 Because complexity economics is based upon non-linear systems theory and standard economics is based upon linear systems theory They are, from a theoretical point of view, essentially the mirror-inverse of each other They're both looking at the same economy, but looking at it through a different paradigm whereas standard economics models the economy in terms of a closed system with homogeneous isolated agents making rational choices that lead to equilibrium static macro-level outcomes Nonlinear models are going to give us a very different picture It is a model of the economy as an open system composed of heterogeneous agents with bounded rationality making choices within a particular context which gives rise to networks of interaction what we call Institutions and a macro-level non-equilibrium to the economy that is constantly changing driven by internal dynamics So that's a lot of very dense information But it's the bigger picture of what we'll be exploring in this module and we'll go over each part in detail As we've previously discussed Standard economics is based upon this concept of equilibrium which is derived from modeling the economy as a closed system Complexity economics will flip this on its head Describing the economy as an open system that is never in equilibrium And this is a paradigm shift a very different way of looking at things from which a whole different theoretical framework is going to emerge Complexity theory studies open systems That is to say systems that are so embedded within their environment Interconnected and interdependent with other systems that we can no longer define them by their boundary condition and their static internal components With linear systems theory we use the process of reasoning called analysis that starts by isolating the system from its environment So as to hold inputs and outputs to the system constant This is what we do when we take something from its environment and put it into a lab It's only by doing this that we can describe the system in terms of the additive properties of its internal constituent elements Creating equations based on invariant transformations between the properties of its constituent elements Complexity theory does not use this method of analysis because this method of analysis is very fundamental to modern science Complexity theory is going to give us a very different way of looking at things Because it does not use analysis it will allow us to study a system as being open That is in terms of its function and relations within its environment This is a process of reasoning called synthesis and it is the opposite from analytical reasoning Within economics, this will mean that unlike with standard economics where the economy is modeled as a closed system Complexity theory will give us a model of the economy as fundamentally an open system Standard economic models will not describe how the system interacts with other systems external to it such as the social or ecological domains It exists in isolation if anything is going to be incorporated into the model It has to be presented as being inside of the economy with complexity economics because we're using open systems models We can represent the economy as one component interacting with other components within its environment from this perspective We'll be able to identify the economy as taking in resources from other components as in social capital or ecological capital Processing these in order to generate outputs that represent both resources of a higher value and also resources of a lower value what we might call entropy Because we're modeling the economy as an open system There will typically be no single closed-form solution Isolated systems tend towards a single equilibrium Open systems in contrast because there is a constant input and output of energy and matter They do not tend towards a single equilibrium. They may have multiple equilibria, which is characteristic of nonlinear systems This does not mean that they are random. They're certainly not They're just governed by different dynamics What this means is that the end result of our model would typically not be a closed-form solution That is to say an equation Even if we do create an equation these equations typically have multiple solutions and they may exhibit chaos In general because we're dealing with open systems that are really defined not by any Equilibrium or equation but instead by their inputs and outputs these inputs and outputs are going to be defined by the system's Relations with its environment and its functioning These functions are most appropriately modeled as algorithms That is to say a set of instructions that define how the components or system maps an input to an output So when using this framework what we're going to spend most of our time doing is trying to understand And capture the algorithm that the components are operating under by the components I mean agents or institutions if we then want to go on and create a high-fidelity model We'll put the algorithm into code although there is definitely a skill involved in doing that Theoretically, it's quite straightforward We will then run this computer model in order to get a simulation of the system's behavior over time If we do all that we will have all the information we might want to know about the system Without ever needing an equation The power behind this technique is one of the basic premise within complexity theory That is the idea that simple rules can create complex phenomena We're defining simple rules and then using the computer to iterate on these simple rules to give us nonlinear Interactions and feedback that will generate a model with a structure that is both complex and intricate a high-fidelity Representation of a real-world economic phenomena But as mentioned, we're not just interested in these algorithms We're very much interested in the interaction between components Unlike standard economics where these interactions have to be additive in order to get general equilibrium Which basically means the relations add or subtract no value to the system and thus can be largely ignored But remember in complexity economics, we're not trying to get an equilibrium outcome These relations don't have to be additive because this is a non linear framework They can be non zero sum which means they may add or subtract value to the system And these non additive relations will be important to the overall makeup of the system and thus of great interest to us Because these interactions are non additive They're going to give rise to non-equilibrium Macroscale patterns of organization that will have their own internal dynamics and structure and what we call a Emergent properties. This is going to give us a heterogeneous Macroscale topology to the system put very succinctly Institutions are going to matter and this is in contrast to standard economics where they are basically seen as markets or clearing houses They don't really have any internal dynamic. They're just an equilibrium point When we stop focusing on general equilibrium and the idea of individual atomized agents and start to focus more on these Interactions, what we're going to see is that these institutions are in fact networks and the structure to these networks is very important Because it's going to define how things flow through the network and remember because we're dealing with open systems This is about inputs and outputs where a component is in a network the network structure And what is flowing through that network is going to be decisive in defining the inputs and outputs to any of its components or subsystems These internal emergent structures or institutions will add or subtract value to the whole system Creating a macro level disequilibrium because the system is open and resources are coming in and out of it from a larger Environment that is a part of the flow of resources in and out of the system may change over time This will add to this state of non-equilibrium When we allow for non-equilibrium on the macro scale We can start to think about how the whole system changes over time and complexity economics Uses the model of evolution in order to describe this macro process of change So what we've presented here is basically a very high level view of the whole approach that complexity economics is going to use I'll flesh this out further in the rest of this video To give us a quick overview to what answers this approach is going to give us to the basic questions That we're trying to model and describe within the domain of economics That is a model to the behavior of agents how these agents interact what economic Institutional structures will get from this and how the whole thing changes But the thing for us to take away from this first section is this the fact that we define the economy as an open system Allows us to talk about non-equilibrium and this non-equilibrium is our first basic principle And it's going to structure and define our whole approach from here on in the same way the general equilibrium Defines the overall workings to standard economics Non-equilibrium and non linearity are going to be defining factors in the whole approach taken by complexity economics As with linear systems theory This approach will enable us to capture and model some things and constrain us from modeling and describing others Because we have a model that is actually embedded within some real environment We can begin to recognize the complexity of the real world as the economist Alex Leonhoft remarked Neoclassical models give us a view of quote smart people in incredibly simple situations While the real world involves simple people coping with incredibly complex situations The implicit expectations of standard economic models is that agents are seen as almost Supercomputers that are able to run an optimization algorithm over thousands or even millions of different choices Within a fraction of a second complexity economics based on this idea of simple rules Instead describes individuals as endowed with only a very finite amount of computing power What is called bounded rationality the idea that when individuals make choices their Rationality is limited by the information that they have the cognitive limitations of their mind and the time available to make the decision Bounded rationality tries to capture the fact that economic phenomena Actually at the end of the day play out in the real world and this has real implications and limitations Again, this goes back to the fact that we use your model that allows us to see the system within its environment Linear systems theory because it's an analytical framework won't allow us to do this Modeling things as closed systems is sometimes a big advantage and sometimes not but in this case it is creating a very large disparity between what empirical data tells us and what the standard models tell us and Central to trying to resolve this is the new area of behavioral economics behavioral economics gives us a much expanded and more complex conception of motives that are driving the individuals as it studies the effects of psychological social Cognitive and emotional factors on the economic decisions of individuals Agents are still seen to be efficiently pursuing their valued ends as part of our definition of economics But these valued ends can represent a much wider spectrum not just purely industrial capital Because we're not constraining our model of the individual towards achieving equilibrium We can begin to think about the individual agent as being in a real environment Embedded within a multiplicity of different networks each exerting its own force over the agent's behavior and thus linear Causality where a causes B begins to break down the net results of bounded rationality and a complex set of motives Means that agents may come to hugely suboptimal economic solutions Of course, we can only come to this conclusion because we're allowing for non-equilibrium outcomes And this leads to a discussion of what theory of value can this nonlinear modeling framework offer? Because we're looking at the economy within the context of its connections with other systems within its environment We can begin to recognize the value of those other things that are not necessarily inside of the economy Using analytical methods We can only ascribe value to anything that is inside the economy Ecological capital is defined within the standard model as mining or agricultural industries It can't have value outside or independent from the economic system But when we flip this model around and see these other domains outside of the system and their relation to it Then we can begin to reason about their independent value How this might translate into primary economic value as long as we're using Analytical methods focused on looking inside the system We will only be able to ascribe value to anything that is inside the model When we use synthetic reasoning to create models for the whole environment We can then ascribe some form of value to all the different domains and begin to reason about how to create metrics for translating between different domains Thus incorporating both extrinsic primary economic value and Intrinsic secondary value and this will be congruent with our model of agents as being under the influence of many different Motives and value systems as they respond to social capital cultural capital Environmental capital and so on it is a much more complex model where we're trying to take account of value in all its different forms Value is not homogeneous a single price determined by a market equilibrium It is instead heterogeneous a Network of different interacting variables and as we saw in the previous module with information technology This is increasingly a practical reality Next we'll talk about the interactions between economic agents This is the domain of game theory game theory models both zero-sum games and non-zero-sum games Zero-sum games give linear solutions and other central to standard economics non-zero-sum games results in non-linear outcomes and thus the nonlinear study of economics is mainly concerned with these non-zero-sum Dynamics it allows us to incorporate relations of Co-operational interference into our models both will give us non-equilibrium results Interference between components means some form of conflict between the agents that makes the combined system Less than the sum of its parts as an example of this we might think about price wars between different businesses inversely Cooperation is a form of synergistic interaction between agents Synergies involve the components both differentiating their functions and combining them towards common ends Through synergies value is added to the composite organization through these relations We get an organization that is greater than the sum of its parts Synergies form the basis for the process of emergence that gives rise to different levels in the economy with diverse institutions serving diverse functions on different levels in the complexity paradigm Macroeconomic patterns are emergent properties of micro level interactions and behavior But because of the non-linear interactions between components that we previously mentioned We cannot analytically derive the properties of the macro system from those of its constituent parts Although we can apply computational techniques to model the behavior of the emergent properties That is to say agent-based models can simulate these emergent phenomena in high fidelity Agents within the complexity economy are embedded within many overlapping networks Social cultural technological financial, etc How an organization or individual succeeds or fails within this economy is a product of these different many Interacting variables across different networks and the makeup of those networks From this perspective, there is no such thing really as an efficient market that allocates resources in an optimal fashion This whole idea is only really relevant when we're thinking about agents in isolation Agents as price takers in pure markets where they face an impersonal price structure And they're computing their rational choices in isolation from the complexity perspective people are interconnected They're embedded within networks of production and consumption Resources flow through these networks and how these resources get distributed out Depends on the structure of the network and where you lie in that network There doesn't have to be any equilibrium here The distribution of resources across the network can be hugely heterogeneous and may remain in a non-equilibrium state indefinitely Complexity economics sees the economy as a complex adaptive system that evolves over time In standard economic theory There is no mechanism for creating novelty or qualitative change within the economy in the complex economy The evolutionary process of diversification Selection and amplification provides a system with novelty and is responsible for the growth in order and complexity over time Eric Beinhacher in his book The Origins of Wealth Describes this process as such Quote an evolutionary search mechanism markets provide incentives for the deductive tinkering process of differentiation They then critically provide a fitness function and selection process that represents the broad needs of the population Finally, they provide a means of shifting resources towards fit models and away from unfit ones Thus amplifying the fit modules influence Complexity economics focuses on the non-equilibrium processes that transform the economy from within Such as technological innovation and new business models created by entrepreneurs that leads to a process of creative destruction Within an economy that is constantly changing as it grows in a somewhat organic fashion Changes in one part lead to new opportunities and niches within another As the whole thing evolves with different industries and sectors becoming interdependent and self-organizing And out of this process of evolution, we get what we might call economic growth Not so much in our traditional sense of an increase in gross throughput to the system But more in terms of its qualitative structural transformation In becoming both more differentiated and integrated to exhibit greater levels of complexity In this module, we've been taking a brief overview to the area of complexity economics Highlighting some of its main characteristics. We started off by talking about how it models the economy as an open system Meaning that it does not need to be an equilibrium Relaxing this constraint allows us to create a whole new paradigm built on non-linear systems theory Within this paradigm, we get a much more complex picture to individual agents the motives they're acting under What they value and how that value may not be a homogeneous thing But instead a heterogeneous composite of different forms of capital We talked about how a non-linear framework will allow us to focus more on non-zero sum interactions Where value is added or subtracted to the organization through the different types of relations between components How synergistic interactions can give rise to emergent macro patterns of organization Next, we looked at how complexity economics understands these institutions as complex networks Where the structure and makeup of those networks defined macro level resource allocation Lastly, we talked about economic development as a process of evolution And how this will allow us to better reason about structural qualitative transformations within the whole economic system As deriving from internal drivers