Complex Systems
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Complex Systems
Complex Systems are ubiquitous both in natural and artificial worlds and Complexity Science is helping understand these systems better.
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Chaos–order transition in foraging behavior of ants

Chaos–order transition in foraging behavior of ants | Complex Systems | Scoop.it

We have studied the foraging behavior of group animals that live in fixed colonies (especially ants) as an important problem in ecology. Building on former findings on deterministic chaotic activities of single ants, we uncovered that the transition from chaotic to periodic regimes results from an optimization scheme of the self-organization of such an animal colony. We found that an effective foraging of ants mainly depends on their nest as well as their physical abilities and knowledge due to experience. As an important outcome, the foraging behavior of ants is not represented by random, but rather by deterministic walks, in a random environment: Ants use their intelligence and experience to navigate.


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Semantic Weak Signal Tracing

Semantic Weak Signal Tracing | Complex Systems | Scoop.it

The weak signal concept according to Ansoff has the aim to advance strategic early warning. It enables to predict the appearance of events in advance that are relevant for an organization. An example is to predict the appearance of a new and relevant technology for a research organization. Existing approaches detect weak signals based on an environmental scanning procedure that considers textual information from the internet. This is because about 80% of all data in the internet are textual information. The texts are processed by a specific clustering approach where clusters that represent weak signals are identified. In contrast to these related approaches, we propose a new methodology that investigates a sequence of clusters measured at successive points in time. This enables to trace the development of weak signals over time and thus, it enables to identify relevant weak signal developments for organization’s decision making in strategic early warning environment.

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Rise of the Drones - Risk & Insurance

Rise of the Drones - Risk & Insurance | Complex Systems | Scoop.it
Insuring unmanned aircraft systems is going to be complicated. (RT @RiskInsurance: Rise of the Drones | The insurance of unmanned aircraft systems is expected to be highly complex.
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Synched

Synched | Complex Systems | Scoop.it

Synched is a piece of software created to visualize synchronization phenomena in complex systems. Due to their ubiquity in science and nature, the study of large networks of coupled oscillators has become a popular research area.

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SourceForge.net: PyCX Project: PyCX 0.2 now available

SourceForge.net: PyCX Project: PyCX 0.2 now available | Complex Systems | Scoop.it
The PyCX Project aims to develop an online repository of simple, crude, yet easy-to-understand Python sample codes for dynamic complex systems simulations, including iterative maps, cellular automata, dynamical networks and agent-based models.

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Corporate structure, Darwinism and random selection | Synthesis

Corporate structure, Darwinism and random selection | Synthesis | Complex Systems | Scoop.it

By Paul Ormerod

 

The corporate world exhibits a wide variety of structures. Co-operatives and partnerships have been around for a long time and have some well known examples. The Co-op, for example, was founded in Rochdale as long ago as 1844 and now is represented worldwide. Goldman Sachs was a partnership for most of its existence. There are more exotic forms of the corporate beast, such as companies limited by guarantee, industrial and provident societies, friendly societies and, recently made possible by legislation in the UK, community interest companies.

 

But by far the dominant form of corporate organisation is that of the joint stock company with limited liability. In other words, companies ultimately controlled by shareholders. These can range from one person bands to the world’s largest firms such as Google. (...)

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Complexity in Reality | Synthesis

Complexity in Reality | Synthesis | Complex Systems | Scoop.it

"Complexity approaches may offer a way to help articulate the issues and opportunities within the systems people operate in, and provide tools to conceptualise and model these vast, complex systems, supporting informed redesign decisions."

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Critical behavior of the SIS epidemic model with time-dependent infection rate

by Nuno Crokidakis, Marcio Argollo de Menezes

 

In this work we study a modified Susceptible-Infected-Susceptible (SIS) model in which the infection rate $\lambda$ decays exponentially with the number of reinfections $n$, saturating after $n=l$. We find a critical decaying rate $\epsilon_{c}(l)$ above which a finite fraction of the population becomes permanently infected. From the mean-field solution and computer simulations on hypercubic lattices we find evidences that the upper critical dimension is 6 like in the SIR model, which can be mapped in ordinary percolation.

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Introduction: social-cognitive complexity, computational models and theoretical frames

Introduction: social-cognitive complexity, computational models and theoretical frames | Complex Systems | Scoop.it

in Computational & Mathematical Organization Theory, Online First™ - SpringerLink

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Important and complex systems may be more controllable than they appear : complexsystems

Important and complex systems may be more controllable than they appear : complexsystems | Complex Systems | Scoop.it
reddit: the front page of the internet (Important and complex systems may be more controllable than they appear submitted by snotskie_[link] [comment] http://t.co/HkSLaaJg2V)...
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A new criterion for tractability of multivariate problems

Tractability of multivariate problems studies their complexity with respect to the number of variables, dd, and the accuracy of the solution εε. Different types of tractability have been used, such as polynomial tractability and weak tractability and others. These tractability types, however, do not express the complexity with respect to the number of bits of accuracy.

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ScienceDirect.com - Mathematical Social Sciences - Complexity and organizational architecture

ScienceDirect.com - Mathematical Social Sciences - Complexity and organizational architecture | Complex Systems | Scoop.it
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IU community mourns passing of Distinguished Professor and Nobel Laureate Elinor Ostrom: IU News Room: Indiana University

IU community mourns passing of Distinguished Professor and Nobel Laureate Elinor Ostrom: IU News Room: Indiana University | Complex Systems | Scoop.it
The entire Indiana University community mourns the passing of Distinguished Professor Elinor Ostrom, who received the 2009 Nobel Prize in Economic Sciences for her groundbreaking research on the ways that people organize themselves to manage...
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Iterated Prisoner’s Dilemma contains strategies that dominate any evolutionary opponent

The two-player Iterated Prisoner’s Dilemma game is a model for both sentient and evolutionary behaviors, especially including the emergence of cooperation. It is generally assumed that there exists no simple ultimatum strategy whereby one player can enforce a unilateral claim to an unfair share of rewards. Here, we show that such strategies unexpectedly do exist. In particular, a player X who is witting of these strategies can (i) deterministically set her opponent Y’s score, independently of his strategy or response, or (ii) enforce an extortionate linear relation between her and his scores. Against such a player, an evolutionary player’s best response is to accede to the extortion. Only a player with a theory of mind about his opponent can do better, in which case Iterated Prisoner’s Dilemma is an Ultimatum Game.


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Emergent Change | Rethinking Complexity

Emergent Change | Rethinking Complexity | Complex Systems | Scoop.it

When we look at change, we can easily distinguish between planned and unplanned change. In simple terms, planned change is change that we seek. Conversely, unplanned change is the type of change we are forced to accept and integrate. This latter type of change may have been planned by others and we are just the unsuspecting recipients of it; or the unplanned change may be totally unexpected by everyone as in the visit of a tornado and its resulting devastation.


Via Alessandro Cerboni
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A quantitative measure, mechanism and attractor for self-organization in networked complex systems

by Georgi Yordanov Georgiev

 

Quantity of organization in complex networks here is measured as the inverse of the average sum of physical actions of all elements per unit motion multiplied by the Planck's constant. The meaning of quantity of organization is the inverse of the number of quanta of action per one unit motion of an element. This definition can be applied to the organization of any complex system. Systems self-organize to decrease the average action per element per unit motion. This lowest action state is the attractor for the continuous self-organization and evolution of a dynamical complex system. Constraints increase this average action and constraint minimization by the elements is a basic mechanism for action minimization. Increase of quantity of elements in a network, leads to faster constraint minimization through grouping, decrease of average action per element and motion and therefore accelerated rate of self-organization. Progressive development, as self-organization, is a process of minimization of action.

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Complex Systems Principles and Education: Focusing on Universal Principles and Individual Differences | Yaneer Bar-Yam

Complex Systems Principles and Education: Focusing on Universal Principles and Individual Differences | Yaneer Bar-Yam | Complex Systems | Scoop.it

"I will present a few perspectives on the current trends in education from the point of view of a complex systems scientist. Among the likely topics: centrally prescribed metrics and standardized testing, charter schools, and innovations in mathematics education. The discussion will be based upon analysis of complexity and scale, the substructure of neural cognition, and other relevant complex systems insights."

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Stochastic Physics, Complex Systems and Biology

In complex systems, the interplay between nonlinear and stochastic dynamics gives rise to an evolution process in Darwinian sense with punctuated equilibrium, random "mutations" and "adaptations". The emergent discrete states in such a system, i.e., attractors, have natural robustness against both internal and external perturbations. Epigenetic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a framework.

 

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