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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.
Official Post from Prof Steve Keen: This talk was a pleasure to give, because for once I was talking to an audience which completely understands Complex Systems (unlike the vast majority of economists). In the case of "EASY"the "Evolutionary and Adaptive Systems Research Group"(http://www.sussex.ac.uk/easy/)they apply this methodo This talk was a pleasure to give, because for once I was talking to an audience which completely understands Complex Systems (unlike the vast majority of economists). In the case of "EASY"the "Evolutionary and Adaptive Systems Research Group"(http://www.sussex.ac.uk/easy/)they apply this methodology to analysing the brain and consciousness, rather than economics. In this talk I: Outline Minsky (downloadable from https://sourceforge.net/projects/minsky/), the system dynamics platform I designed for economics to enable banks, debt and money to be easily incorporated into dynamic models of the economy, explain why mainstream economists believe that you don't have to include banks, debt and money in macroeconomic models and why they are profoundly wrong, discuss the attempt by some Neoclassical economists to get back to that Olde Religion now that the global economy is reviving somewhat, ten years after the Global Financial Crisis, and conclude by showing that macroeconomics does not have to be derived from microeconomics (which is impossible in the first place, because of emergent properties in complex evolutionary systems, which the economy manifestly is), but can instead be derived directly from macroeconomic definitions in a Complex Systems manner.
Via Alessandro Cerboni
This document collects the lecture notes from my minicourse "Complexity Theory, Game Theory, and Economics," taught at the Bellairs Research Institute of McGill University, Holetown, Barbados, February 1923, 2017, as the 29th McGill Invitational Workshop on Computational Complexity. The goal of this minicourse is twofold: (i) to explain how complexity theory has helped illuminate several barriers in economics and game theory; and (ii) to illustrate how gametheoretic questions have led to new and interesting complexity theory, including recent several breakthroughs. It consists of two fivelecture sequences: the Solar Lectures, focusing on the communication and computational complexity of computing equilibria; and the Lunar Lectures, focusing on applications of complexity theory in game theory and economics. No background in game theory is assumed.
Abstract We consider the relationship between economic activity and intervention, including monetary and fiscal policy, using a universal monetary and response dynamics framework. Central bank policies are designed for economic growth without excess inflation. However, unemployment, investment, consumption, and inflation are interlinked. Understanding dynamics is crucial to assessing the effects of policy, especially in the aftermath of the recent financial crisis. Here we lay out a program of research into monetary and economic dynamics and preliminary steps toward its execution. We use general principles of response theory to derive specific implications for policy. We find that the current approach, which considers the overall supply of money to the economy, is insufficient to effectively regulate economic growth. While it can achieve some degree of control, optimizing growth also requires a fiscal policy balancing monetary injection between two dominant loop flows, the consumption and wages loop, and investment and returns loop. The balance arises from a composite of government tax, entitlement, subsidy policies, corporate policies, as well as monetary policy. We further show that empirical evidence is consistent with a transition in 1980 between two regimes—from an oversupply to the consumption and wages loop, to an oversupply of the investment and returns loop. The imbalance is manifest in savings and borrowing by consumers and investors, and in inflation. The latter followed an increasing trend until 1980, and a decreasing one since then, resulting in a zero interest rate largely unrelated to the financial crisis. Three recessions and the financial crisis are part of this dynamic. Optimizing growth now requires shifting the balance. Our analysis supports advocates of greater income and / or government support for the poor who use a larger fraction of income for consumption. This promotes investment due to the growth in expenditures. Otherwise, investment has limited opportunities to gain returns above inflation so capital remains uninvested, and does not contribute to the growth of economic activity.
1) Complex systems are intrinsically hazardous systems. All of the interesting systems (e.g. transportation, healthcare, power generation) are inherently and unavoidably hazardous by the own nature. The frequency of hazard exposure can sometimes be changed but the processes involved in the system are themselves intrinsically and irreducibly hazardous. It is the presence of these hazards that drives the creation of defenses against hazard that characterize these systems. 2) Complex systems are heavily and successfully defended against failure. The high consequences of failure lead over time to the construction of multiple layers of defense against failure. These defenses include obvious technical components (e.g. backup systems, 'safety' features of equipment) and human components (e.g. training, knowledge) but also a variety of organizational, institutional, and regulatory defenses (e.g. policies and procedures, certification, work rules, team training). The effect of these measures is to provide a series of shields that normally divert operations away from accidents. 3) Catastrophe requires multiple failures – single point failures are not enough.. Discover the world's research How complex systems fail (PDF Download Available). Available from: https://www.researchgate.net/publication/228797158_How_complex_systems_fail [accessed Aug 13, 2017].
New math shows how, contrary to conventional scientific wisdom, conscious beings and other macroscopic entities might have greater influence over the future than does the sum of their microscopic components.
Via Complexity Digest
Center for Collective Dynamics of Complex Systems (CoCo) Seminar Series April 27, 2017 Mark Sellers (Systems Science, Binghamton University / Northrop Grumman Laser Systems) "Why Is 'Systems Thinking' So Rare?" Slides are available at http://bit.ly/2p51VEc
Via Complexity Digest
Complexity Theory is an emerging field of scientific study that seeks to offer a better framework for understanding dynamic, complex adaptive systems.
Via Jürgen Kanz, Complexity Digest
Computational physicist Sharon Glotzer is uncovering the rules by which complex collective phenomena emerge from simple building blocks.
An autonomous agent is something that can both reproduce itself and do at least one thermodynamic work cycle. It turns out that this is true of all freeliving cells, excepting weird special cases. They all do work cycles, just like the bacterium spinning its flagellum as it swims up the glucose gradient. The cells in your body are busy doing work cycles all the time.
T. Bossomaier, L. Barnett, M. Harré, J.T. Lizier "An Introduction to Transfer Entropy: Information Flow in Complex Systems" Springer, 2016.
This book considers a relatively new measure in complex systems, transfer entropy, derived from a series of measurements, usually a time series. After a qualitative introduction and a chapter that explains the key ideas from statistics required to understand the text, the authors then present information theory and transfer entropy in depth. A key feature of the approach is the authors' work to show the relationship between information flow and complexity. The later chapters demonstrate information transfer in canonical systems, and applications, for example in neuroscience and in finance. The book will be of value to advanced undergraduate and graduate students and researchers in the areas of computer science, neuroscience, physics, and engineering. SpringerLink access to PDFs: http://bit.ly/tebook2016 Springer hard copy listing: http://bit.ly/tebook2016hardcopy Amazon listing: http://amzn.to/2f5YdYW
Via Complexity Digest

A new study challenges one of the most celebrated and controversial ideas in network science.
This eTextbook contains the systemscientific contents taught at the Institute of Systems Sciences, Innovation and Sustainability Research (SIS) at the University of Graz Organically farmed by Manfred Füllsack
Via Complexity Digest
The need for instilling crisis management capability in organizations Nobody has been promoting Churchman’s systems approach as well as Dr. Ian I. Mitroff. He did so in a variety of (indirect, practical) ways, but his most sustained effort is in the form of promoting crisis management as an essential management capability. Crisis management is: (1)…
Complexity science shows us not only what to do, but also how to do it: build shared infrastructure, improve information flow, enable rapid innovation, encourage participation, support diversity and citizen empowerment.
Via june holley
This book provides a short, handson introduction to the science of complexity using simple computational models of natural complex systemswith models and exercises drawn from physics, chemistry, geology, and biology. By working through the models and engaging in additional computational explorations suggested at the end of each chapter, readers very quickly develop an understanding of how complex structures and behaviors can emerge in natural phenomena as diverse as avalanches, forest fires, earthquakes, chemical reactions, animal flocks, and epidemic diseases. Natural Complexity provides the necessary topical background, complete source codes in Python, and detailed explanations for all computational models. Ideal for undergraduates, beginning graduate students, and researchers in the physical and natural sciences, this unique handbook requires no advanced mathematical knowledge or programming skills and is suitable for selflearners with a working knowledge of precalculus and highschool physics. Selfcontained and accessible, Natural Complexity enables readers to identify and quantify common underlying structural and dynamical patterns shared by the various systems and phenomena it examines, so that they can form their own answers to the questions of what natural complexity is and how it arises.
Via Complexity Digest
Many realworld systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two ubiquitous growth mechanisms that not only explain certain structural properties commonly observed in realworld systems, but are also tied to a number of applications in modeling and inference. While there are standard statistical packages for estimating the structural properties of complex networks, there is no corresponding package when it comes to the estimation of growth mechanisms. This paper introduces the R package PAFit, which implements wellestablished statistical methods for estimating preferential attachment and node fitness, as well as a number of functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure good performance for largescale networks. In this paper, we first introduce the main functionalities of PAFit using simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks. PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks Thong Pham, Paul Sheridan, Hidetoshi Shimodaira
Via Complexity Digest
From Jos Leys, Étienne Ghys and Aurélien Alvarez, the makers of Dimensions, comes CHAOS. It is a film about dynamical systems, the butterfly effect and chaos theory, intended for a wide audience.
Via Dr. Stefan Gruenwald
The main message of this paper is that systemism is best suited for transdisciplinary studies. A description of disciplinary sciences, transdisciplinary sciences and systems sciences is given, along with their different definitions of aims, scope and tools. The rationale for transdisciplinarity is global challenges, which are complex. The rationale for systemism is the concretization of understanding complexity. Drawing upon Ludwig von Bertalanffy’s intention of a General System Theory, three items deserve attention—the worldview of a synergistic systems technology, the world picture of an emergentist systems theory, and the way of thinking of an integrationist systems method.
Weaver differentiates “disorganized complexity”, and “organized complexity”.
A complete concise understanding of the systems approach When I started this blog (CSL4D, i.e. Concept & Systems Learning for Design) almost 5 years ago (January 8, 2012), I had just discovered concept mapping as a great learning tool. At the same time I had a great interest in systems thinking, but found it hard…
Nearly all nontrivial realworld systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed, few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This article has three aims. First, it argues for several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second, it uses these visualizations to introduce the foundations of nonlinear dynamics, chaos, fractals, selfsimilarity and the limits of prediction. Finally, it presents Pynamical, an opensource Python package to easily visualize and explore nonlinear dynamical systems’ behavior. Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, SelfSimilarity and the Limits of Prediction Geoff Boeing Systems 2016, 4(4), 37; doi:10.3390/systems4040037
Via Complexity Digest
