Systems thinking

this course is a comprehensive introduction to the area of systems thinking and theory that is designed to be accessible to a broad group of people the course is focused upon two primary achievements firstly providing students with the key Concepts that will enable them to see the world in a whole new way from the systems perspective what we call systems thinking secondly the aim is to Prov provide you with the standardized language of systems theory through which you will be able to describe and model systems of all kinds in a more coherent fashion whilst also being able to effectively communicate this to others the course is broken down into four main areas firstly we will discuss the distinction between analytic reasoning and the methods of synthesis that form the foundations to systems thinking after this we will build up our model of a system by discussing functions efficiency systems boundary and environment and we will finish the course by taking a look at systems Dynamics this course requires no prior specific knowledge of mathematical modeling or science as we will be starting with the very basic model of a system and then building upon this to create more sophisticated representations all that you will need is a solid grasp of the English languageSystems-thinking is what we call a paradigm. A dictionary definition of a paradigm would read something like this: A worldview underlying the theories and methodology of a particular scientific subject. Thus we can understand a paradigm to be the foundation that shapes our way of seeing the world it is the assumptions and methods out of which we build our theories. Now there are two fundamentally different paradigms within science. One is called analysis and the other synthesis. Analysis is the traditional method of reasoning taken within modern science whereby we try to gain an understanding of a system by breaking it down into its constituent elements. On the other hand synthesis, which is the foundation to systems thinking, works in the reverse direction, trying to gain an understanding of an entity through the context of its relations within a whole that it is part of, but let's start by talking a bit about analysis. Analysis is based upon the premise that our basic unit of interest should be the individual parts of a system. From this follows a process of reasoning called reductionism. Reductionism is the process of breaking down or reducing systems to their constituent parts, and then describing the whole system primarily as simply the sum of these constituent elements. Reductionism is often described in terms of a three-step process that we use for analyzing things. Firstly, we take something and we break it down into its constituent elements. This is deeply intuitive to us when we wish to understand how a car, bird, or business works. The first thing we do is isolate it by taking it into a garage or lab and decompose it into its constituent parts. Secondly, once we've broken down the system into its most elementary components, we analyze these individual components in isolation in order to describe their properties and their functioning in isolation. Lastly, we recombine these components into the original system that can now be described in terms of the properties of its' individual elements. Reductionist approach is the fundamental method behind modern science and, by extension, our modern understanding of the world. It has proven highly successful in many ways from understanding atoms and DNA to designing the modern corporation and nation state, but as successful as it has been, it also has inherent limitations to it. Because we understand systems by breaking the parts down and isolating them, the reductionist paradigm systematically and inherently demotes the relationships between these components. Thus within this paradigm of reductionism, the whole system is implicitly thought to be nothing more than the sum of its parts. Thus, analysis works well when there is a low level of interconnectivity and interdependencies within the system we are modeling. Although this may be true for some systems, it is certainly not always the case. Many of the systems we are interested in describing have a high level of interconnectivity and interdependency. Examples being eco systems, computer networks and many types, of social systems. These systems in contrary are primarily defined by the relations within the system and not the static properties of their elements. We can and often do continue to use analysis to try to describe them, but the reductionist approach is not designed for this and thus we need to change our basic paradigm to one that is more focused upon these relations as opposed to the components and this is where synthesis and Systems thinking comes in. Synthesis means the combination of components or elements to form a connected whole. It is a process of reasoning that describes an entity through the context of its relations and functioning within the whole system that it is a part of. Systems thinking is the process of reasoning called synthesis and it is also referred to as being what is called, holistic meaning, that it is characterized by the belief that the parts of something are intimately interconnected and explicable only by reference to the whole. Thus synthesis focuses on the relations between the elements. That is to say, the way those elements are put together or arranged into a functioning entirety and like with analysis, we can also identify a few key stages in this process of reasoning. The first step in the process is to identify the system that our object of interest is a part of. Examples of this might be a bird being part of a broader ecosystem or person being part of a greater culture. Next, we try to gain a broad outline of how this whole system functions. So for example, a hard drive is part of a computer and to properly understand it we need to have some understanding of the whole computer. Lastly, we try to understand how the parts are interconnected and arranged to function as an entirety. By completing this process we can identify the relations within which our entity is embedded, its place and function within the whole, and within systems thinking this context is considered the primary frame of reference for describing something.in this section we're going to continue our discussion on analysis and synthesis digging a bit deeper into the distinction between the two the first thing to note is that the methods of synthesis and Analysis are not mutually exclusive they should both be a part of any well-developed model but each will have particular relevance depending on the type or properties of the system we are dealing with thus it should not be a surprise to us that physics is the home of the reductionist approach where they are often dealing with inert static and decomposable systems whereas ecologists that deal with highly interconnected and dynamic systems are much more inclined to systems thinking so some of the primary questions we will be asking to determine the type of system we are dealing with and thus the appropriate method of reasoning will be firstly is it primarily a component-based system or does it serve some common function that integrates the various elements is it isolated or connected is it a linear deterministic system or a nonlinear non-determinate system and is it static or dynamic we will be covering many of these topics in more depth later on in the course so we will just be touching on them for the moment firstly are we dealing with an actual system or simply a set of things when we wish to talk about a composite entity that is to say a group of things we can describe it as either a set of objects or system the difference here being that a set is a group of objects that share no common function thus we call a group of Cups on a table a set of cups as they exist independently from each other in contrary if we take the human body again it is a composite entity but this time the elements have been designed to serve some common function and thus we can call it a system and we need to use systems thinking to properly understand it secondly how interconnected is the system analysis starts from a component-based view of the world and builds a description based upon the properties of these components synthesis in contrary focuses upon the relationships between Parts thus from a systems thinking perspective we are often interested in connectivity I.E answering the question what is connected to what and thus is best suited to systems with a high level of interconnectivity thirdly Are We dealing with a linear system or are there feedback loops analytical thinking searches for direct linear relations between the cause of an event and the effect thus we call this linear thinking systems thinking is more inclined to see events as the product of a complex interacting of parts where relations are often cyclical with feedback loops is the system primarily static or dynamic analytical methods often describe entities in terms of static structures with limited reference to their development within time systems thinking takes a more Dynamic view of things often contextualizing entities in terms of The evolutionary forces that have shaped them and thus seeing the process of development as an important phenomena with which to understand the world lastly are we dealing with a system on the micro level or the macro level analysis breaks things down into parts and thus analytical thinking typically focuses upon analyzing and optimizing subsystems in a belief that we can improve the whole system by simply optimizing all of its subcomponents if we are dealing with a system on the macro level what we sometimes call the global level we need to use systems thinking to get a vision of the whole system and an understanding of how the parts interrelate to achieve Global functionalityas we have previously discussed systems are essentially the global functionality that emerges out of the interaction and arrangement of a set of elements thus systems are defined by the function that they perform to see the world from the systems perspective is to not see things but to see their functions so in this section we're going to talk about these things called functions a function is a very Broad and fundamental concept that is Central to systems theory it is also used in many different domains being particularly important within mathematics and Engineering put simply a function is a process that transforms energy or resources from one state to another so there are three key things to note in this definition we have a set of things that is the input we have a process process that changes these things in some way and we have an output firstly inputs involve the capturing and assembling of elements that enter the system to be processed putting fuel into your car is an example of an input to a mechanical system the water taken in by the roots of a plant is an example of an input to a biological system an important thing to note is that any given system can only process a specific range of inputs our car can process a certain type of fuel but not all fuels as we will discuss in a later lecture what a system can and can't process is a defining feature to its boundary that functions to filter inputs to the system for example an electrical power socket is designed in a particular shape to ensure that only the right plug is inputed to it thus it is functioning as a boundary filter to accessing the system secondly resources that are successfully inputed are processed within the system a process is a series of actions performed upon the input in order to achieve a particular end result processing is often understood in terms of information that is an algorithm or set of instructions that are performed upon the input in order to produce the output so baking a cake is an example of a process it takes a set of ingredients such as eggs water flour Etc the cook then has a recipe to follow that represents the set of instructions to be performed upon these raw ingredients such as chopping mixing baking and so on if these stages in the process are correctly performed the result should be the desired output this same process is true for the internal working of a computer biological cell or a financial transaction processes are not necessarily linear in nature they may be cyclical feeding back on each other with the output from one process being the input for another they may also be nested within larger processes or run parallel to them but ultimately there will be some energy or resources produced by this process that travels across the system's boundary to be returned to its environment and this is what is called the system's output one way of understanding a function then is simply as the difference between what goes into the system and what comes out we call a system whose internal functioning we do not know a black box in science Computing and Engineering a black box is a device system or object which can be viewed in terms of its inputs and outputs without any knowledge of its internal workings this can be of great value to us as it helps to hide away the complexity of the internal workings to the system a function is often symbolically denoted as an arrow from one element or set of elements to another and in the language of mathematics may be called a mapping or a transformation functions may be unidirectional meaning the function only Maps an input to an output or the function may be biral meaning it can also invert this process to transform the output back to the input through what is called an inverse function many processes are essentially unidirectional requiring vastly or even infinitely more energy to invert the function than was required to perform it in the first place aging within the human body may be cited as a good example of a unidirectional process whereas the building of a Lego brick house is an example of a function that can be easily inverted and thus bire we should note that the model of a function cannot be properly used to describe a set of things because sets do not perform a common function so if we take a group of Nations at war with each other because they are not working together to perform a function we can only describe them by talking about their attributes and interactions but the model of a function can be effectively used to describe any type of system the concept of a function will appear to be very simple and intuitive to us this is due to its high level of abstraction which also makes it a powerful model and very important tool in our systems thinking toolboxin the previous section about systems functioning but purposely left out some important aspect to the process that is to say what the system is processing and how well it does this whether we are talking about a car political system or a farm we are often interested in answering the question how well does it work that is to say what is the ratio between the resources that the system takes in and those that it outputs language of system theory this is called the system's efficiency answering this question may not be too difficult if we are simply talking about something like a steam engine but if we wish to be able to reason about all types of systems in this way the question becomes a little bit more difficult than it might sound so we need to start by being clear about some of the terms we are using firstly a resource is essentially a stored form of energy an ordered structure that enables a system to perform work examples of this might be the food humans metabolize in order to fuel our bodies or petroleum where energy is stored in chemical bonds the opposite of energy is entropy which is the incapacity to perform work and a measurement of the degree of disorder within a system whereas a stored form of energy is called a resource a stored form of entropy may be loosely equated to the term waste an example of an entropy system might be a vase that has fallen on the ground and shattered the parts are arranged in a random unordered fashion making them incapable of serving their intended function energy and entropy are typically measured using information theory that is to say we can measure the degree of order or disorder within a system in terms of the information correlation between its constituent elements the more patterns there are between the parts the less information it will take to describe the system and thus the more ordered it is said to be thermodynamics is the area that studies energy in relation to heat whilst energetics is the area that studies energy on a broader level within all physical systems the functioning of a system can be either productive or consumptive a function can be said to be productive if the system takes in some resource from its environment and performs work on this resource by transferring energy to it and thus outputting a resource of greater value an example of this might be simply lifting an object off the ground when we model this phenomena as a system we can see that we inputted an object at the low level of potential gravity and in transferring energy from ourself to the object by lifting it we output it an object at a high state of potential energy a resource that now has a greater capacity to perform work than it did before we performed this operation on it inversely a function can be said to be consumptive when the resource that was inputted transfers its energy to the system conserving this energy with the systems boundary whilst outputting entropy otherwise known as waste to its environment probably the simplest example of this is the metabolic process of digestion within mammals here the resource of food is inputted to the system energy is extracted from it and a waste product of excretion is exported from the system so we can define systems efficiency as a ratio between energy inputted and the energy output it but unfortunately what is considered energy and what is considered entropy is by no means objective and is often relative to the system's environment to take an example of this a light bulb consumes some amount of electricity as its input and produces some amount of light as its output though not all electricity is converted to light some is converted to heat energy with respect to the functioning of the light bulb as a light producer this heat would be considered waste or entropy but if we were interested in heating our house then this excess heat energy may be considered a resource in order to understand this better we need to think outside of systems that is start talking about their environment and this is where we will pick up in the next video up until now we've been talking primarily about the internal working of systems but in this section we will start to present models for understanding systems within the context of the broader environment that they operate in the first thing we need to discuss is what is called the systems boundary the system's boundary demarcates a limit to the system's internal components and processes internal to its boundary the system has some degree of Integrity meaning the parts are working together and this Integrity gives the system a degree of autonomy but that is all quite abstract so let's take some examples if we take a tree for example every part of the tree has been designed in some way to function as a part of the entire system the bark the leaves and trunk all serve some function with respect to the whole and thus they are integrated and through this integration they are able to function independently from other systems in their environment thus the leaves in a tree are dependent upon the tree's trunk and all other elements to that tree but they are independent from the leaves and Trunks of other trees that is to say the tree as an entirety has a degree of autonomy A System's boundary is then demarcated by where the next of relations that enable it to function as an integrated and autonomous whole reach their limit Beyond this the system loses its autonomy and has to interact with other systems and its environment the boundary to a nation state is another example of this the nation's border is only a boundary if within this boundary public functions are integrated within the national system as an entirety and by the nation functioning as a whole it can be autonomous from other nations if one region of this nation has a different culture from that of its parent Nation instead sharing its Heritage with a neighboring country this will reduce the internal Integrity of the nation its autonomy to act as an entirety and reduce the degree of definition to its boundary these examples should hopefully help to illustrate that boundaries may have a physical Dimension but can't always be defined in physical terms if we want to be able to achieve sufficient generality to talk about all types of systems which we should remember is the aim of systems theory then we need to understand boundaries within this slightly more abstract language of integrity and autonomy within the language of systems theory systems are said to be open or closed open systems interface and interact with their environment by receiving receiving inputs and delivering outputs external to their boundary these boundaries are permeable meaning that they may permit the exchange of materials Energy Information or ideas conversely closed systems are more prone to resist incorporating new inputs in this resistance at their boundary makes them more strongly defined by the static properties of the boundary by not adopting inputs a Clos system ceasing to properly serve a function within its environment may become deemed unnecessary to its parent environment and risks atrophy an isolated system is more restrictive than a closed system as it does not interact with its surroundings in any way the universe as an entirety might be an example of an isolated system but it is debatable as to whether such a construct could exist in reality so let's take a few quick examples of open and closed systems to try and cement the idea a hospital is an example of an open system continuously taking in new patients and discharging others receiving medical equipment and removing old hiring new Personnel whilst retiring others this rate of input and output to open systems make them dynamic they are constantly changing and have to respond to the changes within their envir environment an example of a closed system might be a boat on the sea it is specifically designed not to take in water from the oceanic environment it is part of another example might be a group of teenage friends in a public park engrossed within the internal cultural dynamics of their peer group they are capable of receiving only a very limited input of Impressions from their broader environment finally we get to the systems environment all systems have a boundary and operate within an environment this environment represents the sum total of other systems and input output resources that the system interacts with during its operation thus the environment consists of the sum total of resources and systems that lie outside of the boundary of the system of interest and interact with it providing its inputs and consumes its outputs from this we should note that A System's environment is primarily relative to its functioning so a biological system that requires the input and output of Natural Resources operates within the natural environment a business or enterprise system that requires the input of economic resources operates within a given Market environment and the political system of a Nation operates within the international political environment we will wrap up here and continue our discussion in the next section where we will be talking about the relations between elements and systemsas the famous scientist Carl Sean once said the beauty of a living thing is not the atoms that go into it but the way those atoms are put together this short quote goes to the heart of the systems Paradigm and tells us why it is the relationship between components that we are really interested in when seeing the world from the systems perspective a relation is a simple but abstract concept it is the connection or interaction between two or more components through this connection there's an exchange of some matter Energy Information or ideas that bind the elements into a state of interdependency where the total gains and losses of any component are correlated with those of others in the relationship these relationships between the system's constituent elements can be fundamentally of two different kinds constructive or destructive we call constructive relations synergies and destructive relations interference starting with synergies a Synergy is an interaction or cooperation of two or more components to produce a combined effect greater than the sum of their separate effects a classical example of a Synergy is the relationship between the honeybee and the flower it pollinates bee and plant interact by exchanging pollen and nectar both elements have a need that they cannot fulfill themselves The Bee needs some resource for its subsistence which it cannot produce itself and the plant that is incapable of Mobility needs some form of transportation for its pollen it can be said that they both get out of this interaction more than they put in and thus the sum total is greater than the simple combination of their resources in isolation examples of synergistic phenomena are ubiquitous in the natural world but another example of a Synergy could be the increased gains resulting from a business merger which can be attributed to various factors such as combined Talent OR economics of scale and cost reduction with synergies the value added by the system as a whole beyond that contributed independently by the parts is created primarily by the relationship among the parts that is how they are interconnected in contrast to synergistic relations we also have relations of interference that are destructive in nature meaning they reduce the combined output of the system to less than the sum of its parts interference is the prevention of a process or activity from being carried out properly due to some interaction between elements or systems an example of this would be the interference between two drugs a situation in which a substance affects the activity of another drug in negative ways when both are administered together thus reducing the overall positive effect to less than the benefit of the individual effects in isolation another simple example of this is destructive interference between sound waves where sound waves that are out of sync lead to their cancelling each other out thus we can see how the degree of synchronization or asynchronization between elements is an important factor in determining the nature of the relations between them we can also note that synergies often arise as a product of differentiation and specialization differenti is a process that occurs in many systems as they develop it is defined by the proliferation of subsystems and specialized Elements internal to the system in order to make it more capable of responding to a greater diversity of States within its environment differentiation occurs most notably during the development of a multicellular organism which originates as a single cell but through cellular division the organism develops velops a multitude of differentiated or specialized cells capable of Performing many different functions the same can be observed in the development of Technologies and social organization the point to take away from this is that this process of differentiation also involves the proliferation of relations with which the now specialized components can Avail of each other's services for example as the global economy has grown with different areas focusing on their specialized domains we have also seen the proliferation of trade relations and this process of specialization and then exchange is a key source of synergistic relations in the next section we will be carrying on our discussion of synergies when we talk about emergenceAccording 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 whereby the interaction between elements gives rise to something that is greater than the sum of their parts is called emergence. So whereas 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 synergistic 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 simplest 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 cited 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 arranged 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 cite from ant colonies to galaxies and cultures. But all of these are types of structures. Whereas emergence is really a process. These systems are then the product of a process of emergence that has played 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 can change qualitatively. Again, there are lots of examples of this such as the phase transition between solid and liquid that a substance goes through when heated. But maybe the most dramatic example is the metamorphosis of a butterfly from being a caterpillar to a mature adult. Not only does the systems 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 name. This illustration helps to bring us to another important theme within emergence. That is the distinction between what is called strong and weak emergence. Weak emergence describes how the emergent phenomena can be traced back to the individual elements. Meaning we can predict and observe higher level emergent phenomena just by looking at individual components. In contrast, strong emergence, also known as irreducible emergence, states that these phenomena cannot be reduced to the individual components. Instead, the emergent phenomena are traced back to the interactions between the multiple components. So quite literally cannot be predicted in any sense by looking at the components on their own. Consciousness is often cited as an example of strong emergence. It would appear that without prior knowledge or experience of what consciousness is, it would be virtually impossible to understand the vastly complex and subtle system that is human consciousness by analyzing the properties of the very simple neurons that formulate it. This distinction between strong and weak emergence may also be formulated within the language of information theory where weekly emergent phenomena are essentially computable. That is to say, if we had sufficient information, we could simulate them. In contrast with strongly emergent phenomena, no amount of information could predict or formulate the end result of the process prior to its completion. The discussion of strong and weak emergence leads us to another key theme in system theory that is hierarchy. The distinction between micro and macro and top down versus bottomup causality. All of which we will be talking about in the next lecture.up until now we've been talking about systems on one level of analysis our model so far has consisted of simple elements making up systems the reality of the world we live in though is of course vastly more complex than that and one way of capturing and structuring this complexity is through the use of abstraction in hierarchical structure abstraction is the process of successively removing layers of detail from our representation in order to capture the most essential features to a system an architect's master plan of a building is an example of an abstract representation it is designed to capture only the most essential features to the building that are required to get an idea of its overall makeup by using abstraction we can Define different levels to our model depending on its degree of detail or granularity it and this is called encapsulation we are encapsulating one model of a system inside of another which in turn may be encapsulated within a third and so on creating a hierarchy within our representation you might ask what the value of this is the value of this is that almost all phenomena exhibit this hierarchical structure whether we're talking about physical systems where atoms make up molecules which make up substances and so on or social institutions where individuals make up organizations which make up societies and Etc in order to give some terminology to these different levels we have at least four different terms we can use at the most basic level of the hierarchy is what are called elements elements are Elemental meaning they do not have constituent components we treat them as a whole they simply have properties an electron is an example of an element we cannot look inside of it because it is not made up of any separate parts next up are subsystems a subsystem is a set of elements which make up a system which in turn is a component of a larger system an example of a subsystem may be the brakes in a car they are made up of elements but are also an integral part of a broader system the car our car which is a system of personal Mobility is in Turn part of a transportation system and we call this level to our analysis a system of systems lastly all of this is encapsulated within our ultimate unit of analysis that is the systems environment different types of systems base their hierarchy upon different features so hierarchies with within ecosystems are based upon where creatures lie in the food chain within social systems hierarchies may be based upon age occupation education or many other factors the theory of Integrative levels tries to describe the underlying Dynamics and characteristics of this ubiquitous feature of organizational levels the theory of Integrative levels deals with the idea that units of matter are organized and integrated into levels of increasing integration and complexity the idea of Integrative levels of organization allows researchers to describe the evolution from the inanimate to the animate and the social World higher integrative levels are thought to be more complex and demonstrate more variation and characteristics than lower integrative levels because of emergence each level has its own unique internal Dynamics and cannot be fully reduced to the level below and thus we have the domains of biology sociology and cultural studies because novel features to systems emerge on each of these particular levels of integration that cannot be described by simple reference to physical structures and processes the last thing to note in this section is that as we have emergence and hierarchical structure we have a new new dynamic between the different levels to the system as emergence implies that the rules governing any given level may be qualitatively different from those of another and this will be particularly pronounced when we take the two extremes of the system's micro and macro level as all of these different levels have to ultimately work together as an entire system the question turns to whether it is the rules that govern the micro level to the system or the rules that govern the macro level that ultimately determine the system's functioning as a whole you may also hear this Dynamic referred to as bottom up versus top down causality and it is another key theme within systems theory so let's take an example of this if a doctor has a patient that is in poor physical health and psychologically depressed does she search for a bottomup cause to the system's dysfunctionality in which case she would look for a physiological explanation something like a virus or infection that is causing the overall problems within the patient's body or inversely does she search for a top- down explanation reasoning that it is the patient psychological state that is inducing their physiological state of poor health debating this question further is beyond the scope of this course but the point to take away is that Within These emergent hierarchical systems such as the human body political regimes or ecosystems there will always be this complex dynamic between the rules that govern the system on the micro level and those that govern it on the macro level we can wrap up then by saying that abstraction is a powerful method of reasoning by using encapsulation to Nest subsystems within systems we can create models that capture the emergent hierarchical structures that we see all around us in the worlduntil now our model of a system has been relatively static in this module we are going to start to deal with how systems change over time what is called system dynamics system dynamics is a branch of systems theory that tries to model and understand the dynamic behavior of complex systems it deals with internal feedback loops and time delays that affect the behavior of the entire system it was first developed by professor Jay Forrester at MIT as a management method but has since gone on to be applied to all types of systems from modeling the dynamics of Earth's systems to those of the economy and political regimes the key elements of system dynamics are feedback loops stocks and flows firstly feedback with analytical thinking we often see the world in terms of linear cause and effect but system's thinking looks for the interplay between elements that is the feedback loops through which elements are interconnected in affecting a joint outcome system dynamics uses what are called causal loop diagrams to do this a causal loop diagram is a simple map of a system with all its constituent components and their interactions by capturing interactions and consequently the feedback loops a causal loop diagram reveals the structure of a system by understanding not only the structure to these relations but also the nature of those relations it becomes possible to model and simulate a systems behavior over a certain time period these feedback loops can then be of two different kinds either positive or negative a positive feedback loop means that values associated with the two nodes within the relation change in the same direction so if the node in which the loop starts decreases the value associated with the other node also decreases similarly if the node in which the loop starts increases the other node increases also economics of scale is an example of a positive feedback loop between a business and its customers the more products a company sells the more revenue it receives from its customers giving it more to invest in scaling up production thus allowing it to reduce costs which in turn means more customers will purchase the product and so on this is also called a virtuous cycle where one party gains the other does so also of course this can't go on forever and that is why positive feedback loops are typically associated with unstable processes that are likely to crash at some time a negative causal link means that two nodes change in opposite directions if the node in which the link starts increases then the other node decreases and vice versa the systems dynamics between predators and prey are an example of a negative feedback loop if the number of predators increases then a number of their prey will decrease which will in turn feed back to effect the Predators by reducing their population which again will feed back to increase the prey population and so on negative feedback loops are typically associated with an overall stable and sustainable pattern of development there are of course many more examples of positive and negative feedback loops but we will move on to talk about the other key feature to the area of systems dynamics that is what we call stock and flow diagrams to perform a more detailed quantitative analysis a causal loop diagram is transformed to a stock and flow diagram which helps in studying and analyzing the system in a quantitative way typically through the use of computer simulations a stock is the term for any entity that accumulates or depletes over time thus it is a simple variable a flow in contrary is the rate of change in a stock so an example of a stock might be a water reservoir it is a store of water and we can ascribe a value to the volume it contains now if we put a tap on the side of our reservoir and started pouring water out of it this would be an example of a flow whereas a stock variable is a measure of some static quantity a flow variable is measured over an interval of time by using these tools of system dynamics we may get a qualitative and/or quantitative idea of how a system of interest is likely to develop over time for example if we create a simple two-dimensional graph with time on the horizontal axis we will see how the different feedback loops create different types of graphs graphs for positive feedback loops typically reveal an initial exponential growth as they shoot upwards rapidly then reach some environmental boundary condition where they crash back down again a financial bubble and ensuing crash could be an example of this whereas the net result of a negative feedback loop will be a wave like graph that will likely be bounded within an upper and lower limit over a prolonged period of time with relatively smooth fluctuations during the system's development that enable it to sustain an overall stable state in the long termshort module we are going to continue our discussion about dynamic systems within the context of their environment many types of systems require both a continuous input of resources from their environment and the capacity to export entropy back to the environment in order to maintain a specific level of functionality an example of this might be a tractor that must receive a periodic input of fuel and be able to export heat and gases back to its environment for it to maintain its functionality a business organization is another example requiring a continuous revenue stream to pay its employees and suppliers while also producing a certain amount of waste material that it must externalize and the same can be said of many other types of systems thus in order for these systems to maintain their intended level of functionality what we might call their normal or equilibrium state they must have an environment that is conducive to providing them with these required conditions with in ecology and biology the term homeostasis is used to describe this phenomena the word homeostasis derives from the Greek word meaning homos or similar and stasis meaning standing still it is the state of a system in which variables are regulated so that internal conditions remain stable and relatively constant despite changes within the system's environment in order for systems to maintain homeostasis there needs to be some kind of regulatory mechanism what we also call a control system this control mechanism has to regulate both the system's internal and external environment to ensure that the environmental conditions are within the given set of parameters that will enable the internal processes of the system to function at a normal or equilibrium State cybernetics is the area of Systems Theory that studies these regulatory mechanisms cybernetics again comes from a Greek word which means to steer or guide and this is exactly what a control system is designed to do it is designed to guide the system in the direction of the set of environmental parameters that are best suited for it to maintain homeostasis so let's take some examples of this in order to maintain the environmental condition best suited to the physiology of a human being we have invented the thermostat thermostats are classical examples of control systems that operate by switching heaters or air conditioners on and off in response to the information given by a temperature sensor thus they regulate the environment in order to maintain a stable or equilibrium condition best suited to the internal workings of the human body another example might be the process control system in a chemical plant or oil refinery which maintains fluid levels pressure temperature and chemical composition within just the right parameters required for the desired chemical process to take place there are many more examples of how adaptive systems maintain homeostasis but the essential characteristic of this phenomenon is to maintain a stable state conducive to performing a set of internal dynamic processes and this is done by monitoring information from feedback loops if the system is in a homeostatic condition it will simply continue with its previous course of action but if one or more of the parameters it is designed to monitor are outside of these parameters it will perform some operation in order to affect the state of its environment the control system then waits for a feedback of information from its environment in order to analyze how this previous activity has adjusted the desired parameters depending on whether this information signals the system moving away or returning to homeostasis it will again react accordingly an example of this is a person driving a car when we are cruising nicely along the road we simply continue doing what we have been previously doing whilst also continuing to monitor feedback loops but as soon as this information signals us approaching the limit of a homeostatic parameter such as getting too close to the side of the road we react by adjusting the steering wheel we then wait a fraction of a second to monitor how this action has affected our status within the environment once this information is fed back to us and we have processed it we then once again react all the time with the aim of returning to our desired homeostatic condition that enables the desired function of the car that is our transit from one location to another we can then see how this concept of homeostasis can be a powerful model for capturing the development of any adaptive system as their course of development is the product of this continuous acting and reacting to feedback loops another thing we may note is how complex a system may become given two or more of these adaptive systems acting and reacting to each other's behavior as the system develops through an evolutionary like dynamic we may also notice how this model captures a lot of dynamics underpinning the development of social systems such as international politics free market economies and almost all types of social relations but this is getting into a whole new area of complex adaptive systems that is the subject of another coursein this last lecture to the course we're going to wrap things up by giving an overview to the application of systems theory to the various domains of science what is called Systems Science systems theory is a formal language meaning like other formal languages such as mathematics it is independent from external reference to any subject matter and thus is solely dependent upon its own internal logic if this logic is consistent then it works if there are logical inconsistencies within the syntax of the language then it does not work the same should be true for any formal language such as the algorithms that run your computer if there is an error in the program's logic then it will crash the system although the term science in its broadest definition may be used to include the formal languages it is essentially an empirical endeavor ever meaning that it is dependent upon reference to some subject matter in order for its validation thus the vast majority of people who call themselves scientists spend their time amassing or analyzing empirical data whereas the formal languages are independent from empirical science science works best when it is supported by some formal language and Mathematics as we know is the formal language that supports most of modern science math matical proofs are considered the gold standard in terms of scientific validation since the turn of the 20th century set theory has been the deao foundation to mainstream mathematics as we have discussed previously set theory and reductionism Paradigm are suited to the modeling of certain types of systems thus modern science supported by mathematics does a very good job of describing the simple deterministic systems that we have to deal with in the Natural Sciences areas like chemistry and particularly physics represent powerful sophisticated and welldeveloped Frameworks but other areas of science most notably the social sciences that have to deal with non-deterministic highly interconnected and emerging systems either try to mimic the Natural Sciences as mainstream economics does or are left with very little in the way of formal foundations out of which to build any kind of robust framew work another aspect to the way modern science has developed under the reductionist Paradigm is its fractured nature science is today a highly specialized and compartmentalized activity of course there is nothing wrong with specialization but when the knowledge and expertise of one domain are very disconnected from those of another then science as a body of knowledge can become too focused on the trees without seeing the forest science serves a function within in society and ultimately a society needs answers to not only these analytical questions but also to bigger questions such as the nature of Order and Chaos in our universe or how the different domains of knowledge really relate to each other the reductionist Paradigm offers us limited means to approaching these bigger questions and if science can't provide Society with plausible answers then people will look elsewhere and it would have failed in providing us with an inte ated picture of how the world works and not just a one-sided picture that reduces everything to some simple interaction between physical components this is where system science comes in with its holistic approach it lets us Focus Less on Specialized knowledge within specific domains and more on how these domains fit together often through the idea of Integrative levels thus system science is a much more interdisciplinary form of science being more more relevant when we are dealing with phenomena that cross the traditional domains such as ecology that doesn't confine itself to dealing with biological systems but also recognizes the important interplay between human industrial activity the biosphere and the abiotic geosphere thus the area of system ecology has proven one of the most successful areas within the system Sciences another interdisciplinary domain system science is proving particularly relevant too is in the study of the interaction between people and Technology what are called soot Technical Systems whereas modern science has supported a technocratic view of the world system science crosses the two boundaries to recognize the importance of the interaction between people and Technology this leads up to what can possibly be the system science's greatest contribution to our scientific framework for centuries people have been trying to apply the success of modern physics to studying social systems with limited results traditional science rests upon an objective view of the world that is to say removing the subjective interpretation of the view from the model this works fine when dealing with inanimate objects but of course there is a subjective Dimension to almost everything that humans do system science is philosophically sophisticated enough to deal with the difficult questions surrounding the subjective nature of the human condition that are required to truly tackle areas like psychology cultural studies and sociology system science and traditional science are often cast in contrary terms but of course they are two sides of the same coin developing a scientific framework powerful enough to describe our world in all its richness will require both the qualitative capacities of system science that allow us to properly contextualize things and the rigorous quantitative methods of analysis that allow us to properly compute this information with the net result being hopefully a fuller picture of how our extraordinary World works