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Systems Thinking
 
05:41
See the full course: https://goo.gl/KfJ6KY Follow along with the course eBook: https://goo.gl/kvpKws A short video explaining the primary differences between analytical methods of reasoning and systems thinking while also discussing the two methods that underpin them; synthesis and reductionism. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 54510 Complexity Labs
Financialization Explained
 
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Short explainer video about financialization. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: Financialization is an economic paradigm where the conversion of real economic value into financial instruments and their exchange within the financial system comes to dominate economic institutions, activity and value creation. Through financialization, the financial industry converts any work product, physical asset or service to an exchangeable financial instrument, that can be traded, speculated upon and ultimately managed through the financial system. As Professor Geta Krippner has stated it, financialization is the "Pattern of accumulation in which profit making occurs increasingly through financial channels rather than through trade and commodity production" Financialization can be thought of as the virtualization of our real economies. Through information technology and lots of financial analysts, we perform what is called securitization. Securitization is the process of taking an illiquid asset, or group of assets, and through financial engineering, transforming them into a security that can be traded. As this process has grown in scale the importance of financial markets and institutions in the operation of the global economy and its governing institutions has also risen to unprecedented levels. This has raised concerns from many, while at the same time society's perspective on finance has changed significantly. Since the liberalization of capital markets in the 1980s, the number and quantity of financial instruments have grown rapidly. Today the financial system dominates over the real economy. In this time financial leverage has tended to override capital equity, and financial markets have tended to dominate over traditional industrial economic activity. Traditionally, prior to the 1980s, the primary occupation of banks was in taking deposits and lending them out to businesses, thus making them strongly integrated with real economic activity. However, recent research has shown that only approximately 15% of the financial flows coming out of the largest financial institutions in the US are now going to business investments. This is a profound shift in what the financial system does and is, that departs significantly from our traditional economic models for the role of finance within the overall economy. Much of the money that has been diverted from real economic activity has gone into the development of the global derivatives market. A key feature of financialization as it has evolved over the past decades has been the proliferation of derivatives of all form. In the year 2006 derivatives trading reached a level of 1,200 trillion dollars, dwarfing the output of the real global economy that was approximate 50 trillion dollars at that time. On a broader level, financialization can be seen to mark a transition from a traditional form of Industrial Age capitalism, that was based on the physical means of production as the primary source of capital, power and value creation, to a new form of information and services based financial capitalism. In this transition, the financial system has ceased to simply play the role of assisting in the running and operation of the real economy of goods and services, but rather has come to dominate, even displace, real economic activity. This broader transformation has fed through to the strong effect the financial system has had in shaping the evolving nature of the corporation within advanced economies, as financial rationale and practices have re-shaped performance metrics within the corporation. As the remuneration going to top management has become increasingly aligned with the interests of the financial system, shareholder equity has increasingly come to replace other metrics for success. Equally, the rise of finance has gone hand in hand with privatization enabling it to affect almost all sectors of the economy including the public sector and utilities. The net result of financialization and globalization is the formation of one of the first truly global complex systems that we are far from understanding, and this lack of understanding creates major vulnerabilities. The critical role that finance plays within a modern economy places the real global economy in a very precarious and unstable situation. Adair Turner - the head of Britain's Financial Services Authority - directly named financialization as the primary cause of the 2007 financial crisis.
Views: 7887 Complexity Labs
Graph Theory Overview
 
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For the full course see: https://goo.gl/iehZHU Follow along with the course eBook: https://goo.gl/i8sfGP In this lecture, we start to lay down some of our basic language for talking about networks that comes to us from graph theory a relatively new area of mathematics that studies the properties of graphs. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: When we hear the word network all sorts of things spring to mind like social networks and the Internet in particular, but the power of network theory is really in its high degree of abstraction, so the first thing for us to do is to try and start back at the beginning by forgetting what we think we know about networks and embrace the abstract language of networks what is called graph theory. In the formal language of mathematics a network is called a graph and graph theory is the area of mathematics that studies these objects called graphs. The first theory of graphs goes back to 1736, the first textbook came about in 1958 but most of the work within this field is less than a few decades old. In its essence a graph is really very simple, it consist of just two parts what are called vertices and edges. Firstly Vertices; a vertex or node is a thing, that is to say it is an entity and we can ascribe some value to it, so a person is an example of a node as is a car, planet, farm, city or molecule. All of these things have static properties that can be quantifies, such as the color of our car, the size of our farm, or the weight of our molecule. Within network science vertices are more often called nodes so we will be typically using this term during the course. Edges can be define as a relation of some sort between two or more nodes, this connection may be tangible as in the cables between computers on a network or the roads between cities within a national transportation system or these edges may by be intangible, such as social relations of friendship. Edges may be also called links, ties or relations and we will be often using this latter term during the course. The nodes belonging to an edge are called the ends, endpoints, or end vertices of the edge. Within graph theory networks are called graphs and a graph is define as a set of edges and a set vertices. A simple graph does not contain loops or multiple edges, but a multigraph is a graph with multiple edges between nodes. So where as a simple graph of a transpiration system would just tell us if there is a connection between two cities, a multigraph would show all the different connections between the two cities. Graphs can be directed or undirected. With an undirected graph edges have no orientation, for example a diplomatic relation between two nations may be mutual and thus have no direction to the edge between the nodes. These undirected graphs have unordered pairs of nodes, that means we can just switch them round, if Jane and Paul are married, we can say Jane is married to Paul or we can say Paul is married to Jane it makes no difference and thus it is an unordered pair.
Views: 38666 Complexity Labs
SocioTechnical Systems Overview
 
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For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB A brief introduction to the area of Socio-Technical systems a new area that takes a holistic approach to the design of engineering projects which involve the interaction of both social and technical elements. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: Socio-technical systems is a relatively new area which takes a more holistic approach to the development of engineering projects that involve that interaction between people and technology. But lets start by take an example of how our traditional approach to the development of technology and social organizations works. Take Bob and Alice, they are both web developers and they both work on the same website But Bot is a back end web developers, he eats databases for breakfast and spends his days crunching code. Whilst Alice is a front end web designer, she makes words sing and graphics come alive in simple and user-friendly interfaces that people love. Every few months the site needs updating and Bob works hard behind the scenes coding away, he then hands the project over for Alice to work her magic on. This works fine initially but as the site grows with more employees being taken on thing become a little less straight forward. We soon have multiple people working on increasingly specialized area of the side with developers and designer often needing to interact and collaborate but finding them self's stuck in their separate departments. Bod and Alice's site now requires a more holistic and Non-linear approach to overcome this stumbling bock as it has now become what we can call a complex socio technical system. But what do we mean by that? Firstly it is complex in that it has multiple elements such as lines of code, databases, graphics and so on, with all of these different things needing to interact and being dependent on each others functioning. And secondly it is socio-technical as a web site represents an interaction between the technical domains of computer software and the human interaction. For the site to function fully we need to design both areas to work together. When we look around us we can being to see socio technical systems every where. Lets take another example,
Views: 21866 Complexity Labs
Nonlinear Dynamics & Chaos
 
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See the full course: https://goo.gl/9qB4CV Follow along with the course eBook: https://goo.gl/wQahvk For many centuries the idea prevailed that if a system was governed by simple rules that were deterministic then with sufficient information and computation power we would be able to fully describe and predict its future trajectory, the revolution of chaos theory in the latter half of the 20th century put an end to this assumption showing how simple rules could, in fact, lead to complex behavior. In this module we will describe how this is possible when we have what is called sensitivity to initial conditions. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription excerpt: Isolated systems tend to evolve towards a single equilibrium, a special state that has been the focus of many-body research for centuries. But when we look around us we don't see simple periodic patterns everywhere, the world is a bit more complex than this and behind this complexity is the fact that the dynamics of a system maybe the product of multiple different interacting forces, have multiple attractor states and be able to change between different attractors over time. Before we get into the theory lets take a few examples to try and illustrate the nature of nonlinear dynamic systems. A classical example given of this is a double pendulum; a simple pendulum with out a joint will follow the periodic and deterministic motion characteristic of linear systems with a single equilibrium that we discussed in the previous section. Now if we take this pendulum and put a joint in the middle of its arm so that it has two limbs instead of one, now the dynamical state of the system will be a product of these two parts interaction over time and we will get a nonlinear dynamic system. To take a second example; in the previous section we looked at the dynamics of a planet orbiting another in a state of single equilibrium and attractor, but what would happen if we added another planet into this equation, physicists puzzled over this for a long time, we now have two equilibrium points creating a nonlinear dynamic system as our planet would be under the influence of two different gravitational fields of attraction. Where as with our simple periodic motion it was not important where the system started out, there was only one basin of attraction and it would simply gravitate towards this equilibrium point and then continue in a periodic fashion. But when we have multiple interacting parts and basins of attraction, small changes in the initial state to the system can lead to very different long-term trajectories and this is what is called chaos. Wikipedia has a good definition for chaos theory so lets take a quote from it. “Chaos theory studies the behavior of dynamical systems that are highly sensitive to initial conditions—a response popularly referred to as the butterfly effect. Small differences in initial conditions yield widely diverging outcomes for such dynamical systems, rendering long-term prediction impossible in general”. We should note that chaos theory really deals with deterministic systems and more over it is primarily focuses on simple systems, in that it often deals with systems that have only a very few elements, as opposed to complex systems where we have very many components that are non deterministic, in these complex systems we would of cause expect all sorts of random, complex and chaotic behavior, but it is not something we would expect in simple deterministic systems.
Views: 26579 Complexity Labs
Systems Thinking
 
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Follow along with the course eBook: https://goo.gl/Z2ekrB For full courses see: http://complexitylabs.io/courses A brief introduction to systems thinking. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: System thinking is a way of describing the world in a more holistic manner based upon the model of a system, but lets start from the beginning. We can understand the world as things, that is parts or components and their relations, that is how they are connected or fit together. So take a car for example, it is made up of parts, car parts such as engine, wheels and so on and these parts are put together or organized in a specific way so as to make them function as a vehicle of transportation. Now we call a group of things that are not organized in this way a set. So we would call a group of cups on a table a set of cups because unlike the parts to our car they have not been designed to serve some collective function. Because the group of cups is simple the sum of its parts we would describe them by describing the individual properties of the each cup and this would tell us everything we needed to know about them. This approach to describing thing is called analysis or reductionism, reductionism is the traditional approach taken within modern science that tries to describe complex phenomena in terms of their individual parts. Now take the human body that is highly organized through a complex set of relations between its parts. Out of the arrangement of these parts in a specific way we get the overall functioning of a living organism. Because the parts are so strongly defined by their connections and function within the body as an entirety, to properly describe the parts we need to first understand the functioning of the whole body. This approach to describing things that is that we can best describe things by understanding their place within the function of the whole that they are apart of is called synthesis and synthesis is the foundations of systems thinking. Thus we have two different approaches to describing thing, analysis that is interested in describing the individual components and syntheses that talks about the relationship between these components and their functioning as a whole. Ok so now that we know a bit about system thinking lets put our new found knowledge to use say a car manufacturing company has employed us to design their next great model. Now we could take two different approaches to this problem, applying analytical thinking or our friend systems thinking. If we approached the problem for a traditional perspective we would start by analyzing the car and looking for ways to optimizes it, we might come up with a design that minimizes the cars drag by reducing its height by a few centimeters to increasing its fuel efficiency. Now if we applied systems thinking to this problem, we would start by identifying the cars function, that is personal transportation and the system it is apart of, the transportation system. From this perspective we might not even need to design a new car. But end up designing some services that connects preexisting resources to provide the same desired functionality. From this example we can see how systems thinking is often employed when the current paradigm or way of doing things has reached its limit and giving us a fresh perspective on things. Systems thinking is the beginning of another closely related area called systems theory that goes on to give us a wholes suit of tools for analysis and modeling systems and their interaction and dynamics as they evolve over time. So we can rap up by saying that systems thinking is an emerging paradigm within many areas for science to engineering and business management, that presents an alternative to our traditional modern analytical methods of enquiry by emphasizing the need for a more holistic and contextualized understanding of the world. but how do we actually do it ? we start by asking what is the function of the thing I am interested in. Leave perform the function of photosynthesis, cars transport people and business produce products. By identifying the function that these things performs with in a broader system we are given the primary context within which to understand them. By understanding the whole system, the other elements within it and its relationship to them we can understand what uniquely defines the thing we are interested in. This is why systems thinking is also called holistic thinking, becomes it starts with an understanding of the whole and works backwards to understand the individual elements. Once we have this context of understanding the elements function we can apply the model of a systems to identify its inputs and out puts and reason about its efficiency As a ratio between the resources it process and the wait produced during its operation
Views: 64313 Complexity Labs
Service-Oriented Architecture
 
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For the full course see: https://goo.gl/S3Q8XD Follow along with the course eBook: https://goo.gl/ZZqUzY Service Oriented 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. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF 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.
Views: 25502 Complexity Labs
Event Driven Architecture
 
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For the full course see: https://goo.gl/S3Q8XD Follow along with the course eBook: https://goo.gl/ZZqUzY Overview to Event Driven Architecture(EDA) in the design of complex systems Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: Complex systems are inherently dynamic systems, schools of fish, transportation systems, social networks and financial markets are examples of complex systems that are constantly changing, change is the norm. When systems reach this critical level of dynamism we have to change our way of modelling and designing them. It is now no-longer the static structural properties in space that define them but increasingly the systems relations in time that come to define how it functions. The appropriate systems architecture for this inherent dynamic nature to complex engineered systems is what is called event driven architecture. Event driven architecture or E.D.A. is a design pattern built around the production, detection, and reaction to events that take place in time. Information technology is key to enabling a new world of event driven architecture, when we start putting chips in all kinds of devices and objects, instrumenting our technologies and putting smart phones in the hands of many, the world around us stops being dumb and static and starts being more dynamic, adaptive, and things start happening in realtime. When the lights in my house or my garage door are instrumented with sensors and actuators, they no longer need me to turn them on. Instead they wait in what is called a restless state, listening for some event to occur and then can instantly respond. This is in contrast to many of our traditional system, where the components are constrained by some centralised coordination mechanism with information often having to be routed from the local level to a centralised control mechanism, then batch process and returned to the component to respond after some delay. the components within complex system are able to adapt locally, this means they can often act and react in realtime. Added to this is the fact that many of these complex engineered systems are loosely couples networks of unassociated components, they don’t really have any structure, sometimes they don’t even exist until some event actually happens. When I make a query on a search engine my computer might be coupling to a data centre in Texas but the next time I make the same query I might be interacting with a server in South Korea depending on the systems load balance at that instance in time the network’s structure is defined dynamically during the systems run time. An event-driven architecture consists primarily of event creators, event managers and event consumers. The event creator, which is the source of the event, only knows that the event has occurred and broadcasts a signal to indicate so. An event manager, as the name implies functions as an intermediary managing events, when the manager receives notification of an event from a creator, it may apply some rules to process the event but ultimately events are passed down stream to event consumers where a single event may initiate numerous downstream activities. Consumers are entities that need to know the event has occurred and typically subscribe to some type of event manager. So a quick example of this might be an online accommodation service, where event creators, that is property owners, broadcast the availability of their accommodation to the event manager, the online platform, which would aggregate these and event consumers, people looking for accommodation, could subscribe to the platform’s mailing list sending them notification for any new relevant listings.
Views: 39564 Complexity Labs
Complexity Science Overview
 
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For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB A brief overview to the area of complexity science. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: Complexity science is a new approach or method to science that has arisen over the past few decades to present an alternative paradigm to our standard method of scientific enquiry. To give it context lets start by talking a bit about our traditional approach to scientific research. We can loosely define science as a type of enquiry into the world around us, as opposed to other areas such as art or religion that are based upon aesthetics or revelation, the scientific method of enquiry claims to be based upon empirical data, otherwise known as facts. The beginning of the modern era, a proximally 500 hundred years ago, saw the development of a systematic and coherent framework for conducting this scientific process. This framework became clearly formulated with the work of Sir Isaac Newton, and thus Newtonian physics became an example or paradigm of how modern science should be conducted. The Newtonian paradigm is a whole way of seeing the world that describing phenomena as the product of linear cause and effect interactions between isolated objects that are determined by mathematical laws, this vision of the things results in a very mechanical world sometimes called the clock world universe. This new paradigm in turn gave rise to a new method of enquiry called reductionism. Reductionism is the process of breaking down complex phenomena into simple components that can be modeled using linear equations. by then reassemble these individual components we can understand the whole system as simply the sum of its individual parts. Having been phenomenally successful within physics this framework for modern science has gone on over the century to be applied to almost all areas of enquiry from biology to engineering and business management, placing it at the hear of our modern understanding of the world. It is only during the 20th century that this approach to science began to be called into question as the revolutions of quantum physics and relativity showed some of its most basic assumption about time, space and causality to be flawed. Whist later in the century chaos theory began to open up a new world of non-linear systems. Out side of science the world has also become very different from the one Newton lived in as globalization, information technology and sustainability present us with the new challenges of understanding, designing and managing systems that are highly interconnect, interdependent and non-linear, that we can now call complex systems. This is where complexity science comes in to provide us with an alternative scientific method better suited to researching these complex system, supported by a paradigm that sees the world as a set of interconnected elements whose interaction give rise to the patterns and phenomena that we observe in the world around us. As opposed to traditional science that tries to eliminate complexity by studying the individual component of a system within an isolated environment. Complexity science places a greater enforces upon open system that is understand systems within the complex of relations that give it context. Where as traditional reductionist science primarily uses linear mathematical models and equations as its theoretical foundation, complexity science uses the concepts of complexity theory, such as self organization, network theory, adaptive and evolution. This new theoretical framework is combined with new methods such as agent based modeling. As opposed to describing the phenomena we observe in terms of the laws of nature encode in equations, agent based modeling takes a more bottom up approach describe them as the emergent phenomena of local level interaction of agents governed by simple rules. Complexity science studies the complex systems in our world that have previously fallen between the gaps of modern science, such as financial networks, cities, ecosystems and social networks, studying these large complex systems typically requires significant amounts of data. Thus what the microscope, telescope and laboratory were for modern science, computation and data are to complexity science, which relies heavily on computer simulations and analysis of the mass of rich and diverse data that information technology, has provided us with...
Views: 31910 Complexity Labs
What is VUCA?
 
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See the book Managing Complexity: https://goo.gl/XH77jH Produced by: http://complexitylabs.io A short video introducing the acronym VUCA. VUCA is an acronym used to describe situations or environments that engender high levels of volatility, uncertainty, complexity and ambiguity. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 54092 Complexity Labs
Long Tail Distributions
 
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See the full course: https://goo.gl/9qB4CV Follow along with the eBook: https://goo.gl/wQahvk One result of the power laws that we discovered in the previous section are long tail distributions which is a type of graph we get when we plot a power law relation between two things. The long tail distribution, sometimes called the fat tail, is so called because it results in there being an extraordinarily large amount of small occurrences to an event and a very few very large occurrences with there being no real average or normal to the distribution. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription excerpt: Part of our definition for linear systems is that the relationship between input and output is related in a linear fashion, the ratio of input to output might be 2 times as much, 10 times as much or even a thousand times it is not important this is merely a quantitative difference, what is important is that this ratio between input and output is itself not increasing or decreasing. But as we have seen when we have feedback loops the previous state to the system feeds back to effect the current state thus enabling the current ration between input and output to be greater or less than its ratio previously and this is qualitatively different. This phenomenon is captured within mathematical notation as an exponential. The exponential symbol describe how we take a variable and we times it by another not just once but we in fact iterate on this process, meaning we take that output and feed it back into compute the next output, thus the amount we are increasing by each time itself increases. So lets take a quick example of exponential grow to give you an intuition for it. Say I wish to create a test tube of penicillin bacteria. Knowing that the bacteria will double every second, I start out in the morning with only two bacteria hoping to have my tube full by noon. As we know the bacteria will grow exponentially as the population at any time will feed into effect the population at the next second, like a snowball rolling down a hill. It will take a number of hours before our tube is just 3% full but within the next five seconds as it approaches noon it will increase to 100% percent of the tube. This type of disproportional change with respect to time, is very much counter to our intuition where we often create a vision of the future as a linear progression of the present and past, we will be discussing farther the implication of this type of grow later when we get into the dynamics of nonlinear systems but for the moment the important thing to note here is that in exponential growth the rate of growth itself is growing and this only happens in nonlinear systems, where they can both grow and decay at an exponential rate. Exponentials are also called powers and the term power law describes a functional relationship between two quantities, where one quantity varies as a power of another. There are lots of example of the power law in action but maybe the simples is the scaling relationship of an object like a cube, a cube with sides of length a will have a volume of a3 and thus the actual number that we are multiplying the volume by grows each time, this would not be the case if there was a simple linear scaling such as the volume being 2 times the side length. Another example from biology is the nonlinear scaling within the metabolic rate vs. size of mammals, the metabolic rate is basically how much energy one needs per day to stay alive and it scales relative to the animals mass in a sub-linear fashion, if you double the size of the organism then you actually only need 75 % more energy. One last example of the power law will help to illustrate how it is the relationships between components within a system that is a key source of this nonlinearity.
Views: 14866 Complexity Labs
Centralized & Scale Free Networks
 
05:46
For the full course see: https://goo.gl/iehZHU Follow along with the course eBook: https://goo.gl/i8sfGP In this module we looked at networks that have the highest degree distribution making their topology very heterogeneous in terms of the distribution of connectivity, these networks may have one or a few nodes with a very high degree of connectivity forming global hubs within the network and very many with a much smaller degree of connectivity Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription excerpt: let's start by taking a few examples of these centralized systems, if we look at his network of global banking activity with nodes representing the absolute size of assets booked in the respective jurisdiction and the edges between them the exchange of financial assets, with data taken from the IMF. We can then see clearly how a very few core nodes dominate this network, there are approximately 200 countries in the world but these 19 largest jurisdictions in terms of capital together are responsible for over 90% of the assets. This type of centralized structure to a network is surprisingly reverent in our world and we could cite many other examples of it, such as social networks where a very few people may have millions of people connected to them and the vast majority very few. These highly centralized networks are more formally called scale free or power law networks, that describe a power or exponential relationship between the degree of connectivity a node has and the frequency of its occurrence. These power law networks are really define by the mathematics that is behind them so lets just take a quick look at that it. In these networks, The number of nodes with degree x is proportional to 1 over x squared So, The number of nodes with degree 2 is one fourth of all the nodes The number of nodes with degree 3 is one ninth of the nodes The number of nodes with degree 10 is proportional to one hundredth If we have a network with a thousand nodes of degree one then we would have 250 nodes with degree two, and proximally 31 with degree ten. If we notice then when we go from degree one to degree two we had a very big drop, but then going from degree two to teen the drop is a lot more gradual and this decline continues to get more gradual giving the graph what is called a long tail. The point to take away from this is that this long tail means there can be nodes with a very high degree but there will also be very many with a very low degree of connectivity giving us our centralized network. This type of power law graph was first discover within the degree distribution of websites on the internet with some websites like Google and Yahoo having very many links into them but there also being very many sites out on the web that have a very few links into them. Since then it has been discovered in many types of very different networks such as in metabolic networks where the essential molecules of ATP and ADP that provide the energy to fuel cells play a central role interacting with a very many different molecules, where as most of the molecule interaction very few others, thus making these two molecule hubs in the metabolic networks fueling the cells in our bodies.
Views: 11282 Complexity Labs
Decentralized Autonomous Organization
 
08:51
Take the full course: https://goo.gl/uMK1h2 Follow along with the course eBook: https://goo.gl/B5Hr52 In this video, we explain the idea of a Decentralized Autonomous Organization(DOA) and talk about the current context surrounding the term. A decentralized autonomous organization is an organization that is run by rules that are created by their members through a consensus process and then written into a set of contracts that are run via computer code, thus enabling the automated management of a distributed organization. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 17270 Complexity Labs
Pareto Optimality
 
04:12
See the full course: https://goo.gl/jhLCSc Follow along with the course eBook: https://goo.gl/ASzGxp Pareto optimality in game theory answers a very specific question of whether an outcome can be better than the other? Pareto optimality is a notion of efficiency or optimality for all the members involved. An outcome of a game is Pareto optimal if there is no other outcome that makes every player at least as well off and at least one player strictly better off. That is to say, a Pareto optimal outcome cannot be improved upon without hurting at least one player. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 11822 Complexity Labs
What is a Complex System?
 
10:24
For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB 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 product of a number of different parameters. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Examples of some definitions for a complex system: "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." 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."
Views: 31943 Complexity Labs
Systems Theory Overview
 
04:11
For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB Short overview to the area of Systems theory. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: Systems theory is a set of theoretical concepts used to describe a wide variety of thing in terms of a model called a system. To give it context lets talk a bit about it's origins. Of cause people have been coming up with abstract theories about how the world work for a long time. Some ancient Greeks thought everything was made of earth, water, fire and air, whilst others came to the conclusion that it was the expression of perfect geometric forms. Over the years our theoretical systems have grown into large and sophisticated bodies of knowledge such as philosophy, mathematics and the many areas of theoretical science, although these theoretical frameworks are often limited to relatively specific area of interest. During the 20th century Systems theory emerged as a new theory that draws upon many core concepts within these pre-existing methods to develop a more abstract framework that is design to be universally applicable to all domains. In order to achieve such a general relevance, system theory starts with the abstract concept of a system and then applies this to modeling various different phenomena from biological to social and technical systems. The model of a system can be loosely defined as a set of parts often called elements that form a whole, which is referred to as the system. A system exists within an environment and has a boundary that differentiated the systems exterior from its interior. An example of this might be a country, interior to which are all the people, institution and other elements that constitutes the nation as an entire system. Whilst exterior to its boundary is the international political environment. A system can be either open or isolated, isolated systems do not interact with their environment, but most systems are open meaning there is an exchange of energy and resources between the system and its environment. The passing of energy or resources from the exterior of the systems boundary to the interior is termed an input whilst the reverse is termed an output. Systems develop or function through the input of energy or resources from their environment, they process this energy by transforming it to create an output, if this output is of some value to its environment it can be termed energy. If on the other hand it is of negative value it may be termed entropy, a scientific term for lack of order, disarrangement or in more familiar terms we might call it waste. An early use of this type of model was during the development of the steam-engine where scientist and engineers were thinking about the amount of fuel inputted to the engine relative to the power out put and heat energy wasted. By using this model they could create a quantifiable ration between them that we would now term the efficiency of the system. Of cause this same reasoning can be applied to a wide variety of phenomena from the processing of energy within a plant cell to the efficiency of a business organization. We can model systems on various scales, thus elements can form part of systems that themselves form part of larger systems and so on, this is termed nesting or encapsulation and helps us to analyst a system on various levels whilst hiding away the underlining complexity. Systems theory explores many other areas such as Emergence that raises key question about the relationship between the parts within a system and the hole, that is how elements can function together or self organize to create some new and emergent structure as an entirety...
Views: 21304 Complexity Labs
Network Centrality
 
05:30
For the full course see: https://goo.gl/iehZHU Follow along with the course eBook: https://goo.gl/i8sfGP In this module, we talk about one of the key concepts in network theory, centrality. Centrality gives us some idea of the node's position within the overall network and it is also a measure that tells us how influential or significant a node is within a network although this concept of significance will have different meanings depending on the context. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: In the previous module we talked about the degree of connectivity of a given node in a network and this leads us to the broader concept of centrality. Centrality is really a measure that tells us how influential or significant a node is within the overall network, this concept of significance will have different meanings depending on the type of network we are analyzing, so in some ways centrality indices are answers to the question "What characterizes an important node?" From this measurement of centrality we can get some idea of the nodes position within the overall network. The degree of a node’s connectivity that we previously looked at is probably the simples and most basic measure of centrality. We can measure the degree of a node by looking at the number of other nodes it is connected to vs. the total it could possibly be connected to. But this measurement of degree only really captures what is happening locally around that node it don’t really tell us where the node lies in the network, which is needed to get a proper understanding of its degree centrality and influence. This concept of centrality is quite a bit more complex than that of degree and may often depend on the context, but we will present some of the most important parameters for trying to capture the significance of any given node within a network. The significance of a node can be thought of in two ways, firstly how much of the networks recourses flow through the node and secondly how critical is the node to that flow, as in can it be replaced, so a bridge within a nations transpiration network may be very significant because it carries a very large percentage of the traffic or because it is the only bridge between two important locations. So this helps us understand significance on a conceptual level but we now need to define some concrete parameters to capture and quantify this intuition. We will present four of the most significant metric for doing this here; Firstly as we have already discussed a nodes degree of connectivity is a primary metric that defined its degree of significance within its local environment. Secondly, we have what are called closeness centrality measures that try to capture how close a node is to any other node in the network that is how quickly or easily can the node reach other nodes. Betweenness is a third metric we might use, which is trying to capture the nodes role as a connector or bridge between other groups of nodes. Lastly we have prestige measures that are trying to describe how significant you are based upon how significant the nodes you are connect to are. Again which one of these works best will be context dependent. So to talk about closeness then; closeness maybe defined as the reciprocal of farness where the farness of a given node is defined as the sum of its distances to all other nodes. Thus, the more central a node is the lower its total distance to all other nodes. Closeness can be regarded as a measure of how long it will take to spread something such as information from the node of interest to all other nodes sequentially; we can understand how this correlates to the node’s significance in that it is a measurement of the nodes capacity to effect all the other elements in the network.
Views: 23247 Complexity Labs
System Dynamics
 
06:08
See the full course: https://goo.gl/KfJ6KY Follow along with the course eBook: https://goo.gl/kvpKws In this video we give an overview to the area of system dynamics, a branch of systems theory that tries to model and understand the dynamic behavior of complex systems over time. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 22369 Complexity Labs
Decentralized Autonomous Organization A Short Film
 
09:55
A short film about the rise of Decentralized Autonomous Organization with narration by Shermin Voshmgir. This video is for educational purposes only. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Excerpt: Blockchain is, I would say, not only a technological revolution but first and foremost a socio-economic revolution. The blockchain is a tool to bring us into a more decentralized world. Why can we disrupt organizations with blockchain in order to understand this we need to understand the history of the internet. So if we look back the first generation Internet in the early 90s revolutionized information and this is why we called it the information data highway. About ten years later we have the so-called web 2.0 - the internet became more mature more programmable and all of a sudden we had on, one the hand social media platforms and on the other hand a peer-to-peer economy where the consumer and the producer came, closer to each other of information of opinion of goods and services. So the original vision of the internet was to be a decentralized world where everyone could put information online. But in the web, it became very centralized with those platforms. It brought us this peer-to-peer economy but with this huge man in the middle this platform in the middle who started to control all the data and dictates the rules of transactions of that platform. So instead of the internet becoming more decentralized it became more centralized, and what we're doing now with blockchain and IPFS on all these other technologies of the decentralized web, we are redesigning data structures given the fact that we are already living in the connected world and if we think of blockchain in the context of the internet it is the driving technology behind the decentralized web, or also called the Web 3.
Views: 8296 Complexity Labs
Critical Thinking: Course Overview
 
03:09
See the full course: https://goo.gl/4ALhrP A short overview of our course on critical thinking. Follow along with the course eBook: https://goo.gl/jWZjwm Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Twitter: https://goo.gl/Nu6Qap Facebook: https://goo.gl/ggxGMT LinkedIn:https://goo.gl/3v1vwF
Views: 5610 Complexity Labs
Phase Transitions & Bifurcations
 
06:24
See the full course: https://goo.gl/9qB4CV Follow along with the course eBook: https://goo.gl/wQahvk A phase transition is the transformation of a system from one state to another through a period of rapid change. The classical example of this is the transition between solid, liquid and gaseous states that water passes through given some change in temperature, phase transitions are another hallmark of nonlinear systems. In this module we discuss the concept in tandem with its counterpart bifurcation theory. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription excerpt: Bifurcations & Phase transitions As we have previously discussed the qualitative dynamic behavior of nonlinear systems is largely defined by the positive and negative feedback loops that regulate their development, with negative feedback working to dampen down or constrain change to a linear progression, while positive feedback works to amplify change typically in an super-linear fashion. As opposed to negative feedback where we get a gradual and often stable development over a prolonged period of time, what we might call a normal or equilibrium state of development, positive feedback is characteristic of a system in a state of nonequilibrium. Positive feedback development is fundamentally unsustainable because all systems in reality exist in an environment that will ultimately place a limit on this grown. From this we can see how the exponential grow enabled by positive feedback loops is what we might say special, it can only exist for a relatively brief period of time, when we look around us we see the vast majority of things are in a stable configuration constrained by some negative feedback loop whether this is the law of gravity, predator prey dynamics or the economic laws of having to get out of bed and go to work every day. These special periods of positive feedback development are characteristic and a key diver of what we call phase transitions. A phase transition may be defined as some smooth, small change in a quantitative input variable that results in a qualitative change in the system’s state. The transition of ice to steam is one example of a phase transition. At some critical temperature a small change in the systems input temperature value results in a systemic change in the substance after which it is governed by a new set of parameters and properties, for example we can talk about cracking ice but not water, or we can talk about the viscosity of a liquid but not a gas as these are in different phases under different physical regimes and thus we describe them with respect to different parameters. Another example of a phase transition may be the changes within a colony of bacteria that when we change the heat and nutrient input to the system we change the local interactions between the bacteria and get a new emergent structure to the colony, although this change in input value may only be a linear progression it resulted in a qualitatively different pattern emerging on the macro level of the colony. It is not simply that a new order or structure has emerged but the actual rules that govern the system change and thus we use the word regime and talk about it as a regime shift, as some small changes in a parameter that affected the system on the local level leads to different emergent structures that then feedback to define a different regime that the elements now have to operate under. Another way of talking about this is in the language of bifurcation theory, whereas with phase transitions we are talking about qualitative changes in the properties of the system, bifurcation theory really talks about how a small change in parameter can causes a topological change in a system’s environment resulting in new attractor states emerging. A bifurcation means a branching, in this case we are talking about a point where the future trajectory of an element in the system divides or branches out, as new attractor states emerge, from this critical point it can go in two different trajectories which are the product of these attractors, each branch represents a trajectory into a new basin of attraction with a new regime and equilibrium.
Views: 11005 Complexity Labs
Network Theory Overview
 
05:31
For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB A short overview to the new area of network theory. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 24318 Complexity Labs
Self-Organization
 
09:25
See the full course: https://goo.gl/Fznhqi Follow along with the course eBook: https://goo.gl/PtCWjN In this video we will be talking about the process of self-organization within complex adaptive systems and the dynamic interplay between order and entropy that is thought to be required to enable it, we will firstly discuss different theories for the emergence of organization in so doing we will look at the first and second laws of thermodynamics, we will then talk about the rise of self- organization theory during the past century and lay down the basic framework through which this process is understood to take place. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF The real question is why or how do we get things to work together, how do we get global level coordination within a system? And there are two fundamentally different approaches to trying to answer this question; firstly, this coordination may be imposed by some external entity or secondly it may be self-generated internally. For thousands of years many different societies came to the former conclusion that this organization we see in the world derives from some external divine entity, religions and spirituality often depict the world in terms of a interplay between super natural forces of order and chaos, but of cause modern science has always rejected any form of divine intervention, as core to its foundation is the law to the conservation of energy and matter. The first law of thermodynamics is an expression of this fundamental conservation, which states that the total energy of an isolated system remains constant or conserved. Energy and matter can be neither created nor be destroyed, but simply transformed from one form to another. The conservation of energy is a fundamental assumption and keystone of the scientific enterprise, if you tell a physics that you have create a perpetual motion machine, that can essentially create energy out of nothing they will just laugh at you, because you are no longer playing the game of science, you have broken its most fundamental rule. The second law of thermodynamics states that the total entropy, which may be understood as disorder, will always increase over time in an isolated system. To understand where this comes from we might think about how if we have some object heated that heat will always try to spread out to become evenly distributed within its environment, but the revers never happens, heat will not spontaneously reverse this process to become concentrated again, likewise whenever rooms are cleaned they become messy again in the future, people get older as time passes and not younger all of these are expressions of the second law of thermodynamics meaning that a system cannot spontaneously increase its order without external intervention that decreases order elsewhere in another system. For many years, the second law of thermodynamics - that systems tend toward disorder - has generally been accepted. Unfortunately none of this helps us in answering Shakespeare’s question as to why our universe has in fact developed to produce at least some systems with extra ordinary high levels of organization, in fact the second law of thermodynamics would predict quite the opposite. The term "self-organizing" was introduced to contemporary science in 1947 by the psychiatrist and engineer W. Ross Ashby. Self-organization as a word and concept was used by those associated with general systems theory in the 1960s, but did not become commonplace in the scientific literature until its adoption by physicists and researchers in the field of complex systems in the 1970s and 1980s. In 1977 the work of Nobel Laureate chemist Ilya Prigogine on dissipative structures, was one of the first to show that the second law of thermodynamics may not be true for all systems. Prigogine was studying chemical and physical systems far-from-equilibrium and looking at how small fluctuations could be amplified through feedback loops to create new patterns. For example when water is heated evenly from below, while cooling down evenly at its surface, since warm liquid is lighter than cold liquid, the heated liquid tries to move upwards towards the surface. However, the cool liquid at the surface similarly tries to sink to the bottom. These two opposite movements cannot take place at the same time without some kind of coordination between the two flows of liquid. The liquid tends to self-organize into a pattern of hexagonal cells call convection cells, with an upward flow on one side of the cell and a downward flow on the other side.
Views: 18750 Complexity Labs
Complexity Economics Overview
 
06:00
For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB Short overview to the area of complexity economics and heterodox economics. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: Complexity economics is a new paradigm within economic theory that see the economy as a complex adaptive systems, composed of multiple diverse agents interaction through networks and evolving overtime. It is one of a number of alternative economic theories that have arisen over the past few decade, due to a growing wariness to the limitations of our existing economic theory. So lets first talk a bit about this standard approach to economic theory. The foundations to modern economics date back to the 18th century where it borrowed much of the formal apparatus of mathematics and the natural sciences, especially from physics with its classical mechanistic view of the world in terms of linear deterministic cause and effect. Within this paradigm of classical economics individual human behavior is comparable to the physical laws of motion, it is both regular, predictable and largely deterministic, meaning the standard tools of mathematic can be applied. Classical economics models the economy as a closed system, that is to say separate from social, environmental and cultural factors, which are not include in the models thus the social domain is constituted by sets of isolated individuals that are governed purely by economic self-interest. Similar to classical physics equilibrium is a fundamental assumption of many economic models, according to the equilibrium paradigm, there are optimal states to the economy, to which the system will automatically and quickly evolve, driven by the market forces of supply and demand. This idea is enshrined in the metaphor of the 'invisible hand'. Lastly standard economic inherited the reductionist view of classical physics implying that the behavior of a society and its institutions does not differ in kind from the sum of its individual agents, thus the behavior of all the agents together can be treated as corresponding to that of an average individual. By applying these assumption standard economics has converted what was once a branch of moral philosophy into a powerful framework based upon formal mathematics, that has proven to be a solid foundation in supported the massive economic transformation that was the industrial revolution. Today major trends such as the rapid development of our global economy, the rise of financial capitalism, the huge growth in the services, knowledge and information economy and environmental awareness are all working to reveal the limitations in the foundation assumptions of classical economics. In response to these changes a number of new economic theories have emerged Under the heading of heterodox economics, that all emphasize a need for an expansion of our economic framework to incorporate new social, cultural and environmental parameters to give a more realistic vision of how economies functions in practice. Primary among these is behavioral economics that ties to go beyond the classical model of the individual motivated by rational self-interest to incorporate a richer set of cultural and social motives driving individuals behavior. Or environmental economics is another area, that tries to address the failure of the current framework to incorporate the value accruing from natural resources and ecosystems services. Complexity economics is part of this alternative theoretical framework, Representing a new paradigm that sees the economy as a complex adaptive system, composed of multiple agents with diverse motives, whose interaction within networks give rise to emergent structures such as enterprises and markets. Instead of seeing the economy as the product of isolated individuals making rational choices with perfect information resulting in efficiency markets, complexity economics see the individual as embed within social and cultural networks that influence there behavior and with limited information that often leads them to make apparently irrational actions, resulting in suboptimal markets. As opposed to seeing the economy as the product of a static equilibrium Complexity economics instead is more focus upon the non-equilibrium processes that transform the economy from within, through continuous adaptation and the emergence of new institutions and technologies as the economy evolves over time. Complexity economic applies this concept of evolution to understanding the dynamics of economic development, which is understood as a process of differentiation, selection and amplification, acting on designs for technologies, social institutions and businesses that drives continuous change within the economy.. see http://www.fotonlabs.com for full transcrition
Views: 9670 Complexity Labs
Decentralized & Small World Networks
 
05:52
For the full course see: https://goo.gl/iehZHU Follow along with the course eBook: https://goo.gl/i8sfGP In this module we continued on with our discussion about how different degree distributions within a network generate different network models this time looking at what we called decentralized networks, a structure that is discernibly different from the random graph that we started with. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcriptions excerpt: In this module we are going continuing on with our discussion about how different degree distributions within a network generate different models to networks, we previously looked at what happens when we have a low level of diversity between the different nodes degree of connectivity by explored distributed networks, that had a very egalitarian degree distribution. Many networks don't show this pattern though. For as we turn up our degree distribution allowing for nodes with a much higher degree of connectivity than others what we see instead is the overall topology to the network becoming more differentiated as local clusters emerge with some nodes playing a central role, that is defined as a hub. We call this network model that has local hubs but still relatively little overall differentiation to it, a decentralized network, there may be some overall center to it but it is still defined largely by what is happening on the local or regional level. To take an example of a decentralized network we could cite the urban network of contemporary Germany, unlike other countries such as Japan or Nigeria whose urban network is dominated by a primary node, Germany’s urban infrastructure and the services that it provide are distributed out into an number of important centers, for example the primary air transportation and financial hub is in Frankford, the political capital in Berlin, with Munich having the strongest economy, each of maybe five or ten centers play a very important role in maintaining the network. There are of cause many more examples we could site, such as conglomerate corporations, political federations or distributed computer networks. So why do we get these local level hub and spoke structures emerging? There are a number of reasons for this but many tie back to the fact that the system is under certain environmental, resource constraints and it will only be possible for nodes to overcome some of these constraints by combining their resources, this coupled with batch processing and the economies of scale that it enables are behind the formation of many hubs, from banks that amass finical resources to be able to fund large project, to international airports, to the emergence of factories as local hubs in manufacturing networks. These hubs then serve the function of connecting nodes locally, but also connecting them globally to other hubs in the network. The result then is local clustering but also some global connections between clusters and this give us the small-world phenomena previously mentioned. A small-world network is a type of graph in which most nodes are not neighbors of one another, but most nodes can be reached from any other by a small number of connections. A certain category of small-world network was identified by Duncan Watts and Steven Strogatz in 1998. Watts and Strogatz measured, that in fact many real-world networks have a small average shortest path length, but also a clustering coefficient significantly higher than expected by random chance.
Views: 11378 Complexity Labs
Random & Distributed Graphs
 
06:47
For the full course see: https://goo.gl/iehZHU Follow along with the course eBook: https://goo.gl/i8sfGP In this module, we talked about random graphs more formally termed the Erdős–Rényi random graph, where connections between nodes are placed at random with a given probability for their concurrence Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: When presenting the different classes of networks, people often start by presenting the random model to a network first, not because most networks in our world are random, quite the contrary, but it is by first understanding what a network create without any specific rules looks like that we can then compare the other networks we encounter around as to see if they differ from this and if they do then we can ask about the rules that created them. A random network is more formally termed the Erdős–Rényi random graph model, so named after two mathematicians who first introduced a set of models for random graphs in the mid 20th century. As the name implies this type of network is generate by simply taking a set of nodes and randomly placing links between them with some given probability. So we just take two nodes in the network and we role a dice to see if there will be a connection between them or not, the higher we set our probability the more likely there will be a connection and thus the more connected the overall graph will become. So this is a simply system in that once you have decided how many nodes there will be, it is then really just defined by a single parameter, that is the probability parameter for the likely hood that any two nodes will form a connection. So if we looked at the degree distribution of this network it would follow a normal distributions, because it was randomly generated there will be some difference in the distribution of degrees of connectivity among the nodes, some will have one degree some five but there will be a well defined normal or average degree, in this distribution there will be very few nodes with a very large degree and very few with a very low degree most will tend to wards the normal amount of connections. Unlike real world networks, there is low clustering in random networks. Therefore, the resulting network very rarely contains highly connected nodes. Consequently, a random network is not a good candidate model for the highly connected architecture that characterizes many of the networks we see around us. Although a useful theoretical exercises, random networks in generally do not represent networks in the real world, they are considered far more random because real world networks are typically create to serve some function and are constrained by some limiting resource, that gives them a more distinct pattern. If we look at some network like the traditional trade routs across the Sahara desert in Africa it may look some what random at first glance but we know that it is not because for the caravans of camels and traders who created these network, setting out to cross the Sahara In any random direction would have of cause been fatal to them.
Views: 13261 Complexity Labs
Systems & Sets
 
04:35
See the full course: https://goo.gl/KfJ6KY Follow along with the course eBook: https://goo.gl/kvpKws In this section we start to give an outline to what we mean by the concept of a system when we contrast it with what we call sets. The concept of a system is defined as a set of things that work together to perform some collective function this is in contrast to a set where elements within the set share no collective function. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 19066 Complexity Labs
Self-Organization Overview
 
05:54
For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB Brief overview of the area of self-organization theory Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: If a system, such as a plant, a building or a car, shows organization we tend to assume that someone or something must have design in that particular order. Self-organization is the idea that this type of global coordination can instead be the product of local interactions. The theory of self-organization has grown out of many different areas from computer science to ecology and economics. Out of these areas have emerged a core set of concepts that are designed to be applicable to all self-organizing systems from galaxies to living cells. But Lets start by talking a bit about Organization itself. Organization is a highly abstract concept but we can loosely equate it to the idea of order with its opposite being what is called entropy or disorder. Order and entropy are typically measured by scientist in terms of information, that is the more information it takes to describe something the more disordered the systems is said to be. An example of this might be a piece of metallic substance consisting of tiny magnets called spins, each spin has an particular magnetic orientation and in general they are randomly directed and thus cancel each other out, This disordered configuration is due to its heat energy causing the random movements of the molecules in the material. When we cool the material down the spins will spontaneously align them self so that they all point in the same direction. To describe the state of the spins in this order system would involve far less information relative to it's original state that requiring unique values for each randomly directed spin. This process of magnetisation is often cited as an example of self-organization, that is the spontaneous appearance of order or global coordination out of local level interactions. But lets take a closer look at how this happens. As we cooled the material down there was some area that had by chance some spins pointing in the same direction, their alignment generated an increased magnetic force that was exerted upon its neighbours, creating what is called an attractor state, attracting other spins to this configuration. Each time another spin aligned itself with this particular attractor state it augmented the force it exerted upon other spins through what is called a positive feedback loop that would cascade through the system until all elements were aligned within this new regime. Another example of self-organization through positive feedback is what is called the network effect, where the more people that use a product or service the greater its value becomes, the telephone and Facebook are such examples becoming more useful as more users join, in this way local connections between individuals can rapidly form into global patterns. The network effect illustrate the positive relations or synergies between elements that can be created when they coordinate, it is due to the presence of these synergistic relations that the system as an entirety can become more than the sum of its parts, in a process called emergence. Ant colonies are a classical example given of emergence, ants governed by very simple rules and only local interactions can through their combined activities generate colonies that exhibit complex structures and behaviour that far exceed the intelligence or capability of any individual ant and thus is said to have emergent properties. Ant colonies also illustrate the decentralised structure to self-organizing system, The queen does not tell the ants what to do, instead each ant reacts to stimuli in the form of chemical scent exchanged with other ants, in this way organization is distributed over the whole of the system. All parts contribute evenly to the resulting arrangement. As opposed to centralized structure such as most social organization that are often dependent upon a single coordinator, this decentralized structure that is inherent to self-organized systems gives them resiliency and robustness, as any element that is damaged can be simple replace by any other given them hug redundancy....
Views: 35907 Complexity Labs
Token Economies
 
13:52
Take the full course: https://goo.gl/uMK1h2 Follow along with the course eBook: https://goo.gl/B5Hr52 A new set of web technologies are enabling a more distributed economic model based upon the blockchain and token markets. These token markets greatly reduce our dependency on centralized organizations and expand markets as systems of distributed organization. In this video we explain the workings of the blockchain, tokens, distributed markets and token investments. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 21116 Complexity Labs
Platform Technologies
 
08:50
For the full course see: https://goo.gl/S3Q8XD Follow along with the course eBook: https://goo.gl/ZZqUzY According to our friend Wikipedia, a platform technology can be defined as a structure or technology from which various products can emerge without the expense of a new process introduction. In this section of our course we discuss the architecture of platform technologies and talk about some of their advantages and disadvantages. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: During the past few decades with the rise of the internet, Platform technologies have become the new cool. Platforms, like the app store or eBay have proven to be some of the most dynamic, innovative and fastest growing services, but of cause the platform model to systems architecture has always been there, since the invention of farms and factories to the making of lego building blocks. When many people see a new technology at work, they don’t usually consider all the pieces that went into its creation. They simply see the amazing capabilities and never give it much thought. But within advance industrial economies many products and services are enabled by the power of abstraction, they are remixes, built out of services from platforms that enable the endless bundling and re-bundling of different components. So what is the difference between bank ABC at one end of your street and bank XYZ at the other, not much really they are both buying their technology from a hand full of platform technology providers like IBM and VISA and bundling the components in different ways to appeal to different customers. According to our friend Wikipedia, a Platform technology can be defined as a structure or technology from which various products can emerge without the expense of a new process introduction. In order to achieve this our system needs to be architected to have two fundamentally different levels, that is, it must have a platform providing the basic services that can be combined into different configurations on the application, to deliver various instances of the technology to the end user. But lets start by thinking about what exactly a non-platform technology is, take a hammer for example, it is a homogenous system, there is no differentiation between the systems infrastructure and its application they are all just one thing, it is an instance of a hammer, it can not generate new and different configurations of itself. The same can be said of a car, it is an instance of a technology, the end user gets and uses the whole thing. To make the comparison clearer we could compare the instance of a car with an automobile platform that allows a motor company to release several vehicles built upon a common chassis, which is the platform, with different engines, interiors and form factors, for the same or different vehicles and brands within the company. Probably the clearest and best example of platform technologies are personal computers, so lets spend some time taking one of these computers to peaces to better understand the different level of abstraction to a platform technology. Our platform in this case is the computers operating system, but before we can get to the platform that’s doing all the great work we need a foundation for it to sit on, that is a set of enabling technologies. In this case our foundation layer is our computer hardware and all the low level firmware that interfaces between it and the operating system, but within a business our foundation layer might be the economic system it is a part of, the public services such as security, rule of law and maintenance of natural resources that would enable our business to function, the same would be true of a city, it rests upon and is enabled by a national infrastructure system. The next layer up from the foundations or hardware is the platform itself, the computers operating system in this case, it essentially manages the computers resources and services that will be required by applications. The platform takes the resources available to it from the infrastructure and creates the lego blocks that we will be using to build things with. These resources are presented to producers on the application level through what are called APIs or application program interfaces. In our automotive factory the platform would be the physical technologies in the production line for creating the car’s parts, our employees can rearrange this production line to create different vehicles. Or in our example of the city this platform level might be the urban utilities that contractors will interface with to build offices and residential spaces and there will be a standard set of procedures for them to do this.
Views: 8643 Complexity Labs
Complexity Systems Theory Overview
 
07:59
For the full course see: https://goo.gl/S3Q8XD Follow along with the course eBook: https://goo.gl/ZZqUzY 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. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 7412 Complexity Labs
Exponentials & Power Laws
 
05:51
See the full course: https://goo.gl/9qB4CV Follow along with the course eBook: https://goo.gl/wQahvk Exponentials are a signature key of nonlinear systems, unlike linear growth exponential grow represents a phenomenon where the actual rate of growth is growing itself to generate an asynchronous development with respect to time and some very counter-intuitive events. In this module we will discuss the dynamics of exponentials and their counterparts power laws that represent a power relation between two entities. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription excerpt: Part of our definition for linear systems is that the relationship between input and output is related in a linear fashion, the ratio of input to output might be 2 times as much, 10 times as much or even a thousand times it is not important this is merely a quantitative difference, what is important is that this ratio between input and output is itself not increasing or decreasing. But as we have seen when we have feedback loops the previous state to the system feeds back to effect the current state thus enabling the current ration between input and output to be greater or less than its ratio previously and this is qualitatively different. This phenomenon is captured within mathematical notation as an exponential. The exponential symbol describe how we take a variable and we times it by another not just once but we in fact iterate on this process, meaning we take that output and feed it back into compute the next output, thus the amount we are increasing by each time itself increases. So lets take a quick example of exponential grow to give you an intuition for it. Say I wish to create a test tube of penicillin bacteria. Knowing that the bacteria will double every second, I start out in the morning with only two bacteria hoping to have my tube full by noon. As we know the bacteria will grow exponentially as the population at any time will feed into effect the population at the next second, like a snowball rolling down a hill. It will take a number of hours before our tube is just 3% full but within the next five seconds as it approaches noon it will increase to 100% percent of the tube. This type of disproportional change with respect to time, is very much counter to our intuition where we often create a vision of the future as a linear progression of the present and past, we will be discussing farther the implication of this type of grow later when we get into the dynamics of nonlinear systems but for the moment the important thing to note here is that in exponential growth the rate of growth itself is growing and this only happens in nonlinear systems, where they can both grow and decay at an exponential rate. Exponentials are also called powers and the term power law describes a functional relationship between two quantities, where one quantity varies as a power of another. There are lots of example of the power law in action but maybe the simples is the scaling relationship of an object like a cube, a cube with sides of length a will have a volume of a3 and thus the actual number that we are multiplying the volume by grows each time, this would not be the case if there was a simple linear scaling such as the volume being 2 times the side length. Another example from biology is the nonlinear scaling within the metabolic rate vs. size of mammals, the metabolic rate is basically how much energy one needs per day to stay alive and it scales relative to the animals mass in a sub-linear fashion, if you double the size of the organism then you actually only need 75 % more energy. One last example of the power law will help to illustrate how it is the relationships between components within a system that is a key source of this nonlinearity.
Views: 13575 Complexity Labs
Systems Overview
 
02:26
See the full course: https://goo.gl/9qB4CV Follow along with the course eBook: https://goo.gl/wQahvk In this module we give an overview to the model of a system that will form the foundations for our discussion on nonlinear systems, we will quickly present the basic concepts from systems theory such as elements, system's boundary, environment etc. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: A nonlinear system is a type of system, so before we can talk about them we need to first have some understanding of the more general concept of a system. There are many definitions for systems, we will just quickly present a few of them for you to get an idea of where they overlap. Your Dictionary defines a system as: An arrangement of things, or a group of related things that work toward a common goal. Webopedia tells us a system is: A group of interdependent items that interact regularly to perform a task. We will then loosely define a system as a set of parts that are interconnected in effecting some joint come. A system then consists of parts, what we call elements and a set of connections between them, what we call relations. An example of a system might be a farm, a farm consist of a set of components such as, fields, seeds, machinery, workers etc. and the set of relations between these things that is how they are interconnect in order to perform some overall function. A bicycle is also an example of a mechanical system with a number of elements that have been designed to interrelate in a specific fashion in order to collectively function as a unit of transportation. A plant cell is another example of a system, this time it is what we would call a biological system, consisting of a set of organelles that are interconnected and interdepend in performing the metabolic processes that enable the cell as an entire system to function. There are of cause many more examples of systems from ecosystems to transportations systems to social systems. In the world of science and mathematics there are fundamentally two different types of systems, what are called linear and nonlinear systems and in the next two lectures we will be discussing each and the distinctions between them.
Views: 9283 Complexity Labs
About Complexity Labs
 
01:35
Short video introducing Complexity Labs, to find out more see: Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 8311 Complexity Labs
Complexity Theory Course Introduction
 
01:40
For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB Brief overview to our introduction to complexity theory course Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 21262 Complexity Labs
Complexity Theory Overview
 
10:52
For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB In this video we will be giving an overview to the area 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. For full courses see: http://complexitylabs.io/courses Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF 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.
Views: 26977 Complexity Labs
Social Network Analysis Overview
 
04:45
For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB A brief overview to the new area of social network analysis that applies network theory to the analysis of social relations. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: Social network analysis is the application of network theory to the modeling and analysis of social systems. it combine both tools for analyzing social relations and theory for explaining the structures that emerge from the social interactions. Of course the idea of studying societies as networks is not a new one but with the rise in computation and the emergence of a mass of new data sources, social network analysis is beginning to be applied to all type and scales of social systems from, international politics to local communities and everything in between. Traditionally when studying societies we think of them as composed of various types of individuals and organizations, we then proceed to analysis the properties to these social entities such as their age, occupation or population, and them ascribe quantitative value to them. This allows social science to use the formal mathematical language of statistical analyst to compare the values of these properties and create categories such as low in come house holds or generation x, we then search for quasi cause and effect relations that govern these values. This component-based analysis is a powerful method for describing social systems. Unfortunately though is fails to capture the most important feature of social reality that is the relations between individuals, statistical analysis present a picture of individuals and groups isolates from the nexus of social relations that given them context. Thus we can only get so far by studying the individual because when individuals interact and organize, the results can be greater than the simple sum of its parts, it is the relations between individuals that create the emergent property of social institutions and thus to understand these institutions we need to understand the networks of social relations that constitute them. Ever since the emergence of human beans we have been building social networks, we live our lives embed in networks of relations, the shape of these structures and where we lie in them all effect our identity and perception of the world. A social network is a system made up of a set of social actors such as individuals or organizations and a set of ties between these actors that might be relations of friendship, work colleagues or family. Social network science then analyze empirical data and develops theories to explaining the patterns observed in these networks In so doing we can begin to ask questions about the degree of connectivity within a network, its over all structure, how fast something will diffuse and propagate through it or the Influence of a given node within the network. lets take some examples of this Social network analysis has been used to study the structure of influence within corporations, where traditionally we see organization of this kind as hierarchies, by modeling the actual flow of information and communication as a network we get a very different picture, where seemingly irrelevant employees within the hierarchy can in fact have significant influence within the network. Researcher also study innovation as a process of diffusion of new ideas across networks, where the oval structure to the network, its degree of connectivity, centralization or decentralization are a defining feature in the way that innovation spreads or fails to spread. Network dynamics, that is how networks evolve overtime is another important area of research, for example within Law enforcement agencies social network analysis is used to study the change in structure of terrorists groups to identify changing relations through which they are created, strengthened and dissolved? Social network analysis has also been used to study patterns of segregation and clustering within international politics and culture, by mapping out the beliefs and values of countries and cultures as networks we can identify where opinions and beliefs overlap or conflict. Social network analysis is a powerful new method we now have that allows us to convert often large and dense data sets into engaging visualization, that can quickly and effectively communicate the underlining dynamics within the system. By combine new discoveries in the mathematics of network theory, with new data sources and our sociological understanding, social network analysis is offering huge potential for a deeper, richer and more accurate understanding, of the complex social systems that make up our world.
Views: 35744 Complexity Labs
Linear Systems Theory
 
05:59
See the full course: https://goo.gl/9qB4CV Follow along with the course eBook: https://goo.gl/wQahvk In this lecture we will discuss linear systems theory which is based upon the superposition principles of additivity and homogeneity, we will explore both of these principal separately to get a clear understanding of what they mean and the basic assumptions behind each Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: Linear Systems Theory Before we talk about nonlinear systems we need to first have a basic understand what a linear system is. Linear systems are defined by their adherence to what is called the superposition principles. There are just two superposition principles and they are called homogeneity and additively. Firstly additively, which states that we can add the effect or output of two systems together and the resulting combined system will be nothing more that the simple addition of each system’s output in isolation. So for example, if I have two horses that can each pull a hundred kilograms of weight on a cart in isolation, well if I then combine these two horses to tow a single larger cart they will be able to pull twice as much weight. Another way of stating the additivity principle is that for all linear systems, the net response caused by two or more stimuli is the sum of the response which would have been caused by each stimulus individually. Our second superposition principle, homogeneity, states that the output to a linear system is always directly proportional to the input, so if we put twice as much into the system we will in turn get out twice as much. For example, if I pay 50 dollars for a hotel room, for which I will get a certain quality of service, this principle states that if I pay twice as much I will then get an accommodation service that is twice a good. When we plot this on a graph we will see why linear systems are called linear because the result will always be a straight line. These principles are of cause deeply intuitive to us and will appear very simple, but behind them are a basic set of assumptions about how the world works, so lets take a closer look at these assumptions that support the theory of linear systems. Essentially what these principles are saying is that it is the properties of the system in isolation that really matter and not the way these thing are put together or the nature of the relationships between them, this is of cause very abstract so lets illustrate it with some examples. Imagine you have some ailment and you have two drugs that you know are meant to cure this problem, so you take them both at the same time. The result of this, or we might say the output to this system, will depend on whether the two drugs have any effect on each other when taken in combination. If the drugs have no effect on each other then it will be the properties of each drug in isolation that will define the overall output to the system, and because of this lack of interaction between the components our linear model will be able to fully capture and describe this phenomena.
Views: 12615 Complexity Labs
Network Theory Overview
 
05:50
For the full course see: https://goo.gl/iehZHU Follow along with the course eBook: https://goo.gl/i8sfGP In this module we will give an overview to the different questions that we are interested in trying to answer when it comes to analysing networks, this module also works as an overview to the content we will be covering during the rest of the course.​ Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: In this section we are going to give an overview to network theory that will also work as an overview to the structure of this course and the content we will be covering. As the name implies network theory is all about the study of networks, we are trying to create models so as to analyze them, in order to be able to do this the first thing we need is some kind of formal language and this formal language is called graph theory. We will be going into the details of graph theory in the next lecture but it is a relatively new area of mathematics that gives us some kind of standardized language with which to talk about and quantify the structure and properties of networks. So once we have this basic vocabulary, say you give me a network to analyze the question then turns to what are the features and properties of this network that we should be really interested. The first set of questions we might like to ask relate to individual elements within the network. We want to know what are the nodes within the network what are the connections between them and what properties are we really interested in, for example in a computer network we might not be interested in who owns the different computers and connections but just interested in the speed of the computers and the bandwidth of the connections, so we need to define what it is about our network we are interested in because as with all models we will be focusing on some information and excluding other. There is lots of other information we want to know about these individual elements and the connections, such as asking whether they are weighted or not, meaning can we ascribe a value to them, we can talk about a computer network’s bandwidth in megabits per second, but it might not be so easy to do the same with a social network where the relations are of friendship or kinship. We can also ask if these relations go both ways or are just unidirectional. Other questions we will be asking here is how connected is any individual node or how central is it within the overall network. The next major set of questions we will be asking about our network will relate to its overall structure, networks are defined by both what happens on the local level, that is how central or connected you are, but also what happens on the global level, because the dynamics of the network on the global level feeds back to effect the elements on the local level. Here some of the key questions we will be asking about the overall structure to the network is firstly how connected is it? Are there connections between all the parts or are some parts disconnect and separate from others? How dense is this set of connections? If we compare a group of unassociated people waiting at a bus stop with a close knit group of friends we will see the density of the network will vary greatly. What are the patters of clustering within the system? Do we see many small groups or just a few large groups? These are the types of features that will define the overall makeup of the network structure. One key question we are interested in answering here is if we change some parameter to one of
Views: 11114 Complexity Labs
Emergence
 
06:02
See the full course: https://goo.gl/KfJ6KY Follow along with the course eBook: https://goo.gl/kvpKws This video presents the ideas of emergence, phase transitions, and strong vs. weak emergence. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF According to Wikipedia, emergence is conceived as a process whereby larger entities, patterns, and regularities arise through interactions among smaller or simpler entities that themselves do not exhibit such properties. In the previous section we discussed how synergistic relations give rise to the phenomena of two or more elements having a greater combined output or effect than the simple product of each in isolation. This process where by the interaction between elements gives rise to something that is greater than the sum of their parts is called emergence. So where as when we were talking about synergies we simply said that the combined effect was greater than its parts in isolation. The concept of emergence though implies that what is created out of these synergic relations is not just quantitatively different it is in fact qualitatively different. That is to say none of the elements that contribute to the emergence of this new phenomena have its qualities when taken in isolation. There are many examples of this but maybe the simples is the example of water, water is made up of hydrogen and oxygen atoms, neither of these two elements that make up the system have the property or quality of wetness, but when we combine them we get a substance called water that has the quality of being wet, this property of wetness has emerged out of the interaction of the systems elements and it only exists on the systems level. Another often sited example of emergence is the phenomena of life, biological systems such as a plant cell consist of a set of inanimate molecules none of which in isolation have the property of life, but it is the particular way that these elements are arrange into structures and processes that enable the emergent phenomena of the living system as an entirety. Our world is full of examples of emergence that we could site, from ant colonies to galaxies and cultures, but all of these are types of structures, where as emergence is really a process, these systems are then the product of a the process of emergence that has play out to create two qualitatively different levels to the system. Emergence then is a process through which systems develop or we might say grow. During this process unassociated elements interact, synchronize to form synergies and out of this emerges some new and novel phenomena that previously did not exist. In order to create some qualitatively different and new phenomena the system must go through what we call a phase transition. A phase transition is an often rapid or accelerated period during the process of a systems development, either side of which the fundamental parameters with which we describe the system change qualitatively. Again there are lots of examples of this such a the phase transition between solid and liquid that a substance goes through when heated, but maybe the most dramatic example is the metamorphose of a butterfly from being a caterpillar to a mature adult. Not only dose the system’s morphology change but the whole set of parameters that we define it with are so drastically altered prior and post the phase transition that we give the creature a whole new names.
Views: 21260 Complexity Labs
Emergence & Systems Thinking
 
09:13
For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB In this video, we talk about systems thinking and how it is related to the idea of emergence. A system is a set of elements and relationships between those elements through which they form a whole. Thus when we look at a composite entity like a tree or a chair we could equally ask a different question from talking about the parts. We could ask how are the parts interrelated to form the whole organization. This is a very different way of looking at the world, this approach to reasoning about some entity is call synthesis. Where synthesis means "the combination of components or elements to form a connected whole." Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 9946 Complexity Labs
Self-Organization Far-From-Equilibrium
 
08:48
See the full course: https://goo.gl/Fznhqi Follow along with the course eBook: https://goo.gl/PtCWjN In this module we will be talking about the theory of far-from-equilibrium self-organization. We will firstly discuss the concepts of order and randomness in terms of symmetry and information theory. We will then talk about complexity as the product of an in-between or phase transition state and finally we will discuss the term edge-of-chaos and talk about how self-organization is thought to be dependent upon noise and random fluctuations in order to stay generating variety. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Far-from-equilibrium self-organization is a model that describes the process of self-organization as taking place at a critical phase transitions space between order and chaos when the system is far-from its equilibrium. But lets start by talking about organization. Organization is an ordered structure to the arrangement of elements within a system that enables them to function, as such we can loosely equate it to the concept of order, both order and organization are highly abstract concepts neither of which are well defined within the language of mathematics and science but probably the most powerful method we have for formalizing them is through the theory of symmetry. The theory of symmetry within mathematics is an ancient area of interest originally coming from classical geometry but within modern mathematics and physics it has been abstracted to the concept of invariance. In this way symmetry describes how two things are the same under some transformation. So if we have two coins one showing heads the other tails, by simply flipping one of the coins over it will come to have the same state as the other, thus we don’t need two pieces of information to describe the states within this system, we can describe this system in terms of just one state and a flipping transformation that when we perform it will give us the other state. Now say instead of having two coins we had an apple and an orange, well there is no transformation we know of that can map an apple to an orange, they are different things there is no trivial symmetry or order between them and thus we need at least two distinct pieces of information to describe this system. This second system requires more bits of information to describe its state, thus we can say it has higher statistical entropy. The point to take away here is that we can talk about and quantify order and randomness in terms of information theory, that ordered systems can be described in terms of these transformations which we encode in equations, ordered systems are governed by equations whereas random systems are not, but because there is no correlation between the element’s states in these random systems, they are governed by probability theory the branch of mathematics that analyzes random phenomena. Complex systems are by any definition nonlinear. Complexity is always a product of an irreducible interaction or interplay between two or more things, if we can just do away with this core dynamic and interplay then we simply have a linear system, if the system is homogeneous and everything can be reduced to one level then it might be a complicated system but it is certainly not a complex system. Thus one of the main ideas or findings of complexity theory is that complexity is found at what is sometimes called the interesting in between, if we take some parameter to a system, say its rate of change or its degree of diversity and turn this parameter fully up what we often get is randomness, continuous change or total divinity of states without any pattern, or if we turn it fully down we get complete stasis and homogeneity with very stable and simple patterns. It is often the case that with too much order the system becomes governed by a simple set of symmetries, too much disorder results in randomness and the system becomes subject to statistical regularities. It is only in-between that we get complexity, on either side of this there is a single dominant regime or attractor that will come to govern the system’s behavior.
Views: 7284 Complexity Labs
Complex Adaptive Systems
 
10:23
See the full course: https://goo.gl/Fznhqi Follow along with the course eBook: https://goo.gl/PtCWjN In this module we will be giving an overview to complex adaptive systems, we will first define what we mean they this term, before briefly covering the main topics in this area. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF 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.
Views: 16782 Complexity Labs
Dynamical Systems Introduction
 
06:41
See the full course: https://goo.gl/9qB4CV Follow along with the course eBook: https://goo.gl/wQahvk Dynamical systems is a area of mathematics and science that studies how the state of systems change over time, in this module we will lay down the foundations to understanding dynamical systems as we talk about phase space and the simplest types of motion, transients and periodic motion, setting us up to approach the topic of nonlinear dynamical systems in the next module. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription excerpt: Within science and mathematics, dynamics is the study of how things change with respect to time, as opposed to describing things simply in terms of their static properties the patterns we observe all around us in how the state of things change overtime is an alternative ways through which we can describe the phenomena we see in our world. A state space also called phase space is a model used within dynamic systems to capture this change in a system’s state overtime. A state space of a dynamical system is a two or possibly three-dimensional graph in which all possible states of a system are represented, with each possible state of the system corresponding to one unique point in the state space. Now we can model the change in a system’s state in two ways, as continuous or discrete. Firstly as continues where the time interval between our measurements is negligibly small making it appear as one long continuum and this is done through the language of calculus. Calculus and differential equations have formed a key part of the language of modern science since the days of Newton and Leibniz. Differential equations are great for few elements they give us lots of information but they also become very complicated very quickly. On the other hand we can measure time as discrete meaning there is a discernable time interval between each measurement and we use what are called iterative maps to do this. Iterative maps give us less information but are much simpler and better suited to dealing with very many entities, where feedback is important. Where as differential equations are central to modern science iterative maps are central to the study of nonlinear systems and their dynamics as they allow us to take the output to the previous state of the system and feed it back into the next iteration, thus making them well designed to capture the feedback characteristic of nonlinear systems. The first type of motion we might encounter is simple transient motion, that is to say some system that gravitates towards a stable equilibrium and then stays there, such as putting a ball in a bowl it will role around for a short period before it settles at the point of least potential gravity, its so called equilibrium and then will just stay there until perturbed by some external force. Next we might see periodic motion, for example the motion of the planets around the sun is periodic. This type of periodic motion is of cause very predictable we can predict far out into the future and way back into the past when eclipses happen. In these systems small disturbances are often rectified and do not increase to alter the systems trajectory very much in the long run. The rising and receding motion of the tides or the change in traffic lights are also example of periodic motion. Whereas in our first type of motion the system simply moves towards its equilibrium point, in this second periodic motion it is more like it is cycling around some equilibrium. All dynamic systems require some input of energy to drive them, in physics they are referred to as dissipative systems as they are constantly dissipating the energy being inputted to the system in the form of motion or change. A system in this periodic motion is bound to its source of energy and its trajectory follows some periodic motion around it or towards and away from it. In our example of the planet’s orbit, it is following a periodic motion because of the gravitational force the sun exerts on it, if it were not for this driving force, the motion would cease to exist.
Views: 20278 Complexity Labs
Network Dynamics
 
06:48
For the full course see: https://goo.gl/iehZHU Follow along with the course eBook: https://goo.gl/i8sfGP Almost all real networks are dynamic in nature and how they have evolved and change over time is a defining feature to their topology and properties. As network theory is a very new subject much of it is still focused on trying to explore the basics of static graphs, as the study of their dynamics results in the additional of a whole new sets of parameters to our models and takes us into a new level of complexity, much of which remains unexplored, and is the subject of active research. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription excerpt: So lets start by talking about growing a random network to see what it looks like, when we say growing a network we might mean adding more nodes to it, but also, more interestingly adding links to it, that increases the overall connectivity. In our random model links were just placed between nodes at random with some given probability, growing the network here just meant increasing this probability so as to have more links develop over time. One interesting thing we find when we do this is that there are thresholds and phase transitions during the network's development, by thresholds we simply mean that, by gradually increasing our link probability parameter, some property to the network suddenly appearing when we pass a critical value. So for example our first threshold is when the average degree goes above 1 over the total number of nodes in the network, as at this threshold we start to get our first connection. At degree one that is when every node has on average one connection the network stars to appear connected, we see one giant component emerging within the network, that is one dominant cluster and we start to have cycles, which means there are feedback loops in the network. Another threshold occurs when nodes have an average degree of log(n) at this point everything starts to be connected meaning there is typically a path to all other nodes in the network. So this is what we see in random network but as we know most real world networks are not random as they are subject to some resource constraints and they have preferential attachment giving them clusters that we do not see in these random graphs. One way of thinking about how real world networks form is through the lens of percolation theory, percolation theory looks at how something filters all percolates through something else like a liquid filtering through some mash structure in a material or we might think about some water running down the side of the hill, as it does the water will find the path of least resistance creating channels and furrows in the side of the hill. This network formation is then the product of the resource constraints that its environment placed upon it, but the constraints are unevenly distributed and the networks topology is then reflecting this as it follows the paths of least resistance as it avoids toughest material. In order to demonstrate the general relevance of this we will take some other examples, if say we put on a cheap flights from one city to another then people will start using that transportation link because of financial constraints. Or because of phenomena of Homophily within social networks we will get the same peculation dynamic where it will be easier for people to make links with people who are similar to themselves than with others, again creating a particular structure based on the social constraints within the system.
Views: 8812 Complexity Labs
Synergistic Relations
 
05:31
See the full course: https://goo.gl/KfJ6KY Follow along with the course eBook: https://goo.gl/kvpKws A synergy is the creation of a whole that is greater than the simple sum of its parts. In this lecture we discuss the relation between components within a system and the two fundamentally different types; Synergies and interference. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 10396 Complexity Labs
Synthesis & Analysis
 
04:13
See the full course: https://goo.gl/KfJ6KY Follow along with the course eBook: https://goo.gl/kvpKws In this section we discuss when to use systems thinking, we talk about how systems thinking is most relevant when dealing with systems that are highly interconnected; that are dynamic in nature (meaning they change over time) and also when we are dealing with a system on the macro scale. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 24150 Complexity Labs
What is DIKW?
 
03:48
http://complexitylabs.io A short explainer video introducing the model of the DIKW pyramid. The DIKW Pyramid refers loosely to a class of models for representing purported structural and/or functional relationships between data, information, knowledge, and wisdom. “Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge”. It represents an irreducible structure to information and knowledge and by extension a fundamental structure to information societies. In this video, we give a quick outline to each of the different levels and their characteristics and then summarize the process of going up or down the hierarchy. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 8469 Complexity Labs
Network Robustness & Resilience
 
08:07
For the full course see: https://goo.gl/iehZHU Follow along with the course eBook: https://goo.gl/i8sfGP Robustness and resilience are often defined in terms of a system’s capacity to maintain functionality in the face of external perturbations. In this model we give an overview to network robustness looking at what happens when we remove links and node, both strategically and randomly. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription excerpt: Robustness & resilience are often thought of in terms of a systems capacity to maintain functionality in the face of external perturbations. We see some extraordinary example of this, ecological networks that persist despite extreme environmental changes, Communication networks like the internet can often deal with malfunction, errors and attacks, without these local events lead to catastrophic global failures, but we also see the opposite where some small failure in say a financial system can propagate to affect the whole systems. Trying to understand how and why this happens is the study of network robustness. Robustness can be correlated with connectivity in that connectivity enables system integration, as we previously noted without connectivity parts to the system may become disconnected and disintegrated. If blood stops flowing to some part of the body then it will become atrophied and waste away or if a child stops talking to their parents then the family unit disintegrates through lack of communications. Thus when we are talking about robustness & resilience we are often asking what will happen to the networks overall connectivity and integration if we remove some components or connections and equally how will this failure then spread within the network system. We can think about failure either with respect to the networks nodes asking what will happen if we remove a certain amount, this is called node percolation, but we can also talk about failure in terms of the removal of edges, that is edge percolation and another key factor here is whether the attack is random or strategic. When we talking about robustness with respect to the nodes in the network then a key factor is the degree distribution between the nodes, the higher that degree distribution, meaning there will be more hubs, the more vulnerable it is to strategic attack, but if it is a random attack then the degree distribution is not so important, as these hubs that are part of centralized network will be particularly vulnerable to strategic attack. Thus as we have previously noted distributed networks will be robust to strategic attack but scale free centralize networks are particularly susceptible. A strategic attack on large hubs will drastically reduce the number of connections within the system increase the average path length significantly, which is a key measure of the systems overall integration. This has been conferred in empirical data from the internet and world wide web which show robustness to random attack but are significantly affected by strategic attract due to the presence of major hubs in the network. Next edge percolating, that is when connections fail or are removed, an important factor here is the degree of betweenness to the network, if we remember betweenness is really measuring the number of bridging edges, that represent the critical, irreplaceable connections between one cluster and other. An example of this might be the Malacca Straits, a stretch of sea between the cost of Malaysia and Indonesia that connects maritime transport in Asia with the Middle East and Europe. Approximately 40 percent of the world's trade and 25 percent of all crude petroleum is thought to pass through this critical link in the global logistics network. This is an example of a bridging link that reduces the systems robustness and makes it much more susceptible to strategic attack.
Views: 6548 Complexity Labs
Holism & Reductionism
 
12:58
See the full course: https://goo.gl/H86iXb Follow along with the course eBook: https://goo.gl/fduVjt Holism and reductionism represent two paradigms or worldviews within science and philosophy that provide fundamentally different accounts as how to best view, interpret and reason about the world around us. Reductionism places an emphasis on the constituent parts of a system, while holism places an emphasis on the whole system. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 14080 Complexity Labs

ICO performance tracker
Ethereum`s smart contracts platform has gathered lots of attention from a vast range of industries since it could potentially be utilized to digitize and streamline inefficient business processes later on. The majority of the altcoin technologies continue to be significantly underdeveloped. As it is a JavaScript run-time environment, it`ll be familiar to the majority of programmers and therefore will permit you access to an immense pool of talented software developers. Better management indeed should work as a catalyst for those investors. It`s supplied for informational services only. Hence, it might be used for IoT networks, that`s the project`s aim.
The token will be essential to execute many kinds of transactions within the network, like the majority of the token schemes that will do the job. The token will be called ICX and it is going to be one of many that may be transacted in the computer system. The IOTA token is the sole major digital currency that doesn`t use a complete blockchain to conduct transactions.
Each blockchain is made for a particular intent. In general, blockchain powered systems appear to supply benefits to everyone. The really exciting portion of blockchain is the fact that it operates on a decentralized network as opposed to via the traditional client-server model.
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The acryptoa portion of the word denotes the cryptography used to ensure the blockchain, and the acurrencya portion of the word denotes the ability to exchange the tokens for goods and solutions. The absolute pacifism part is assumed I think, in spite of someone having the capability to locate some examples where it may appear otherwise. What you share is is an instance of absolute terror onto people who ought to be fought if it may be. The numbers won`t be good. Today they are much higher.
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When the project is completed, the tokens value is probably going to increase because there is currently a tangible item, instead of simply an idea. After all, research demonstrates that clients use their networks to choose consultants more than every other method, and they, without doubt, know somebody who knows you. Getting knowledge in the region of property investment is insufficient. You may observe I didn`t speak about a few skills. After all, in case you have a saleable skill, it`s a simple business to enter.
A collection company cannot collect any sum of money that isn`t permitted by law or by agreement. Furthermore, it would be required to show the terms of that agreement in court. It is not surprising that consulting is attractive to numerous individuals who wish to launch a company. You also have to know the way the corporation will account for the exchanging of tokens. Starting a public company is a complicated and costly undertaking. Doing so makes a contract that might be binding. If you haven`t signed a contract with the collection business, you owe them nothing.
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Enterprises and entire countries are now able to block Tor traffic without any issues. Nobody is suggesting that it isn`t the work of liberal Whites to oppose what`s wrong. To the contrary, the investments with the period of time of over five years are called as long-term investments. Original investors can then opt to sell their tokens to earn a significant profit.