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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…
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. Counterintuitively, 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 hyperchaotic 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.
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/howthehiddenmathematicsoflivingcellscouldhelpusdecipherthebrain59483
Via Complexity Digest
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),…
Springing from my recent post distinguishing types of interdisciplinary 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.
After years of development in increasingly fracturing subdisciplines 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
sociology and complexity science web
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 largescale, quantitative data. We analyse activity traces and success levels for approximately 150 000 selforganized, 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 nonteam 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
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 rearrange 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…
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…
In the Near East, nomadic huntergatherer 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 agentbased 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.
Spin models are used in virtually every study of complex systemsbe it condensed matter physics [14], 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 coarsegraining 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 "finegraining" 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.
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 socalled linear problem solving methods. This changed in the early 1960s when Horst Rittel found…

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 datarich, 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 datarich 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.
About the Course: This course will explore how to use agentbased modeling to understand and examine a widely diverse and disparate set of complex problems. During the course, we will explore why agentbased modeling is a powerful new way to understand complex systems, what kinds of systems are amenable to complex systems analysis, and how agentbased 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
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
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
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
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
Hybrid societies are selforganizing, collective systems, which are composed of different components, for example, natural and artificial parts (biohybrid) or human beings interacting with and through technical systems (sociotechnical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for reuse of methods and create significant synergies. We identify three main areas of challenges in the design of selforganizing 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 selforganizing hybrid societies stretches over many different fields and hence requires the reuse 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 Selforganizing Systems Heiko Hamann, Yara Khaluf, Jean Botev, Mohammad Divband Soorati, Eliseo Ferrante, Oliver Kosak, JeanMarc 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
Systems Thinking, is well suited to mass customization and knowledge work.Dealing with complex systems requires an experimental ‘systems tinkering’ approach
Via Jürgen Kanz
Being around people who are different from us makes us more creative, more diligent and harderworking
Via june holley
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 nonsystems practitioners can think that they can and must address…
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…
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 phaseparametric 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, KSentropies, horizons of predictability, and Lyapunov dimensionalities of strange attractors are calculated. Some conclusions about the structuralfunctional 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.
We extend previously proposed measures of complexity, emergence, and selforganization 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 scalefree 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 SantamaríaBonfil, Nelson Fernández, and Carlos Gershenson Entropy 2016, 18(3), 72 http://www.mdpi.com/10994300/18/3/72
Via Complexity Digest
