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Mapping Philanthropic Support of Science

Louis M. Shekhtman, Alexander J. Gates, Albert-László Barabási
While philanthropic support plays an increasing role in supporting research, there is limited quantitative knowledge about the patterns that characterize the distribution of philanthropic support. Here, we map philanthropic funding to universities and research institutions based on IRS tax forms from 685,397 non-profit organizations. We identify nearly one million grants supporting institutions involved in science, finding that in volume and scope, philanthropic funding is comparable to federal research funding. However, whereas federal funding relies on a few large organizations to distribute grants, the philanthropic ecosystem's support is fragmented among a large number of funders with diverse focus that support research institutions at varying levels. Furthermore, we find that distinct from government support, philanthropic funders tend to focus locally, indicating that other criteria, beyond research excellence, play a role in their funding decisions. We also show evidence of persistence, i.e., once a grant-giving relationship begins, it tends to continue in time. Finally, we discuss the policy implications of our findings for philanthropic funders, individual researchers, the science of science, and for quantitative studies of philanthropy in general.

Read the full article at: arxiv.org

Phillip Trotter's curator insight, June 29, 2:49 PM

Interesting insights into philanthropic support of science and research in USA

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Dynamics of cross-platform attention to retracted papers

Dynamics of cross-platform attention to retracted papers | Papers | Scoop.it

Hao Peng, Daniel M. Romero, and Emőke-Ágnes Horvát

PNAS June 14, 2022 119 (25) e2119086119

Scientific retraction has been on the rise recently. Retracted papers are frequently discussed online, enabling the broad dissemination of potentially flawed findings. Our analysis spans a nearly 10-y period and reveals that most papers exhaust their attention by the time they get retracted, meaning that retractions cannot curb the online spread of problematic papers. This is striking as we also find that retracted papers are pervasive across mediums, receiving more attention after publication than nonretracted papers even on curated platforms, such as news outlets and knowledge repositories. Interestingly, discussions on social media express more criticism toward subsequently retracted results and may thus contain early signals related to unreliable work.

Read the full article at: www.pnas.org

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Influence maximization in Boolean networks

Influence maximization in Boolean networks | Papers | Scoop.it

Thomas Parmer, Luis M. Rocha & Filippo Radicchi 
Nature Communications volume 13, Article number: 3457 (2022)

The optimization problem aiming at the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired long-term behaviors is central for some applications, as for example the detection of key therapeutic targets to control pathways in models of biological signaling and regulatory networks. Here, we develop a method to solve such an optimization problem taking inspiration from the well-studied problem of influence maximization for spreading processes in social networks. We validate the method on small gene regulatory networks whose dynamical landscapes are known by means of brute-force analysis. We then systematically study a large collection of gene regulatory networks. We find that for about 65% of the analyzed networks, the minimal driver sets contain less than 20% of their nodes.

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Lithbea, A New Domain outside the Tree of Life

Gomez-Marquez, J. Lithbea, A New Domain outside the Tree of Life . Preprints 2022, 2022060094 (doi: 10.20944/preprints202206.0094.v1)

As synthetic/artificial life forms become more abundant and sophisticated, an increasing number of bizarre creatures - xenobots, robots, soft A-life entities, genetically engineered organisms, etc. - are invading our society. Therefore, we need to bring order to all this, to establish what is living and what is not. Here, I intend to classify all these non-natural entities and clarify their status with reference to their consideration or not as living beings, leaving the door open to an uncertain future in which perhaps we can see how "the artificial" and "the natural" merge to originate something new. To order all this "new biodiversity" and to also give entry to viruses (which are excluded of the three-domains tree of life), I propose the creation of a new domain, Lithbea (from the name: life-in-the-border entities), in which all these new human-made entities as well as the viruses will be included. Within this domain there would be two kingdoms, Virus and Humade (contraction of human-made), based on their origin, natural or human-made. A brief description of each component of Lithbea is included and the implications for society and biology of this “new biodiversity” is briefly discussed.

Read the full article at: www.preprints.org

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Biology, geometry and information

Jürgen Jost
Theory in Biosciences volume 141, pages 65–71 (2022)

The main thesis developed in this article is that the key feature of biological life is the a biological process can control and regulate other processes, and it maintains that ability over time. This control can happen hierarchically and/or reciprocally, and it takes place in three-dimensional space. This implies that the information that a biological process has to utilize is only about the control, but not about the content of those processes. Those other processes can be vastly more complex that the controlling process itself, and in fact necessarily so. In particular, each biological process draws upon the complexity of its environment.

Read the full article at: link.springer.com

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Flat teams drive scientific innovation

Flat teams drive scientific innovation | Papers | Scoop.it

Fengli Xu, Lingfei Wu, and James Evans

PNAS 119 (23) e2200927119

With teams growing in all areas of scientific and scholarly research, we explore the relationship between team structure and the character of knowledge they produce. Drawing on 89,575 self-reports of team member research activity underlying scientific publications, we show how individual activities cohere into broad roles of 1) leadership through the direction and presentation of research and 2) support through data collection, analysis, and discussion. The hidden hierarchy of a scientific team is characterized by its lead (or L) ratio of members playing leadership roles to total team size. The L ratio is validated through correlation with imputed contributions to the specific paper and to science as a whole, which we use to effectively extrapolate the L ratio for 16,397,750 papers where roles are not explicit. We find that, relative to flat, egalitarian teams, tall, hierarchical teams produce less novelty and more often develop existing ideas, increase productivity for those on top and decrease it for those beneath, and increase short-term citations but decrease long-term influence. These effects hold within person—the same person on the same-sized team produces science much more likely to disruptively innovate if they work on a flat, high-L-ratio team. These results suggest the critical role flat teams play for sustainable scientific advance and the training and advancement of scientists.

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Nutrient concentrations in food display universal behaviour

Nutrient concentrations in food display universal behaviour | Papers | Scoop.it

Giulia Menichetti & Albert-László Barabási 

Nature Food volume 3, pages375–382 (2022)

Extensive programmes around the world endeavour to measure and catalogue the composition of food. Here we analyse the nutrient content of the full US food supply and show that the concentration of each nutrient follows a universal single-parameter scaling law that accurately captures the eight orders of magnitude in nutrient content variability. We show that the universality is rooted in the biochemical constraints obeyed by the metabolic pathways responsible for nutrient modulation, allowing us to confirm the empirically observed scaling law and to predict its variability in agreement with the data. We propose that the natural nutrient variability in food can be quantitatively formalized. This provides a mathematical rationale for imputing missing values in food composition databases and paves the way towards a quantitative understanding of the impact of food processing on nutrient balance and health effects.

Read the full article at: www.nature.com

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Group mixing drives inequality in face-to-face gatherings

Group mixing drives inequality in face-to-face gatherings | Papers | Scoop.it

Marcos Oliveira, Fariba Karimi, Maria Zens, Johann Schaible, Mathieu Génois & Markus Strohmaier
Communications Physics volume 5, Article number: 127 (2022)

Uncovering how inequality emerges from human interaction is imperative for just societies. Here we show that the way social groups interact in face-to-face situations can enable the emergence of disparities in the visibility of social groups. These disparities translate into members of specific social groups having fewer social ties than the average (i.e., degree inequality). We characterize group degree inequality in sensor-based data sets and present a mechanism that explains these disparities as the result of group mixing and group-size imbalance. We investigate how group sizes affect this inequality, thereby uncovering the critical size and mixing conditions in which a critical minority group emerges. If a minority group is larger than this critical size, it can be a well-connected, cohesive group; if it is smaller, minority cohesion widens inequality. Finally, we expose group under-representation in degree rankings due to mixing dynamics and propose a way to reduce such biases. The emergence of inequality in social interactions can depend on a number of factors, among which the intrinsic attractiveness of individuals, but also group size the presence of pre-formed social ties. Here, the authors propose “social attractiveness” as a mechanism to account for the emergence of inequality in face-to-face social dynamics and show this reproduces real-world gathering data, predicting the existence of a critical group size for the minority group below which higher cohesion among its members leads to higher inequality.


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The evolution and development of consciousness: the subject-object emergence hypothesis

John E. Stewart

Biosystems, Volume 217, July 2022, 104687

A strategy for investigating consciousness that has proven very productive has focused on comparing brain processes that are accompanied by consciousness with processes that are not. But comparatively little attention has been given to a related strategy that promises to be even more fertile. This strategy exploits the fact that as individuals develop, new classes of brain processes can transition from operating ‘in the dark’ to becoming conscious. It has been suggested that these transitions occur when a new class of brain processes becomes object to a new, emergent, higher-level subject. Similar transitions are likely to have occurred during evolution. An evolutionary/developmental research strategy sets out to identify the nature of the transitions in brain processes that shift them from operating in the dark to ‘lighting up’. The paper begins the application of this strategy by extrapolating the sequence of transitions back towards its origin. The goal is to reconstruct a minimally-complex, subject-object subsystem that would be capable of giving rise to consciousness and providing adaptive benefits. By focusing on reconstructing a subsystem that is simple and understandable, this approach avoids the homunculus fallacy. The reconstruction suggests that the emergence of such a minimally-complex subsystem was driven by its capacity to coordinate body-environment interactions in real time e.g. hand-eye coordination. Conscious processing emerged initially because of its central role in organising real-time sensorimotor coordination. The paper goes on to identify and examine a number of subsequent major transitions in consciousness, including the emergence of capacities for conscious mental modelling. Each transition is driven by its potential to solve adaptive challenges that cannot be overcome at lower levels. The paper argues that mental modelling arose out of a pre-existing capacity to use simulations of motor actions to anticipate the consequences of the actions. As the capacity developed, elements of the simulations could be changed, and the consequences of these changes could be ‘thought through’ consciously. This enabled alternative motor responses to be evaluated. The paper goes on to predict significant new major transitions in consciousness.

Read the full article at: www.sciencedirect.com

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Physicists Trace the Rise in Entropy to Quantum Information

Physicists Trace the Rise in Entropy to Quantum Information | Papers | Scoop.it

The second law of thermodynamics is among the most sacred in all of science, but it has always rested on 19th century arguments about probability. New arguments trace its true source to the flows of quantum information.

Read the full article at: www.quantamagazine.org

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The penumbra of open source: projects outside of centralized platforms are longer maintained, more academic and more collaborative

The penumbra of open source: projects outside of centralized platforms are longer maintained, more academic and more collaborative | Papers | Scoop.it

Milo Z. Trujillo, Laurent Hébert-Dufresne & James Bagrow 

EPJ Data Science volume 11, Article number: 31 (2022)

GitHub has become the central online platform for much of open source, hosting most open source code repositories. With this popularity, the public digital traces of GitHub are now a valuable means to study teamwork and collaboration. In many ways, however, GitHub is a convenience sample, and may not be representative of open source development off the platform. Here we develop a novel, extensive sample of public open source project repositories outside of centralized platforms. We characterized these projects along a number of dimensions, and compare to a time-matched sample of corresponding GitHub projects. Our sample projects tend to have more collaborators, are maintained for longer periods, and tend to be more focused on academic and scientific problems.

Read the full article at: epjdatascience.springeropen.com

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Guided self-organization through an entropy-based self-advising approach

Guided self-organization through an entropy-based self-advising approach | Papers | Scoop.it

Somayeh Kalantari, Eslam Nazemi & Behrooz Masoumi
Computing (2022)

Nowadays, the study of self-organizing systems has attracted much attention. However, since these systems are run in dynamic, changing, and evolving environments, it is possible that undesirable behaviors that are contrary to the system goals occur. Therefore, it is necessary to provide mechanisms to guide the self-organizing system. However, several approaches were proposed to guide self-organizing systems, more effective approaches are required due to the variation of the contexts in which they are deployed and their complexity. This paper aims to use the self-advising property to provide guidelines about the context of self-organizing systems. The agents of these systems are guided implicitly by using the guidelines provided. In the proposed approach, contextual data is made by an advisor agent that produces them based on the agents’ behavioral entropy. The proposed approach is evaluated using a case study based on the NASA ANTS mission. According to experiments, the proposed approach causes adaptation activities’ costs to decrease at all radio ranges. Besides, in some radio ranges, i.e., 110 and 120 GHz, the guiding state’s adaptive time is less than the no-guiding state’s adaptive time. The evaluations also show that the ruler agents’ mean entropy in the guiding state is less than the no-guiding state in 75 % of radio ranges. This approach’s success in reducing the agents’ entropy indicates its ability to guide self-organizing systems.

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‘Machine Scientists’ Distill the Laws of Physics From Raw Data

‘Machine Scientists’ Distill the Laws of Physics From Raw Data | Papers | Scoop.it

Researchers say we’re on the cusp of “GoPro physics,” where a camera can point at an event and an algorithm can identify the underlying physics equation.

Read the full article at: www.quantamagazine.org

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Critical drift in a neuro-inspired adaptive network

Silja Sormunen, Thilo Gross, Jari Saramäki
It has been postulated that the brain operates in a self-organized critical state that brings multiple benefits, such as optimal sensitivity to input. Thus far, self-organized criticality has typically been depicted as a one-dimensional process, where one parameter is tuned to a critical value. However, the number of adjustable parameters in the brain is vast, and hence critical states can be expected to occupy a high-dimensional manifold inside a high-dimensional parameter space. Here, we show that adaptation rules inspired by homeostatic plasticity drive a neuro-inspired network to drift on a critical manifold, where the system is poised between inactivity and persistent activity. During the drift, global network parameters continue to change while the system remains at criticality.

Read the full article at: arxiv.org

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A Ubiquitous Collective Tragedy in Transport

A Ubiquitous Collective Tragedy in Transport | Papers | Scoop.it

Rafael Prieto Curiel, Humberto González Ramírez, and Steven Bishop

Front. Phys., 16 June 2022

A tragedy of the commons is said to occur when individuals act only in their own interest but, in so doing, create a collective state of a group that is less than optimal due to uncoordinated action. Here, we explore the individual decision-making processes of commuters using various forms of transport within a city, forming a modal share which is then built into a dynamical model using travel time as the key variable. From a randomised start in the distribution of the modal share, assuming that some individuals change their commuting method, favouring lower travel times, we show that a stable modal share is reached corresponding to an equilibrium in the model. Considering the average travel time for all commuters within the city, we show that an optimal result is achieved only if the direct and induced factors and the number of users are equal for all transport modes. For asymmetric factors, the equilibrium reached is always sub-optimal, leading to city travel trajectories being “tragic”, meaning that individuals choose a faster commuting time but create a slower urban mobility as a collective result. Hence, the city evolves, producing longer average commuting times. It is also shown that if a new mode of transport has a small baseline commuting time but has a high induced impact for other users, then introducing it might result in a counter-intuitive result producing more congestion, rather than less.

Read the full article at: www.frontiersin.org

Phillip Trotter's curator insight, June 29, 2:53 PM

Interesting study of transportation models and traffic congestion with a key insight being when being when individuals choose a faster commuting time their decision can often contribute to creating a slower urban mobility as a collective result m- something many of us experience daily.

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A Formal Definition of Scale-dependent Complexity and the Multi-scale Law of Requisite Variety

Alexander F. Siegenfeld, Yaneer Bar-Yam
Ashby's law of requisite variety allows a comparison of systems with their environments, providing a necessary (but not sufficient) condition for system efficacy: a system must possess at least as much complexity as any set of environmental behaviors that require distinct responses from the system. However, the complexity of a system depends on the level of detail, or scale, at which it is described. Thus, the complexity of a system can be better characterized by a complexity profile (complexity as a function of scale) than by a single number. It would therefore be useful to have a multi-scale generalization of Ashby's law that requires that a system possess at least as much complexity as the relevant set of environmental behaviors *at each scale*. We construct a formalism for a class of complexity profiles that is the first, to our knowledge, to exhibit this multi-scale law of requisite variety. This formalism not only provides a characterization of multi-scale complexity but also generalizes the single constraint on system behaviors provided by Ashby's law to an entire class of multi-scale constraints. We show that these complexity profiles satisfy a sum rule, which reflects the important tradeoff between smaller- and larger-scale degrees of freedom, and we extend our results to subdivided systems and systems with a continuum of components.

Read the full article at: arxiv.org

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Complex systems for the most vulnerable

Elisa Omodei, Manuel Garcia-Herranz, Daniela Paolotti and Michele Tizzoni

Journal of Physics: Complexity, Volume 3, Number 2

In a rapidly changing world, facing an increasing number of socioeconomic, health and environmental crises, complexity science can help us to assess and quantify vulnerabilities, and to monitor and achieve the UN sustainable development goals. In this perspective, we provide three exemplary use cases where complexity science has shown its potential: poverty and socioeconomic inequalities, collective action for representative democracy, and computational epidemic modeling. We then review the challenges and limitations related to data, methods, capacity building, and, as a result, research operationalization. We finally conclude with some suggestions for future directions, urging the complex systems community to engage in applied and methodological research addressing the needs of the most vulnerable.

Read the full article at: iopscience.iop.org

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Scale, context, and heterogeneity: the complexity of the social space

Scale, context, and heterogeneity: the complexity of the social space | Papers | Scoop.it

José Balsa-Barreiro, Mónica Menendez & Alfredo J. Morales 

Scientific Reports volume 12, Article number: 9037 (2022)

The social space refers to physical or virtual places where people interact with one another. It decisively influences the emergence of human behaviors. However, little is known about the nature and complexity of the social space, nor its relationship to context and spatial scale. Recently, the science of complex systems has bridged between fields of knowledge to provide quantitative responses to fundamental sociological questions. In this paper, we analyze the shifting behavior of social space in terms of human interactions and wealth distribution across multiple scales using fine-grained data collected from both official (US Census Bureau) and unofficial data sources (social media). We use these data to unveil how patterns strongly depend upon the observation scale. Therefore, it is crucial for any analysis to be framed within the appropriate context to avoid biased results and/or misleading conclusions. Biased data analysis may lead to the adoption of fragile and poor decisions. Including context and a proper understanding of the spatial scale are essential nowadays, especially with the pervasive role of data-driven tools in decision-making processes.

Read the full article at: www.nature.com

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The Hidden Benefits of Limited Communication and Slow Sensing in Collective Monitoring of Dynamic Environments

T. Aust, M. S. Talamali, M. Dorigo, H. Hamann, and A. Reina

IRIDIA – Technical Report No. TR/IRIDIA/2022-005

Most of our experiences and also our intuition is usually built
on a linear understanding of systems and processes. Complex systems
in general and more specifically swarm robotics in this context leverage
non-linear effects to self-organise and to ensure that ‘more is different’. In
previous work the non-linear and therefore counter-intuitive effect of ‘less
is more’ was shown for a site-selection swarm scenario. Although it seems
intuitive that being able to communicate over longer distances should be
beneficial, swarms were found to sometimes profit from communication
limitations. Here, we built on this work and show the same effect for the
collective perception scenario in a dynamic environment. We found an
additional effect of ‘slower is faster’. In certain situations, swarms benefit
from sampling their environment less frequently. All our work is based on
simulations using the ARGoS simulator extended with a simulator of the
smart environment for the Kilobot robot called Kilogrid. Our findings are
supported by an intensive empirical approach and a mean-field model.
Both effects seem important for designing swarms

Read the full article at: iridia.ulb.ac.be

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The emergence of polarization in coevolving networks

Jiazhen Liu, Shengda Huang, Nathan Aden, Neil Johnson, Chaoming Song
Polarization is a ubiquitous phenomenon in social systems. Empirical studies show substantial evidence for opinion polarization across social media. Recent modeling works show qualitatively that polarization emerges in coevolving networks by integrating reinforcing mechanisms and network evolution. However, a quantitative and comprehensive theoretical framework capturing generic mechanisms governing polarization remains unaddressed. In this paper, we discover a universal scaling law for opinion distributions, characterized by a set of scaling exponents. These exponents classify social systems into polarization and depolarization phases. We find two generic mechanisms governing the polarization dynamics, and propose a coevolving framework that counts for opinion dynamics and network evolution simultaneously. We show analytically three different phases including polarization, partial polarization, and depolarization, and the corresponding phase diagram. In the polarized phase, our theory predicts that a bi-polarized community structure emerges naturally from the coevolving dynamics. These theoretical predictions are in line with observations in empirical datasets. Our theory not only accounts for the empirically observed scaling laws but also allows us to quantitatively predict scaling exponents.

Read the full article at: arxiv.org

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Searching for Life, Mindful of Lyfe’s Possibilities

Searching for Life, Mindful of Lyfe’s Possibilities | Papers | Scoop.it

Searching for Life, Mindful of Lyfe’s Possibilities

by Michael L. Wong, Stuart Bartlett, Sihe Chen, and Louisa Tierney

We are embarking on a new age of astrobiology, one in which numerous interplanetary missions and telescopes will be designed, built, and launched with the explicit goal of finding evidence for life beyond Earth. Such a profound aim warrants caution and responsibility when interpreting and disseminating results. Scientists must take care not to overstate (or over-imply) confidence in life detection when evidence is lacking, or only incremental advances have been made. Recently, there has been a call for the community to create standards of evidence for the detection and reporting of biosignatures. In this perspective, we wish to highlight a critical but often understated element to the discussion of biosignatures: Life detection studies are deeply entwined with and rely upon our (often preconceived) notions of what life is, the origins of life, and habitability. Where biosignatures are concerned, these three highly related questions are frequently relegated to a low priority, assumed to be already solved or irrelevant to the question of life detection. Therefore, our aim is to bring to the fore how these other major astrobiological frontiers are central to searching for life elsewhere and encourage astrobiologists to embrace the reality that all of these science questions are interrelated and must be furthered together rather than separately. Finally, in an effort to be more inclusive of life as we do not know it, we propose tentative criteria for a more general and expansive characterization of habitability that we call genesity.

Read the full article at: www.mdpi.com

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From the origin of life to pandemics: emergent phenomena in complex systems

Oriol Artime and Manlio De Domenico

Phil. Trans. Roy. Soc. A, Volume 380 Issue 2227

Theme issue ‘Emergent phenomena in complex physical and socio-technical systems: from cells to societies’

When a large number of similar entities interact among each other and with their environment at a low scale, unexpected outcomes at higher spatio-temporal scales might spontaneously arise. This non-trivial phenomenon, known as emergence, characterizes a broad range of distinct complex systems—from physical to biological and social—and is often related to collective behaviour. It is ubiquitous, from non-living entities such as oscillators that under specific conditions synchronize, to living ones, such as birds flocking or fish schooling. Despite the ample phenomenological evidence of the existence of systems’ emergent properties, central theoretical questions to the study of emergence remain unanswered, such as the lack of a widely accepted, rigorous definition of the phenomenon or the identification of the essential physical conditions that favour emergence. We offer here a general overview of the phenomenon of emergence and sketch current and future challenges on the topic. Our short review also serves as an introduction to the theme issue Emergent phenomena in complex physical and socio-technical systems: from cells to societies, where we provide a synthesis of the contents tackled in the issue and outline how they relate to these challenges, spanning from current advances in our understanding on the origin of life to the large-scale propagation of infectious diseases.

Read the full article at: royalsocietypublishing.org

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Companies need a business leader to be the contrarian that combats herd mentality 

Leaders need to recognize herd mentality when it happens--and explore the contrarian view to help break the spell.

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The games we play: critical complexity improves machine learning

Abeba Birhane, David J. T. Sumpter
When mathematical modelling is applied to capture a complex system, multiple models are often created that characterize different aspects of that system. Often, a model at one level will produce a prediction which is contradictory at another level but both models are accepted because they are both useful. Rather than aiming to build a single unified model of a complex system, the modeller acknowledges the infinity of ways of capturing the system of interest, while offering their own specific insight. We refer to this pragmatic applied approach to complex systems -- one which acknowledges that they are incompressible, dynamic, nonlinear, historical, contextual, and value-laden -- as Open Machine Learning (Open ML). In this paper we define Open ML and contrast it with some of the grand narratives of ML of two forms: 1) Closed ML, ML which emphasizes learning with minimal human input (e.g. Google's AlphaZero) and 2) Partially Open ML, ML which is used to parameterize existing models. To achieve this, we use theories of critical complexity to both evaluate these grand narratives and contrast them with the Open ML approach. Specifically, we deconstruct grand ML `theories' by identifying thirteen 'games' played in the ML community. These games lend false legitimacy to models, contribute to over-promise and hype about the capabilities of artificial intelligence, reduce wider participation in the subject, lead to models that exacerbate inequality and cause discrimination and ultimately stifle creativity in research. We argue that best practice in ML should be more consistent with critical complexity perspectives than with rationalist, grand narratives.

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[Classics] Principles of the self-organizing system

W. Ross Ashby

The brilliant British psychiatrist, neuroscientist, and mathematician Ross Ashby was one of the pioneers in early and mid-phase cybernetics and thereby one of the leading progenitors of modern complexity theory. Not one to take either commonly used terms or popular notions for granted, Ashby probed deeply into the meaning of supposedly self-organizing systems. At the time of the following article, he had been working on a mathematical formalism of his homeostat, a hypothetical machine established on an axiomatic, set theoretical foundation that was supposed to offer a sufficient description of a living organism’s learning and adaptive intelligence. Ashby’s homeostat had a small number of essential variables serving to maintain its operation over a wide range of environmental conditions so that if the latter changed and thereby shifted the variables beyond the range where the homeostat could safely function, a new ‘higher’ level of the machine was activated in order to randomly reset the lower level’s internal connections or organization (see Dupuy, 2000). Like the role of random mutations during evolution, if the new range set at random proved functional, the homeostat survived, otherwise it expired.

One of Ashby’s goals was to repudiate that interpretation of the notion of self-organization, one commonly held to this day, which would have it that either a machine or a living organism could by itself change its own organization (or, in his phraseology, the functional mappings). For Ashby, self-organization in this sense was a bit of superfluous metaphysics since he believed not only could his formalism by itself completely delineate the homeostat’s lower level organization, the adaptive novelty of his homeostat was purely the result of its upper level randomization that could reorganize the lower level and not some innate propensity for autonomous change. We offer Ashby’s careful reasoning here as an enlightening guide for coming to terms with key ideas in complexity theory whose genuine significance lies less with facile bandying about and more with an intensive and extensive examination of the underlying assumptions.

Read the full article at: journal.emergentpublications.com

Also at http://femto4.chem.elte.hu/entropia/InfoTheoryPapers/Ashby1962.pdf 

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