurring networks with distinct dynamical origins, and find that a large number of these follow the finite size scaling hypothesis without any self-tuning. Notably this is the case of biological protein interaction networks, technological computer and hyperlink networks and informational citation and lexical networks. Marked deviations appear in other examples, especially infrastructure and transportation networks, but also social, affiliation and annotation networks. Strikingly, the values of the scaling exponents are not independent but satisfy an approximate exponential relationship.

 

Scale-free networks revealed from finite-size scaling
Matteo Serafino, Giulio Cimini, Amos Maritan, Samir Suweis, Jayanth R. Banavar, Guido Caldarelli

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