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Chasing Ecological Interactions

Chasing Ecological Interactions | Papers | Scoop.it

Basic research on biodiversity has concentrated on individual species—naming new species, studying distribution patterns, and analyzing their evolutionary relationships. Yet biodiversity is more than a collection of individual species; it is the combination of biological entities and processes that support life on Earth. To understand biodiversity we must catalog it, but we must also assess the ways species interact with other species to provide functional support for the Tree of Life. Ecological interactions may be lost well before the species involved in those interactions go extinct; their ecological functions disappear even though they remain. Here, I address the challenges in studying the functional aspects of species interactions and how basic research is helping us address the fast-paced extinction of species due to human activities.

 

Jordano P (2016) Chasing Ecological Interactions. PLoS Biol 14(9): e1002559. doi:10.1371/journal.pbio.1002559

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Because Biodiversity serves Humankind and has inherent value
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Supplemented Alkaline Phosphatase Supports the Immune Response in Patients Undergoing Cardiac Surgery: Clinical and Computational Evidence

Supplemented Alkaline Phosphatase Supports the Immune Response in Patients Undergoing Cardiac Surgery: Clinical and Computational Evidence | Papers | Scoop.it

Alkaline phosphatase (AP) is an enzyme that exhibits anti-inflammatory effects by dephosphorylating inflammation triggering moieties (ITMs) like bacterial lipopolysaccharides and extracellular nucleotides. AP administration aims to prevent and treat peri- and post-surgical ischemia reperfusion injury in cardiothoracic surgery patients. Recent studies reported that intravenous bolus administration and continuous infusion of AP in patients undergoing coronary artery bypass grafting with cardiac valve surgery induce an increased release of liver-type “tissue non-specific alkaline phosphatase” (TNAP) into the bloodstream. The release of liver-type TNAP into circulation could be the body's way of strengthening its defense against a massive ischemic insult. However, the underlying mechanism behind the induction of TNAP is still unclear. To obtain a deeper insight into the role of AP during surgery, we developed a mathematical model of systemic inflammation that clarifies the relation between supplemented AP and TNAP and describes a plausible induction mechanism of TNAP in patients undergoing cardiothoracic surgery. The model was validated against clinical data from patients treated with bovine Intestinal AP (bIAP treatment) or without AP (placebo treatment), in addition to standard care procedures. We performed additional in-silico experiments adding a secondary source of ITMs after surgery, as observed in some patients with complications, and predicted the response to different AP treatment regimens. Our results show a strong protective effect of supplemented AP for patients with complications. The model provides evidence of the existence of an induction mechanism of liver-type tissue non-specific alkaline phosphatase, triggered by the supplementation of AP in patients undergoing cardiac surgery. To the best of our knowledge this is the first time that a quantitative and validated numerical model of systemic inflammation under clinical treatment conditions is presented.

 

Alva Presbitero, Emiliano Mancini, Ruud Brands, Valeria V. Krzhizhanovskaya, and Peter M. A. Sloot

Supplemented Alkaline Phosphatase Supports the Immune Response in Patients Undergoing Cardiac Surgery: Clinical and Computational Evidence
Front. Immunol., 11 October 2018 | https://doi.org/10.3389/fimmu.2018.02342

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The Resilience of Public Policies in Economic Development

This paper studies the resilience of public policies that governments design for catalyzing economic development. This property depends on the extent to which behavioral heuristics and spillover effects allow policymakers to attain their original goals when a particular policy cannot be funded as originally planned. This scenario takes place, for example, when unanticipated events such as natural disasters or political turmoil obstruct the use of resources to advance certain policy issues, e.g., infrastructure or labor reforms. Here, we analyze how the adaptive capacity of the policy-making process generates resilience in the face of disruptions. In order to estimate the allocation of resources across policies, we employ a computational model that accounts for diverse social mechanisms, for example, coevolutionary learning and network interdependencies. In our simulations, we use a data set of 117 countries on 79 development indicators over an 11-year period. Then, we calculate a resilience score corresponding to each development indicator via counter-factual analysis of policy disruptions. Next, we assess whether some development strategies produce resilient/fragile policy profiles. Finally, by studying the relationship between policy resilience and policy priority, we determine which issues are bottlenecks to economic development.

 

The Resilience of Public Policies in Economic Development
Gonzalo Castañeda and Omar A. Guerrero

Complexity
Volume 2018, Article ID 9672849, 15 pages
https://doi.org/10.1155/2018/9672849

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Smeared phase transitions in percolation on real complex networks

Percolation on complex networks is used both as a model for dynamics on networks, such as network robustness or epidemic spreading, and as a benchmark for our models of networks, where our ability to predict percolation measures our ability to describe the networks themselves. In many applications, correctly identifying the phase transition of percolation on real-world networks is of critical importance. Unfortunately, this phase transition is obfuscated by the finite size of real systems, making it hard to distinguish finite size effects from the inaccuracy of a given approach that fails to capture important structural features. Here, we borrow the perspective of smeared phase transitions and argue that many observed discrepancies are due to the complex structure of real networks rather than to finite size effects only. In fact, several real networks often used as benchmarks feature a smeared phase transition where inhomogeneities in the topological distribution of the order parameter do not vanish in the thermodynamic limit. We find that these smeared transitions are sometimes better described as sequential phase transitions within correlated subsystems. Our results shed light not only on the nature of the percolation transition in complex systems, but also provide two important insights on the numerical and analytical tools we use to study them. First, we propose a measure of local susceptibility to better detect both clean and smeared phase transitions by looking at the topological variability of the order parameter. Second, we highlight a shortcoming in state-of-the-art analytical approaches such as message passing, which can detect smeared transitions but not characterize their nature.

 

Smeared phase transitions in percolation on real complex networks
Laurent Hébert-Dufresne, Antoine Allard

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Social style and resilience of macaques’ networks, a theoretical investigation

Group-living animals rely on efficient transmission of information for optimal exploitation of their habitat. How efficient and resilient a network is depend on its structure, which is a consequence of the social interactions of the individuals that comprise the network. In macaques, network structure differs according to dominance style. Networks of intolerant species are more modular, more centralized, and less connected than those of tolerant ones. Given these structural differences, networks of intolerant species are potentially more vulnerable to fragmentation and decreased information transmission when central individuals disappear. Here we studied network resilience and efficiency in artificial societies of macaques. The networks were produced with an individual-based model that has been shown to reproduce the structural features of networks of tolerant and intolerant macaques. To study network resilience, we deleted either central individuals or individuals at random and studied the effects of these deletions on network cohesiveness and efficiency. The deletion of central individuals had more negative effects than random deletions from the networks of both tolerant and intolerant artificial societies. Central individuals thus appeared to aid in the maintenance of network cohesiveness and efficiency. Further, the networks of both intolerant and tolerant societies appeared to be robust to the loss of individuals, as network fragmentation was never observed. Our results suggest that despite differences in network structure, networks of tolerant and intolerant macaques may be equally resilient.

 

Social style and resilience of macaques’ networks, a theoretical investigation
Ivan Puga-Gonzalez, Sebastian Sosa, Cedric Sueur

Primates

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The Adaptive Behavior of a Soccer Team: An Entropy-Based Analysis

To optimize its performance, a competitive team, such as a soccer team, must maintain a delicate balance between organization and disorganization. On the one hand, the team should maintain organized patterns of behavior to maximize the cooperation between its members. On the other hand, the team’s behavior should be disordered enough to mislead its opponent and to maintain enough degrees of freedom. In this paper, we have analyzed this dynamic in the context of soccer games and examined whether it is correlated with the team’s performance. We measured the organization associated with the behavior of a soccer team through the Tsallis entropy of ball passes between the players. Analyzing data taken from the English Premier League (2015/2016), we show that the team’s position at the end of the season is correlated with the team’s entropy as measured with a super-additive entropy index. Moreover, the entropy score of a team significantly contributes to the prediction of the team’s position at the end of the season beyond the prediction gained by the team’s position at the end of the previous season.

 

Neuman, Y.; Israeli, N.; Vilenchik, D.; Cohen, Y.

The Adaptive Behavior of a Soccer Team: An Entropy-Based Analysis.

Entropy 2018, 20, 758.

https://www.mdpi.com/1099-4300/20/10/758

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Large-scale investigation of the reasons why potentially important genes are ignored

Biomedical research is one of the largest areas of present-day science and embeds the hope and potential to improve the lives of the general public. In order to understand how individual scientists choose individual research questions, we study why certain genes are well studied but others are not. While it has been previously observed that most research on human genes only concentrates on approximately 2,000 of the 19,000 genes of the human genome, the reasons for this ignorance are largely unknown. We systematically test explanations for this observation by compiling an extensive resource that characterizes biomedical research, including but not limited to hundreds of chemical and biological properties of gene-encoded proteins, and the published scientific literature on individual genes. Using machine learning methods, we can predict the number of publications on individual genes, the year of the first publication about them, the extent of funding by the National Institutes of Health, and the existence of related medical drugs. We find that biomedical research is primarily guided by a handful of generic chemical and biological characteristics of genes, which facilitated experimentation during the 1980s and 1990s, rather than the physiological importance of individual genes or their relevance to human disease.

 

Stoeger T, Gerlach M, Morimoto RI, Nunes Amaral LA (2018) Large-scale investigation of the reasons why potentially important genes are ignored. PLoS Biol 16(9): e2006643. https://doi.org/10.1371/journal.pbio.2006643

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Decentralized Collective Learning for Self-managed Sharing Economies

The Internet of Things equips citizens with phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance their resource consump- tion and production, while these choices have a collective system-wide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This paper envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that in- volve non-local brute-force operations or exchange full information. This paper contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic trade-offs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.

 

Decentralized Collective Learning for Self-managed Sharing Economies
EVANGELOS POURNARAS, PETER PILGERSTORFER, and THOMAS ASIKIS,

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Rule Primality, Minimal Generating Sets and Turing-Universality in the Causal Decomposition of Elementary Cellular Automata

Rule Primality, Minimal Generating Sets and Turing-Universality in the Causal Decomposition of Elementary Cellular Automata | Papers | Scoop.it

New Turing-universality results in Elementary Cellular Automata in recent published paper: "Rule Primality, Minimal Generating Sets and Turing-Universality in the Causal Decomposition of Elementary Cellular Automata" by Jürgen Riedel and Hector Zenil

 

New paper discovers and proves new universality results in ECA, namely, that the Boolean composition of ECA rules 51 and 118, and 170, 15 and 118 can emulate ECA rule 110 and are thus Turing-universal coupled systems. It is introduced a 4-colour Turing-universal cellular automaton that carries the Boolean composition of 2 and 3 ECA rules emulating ECA rule 110 under multi-scale coarse-graining. They find that rules generating the ECA rulespace by Boolean composition are of low complexity and comprise prime rules implementing basic operations that when composed enable complex behaviour. They also found a candidate minimal set with only 38 ECA prime rules — and several other small sets — capable of generating all other (non-trivially symmetric) 88 ECA rules under Boolean composition.

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Cognitive mechanisms for human flocking dynamics

Low-level “adaptive” and higher-level “sophisticated” human reasoning processes have been proposed to play opposing roles in the emergence of unpredictable collective behaviors such as crowd panics, traffic jams, and market bubbles. While adaptive processes are widely recognized drivers of emergent social complexity, complementary theories of sophistication predict that incentives, education, and other inducements to rationality will suppress it. We show in a series of multiplayer laboratory experiments that, rather than suppressing complex social dynamics, sophisticated reasoning processes can drive them. Our experiments elicit an endogenous collective behavior and show that it is driven by the human ability to recursively anticipate the reasoning of others. We identify this behavior, “sophisticated flocking”, across three games, the Beauty Contest and the “Mod Game” and “Runway Game”. In supporting our argument, we also present evidence for mental models and social norms constraining how players express their higher-level reasoning abilities. By implicating sophisticated recursive reasoning in the kind of complex dynamic that it has been predicted to suppress, we support interdisciplinary perspectives that emergent complexity is typical of even the most intelligent populations and carefully designed social systems.

 

Cognitive mechanisms for human flocking dynamics
Seth Frey, Robert L. Goldstone

Journal of Computational Social Science
September 2018, Volume 1, Issue 2, pp 349–375

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Measuring accessibility using gravity and radiation models

Since the presentation of the radiation model, much work has been done to compare its findings with those obtained from gravitational models. These comparisons always aim at measuring the accuracy with which the models reproduce the mobility described by origin–destination matrices. This has been done at different spatial scales using different datasets, and several versions of the models have been proposed to adjust to various spatial systems. However, the models, to our knowledge, have never been compared with respect to policy testing scenarios. For this reason, here we use the models to analyse the impact of the introduction of a new transportation network, a bus rapid transport system, in the city of Teresina in Brazil. We do this by measuring the estimated variation in the trip distribution, and formulate an accessibility to employment indicator for the different zones of the city. By comparing the results obtained with the two approaches, we are able to not only better assess the goodness of fit and the impact of this intervention, but also understand reasons for the systematic similarities and differences in their predictions.

 

Measuring accessibility using gravity and radiation models
Duccio Piovani, Elsa Arcaute, Gabriela Uchoa, Alan Wilson, Michael Batty
Published 12 September 2018.DOI: 10.1098/rsos.171668

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Physical foundations of biological complexity

Living organisms are characterized by a degree of hierarchical complexity that appears to be inaccessible to even the most complex inanimate objects. Routes and patterns of the evolution of complexity are poorly understood. We propose a general conceptual framework for emergence of complexity through competing interactions and frustrated states similar to those that yield patterns in striped glasses and cause self-organized criticality. We show that biological evolution is replete with competing interactions and frustration that, in particular, drive major transitions in evolution. The key distinction between biological and nonbiological systems seems to be the existence of long-term digital memory and phenotype-to-genotype feedback in living matter.

 

Yuri I. Wolf, Mikhail I. Katsnelson, and Eugene V. Koonin
PNAS September 11, 2018 115 (37) E8678-E8687; published ahead of print August 27, 2018 https://doi.org/10.1073/pnas.1807890115

Via Samir
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Closed Loophole Confirms the Unreality of the Quantum World

Closed Loophole Confirms the Unreality of the Quantum World | Papers | Scoop.it
After researchers found a loophole in a famous experiment designed to prove that quantum objects don’t have intrinsic properties, three experimental groups quickly sewed the loophole shut. The episode closes the door on many “hidden variable” theories.
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Physics of humans, physics for society

Today, the massive use of information and communication technologies (ICT) has made it possible to attach a traceable set of data to almost any person. We argue that these data provide the opportunity to build a ‘physics of society’: describing a society — composed of many interacting heterogeneous entities (people, businesses, institutions) — as a physical system. While important ethical implications have to be taken into account, the benefits in developing such physics of society would be tremendous. Indeed, it could help understanding, anticipating and forecasting future societal trends and human behavioural responses, and their associated uncertainty; or address societal challenges in which globally networked risks play a role. A case in point is modern epidemiology and its success in predicting the large-scale spreading of infectious diseases.

 

Physics of humans, physics for society
Guido Caldarelli, Sarah Wolf & Yamir Moreno 
Nature Physics volume 14, page 870 (2018)

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Learning from Mixed Signals in Online Innovation Communities

We study how contributors to innovation contests improve their performance through direct experience and by observing others as they synthesize learnable signals from different sources. Our research draws on a 10-year panel of more than 55,000 individuals participating in a firm-hosted online innovation community sponsoring creative t-shirt design contests. Our data set contains almost 180,000 submissions that reflect signals of direct performance evaluation from both the community and the firm. Our data set also contains almost 150 million ratings that reflect signals for learning from observing the completed work of others. We have three key findings. First, we find a period of initial investment with decreased performance. This is because individuals struggle to synthesize learnable signals from early performance evaluation. This finding is contrary to other studies that report faster learning from early direct experience when improvements are easiest to achieve. Second, we find that individuals consistently improve their performance from observing others’ good examples. However, whether they improve from observing others’ bad examples depends on their ability to correctly recognize that work as being of low quality. Third, we find that individuals can successfully integrate signals about what is valued by the firm hosting the community, not just about what is valued by the community. We thus provide important insights into the mechanisms of how individuals learn in crowdsourced innovation and provide important qualifications for the often-heralded theme of “learning from failures.”

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Entraining chaotic dynamics: A novel movement sonification paradigm could promote generalization

Entraining chaotic dynamics: A novel movement sonification paradigm could promote generalization | Papers | Scoop.it

Tasks encountered in daily living may have instabilities and more dimensions than are sampled by the senses such as when carrying a cup of coffee and only the surface motion and overall momentum are sensed, not the fluid dynamics. Anticipating non-periodic dynamics is difficult but not impossible because mutual coordination allows for chaotic processes to synchronize to each other and become periodic. A chaotic oscillator with random period and amplitude affords being stabilized onto a periodic trajectory by a weak input if the driver incorporates information about the oscillator. We studied synchronization with predictable and unpredictable stimuli where the unpredictable stimuli could be non-interactive or interactive. The latter condition required learning to control a chaotic system. We expected better overall performance with the predictable but more learning and generalization with unpredictable interactive stimuli. Participants practiced an auditory-motor synchronization task by matching their sonified hand movements to sonified tutors: the Non-Interactive Predictable tutor (NI-P) was a sinusoid, the Non-Interactive Unpredictable (NI-U) was a chaotic system, the Interactive Unpredictable (I-U) was the same chaotic system with an added weak input from the participant’s movement. Different pre/post-practice stimuli evaluated generalization. Quick improvement was seen in NI-P. Synchronization, dynamic similarity, and causal interaction increased with practice in I-U but not in NI-U. Generalization was seen for few pre-post stimuli in NI-P, none in NI-U, and most stimuli in I-U. Synchronization with novel chaotic dynamics is challenging but mutual interaction enables the behavioral control of such dynamics and the practice of complex motor skills.

 

Entraining chaotic dynamics: A novel movement sonification paradigm could promote generalization
Dobromir Dotov, Tom Froese

Human Movement Science
Volume 61, October 2018, Pages 27-41

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Dynamical Criticality in Gene Regulatory Networks

A well-known hypothesis, with far-reaching implications, is that biological evolution should preferentially lead to states that are dynamically critical. In previous papers, we showed that a well-known model of genetic regulatory networks, namely, that of random Boolean networks, allows one to study in depth the relationship between the dynamical regime of a living being’s gene network and its response to permanent perturbations. In this paper, we analyze a huge set of new experimental data on single gene knockouts in S. cerevisiae, laying down a statistical framework to determine its dynamical regime. We find that the S. cerevisiae network appears to be slightly ordered, but close to the critical region. Since our analysis relies on dichotomizing continuous data, we carefully consider the issue of an optimal threshold choice.

 

Dynamical Criticality in Gene Regulatory Networks
Marco Villani, Luca La Rocca, Stuart Alan Kauffman, and Roberto Serra

Complexity
Volume 2018, Article ID 5980636, 14 pages
https://doi.org/10.1155/2018/5980636

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Cross-boundary Behavioural Reprogrammability Reveals Evidence of Pervasive Turing-Universality

Cross-boundary Behavioural Reprogrammability Reveals Evidence of Pervasive Turing-Universality | Papers | Scoop.it

New paper sheds light on the pervasiveness of Turing universality by showing a series of behavioural boundary crossing results, including emulations (for all initial conditions) of Wolfram class 2 Elementary Cellular Automata (ECA) by Class 1 ECA, emulations of Classes 1, 2 and 3 ECA by Class 2 and 3 ECA, and of Classes 1, 2 and 3 by Class 3 ECA, along with results of even greater emulability for general CA (neighbourhood r = 3/2), including Class 1 CA emulating Classes 2 and 3, and Classes 3 and 4 emulating all other classes (1, 2, 3 and 4). The emulations occur with only a linear overhead and can be considered computationally efficient. The paper also introduces a concept of emulation networks, deriving a topologically-based measure of complexity based upon out- and in-degree connectivity establishing bridges to fundamental ideas of complexity, universality, causality and dynamical systems.

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Modeling Collective Rule at Ancient Teotihuacan as a Complex Adaptive System: Communal Ritual Makes Social Hierarchy More Effective

Modeling Collective Rule at Ancient Teotihuacan as a Complex Adaptive System: Communal Ritual Makes Social Hierarchy More Effective | Papers | Scoop.it

Experts remain divided about the nature of the sociopolitical system of ancient Teotihuacan, which was one of the earliest and largest urban civilizations of the Americas. Excavations hoping to find compelling evidence of powerful rulers, such as a royal tomb, keep coming away empty-handed. But the alternative possibility of collective rule still remains poorly understood as well. Previously we used a computational model of this city’s hypothetical sociopolitical network to show that in principle collective rule based on communal ritual could be an effective strategy of ensuring widespread social coordination, as long as we assume that the network’s structure could be transformed via social learning and local leaders were not strongly subdivided. Here we extended this model to investigate whether increased social hierarchy could mitigate the negative effects of such strong divisions. We found a special synergy between social hierarchy and communal ritual: only their combination improved the extent of social coordination, whereas the introduction of centralization and top-down influence by themselves had no effect. This finding is consistent with portrayals of the Teotihuacan elite as religious specialists serving the public good, in particular by synchronizing the city’s ritual calendar with the rhythms of the stars.

 

Modeling Collective Rule at Ancient Teotihuacan as a Complex Adaptive System: Communal Ritual Makes Social Hierarchy More Effective
Tom Froese & Linda R. Manzanilla

Cognitive Systems Research

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What an Entangled Web We Weave: An Information-centric Approach to Time-evolving Socio-technical Systems

A new layer of complexity, constituted of networks of information token recurrence, has been identified in socio-technical systems such as the Wikipedia online community and the Zooniverse citizen science platform. The identification of this complexity reveals that our current understanding of the actual structure of those systems, and consequently the structure of the entire World Wide Web, is incomplete, which raises novel questions for data science research but also from the perspective of social epistemology. Here we establish the principled foundations and practical advantages of analyzing information diffusion within and across Web systems with Transcendental Information Cascades, and outline resulting directions for future study in the area of socio-technical systems. We also suggest that Transcendental Information Cascades may be applicable to any kind of time-evolving system that can be observed using digital technologies, and that the structures found in such systems comprise properties common to all naturally occurring complex systems.

 

Luczak-Roesch, M., O’Hara, K., Dinneen, J.D. et al. Minds & Machines (2018). https://doi.org/10.1007/s11023-018-9478-1

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Good Things for Those Who Wait: Predictive Modeling Highlights Importance of Delay Discounting for Income Attainment

Income is a primary determinant of social mobility, career progression, and personal happiness. It has been shown to vary with demographic variables like age and education, with more oblique variables such as height, and with behaviors such as delay discounting, i.e., the propensity to devalue future rewards. However, the relative contribution of each these salary-linked variables to income is not known. Further, much of past research has often been underpowered, drawn from populations of convenience, and produced findings that have not always been replicated. Here we tested a large (n = 2,564), heterogeneous sample, and employed a novel analytic approach: using three machine learning algorithms to model the relationship between income and age, gender, height, race, zip code, education, occupation, and discounting. We found that delay discounting is more predictive of income than age, ethnicity, or height. We then used a holdout data set to test the robustness of our findings. We discuss the benefits of our methodological approach, as well as possible explanations and implications for the prominent relationship between delay discounting and income.

 

Good Things for Those Who Wait: Predictive Modeling Highlights Importance of Delay Discounting for Income Attainment
William H. Hampton, Nima Asadi, and Ingrid R. Olson

Front. Psychol., 03 September 2018 | https://doi.org/10.3389/fpsyg.2018.01545

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The Silent Cooperator: An Epigenetic Model for Emergence of Altruistic Traits in Biological Systems

Spatial evolutionary game theory explains how cooperative traits can survive the intense competition in biological systems. If the spatial distribution allows cooperators to interact with each other frequently, the benefits of cooperation will outweigh the losses due to exploitation by selfish organisms. However, for a cooperative behavior to get established in a system, it needs to be found initially in a sufficiently large cluster to allow a high frequency of intracooperator interactions. Since mutations are rare events, this poses the question of how cooperation can arise in a biological system in the first place. We present a simple model which captures two concepts from genetics that can explain how evolution overcomes the emergence problem. The first concept is, often in nature, a gene may not express its phenotype except under specific environmental conditions, rendering it to be a “silent” gene. The second key idea is that a neutral gene, one that does not harm or improve an organism’s survival chances, can still spread through a population if it is physically near to another gene that is positively selected. Through these two ideas, our model offers a possible solution to the fundamental problem of emergence of cooperation in biological systems.

 

The Silent Cooperator: An Epigenetic Model for Emergence of Altruistic Traits in Biological Systems
I. Hashem, D. Telen, P. Nimmegeers, and J. Van Impe

Complexity
Volume 2018, Article ID 2082037, 16 pages
https://doi.org/10.1155/2018/2082037

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Zipf's and Taylor's laws

Zipf's law states that the frequency of an observation with a given value is inversely proportional to the square of that value; Taylor's law, instead, describes the scaling between fluctuations in the size of a population and its mean. Empirical evidence of the validity of these laws has been found in many and diverse domains. Despite the numerous models proposed to explain the presence of Zipf's law, there is no consensus on how it originates from a microscopic process of individual dynamics without fine-tuning. Here we show that Zipf's law and Taylor's law can emerge from a general class of stochastic processes at the individual level, which incorporate one of two features: environmental variability, i.e., fluctuations of parameters, or correlations, i.e., dependence between individuals. Under these assumptions, we show numerically and with theoretical arguments that the conditional variance of the population increments scales as the square of the population, and that the corresponding stationary distribution of the processes follows Zipf's law.

 

Zipf's and Taylor's laws

Charlotte James, Sandro Azaele, Amos Maritan, and Filippo Simini
Phys. Rev. E 98, 032408 – Published 12 September 2018

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Criticality distinguishes the ensemble of biological regulatory networks

The hypothesis many living systems should exhibit near-critical behavior is well-motivated theoretically, and an increasing number of cases have been demonstrated empirically. However, a systematic analysis across biological networks, which would enable identification of the network properties that drive criticality, has not yet been realized. Here, we provide a first comprehensive survey of criticality across a diverse sample of biological networks, leveraging a publicly available database of 67 Boolean models of regulatory circuits. We find all 67 networks to be near-critical. By comparing to ensembles of random networks with similar topological and logical properties, we show criticality in biological networks is not predictable solely from macroscale properties such as mean degree Kand mean bias in the logic functions p, as previously emphasized in random Boolean network theory. Instead, the ensemble of real biological circuits is jointly constrained by the local causal structure and logic of each node. In this way, biological regulatory networks are more distinguished from random networks by their criticality than by other macroscale network properties such as degree distribution, edge density, or fraction of activating conditions.

 

Criticality distinguishes the ensemble of biological regulatory networks
Phys. Rev. Lett.
Bryan C. Daniels, Hyunju Kim, Douglas Moore, Siyu Zhou, Harrison Smith, Bradley Karas, Stuart A. Kauffman, and Sara I. Walker

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Gaia 2.0

Gaia 2.0 | Papers | Scoop.it

According to Lovelock and Margulis's Gaia hypothesis, living things are part of a planetary-scale self-regulating system that has maintained habitable conditions for the past 3.5 billion years (1, 2). Gaia has operated without foresight or planning on the part of organisms, but the evolution of humans and their technology are changing that. Earth has now entered a new epoch called the Anthropocene (3), and humans are beginning to become aware of the global consequences of their actions. As a result, deliberate self-regulation—from personal action to global geoengineering schemes—is either happening or imminently possible. Making such conscious choices to operate within Gaia constitutes a fundamental new state of Gaia, which we call Gaia 2.0. By emphasizing the agency of life-forms and their ability to set goals, Gaia 2.0 may be an effective framework for fostering global sustainability.

 

Gaia 2.0
Timothy M. Lenton, Bruno Latour
Science  14 Sep 2018:
Vol. 361, Issue 6407, pp. 1066-1068
DOI: 10.1126/science.aau0427

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What has and hasn’t changed since the global financial crisis?

What has and hasn’t changed since the global financial crisis? | Papers | Scoop.it
The world economy has returned to robust growth after the 2008 global financial crisis. But some familiar risks are creeping back, and new ones have emerged.
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