Complexity & Systems
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# Competition-induced criticality in a model of meme popularity

Heavy-tailed distributions of meme popularity occur naturally in a model of meme diffusion on social networks. Competition between multiple memes for the limited resource of user attention is identified as the mechanism that poises the system at criticality. The popularity growth of each meme is described by a critical branching process, and asymptotic analysis predicts power-law distributions of popularity with very heavy tails (exponent $\alpha<2$, unlike preferential-attachment models), similar to those seen in empirical data.

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# Complexity & Systems

Complex systems present problems both in mathematical modelling and philosophical foundations. The study of complex systems represents a new approach to science that investigates how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment. The equations from which models of complex systems are developed generally derive from statistical physics, information theory and non-linear dynamics, and represent organized but unpredictable behaviors of natural systems that are considered fundamentally complex.  wikipedia (en)
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## The Hidden Power Laws of Ecosystems - Issue 29: Scaling - Nautilus

Here’s how to cause a ruckus: Ask a bunch of naturalists to simplify the world. We usually think in terms of a web of complicated…
Gary Bamford's curator insight,

The complexity of complexity!

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## [1607.00462] Suppression of chaos through coupling to an external chaotic system

We explore the behaviour of an ensemble of chaotic oscillators coupled only to an external chaotic system, whose intrinsic dynamics may be similar or dissimilar to the group. Counter-intuitively, we find that a dissimilar external system manages to suppress the intrinsic chaos of the oscillators to fixed point dynamics, at sufficiently high coupling strengths. So, while synchronization is induced readily by coupling to an identical external system, control to fixed states is achieved only if the external system is dissimilar. We quantify the efficacy of control by estimating the fraction of random initial states that go to fixed points, a measure analogous to basin stability. Lastly, we indicate the generality of this phenomenon by demonstrating suppression of chaotic oscillations by coupling to a common hyper-chaotic system. These results then indicate the easy controllability of chaotic oscillators by an external chaotic system, thereby suggesting a potent method that may help design control strategies.
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## How the hidden mathematics of living cells could help us decipher the brain

So will we ever be able to model something as complex as the human brain using computers? After all, biological systems use symmetry and interaction to do things that even the most powerful computers cannot do – like surviving, adapting and reproducing. This is one reason why binary logic often falls short of describing how living things or human intelligence work. But our new research suggests there are alternatives: by using the mathematics that describe biological networks in the computers of the future, we may be able to make them more complex and similar to living systems like the brain.

How the hidden mathematics of living cells could help us decipher the brain

Chrystopher Nehaniv

https://theconversation.com/how-the-hidden-mathematics-of-living-cells-could-help-us-decipher-the-brain-59483

Via Complexity Digest
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## How to get from a ‘problematic situation’ to a ‘systemic intervention’?

How to get from a 'problematic situation' to a 'systemic intervention'? While reading '15 praktijkverhalen over kennismanagement' [Dutch for '15 practical cases of knowledge management'] I came across one story (about Kennisland, Dutch for 'knowledgeland') which triggered my curiosity. It led me to MaRS (originally 'Medical and Related Sciences', but now an acronym no more),…
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## ComplexityBlog » Studying Complexity vs Studying Complex Systems

Springing from my recent post distinguishing types of inter-disciplinary research, I now will go into more detail on a related topic: the difference between studying particular systems that happen to be complex, and studying complexity itself. The main point is that complexity theory includes several commitments related to levels of organization and to there being shared principles/mechanisms underpinning the dynamics of disparate systems. Studying complexity is the overt researching of these commitments and underpinnings. However, most scientists that describe themselves as doing complexity research are not doing that. Instead they are studying particular complex systems and typically ignore the commitments and underpinnings that define complexity science.
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## Systems Science Ascending

After years of development in increasingly fracturing sub-disciplines it seems that systems science as an integrated whole domain of knowledge is rising again. For those familiar with the history of systems science you will recall that in the earl
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## case-based modeling, case based modeling, sacs toolkit, brian castellani, rajeev rajaram, sociology and complexity, complexity map

sociology and complexity science web
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## Understanding the group dynamics and success of teams

Complex problems often require coordinated group effort and can consume significant resources, yet our understanding of how teams form and succeed has been limited by a lack of large-scale, quantitative data. We analyse activity traces and success levels for approximately 150 000 self-organized, online team projects. While larger teams tend to be more successful, workload is highly focused across the team, with only a few members performing most work. We find that highly successful teams are significantly more focused than average teams of the same size, that their members have worked on more diverse sets of projects, and the members of highly successful teams are more likely to be core members or ‘leads’ of other teams. The relations between team success and size, focus and especially team experience cannot be explained by confounding factors such as team age, external contributions from non-team members, nor by group mechanisms such as social loafing. Taken together, these features point to organizational principles that may maximize the success of collaborative endeavours.

Understanding the group dynamics and success of teams
Michael Klug, James P. Bagrow

Royal Society Open Science

http://dx.doi.org/10.1098/rsos.160007

Via Complexity Digest
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## The systems approach: theory for practice

Over the past 4 years I have written a good number of posts on various aspects of the systems approach. In this post I will re-arrange more than 30 of them, to provide a more or less coherent body of theoretical insights underlying  Wicked Solutions. Along the way you will learn why “it is tempting, if the…
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## The origins of the systems approach

Churchman's personal journey This post about the origins (and future!) of the systems approach is a bit complicated. You may prefer to get yourself intellectually geared up by first reading my previous post on the reasons why people don't apply the systems approach more often. Biography of the systems approach          In the first chapter of…
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## Systems of Interaction between the First Sedentary Villages in the Near East Exposed Using Agent-Based Modelling of Obsidian Exchange

In the Near East, nomadic hunter-gatherer societies became sedentary farmers for the first time during the transition into the Neolithic. Sedentary life presented a risk of isolation for Neolithic groups. As fluid intergroup interactions are crucial for the sharing of information, resources and genes, Neolithic villages developed a network of contacts. In this paper we study obsidian exchange between Neolithic villages in order to characterize this network of interaction. Using agent-based modelling and elements taken from complex network theory, we model obsidian exchange and compare results with archaeological data. We demonstrate that complex networks of interaction were established at the outset of the Neolithic and hypothesize that the existence of these complex networks was a necessary condition for the success and spread of a new way of living.
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## [1406.5955] Simple universal models capture all spin physics

Spin models are used in virtually every study of complex systems---be it condensed matter physics [1-4], neural networks [5] or economics [6,7]---as they exhibit very rich macroscopic behaviour despite their microscopic simplicity. It has long been known that by coarse-graining the system, the low energy physics of the models can be classified into different universality classes [8]. Here we establish a counterpart to this phenomenon: by "fine-graining" the system, we prove that all the physics of every classical spin model is exactly reproduced in the low energy sector of certain `universal models'. This means that (i) the low energy spectrum of the universal model is identical to the entire spectrum of the original model, (ii) the corresponding spin configurations are exactly reproduced, and (iii) the partition function is approximated to any desired precision. We prove necessary and sufficient conditions for a spin model to be universal, which show that complexity in the ground state alone is sufficient to reproduce full energy spectra. We use this to show that one of the simplest and most widely studied models, the 2D Ising model with fields, is universal.
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## The essence of the systems approach

Why 'Wicked Solutions' works? Wicked problems now recognized         There was a time when wicked problems did not seem to exist. No matter how complex the problem, there was a general belief that a solution could be found, more typically by so-called linear problem solving methods. This changed in the early 1960s when Horst Rittel found…
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## The Physics behind Systems Biology

Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.

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 Rescooped by Bernard Ryefield from Systems Thinking

## Introduction to Agent-Based Modelling | Complexity Explorer

About the Course: This course will explore how to use agent-based modeling to understand and examine a widely diverse and disparate set of complex problems. During the course, we will explore why agent-based modeling is a powerful new way to understand complex systems, what kinds of systems are amenable to complex systems analysis, and how agent-based modeling has been used in the past to study everything from economics to biology to political science to business and management. We will also teach you how to build a model from the ground up and how to analyze and understand the results of a model using the NetLogo programming language. We will also discuss how to build models that are sound and rigorous. No programming background or knowledge is required, and the methods examined will be useable in any number of different fields.....

Via Jürgen Kanz
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## Cooperation, competition and the emergence of criticality in communities of adaptive systems

The hypothesis that living systems can benefit from operating at the vicinity of critical points has gained momentum in recent years. Criticality may confer an optimal balance between too ordered and exceedingly noisy states. Here we present a model, based on information theory and statistical mechanics, illustrating how and why a community of agents aimed at understanding and communicating with each other converges to a globally coherent state in which all individuals are close to an internal critical state, i.e. at the borderline between order and disorder. We study—both analytically and computationally—the circumstances under which criticality is the best possible outcome of the dynamical process, confirming the convergence to critical points under very generic conditions. Finally, we analyze the effect of cooperation (agents trying to enhance not only their fitness, but also that of other individuals) and competition (agents trying to improve their own fitness and to diminish those of competitors) within our setting. The conclusion is that, while competition fosters criticality, cooperation hinders it and can lead to more ordered or more disordered consensual outcomes.

Via Samir, Complexity Digest
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## Organized Complexity

While I've focused this week thus far on Cities and the Wealth of Nations, Jane Jacobs' most popular book among planners is, of course, The Death and Life of Great American Cities. This is because the latter book contains all the of the happy things
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## Complexity Labs

Complexity Labs is an online resource dedicated to the area of complex systems providing a wide variety of users with information, research, learning and media content relating to this exciting new area. Our mission statement is to assist in the development of a coherent, robust and accessible framework for modelling, designing and managing complex systems.

http://complexitylabs.io

Via Complexity Digest
Complexity Digest's curator insight,

 Rescooped by Bernard Ryefield from Crisis, collapse and transition

## Collapse des systèmes complexes

Pourquoi parler d’effondrement et de collapse de notre civilisation ? Parce que le faisceau d’informations factuelles est très convergent, parce que cela a à voir avec les systèmes complexes, et parce que la résilience, individuelle et collective, commence par l’acceptation de la réalité telle qu’elle est.

Via Philippe Vallat
Pierre Mongin 's curator insight,
juin excellente synthése du film Demain
Jürgen Kanz's curator insight,
The authors are referring to the paper of Graham M. Turner "On the cusp of global collapse?". This document is available here: https://www.ethz.ch/content/dam/ethz/special-interest/usys/ites/ecosystem-management-dam/documents/EducationDOC/Readings_DOC/Turner_2012_GAIA_LimitsToGrowth.pdf
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## Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-organizing Systems

Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies. We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge. There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area.

Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-organizing Systems

Heiko Hamann, Yara Khaluf, Jean Botev, Mohammad Divband Soorati, Eliseo Ferrante, Oliver Kosak, Jean-Marc Montanier, Sanaz Mostaghim, Richard Redpath, Jonathan Timmis, Frank Veenstra, Mostafa Wahby, Aleš Zamuda
Front. Robot. AI, 11 April 2016 | http://dx.doi.org/10.3389/frobt.2016.00014

Via Complexity Digest
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 Rescooped by Bernard Ryefield from Systems Thinking

## Systems tinkering versus systems thinking - All Things ITSM

Systems Thinking, is well suited to mass customization and knowledge work.Dealing with complex systems requires an experimental ‘systems tinkering’ approach
Via Jürgen Kanz
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 Rescooped by Bernard Ryefield from Self-organizing and Systems Mapping

## How Diversity Makes Us Smarter

Being around people who are different from us makes us more creative, more diligent and harder-working

Via june holley
Marcelo Errera's curator insight,
Indeed, as new ideas flow across groups of people, new degrees of freedom become available in the design evolution process.

It's a physics phenomenon.

Lexie stroud's curator insight,
Although many people, specifically from homogenous groups, might argue this, I think it is an interesting concept. I think that it is a good thing for people of different backgrounds to come together and find ways to effectively solve problems. It seems obvious that they might be better at it. L.S.
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## Some problems with the systems approach

Some problems with the systems approach Why isn't it applied more often? Simple explanations        I am convinced that the systems approach is a very good thing. Like many 'believers' it is hard for me to understand why so many people think otherwise. In other words, how non-systems practitioners can think that they can and must address…
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## The systems approach according to Churchman

The environmental fallacy and other notions The systems approach and its enemies      Too often human realities are ignored, with the result that planning efforts are sterile, unsatisfying, and irrelevant. In 'The systems approach and its enemies' (1979), Churchman draws on his wide and deep experience as a both a thinker and planner to show that…
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## [1602.09054] Lyapunov Indices and the Poincaré Mapping in a Study of the Stability of the Krebs Cycle

On the basis of a mathematical model, we continue the study of the metabolic Krebs cycle (or the tricarboxilic acid cycle). For the first time, we consider its consistency and stability, which depend on the dissipation of a transmembrane potential formed by the respiratory chain in the plasmatic membrane of a cell. The phase-parametric characteristic of the dynamics of the ATP level depending on a given parameter is constructed. The scenario of formation of multiple autoperiodic and chaotic modes is presented. Poincar\'{e} sections and mappings are constructed. The stability of modes and the fractality of the obtained bifurcations are studied. The full spectra of Lyapunov indices, divergences, KS-entropies, horizons of predictability, and Lyapunov dimensionalities of strange attractors are calculated. Some conclusions about the structural-functional connections determining the dependence of the cell respiration cyclicity on the synchronization of the functioning of the tricarboxilic acid cycle and the electron transport chain are presented.
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## Measuring the Complexity of Continuous Distributions

We extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. Given that the measures were based on Shannon’s information, the novel continuous complexity measures describe how a system’s predictability changes in terms of the probability distribution parameters. This allows us to calculate the complexity of phenomena for which distributions are known. We find that a broad range of common parameters found in Gaussian and scale-free distributions present high complexity values. We also explore the relationship between our measure of complexity and information adaptation.

Measuring the Complexity of Continuous Distributions
Guillermo Santamaría-Bonfil, Nelson Fernández,  and Carlos Gershenson

Entropy 2016, 18(3), 72

http://www.mdpi.com/1099-4300/18/3/72

Via Complexity Digest
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