Complexity & Systems
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# Information Path from Randomness and Uncertainty to Information, Thermodynamics, and Intelligence of Observer

Introduced path connects the topics in common concept: integral information measure and symmetry. Initial path' sequence axiomatic probability distributions of stochastic multidimensional process transfers each priory to posteriori probabilities alternating probabilities over process trajectory. Emerging Bayesian probabilities entropy defines process uncertainty measure. Probability transitions model interactive random process generated by idealized virtual measurements of uncertainty as observable process of potential observer. When the measurements test uncertainty by interactive impulses its inferring certain posteriori probability starts converting uncertainty to certainty information. Observable uncertain impulse becomes certain control extracting maximum information from each observed minima and initiating information observer with internal process during conversion. Multiple trial actions produce observed frequency of the events measured probability actually occurred. Dual minimax principle of maxmim extraction and minimax consumption information is mathematical law whose variation equations determine observer structure and functionally unify regularities. Impulse controls cutoff the minimax, convert external process to internal information micro and macrodynamics through integral measuring, multiple trials, verification symmetry, cooperation, enfoldment in logical hierarchical information network IN and feedback path to observations; IN high level logic originates observer intelligence requesting new quality information. Functional regularities create integral logic selfoperating observations, inner dynamical and geometrical structures with boundary shaped by IN information geometry in timespace cooperative processes, and physical substances, observer cognition,intelligence. Logic holds invariance information and physical regularities of minimax law.

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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 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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## Scant Evidence of Power Laws Found in Real-World Networks

A new study challenges one of the most celebrated and controversial ideas in network science.
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## 2018 version of the map of the complexity sciences

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## A (partially) interactive introduction to Systems Sciences

This eTextbook contains the system-scientific 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
William Smith's curator insight,
Interesting content and an intriguing interface. Check it out.
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## Crisis management and the systems approach

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)…
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## The Future of Economics: From Complexity to Commons

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
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## Why teach modeling & simulation in schools?

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## Natural Complexity: A Modeling Handbook (Primers in Complex Systems) by Paul Charbonneau

### Natural Complexity: A Modeling Handbook (Primers in Complex Systems)

~ Paul Charbonneau (author) More about this product
 List Price: \$49.50 Price: \$47.03 You Save: \$2.47 (5%)

This book provides a short, hands-on introduction to the science of complexity using simple computational models of natural complex systems--with 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 self-learners with a working knowledge of precalculus and high-school physics.

Self-contained 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.

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## PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks

Many real-world 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 real-world 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 well-established 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 large-scale 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

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## Chaos - A mathematical adventure

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
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## Transdisciplinarity Needs Systemism

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 world-view of a synergistic systems technology, the world picture of an emergentist systems theory, and the way of thinking of an integrationist systems method.
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## Science and Complexity - Warren Weaver 1948

Weaver differentiates “disorganized complexity”, and “organized complexity”.
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## Systems approach as teleological concordance

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…
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## CHAOS: A MATHEMATICAL ADVENTURE

From Jos Leys, Étienne Ghys and Aurélien Alvarez, the makers of Dimensions, comes CHAOS, a math movie with nine 13-minute chapters. It is a film about dynamical systems, the butterfly effect and chaos theory, intended for a wide audience. CHAOS is available in a large choice of languages and subtitles.

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## Talking Complex Systems Economics to people who understand complex systems | Prof Steve Keen on

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
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## [1801.00734] Complexity Theory, Game Theory, and Economics - Tim Roughgarden

This document collects the lecture notes from my mini-course "Complexity Theory, Game Theory, and Economics," taught at the Bellairs Research Institute of McGill University, Holetown, Barbados, February 19--23, 2017, as the 29th McGill Invitational Workshop on Computational Complexity. The goal of this mini-course is twofold: (i) to explain how complexity theory has helped illuminate several barriers in economics and game theory; and (ii) to illustrate how game-theoretic questions have led to new and interesting complexity theory, including recent several breakthroughs. It consists of two five-lecture 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.

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## [1512.06808] Game Theory (Open Access textbook with 165 solved exercises)

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## Preliminary Steps Toward a Universal Economic Dynamics for Monetary and Fiscal Policy | NECSI

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.
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## How complex systems fail

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].
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## A Theory of Reality as More Than the Sum of Its Parts

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.

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## Why Is 'Systems Thinking' So Rare?

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

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## Complexity Theory and Dynamical Systems | Demetri Kofinas Interviews W. Brian Arthur of the Santa Fe Institute on Complexity Science and Chaos

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
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## ‘Digital Alchemist’ Seeks Rules of Emergence | Quanta Magazine

Computational physicist Sharon Glotzer is uncovering the rules by which complex collective phenomena emerge from simple building blocks.
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## THE ADJACENT POSSIBLE | Edge.org - A Talk with Stuart A. Kauffman

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 free-living 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.

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## Why should students study System Dynamics?

In this video, Jay Forrester talked about why students should study System Dynamics. Please find the transcript here: http://www.systemdynamics.org/upload/...

Via Alessandro Cerboni
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## An Introduction to Transfer Entropy: Information Flow in Complex Systems

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/te-book-2016

Springer hard copy listing: http://bit.ly/te-book-2016-hardcopy

Amazon listing: http://amzn.to/2f5YdYW

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