Complex Systems

Introduction to Complex Systems: Patterns in Nature

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Duration: 7:52
Updated: 31 Aug 2014
views: 5814
author: Ilana Schoenfeld

This video provides a basic introduction to the science of complex systems, focusing on patterns in nature. (For more information on agent-based modeling, vi...

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TEDxRotterdam - Igor Nikolic - Complex adaptive systems

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Duration: 16:07
Updated: 31 Aug 2014
views: 25658
author: TEDx Talks

Igor Nikolic graduated in 2009 on his dissertation: co-evolutionary process for modelling large scale socio-technical systems evolution. He received his MSc ...

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Complexity Theory: What Is A Complex System

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Duration: 10:50
Updated: 02 May 2015
views: 145

In this module we will be trying to define what exactly a complex system is, we will firstly talk about systems in general before going on to look at complexity as a produce of a number of different parameters Produced by Complexity lab: http://complexitylab.io Transcription excerpt: We will be discussing system’s hierarchy, nonlinearity, connectivity, adaptation and self-organization. Before we start we should note that there is no formal definition for what a complex system is and thus there remains many different perspective and opinions on the subject, so it might be of some value to us to start off by taking a quick look a some of these different definitions to get an idea for some of their commonalities. Firstly the Advances in Complex Systems Journal, gives us this definition; "A system comprised of a (usually large) number of (usually strongly) interacting entities, processes, or agents, the understanding of which requires the development, or the use of, new scientific tools, nonlinear models, out-of equilibrium descriptions and computer simulations." Next the social scientist Herbert Simons gives us this definition; "A system that can be analyzed into many components having relatively many relations among them, so that the behavior of each component depends on the behavior of others." Jerome Singer tells us that a complex system is; "A system that involves numerous interacting agents whose aggregate behaviors are to be understood. Such aggregate activity is nonlinear, hence it cannot simply be derived from summation of individual components behavior." Firstly a complex system is a special class of system. A system is simply a set of parts called elements and a set of connections between these parts called relations. These parts can be ordered or unordered, an unordered system is simply a set of things, because there is not specific structure or order we can describe a set by simply listing all of its elements and their properties. So a pile of stones on the ground is an example of an unordered set, as there is no pattern or order to the system we can only describe it by describing the properties of each element in isolation and then adding them all up, with the whole set being nothing more than the sum of its individual parts. If in contrast, through the relations these parts are ordered in a specific way then they can function together as an entirety and out of these parts working together we get the emergence of a global pattern of organization that is capable of functioning as a coherent whole, for example if all the parts in our car are arranged in a specific way then we will have the global functionality of a vehicle of transportation or out of the specific arrangement of billions of cells and the different specialized organs that make up our body we get the emergence of a global system that enables us to operate as an entire organism. So that is the basic model of a system, it consists of elements and relations, when those elements work together we get the emergence of a new level of organization.

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On the Nature of Causality in Complex Systems, George F.R. Ellis

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Duration: 42:50
Updated: 03 Sep 2014
views: 7268
author: Copernicus Center for Interdisciplinary Studies

Big Bang cosmology, chemical and biological evolutionary theory, and associated sciences have been extraordinarily successful in revealing and enabling us to...

wn.com/On The Nature Of Causality In Complex Systems, George F.R. Ellis

How complex systems will save us | Bud Caddell | TEDxIndianapolis

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Duration: 20:22
Updated: 19 Nov 2014
views: 64

This talk was given at a local TEDx event, produced independently of the TED Conferences. A global recession sparked by a speculative bubble in a single domestic market, a regional uprising sparked by one man’s self immolation, a devastating storm sparked by individual fossil fuel consumption – in the aftermath of each event, millions of people found themselves at the mercy of conditions outside of their control. The script for the 21st century continues to repeat itself: connectedness begets complexity, complexity begets uncertainty, and uncertainty begets chaos. How do organizations prepare for events they can’t foresee? The answer lies in the science of complex systems, the most ambitious organizations of our time, and our own courage to embrace uncertainty. Bud Caddell (Los Angeles, CA USA) heads up Undercurrent’s Los Angeles-based office and is a strategic consultant, speaker, and author. In 2012, Business Insider named Bud the most creative person under 30. Adweek listed him in their top 50 industry professionals of 2012, and The Guardian placed him in their 10 digital strategists to watch in 2013. He’s been cited byNYMag and the Harvard Business Review, and his work has been featured in The New York Times, The Wall Street Journal, Forbes, and AdAge. About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)

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Modeling Complex Systems

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Duration: 33:11
Updated: 03 Sep 2014
views: 1951
author: Stanford Complexity Group

Mathematics of Complexity Lecture 2 by Joel Thompson. Class description: We've all heard the buzzwords - chaos, fractals, networks, power laws. What do these...

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Modeling Complex Systems

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Duration: 6:29
Updated: 30 Oct 2014
views: 140

Watertown High School Biotech students explore ideas of complex systems & scientific models through the use of Hexbugs.

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Complex systems design: Abstraction & Fractals

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Duration: 7:42
Updated: 21 Dec 2014
views: 6

As part of our course on Complex systems design and engineering this section discuses the use of abstraction as a powerful tool employed in design in order to structure and solve for excessive complexity. From http://www.fotonlabs.com Transcription In complex systems there is always two fundamentally different levels to the system, the micro and the macro or what we might call the local and the global. This is in contrast to simple linear systems where it is possible to reduce the whole system to one level. So lets first unpack this statement a bit to see why this is so. Firstly complex systems are composed of many parts we may be talking, millions as in the number of inhabitance of a city or billions as in the number of devices connected to the internet. For those of you who aren’t mathematicians a number like a million is a highly abstract things. Trying to relate a number like a billion to our everyday physical experience where we are really dealing with numbers like 5, 10 or possibly 100, should give you an idea of the vast gap or difference in scale difference between any individual node on the local level and the system as an entirety. Secondly, because the components have some degree of autonomy they are adapting to their local environment, these components are often very simple and they do not respond to information on the global level, thus one pattern of order can and often dose develop on the micro level and a second pattern emerges or is imposed on the macro scale as a result of try to design the system to have some form of global coordination. An example of this might be the official use of two different languages in many parts of the world, where we have the local level that has emerged organically and English that has been placed on top of this so as to make the system more interoperable on the global level. Lastly because of the high degree of connectivity within the system we have many interactions, these interactions inevitably lead to elements synchronising which gives rise to macro scale patters, this is what is called emergence. Traffic jams are a good example of emergence as are bank runs. The net result of all this is that we have two qualitatively different levels within complex systems, meaning they can not be reduced to a single level, this makes designing and managing these systems much more difficult. We need to be firstly aware of this multidimensional nature to complex systems and aware that if we try to reduce them to simple mono-dimensional systems there will be unintended and unfortunate consequences, thus we need to learn to design for this multi-dimensional nature to complex systems. In order to do this we have to be able to structure and model the system we are designing according to its different levels of abstraction, but what is abstraction? Abstraction is a powerful tool used in all areas of math, science and engineering, maybe the easiest way to understand it is as the process of removing successive layers of detail from our representation of the system in order to capture its essential features, what we might call its global features, they are common to all the components and are thus on the systems level. This is like zooming in on a satellite map of a city each level will have a certain degree of detail, creating a certain type of structure that will feed into defining the patters on the level bellow. Thus we see how complex engineered systems are what we call systems of systems but unlike mechanistic systems where each level is just a scaled up versions of the components bellow it, what mathematicians call scale invariance, the levels to complex systems can be understood to be scale invariant but they also have their own internal variation and dynamics that can not be fully abstracted away. So you may or may not have heard of things call fractals, they are geometric structures that have this scale invariant property, examples being the arteries in the human body, the structure to snow flakes, the formation of rigid mountains, and sea coastlines. If we make one of these, such as the coastline and zoom in on it the overall structure on each level will be the same but it will not be exactly the same there will be variation and unique differences on each level. To make this more relevant to us as designers and engineers lets take an example of what this means in practical terms for the systems we are developing. So say we are designing a structural adjustment program for the Mongolian economy, no amount of data crunching and analysis from our IMF headquarters will tell us how things will really play out on the ground, yes our abstract economic models will tell us how the system works on the global generic level but there is another level to the Mongolia economy that represents a particular social, cultural and geographical mix, that is unique to this particular instance of the global economy.

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Complexity Theory Overview

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Duration: 9:35
Updated: 03 May 2015
views: 59

In this video we will be giving an overview to the areas of complexity theory by looking at the major theoretical frameworks that are considered to form part of it and contribute to the study of complex systems. Produced by http://www.complexitylab.io Transcription excerpt: Complexity theory is a set of theoretical frameworks used for modeling and analyzing complex systems within a variety of domains. Complexity has proven to be a fundamental feature to our world that is not amenable to our traditional methods of modern science, and thus as researchers have encountered it within many different areas from computer science to ecology to engineering they have had to develop new sets of models and methods for approaching it. Out of these different frameworks has emerged a core set of commonalities that over the past few decades has come to be recognized as a generic framework for studying complex systems in the abstract. Complexity theory encompasses a very broad and very diverse set of models and methods, as yet there is no proper formulation to structure and give definition to this framework, thus we will present it as a composite of four main areas that encompasses the different major perspective on complex systems and how to best interpret them. Firstly systems theory; Systems theory is in many ways the mother of complexity theory, before there was complexity theory, systems theory was dealing with the ideas of complexity, self-organization, adaptation and so on, almost all interpretations to complexity depend upon the concept of a system. In the same way that modern science can be formalized within the formal language of mathematics, all of complex systems science can be formalized within the language of systems theory but, systems theory is a very abstract and powerful formal language and it is typically too abstract for most people and thus is understood and used relatively little. Cybernetics is another closely related area of systems theory, it was also part in forming the foundation to complexity theory, cybernetics during the mid to late 20th century studied control systems and provided a lot of the theoretical background to modern computing, and thus we can see how the interplay between computing and complexity science goes all the way back to its origins as the two have developed hand-in-hand. A lot of systems theory is associated with and has come out of the whole area of computation. The areas of computer science and its counter part information theory have continued to be one of the few major contributors to complexity theory in many different ways, though systems theory is about much more than just computers it is a fully fledged formal language. Next nonlinear systems and chaos theory; Nonlinearity is an inherent feature and major theme that crosses all areas of complex systems. A lot of nonlinear systems theory has its origins in quite dense and obscure mathematics and physics. Out of the study of certain types of equations, weather patterns, fluid dynamics and particular chemical reactions has emerged some very counter intuitive phenomena in the form of the butterfly effect and chaos. Chaos theory, which is the study of nonlinear dynamical systems, was one of the first major challenges to the Newtonian paradigm that was except into the mainstream body of scientific knowledge. Our modern scientific framework is based upon linear systems theory and this places significant constrains upon it, linear systems theory is dependent upon the concept of a system having an equilibrium, although linear systems theory often works as an approximation, the fact is that many of the phenomena we are interested in describing are nonlinear and process of change such as regime shifts within ecosystems and society, happen far-from-equilibrium they are governed by the dynamics of feedback loops and not linear equations. Trying to model complex systems by using traditional linear systems theory is like trying to put a screw into a piece of wood with a hammer, we are simply using the wrong tool because it is the only one we have. Thus the areas of nonlinear systems and their dynamics is another major part to the framework of complexity theory that has come largely from physics, mathematics and the study of far-from-equilibrium processes in chemistry.

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Complex systems design: Design Thinking

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Duration: 8:11
Updated: 04 Jan 2015
views: 12

Design thinking is a design process that enables us to solve complex problems. It combines deep end-user experience, systems thinking, iterative rapid prototyping and multi-stakeholder feedback to guid us through the successive stages in our design. In this section of our course on complex systems design we discuss design thinking as an out line to the design process best suited to the engineering of complex systems Full course available at https://www.udemy.com/complex-systems-design Produced by http://www.fotonlabs.com Transcription: Design thinking, is a design process that enables us to solve complex problems, It combines deep end-user experience, systems thinking, iterative rapid prototyping and multi-stake holder feedback to guid us through the successive stages in our design. Design thinking like complex systems is interdisciplinary. It cuts across traditional domains by recognising that everything in our world is designed, thus it takes design out of its comfort zone of building chairs and fancy coffee cups to apply it to all areas from designing effective organisations to creating health care and financial services. The design process is a bit like blowing up a balloon and then slowly letting all the air out of it again, it requires an initial phase of divergent thinking where we ask expansive question to explore the full context and many different possible options, before having to narrow our vision down upon a single solution and refining it through convergent thinking. But this process is not mechanical it is more evolutionary meaning we can not fully foresee the end product from inception, it emerges and thus we need to think about the future in a open way, that means having confidence in the possibility that an unknown outcome is feasible as the whole point of the design process is that we will create something that does not yet exist and thus is unforeseen. But we don’t have to reinvent the design process wheel every time, there are a few broad stages to it which different people will define in different ways but we are going to talk about some of the most often identified phases in the design thinking process, they include the stages of researching ideating, prototyping and testing. These steps don’t necessary follow a linear path; they can occur simultaneously and be repeated. Firstly the researching phase, what we are doing here is not creating a thing, what we are creating is a solution, and this solution is a solution to a problem that a particular person or people have. Thus we need to understand the context within which our system will exist and where it lies in relation to other thing within that environment, it is only when we see the given context within which a pre-existing version of the system operates that we get a full insight into why it is the way it is and from this can begin to conceive of an improved solution. When we don’t understand the context then we will be likely to simply go round in circles, simply reacting to the pre-exists existing solution. One generation of designers decide that straight lines are the greatest thing extolling all their virtues making everything square and rectangular with pointy corners, until the next generation of designers come along who are now sick of straight lines so they start a new revolution of curves and rounded corners, until everyone gets tired of all the curves and rediscovers the straight line again and so on. By understanding the context and the history of the context to a design we can see its parameters, the advantages and disadvantages of both extremes and try to find integrative solution. If we remember there is aways two qualitatively different level to a complex system, the local and the global, as designers of the system we will be dealing with it primary on the macro scale, but at the end of the day everything really plays out on the local level and we need to understand that local context where people interact and live out their lives through these products and services. People can’t always express what exactly the problem is or know exactly what it is they want, so we need deep emersion to piece it together for ourselves, ethnographic studies, customer journey maps, all forms of enduser experience and importantly empathy.

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Velocity 2012: Richard Cook, "How Complex Systems Fail"

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Duration: 28:21
Updated: 30 Aug 2014
views: 16443
author: O'Reilly

Richard Cook Royal Institute of Technology, Stockholm Dr. Richard Cook is the Professor of Healthcare Systems Safety and Chairman of the Department of Patien...

wn.com/Velocity 2012 Richard Cook, How Complex Systems Fail

Data explains complex systems.

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Duration: 0:27
Updated: 18 Aug 2013
views: 5803
author: SkaIathrax

S07E23.

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Complex systems design:Networks

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Duration: 7:37
Updated: 22 Dec 2014
views: 27

Networks are the true structure to complex engineered systems and in this section we discuss the importance of seeing these systems from the perspective of access, connectivity and networks. Produced by http://www.fotonlabs.com Transcription: Complex systems are by many definitions highly interconnect, examples being social networks, financial networks and transportation networks. In these highly interconnected systems, it is increasingly the connections that define the system as opposed to the properties of their constituent components. This is quite an abstract concept so some examples might help us to grasp it. Think of the expression “it is not what you know but who you know”. It would be more accurate to say, in an isolation system it is what you know that matters, but in an interconnected system it is increasingly who you know that matters, that is the connections that you have. Another more concrete example might help to illustrate this important concept better, think of an expensive sports car, out on the highway it is king doing 0-60 in under three seconds and up to 250 kilometres an hour, these properties of the car are admittedly pretty cool, but put this car in urban traffic and it will be gridlocked like any other car no matter how great the properties of the car it will only be going as fast as the transportation network allows it. This should demonstrates that in these complex systems it is the structure and dynamics of the network that really matter, it is not about being bigger, faster or stronger it is about access and access is defined by where you lie in the network and the structure of that network. Think of the air transportation system, it is not so much the static properties of your location in space and how far away your destination is, but more importantly where you are located in the network, if you are beside a major hub it can be quicker and easier to travel to another major hub on the other side of the planet as it would be to travel from one disconnect hub to another that is a fraction of a the distance away. So hopefully these examples illustrate to you the importance of seeing these complex engineered systems from the perspective of connectivity and networks as opposed to seeing them as things, irrespective of whether we explicitly call them networks or just systems, networks are the true geometry behind complex systems and thus it is very important to think about designing them from this perspective of access, connectivity and network structure. In order to do this we first need to understand a bit about the nature of networks, and network theory is the area of math and science that provides us with the models for analysing networks, so lets take a look at some of the key features to networks and how they will effect the system as a whole. Probably the most important feature to a network is its degree of connectivity, that is how connected is the whole system? Designing for a densely populated urban environment like Hong Kong will be very different to designing for a city like Los Angela which is dispersed. In highly interconnect systems the dense interconnections can require much greater layering, the components can be much more specialised and there may be a much higher level of dependencies. As a result of this failures can quickly propagate, a small security scare in one airport for example can result in delays across large areas of the air transportation system within a nation. In these large, highly interconnected system we don’t always know the dependencies, no one has complete knowledge of all the interlinkages that regulate complex systems like large urban centres or our global supply chain. Thus our aim should not be to design these systems to be perfect, 100% fault tolerant, this is not realistic, instead they need to be engineered so as to be robust to failure, the internet again is a good example of this, it is what is called a “best effort network” this means it tries its best, but if something goes wrong, then it is no big problem it just drops your packet and tries again, it happens all the time but the internet still works, the occurrence of failure should be designed into these systems and not out of them in order to achieve robustness. Another key consideration in the design of these networked systems is their degree of centralisation verse decentralisation, as this is a defining factor in the structure and makeup to networks. In centralised networks we have a node or small set of nodes that have a strong influence on the system and the network will be largely defined by the properties of these primary nodes.

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Complex systems design: Service Orientated Architecture

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Duration: 9:05
Updated: 02 Jan 2015
views: 10

Service orientated architecture or SOA for short, is an approach to distributed systems architecture that employs loosely coupled services, standard interfaces and protocols to deliver seamless cross platform integration. It is used to integrate widely divergent components by providing them with a common interface and set of protocols for them to communicate through what is called a service bus. In this video we discuss the use of SOA as a new architecture paradigm ideally suited to the design of complex systems. Full course available at https://www.udemy.com/complex-systems-design Produced by http://www.fotonlabs.com Transcription: As we have discussed in previous sections the structure and make up to complex engineered systems is fundamentally different to that of our traditional engineered systems which are homogenous, well bounded, monolithic and relatively static, our complex systems are in contrary, heterogeneous, dynamics, unbounded and composed of autonomous elements. Modelling and designing these new complex engineered systems requires intern a alternative paradigm in systems architecture, our new architecture will need to be able to deal with the key features to complex engineered systems that we discussed in previous sections. Firstly it will need to be focus on services over the properties of components. It will also need to be focused upon interpretability and cross platform functionality to deal with a high level of diversity between components. So as to deal with the autonomy of the components it will need to be flexible, distributed and what we call loosely coupled. Lastly It will also need to employ a high level of abstraction to be able to deal with the overwhelming complex of these systems. Over the past few decades a new systems architecture paradigm has emerged within I.T. called Service Orientated Architecture. It is a response to having to build software adapted to distributed and heterogeneous environments that the internet has made more prevalent and thus is an architecture paradigm that fits the design of complex systems well. Service orientated architecture, S.O.A. or SOA for short, is an approach to distributed systems architecture that employs loosely coupled services, standard interfaces and protocols to deliver seamless cross platform integration. It is used to integrate widely divergent components by providing them with a common interface and set of protocols for them to communicate through what is called a service bus. Because SOA originally comes form software development lets take an example from I.T. Imagine I want to build a new web application that allows people to pay their parking tickets online. Well I could spend years developing a subsystem that functions as a street map and then another subsystem for dealing with the payments and yet other for login, user authentication and so one. Or I could simply avail of Google’s map service, a payment gateway service from Paypal and a user login service from Facebook, my job then would be to integrate these diverse service by creating some common process that guides the user though the use of these different services to deliver the desired functionality, Thus instead of building a system that was based around all my different internal components within my well bounded piece of software, my new application would instead be built with an architecture that is orientated around services, a service orientated architecture. Now lets take an example outside of I.T. to illustrate its more generic relevance. Imagine I am a coffee shop owner, my interest is in providing customers with food and beverage in a pleasant environment, in order to do this I need to bring many different things together, from coffee beens to equipment to employees and so on. I need to design some common platform for all these things to interoperate and deliver the final service. But lets think about this system within the more formal language of SOA. Firstly each component in the system is providing a service, whether it is the employee pouring the coffee or the chairs on which people sit, we as designers of the system are not interested in the internal functioning of these components, because we don’t need that information we abstract it away by encapsulating it, only the provider of the service needs to know the internal logic of the component, to us they are simply services. So when it comes to a customer paying with credit card, they simply swipe their card and input the pin number, no one in the shop understands how the transaction is actually completed, only the financial service provider has that information, for the rest of us it is abstracted away through encapsulation. We may also note that the financial service provider has almost complete control over the logic they encapsulate at least during the systems runtime, as is the case for many other components in the system, this is called service autonomy.

wn.com/Complex Systems Design Service Orientated Architecture

Healthy Together Victoria : Complex Systems Thinking

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Duration: 3:21
Updated: 16 Dec 2013
views: 20

Explaining our approach to prevention.

wn.com/Healthy Together Victoria Complex Systems Thinking

Scaling Laws In Biology And Other Complex Systems

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Duration: 54:34
Updated: 28 Mar 2014
views: 8669
author: GoogleTechTalks

Google Tech talks August 1, 2007 ABSTRACT Life is very likely the most complex phenomenon in the Universe manifesting an extraordinary diversity of form and ...

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Complex systems design: Adaptive systems

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Duration: 8:48
Updated: 23 Dec 2014
views: 2

Adaptation is the capacity for a system to respond and alter its state in response to so change within its environment, in this section of our course on complex systems design we discuss the dynamics of complex adaptive engineered systems and how to approach designing them. Transcription: Adaptation is the capacity of a system to alter its state in response to some event within its environment, this capacity of adaptation is something we more often associate with biological systems as apposed to the technologies we design. The industrial world we have engineered is in may ways a relatively static one, we produce things like electrical power grids, buildings and chairs and then they sit there, specifically designed not to change. Every day we walk by the same advertisement on the street, it will present the same information to thousands of people that day but it will only be of any relevance to a very small percentage of them and because it is static they will only take note of it the first time before tuning it out to become simply background noise. Now imagine if that advertisement changed every day, that is to say it was dynamic, instead of a poster we put in a screen that could be updated, it would be of more relevance, more functional. Now lets go even a step further, imagine if this screen could receive information about the profiles and preference of the users that were in its vicinity and dynamically deliver content relevant to their interests. This is the world of complex adaptive engineered systems and as information technology provides us with the tools for building smarter technologies it is increasingly the would we have to design for. To understand this transition from static to adaptive systems lets take the history of the web as a good example. Web 1.0 was a very static systems where web developers hard coded web pages, when you visited a site the server just give you the same page that had been written possibly two or three years earlier with no changes. Web 2.0 that we all know and love leveraged new server side scripting technologies to get information in and out of databases and thus dynamically updating webpages making them interactive and change overtime. The emerging web 3.0 uses semantic technologies and social networking to adapt content relevant to your specific profile and interests, thus making it not just dynamic but also responsive to the context. Out side of the web the massive cost reduction in integrated circuits is leading to sensors and actuators being placed in many devices and objects, as packages within supply changes, cars in traffic and electrical power grids are becoming smarter, they can respond to events within their local environment through realtime mesh networks. But they can also feed data into large centralised systems for analysis, allowing for greater optimisation through dynamic load balancing, as things like washing machines and street lights begin to have the capacity to adapt their power demands to the current load on the system. There are essentially two levels to these complex adaptive engineered systems that we need to consider, the micro and the macro. On the micro level we need elements with some form of control system, a control system is a mechanism for taking in information, processing it according to some set of instructions and generation a response that alters the state of the component. Of cause all living creatures have this, from the simplest single celled organisms to the most complex, the human brain. But increasingly we are using what we call cyber-physical systems to enable all kinds of technology to have this adaptive functionality as they become part of networks of technologies that can communicate and respond to the changes in state of other technologies in real time. As is the case in automated production lines, aircrafts and mass transit systems. On the macro scale, when we are designing these adaptive systems we can no longer rigidly control the system and determine its functionality in the way we can when say designing a bridge, as the end result is going to be more organic, like an ecosystem of products, devices and people interacting and adapting within networks rather than the ridged mechanical systems we are used to. So if we take away this key feature of control that is central to our tradition conception of being able to design, where we see constraining the autonomy of the components as a prerequisite to design, how then can we engineer these adaptive systems at all? The answer is to work with the innate features of adaptive systems not against them, that sounds nice, but what dose this mean exactly? Adaptive systems by definition adapt to their environment, if we place a plant in a new pot or a child in a new school they will adapt to that particular environment, this is part of the dynamics of what biologist and ecologist call homeostasis.

wn.com/Complex Systems Design Adaptive Systems

Modeling Complex Adaptive Systems

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Duration: 71:03
Updated: 01 Sep 2014
views: 19057
author: case

Series: Year of Darwin Title: Modeling Complex Adaptive Systems Recorded on October 30, 2008 in the Peter B. Lewis Bldg., Room 106. A talk for a general univ...

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Controllability and Observability of Complex Systems - Yang-Yu Liu

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Duration: 57:41
Updated: 15 Jul 2014
views: 32
author: Guillaume Chicoisne

Summer School in cognitive Science: Web Science and the Mind Institut des sciences cognitives, UQAM, Montréal, Canada http://www.summer14.isc.uqam.ca/ http:/...

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Christoph Pojer: Evolving Complex Systems Incrementally | JSConf EU 2015

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Duration: 33:27
Updated: 10 Oct 2015
views: 537

JavaScript that writes JavaScript: Christoph will give an intro to jscodeshift and the underlying tools like recast and ast-types that help rewrite and modernize a lot of Facebook’s JavaScript code day-to-day. We’ll explore why these tools become increasingly important and how they change how we think about open source and breaking API changes at Facebook. At the end of the talk everyone will be able to run their own code transformations across all of their projects safely and efficiently. Intro music by @halfbyte

wn.com/Christoph Pojer Evolving Complex Systems Incrementally | Jsconf Eu 2015

Complex Adaptive Systems: 1 Overview

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Duration: 10:23
Updated: 04 May 2015
views: 36

In this module we will be giving a overview to complex adaptive systems, we will firstly define what we mean they this term, before briefly covering the main topics in this area. For full courses, transcriptions & downloads please see: http://complexitylab.io/learning Twitter: https://twitter.com/complexity_lab Facebook: https://www.facebook.com/complexitylab G+: https://plus.google.com/u/0/+FotonLabs/posts Transcriptions excerpt: A complex adaptive system is a special class of complex system that has the capacity for adaptation. Thus like all complex systems they consist of many elements, what are called agents, with these agents interacting in a nonlinear fashion creating a network of connections within which agents are acting and reacting to each other’s behavior. Through adaptation agents have the capacity to synchronize their states or activities with other agents locally, out of these local interactions the system can self-organize with the emergence of globally coherent patterns of organization developing. This macro scale organization then feeds back to the micro level, as the system has to perform selection upon the agents based upon their contribution to the whole system’s functioning. And thus there develops a complex dynamic between the bottom up motives of the individual agents and the top down macro scale system of organization, both of which are often driven by different agendas but are ultimately interdependent. It is this interaction between bottom-up differentiation of agents with different agendas going in different directions and top-down integration in order to maintain the global pattern of organization that creates the core dynamic of complexity within these systems. This is a lot of very dense information so we will now try to flesh it out in greater detail through examples. There are many examples of complex adaptive systems from ant colonies to financial market to the human immune system, to democracies and all types of ecosystems, but we will start on the micro level by talking about the agents and adaptation. An agent is an actor that has the capacity to adapt their state, meaning that given some change within their environment they can in response adjust their own state, so say our agent is a player within a sports game, well if we throw a ball to the person he or she can catch that ball. They are able to do this because they have what is called a regulatory or control system, a control system of this kind consist of a sensor, controller and an actuator, the person is using their optical sense to input information to their brain, the controller, that is then sending out a response to their mussels, the actuator, and through this process they can adjust to generate the appropriate response to this change in their environment. And it is this same process through which a bird in an ecosystem or a trader within a market is receiving information, processing it and generating a response. Typically these agents can only intercept and process a limited amount of local information, like a snail following a trail on the ground it does not have a global vision of the whole terrain around it and it must simply respond to the local information available to it.

wn.com/Complex Adaptive Systems 1 Overview

Complex Systems Design: 2 Complexity Theory Overview

Order: Reorder
Duration: 7:59
Updated: 03 May 2015
views: 21

This video is designed to give you a brief overview to complexity theory and a grounding in the basic concepts that we will be using throughout the rest of the course For full courses, transcriptions & downloads please see: http://complexitylab.io/learning Twitter: https://twitter.com/complexity_lab Facebook: https://www.facebook.com/complexitylab G+: https://plus.google.com/u/0/+FotonLabs/posts

wn.com/Complex Systems Design 2 Complexity Theory Overview

Understanding the Complex Systems Around Us: Martin Schmidt at TEDxMcDonogh

Order: Reorder
Duration: 17:19
Updated: 20 Dec 2012
views: 218
author: TEDxTalks

We are part of and surrounded by many biological and physical systems - they affect us and we affect them. Though they are very varied in their nature, these...

wn.com/Understanding The Complex Systems Around US Martin Schmidt At Tedxmcdonogh