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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…
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…
BnF  11 mai 2011 Conférence donnée dans le cadre du cycle "Un texte, un mathématicien", organisée par la Société…
The tremendous popular success of Chaos Theory shares some common points with the not less fortunate Relativity: they both rely on a misunderstanding. Indeed, ironically , the scientific meaning of these terms for mathematicians and physicists is quite opposite to the one most people have in mind and are attracted by. One may suspect that part of the psychological roots of this seductive appeal relies in the fact that with these ambiguous names, together with some superficial clichés or slogans immediately related to them ("the butterfly effect" or "everything is relative"), some have the more or less secret hope to find matter that would undermine two pillars of science, namely its ability to predict and to bring out a universal objectivity. Here I propose to focus on Chaos Theory and illustrate on several examples how, very much like Relativity, it strengthens the position it seems to contend with at first sight: the failure of predictability can be overcome and leads to precise, stable and even more universal predictions.
Information is a precise concept that can be defined mathematically, but its relationship to what we call "knowledge" is not always made clear. Furthermore, the concepts "entropy" and "information", while deeply related, are distinct and must be used with care, something that is not always achieved in the literature. In this elementary introduction, the concepts of entropy and information are laid out one by one, explained intuitively, but defined rigorously. I argue that a proper understanding of information in terms of prediction is key to a number of disciplines beyond engineering, such as physics and biology.
The SIRnetwork model, introduced in [S. Boatto et al., SIRNetwork Model for Epidemics Dynamics in a City, in preparation] and [L. Stolerman, Spreading of an Epidemic over a City: A Model on Networks, Master's thesis, 2012 (in Portuguese)], deals with the propagation of disease epidemics in highly populated cities. The nodes, or vertices, are the city's neighborhoods, in which the local populations are assumed to be wellmixed. The directed edges represent the fractions of people moving from their neighborhoods of residence to those of daily activities. First, we present some fundamental properties of the basic reproduction number ($R_o$) for this model. In particular, we focus on how $R_o$ depends upon the geometry and the heterogeneity (different infection rates in each vertex) of the network. This allows us to conclude whether an epidemic outbreak can be expected or not. Second, we submit the SIRnetwork model to data fitting, using data collected during the 2008 Rio de Janeiro dengue fever epidemic. Important conclusions are drawn from the fitted parameters, and we show that improved results are found when a timedependent infection parameter is introduced.
Read More: http://epubs.siam.org/doi/10.1137/140996148
“The study of cas is a difficult, exciting task. The returns are likely to be proportionate to the difficulty.” Holland ([2006]) On August 9, 2015, cancer took Prof. John Henry Holland away from us. Prof. Holland was a pioneer of Complex Adaptive Systems (CAS) research and a true inspiration. He is known not only for his work on CAS, Holland ([1962], [1992])—which he would fondly write as “cas”—but also for his seminal work on adaptation in natural and artificial systems leading to the creation of genetic algorithms and eventually the fields of evolutionary computation, Holland ([1995]) and Learning Classifier Systems, Holland and Holyoak ([1989]). Holland was a truly interdisciplinary academic. He had an undergraduate degree in Physics from MIT (1950), an M.A. in Mathematics (1954) and possibly the first ever PhD in Computer Science (1959), both from the University of Michigan—a place where he also subsequently served as a Professor of Psychology, Electrical Engineering and Computer Science. Holland leaves behind his legacy in the form of a large number of thoughtprovoking articles, video lectures, books, and inspired people—ranging from colleagues, fellows and students to budding complexity enthusiasts. Two of his recent books summarize his views on CAS in both a longer, Holland ([2012]) as well as a shorter form, Holland ([2014]). It is easy to foresee that these works will serve not only as a guide to CAS but also guidance for future generations. Holland will indeed be greatly missed.

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
BnF  30 avril 2014 Conférence donnée dans le cadre du cycle "Un texte, un mathématicien", organisée par la Société…
Computing pioneer Jay Forrester, SM ’45, developed magneticcore memory. Then he founded the field of system dynamics. Those are just two of his varied pursuits.
Via Christophe Bredillet
Complex systems may have billion components making consensus formation slow and difficult. Recently several overlapping stories emerged from various disciplines, including protein structures, neuroscience and social networks, showing that fast responses to known stimuli involve a network core of few, strongly connected nodes. In unexpected situations the core may fail to provide a coherent response, thus the stimulus propagates to the periphery of the network. Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i.e. the slow democracy exercising the 'wisdom of crowds'. This mechanism resembles to Kahneman's "Thinking, Fast and Slow" discriminating fast, patternbased and slow, contemplative decision making. The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution. The duality of fast core and slow majority may increase our understanding of metabolic, signaling, ecosystem, swarming or market processes, as well as may help to construct novel methods to explore unusual network responses, deeplearning neural network structures and coreperiphery targeting drug design strategies. (Illustrative videos can be downloaded from here:this http URL)
Fast and slow thinking  of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks Peter Csermely http://arxiv.org/abs/1511.01238 ;
Via Complexity Digest
Scientists are homing in on a warning signal that arises in complex systems like ecological food webs, the brain and the Earth’s climate. Could it help prevent future catastrophes?
