By: Daksh Sharma
Studying the connections between the parts in the system reveals more information than strictly studying the separate fragments
In the last few decades, a quiet revolution has taken place in the field of economic theory. It is called Complexity Economics, and it is firmly rooted in the science of complex systems. A complex system is system of individual components which can interact with each other. An ecosystem is an example of a complex system. Animals, plants, air, soil, and water all interact with each other in various ways to create something that is whole. But the most important insight of studying complex systems is not about the individual components, nor about the whole picture. It is about the connections between the individual variables in the system. In Aristotle’s words, “The whole is more than the sum of its parts.” Studying the connections between the parts in the system reveals more information than strictly studying the separate fragments. While the idea of complex systems has been examined and noted upon for hundreds of years, the explicit study of complex systems started in the 1970s. Since then, biology, chemistry, physics and economics have incorporated this way of thinking into their respective fields. In modern economic theory, complex systems provide a markedly different way of modeling the economy which could better reflect reality.
One of the major questions in economics deals with the concept of general equilibrium: under which conditions are the markets stable and operating at maximum efficiency? Economists come up with models to help explain how such an equilibrium is reached under specific circumstances. The major critique of most of these models comes from their unrealistic assumptions about the economy. For example, they assume that all the agents in the system have perfect knowledge, act rationally, and behave selfishly. In other words, each component is statically connected to another component and is thus, nicely defined. More technically, general equilibrium models do not have many elements with many degrees of freedom; they are too interconnected. As a result, these models are very rigid and ignore the possibility of evolution, creation, transition, and adaptation in an economy.
But by utilizing the study of complex systems, models can become more accurate because the study of complex systems necessarily leads to a more complete picture of the economy. Instead of modeling what agents would do given a certain static scenario, complexity economics asks how agents would react to a transient environment, which consequently reacts to the agents. This so called feedback loop is one of the core ideas of a complex adaptive system. It views the economy as more flexible and, more importantly, more chaotic.
With the idea of complex systems, economists can now model an organic economy that is constantly shifting. The economy is a “complex adaptive system” where the system’s parts are intricately interacting with each other, absorbing information from its environment and creating knowledge to aid its future actions. Moreover, a time dimension must now be considered. Take the stock market for example. Not only do traders keep track of what other traders are doing, but they also use their past experiences to shape future decisions. Unlike a lot of general equilibrium models that attempt to explain the economy in a snapshot, complexity economics accounts for the passage of time and expansion of the system’s knowledge.
This consideration of a time dimension leads to the idea of nonequilibrium. Complexity economics argues that equilibrium is never truly achieved because the economy is always open to change. Of course, general equilibrium models attempt to explain these changes. However, they call them “exogenous shocks,” implying that they are unnatural phenomena. Complexity economics treats these “phenomena” as the realistic effects that they are. But why exactly should economists consider this way of thinking? Why is nonequilibrium a valid concept?
Through two concepts, called fundamental uncertainties and continuous technological disruption, economists can more fully consider all the variables of the true economy as well as the connections between them. Accepting nonequilibrium, and the time dimension associated with it, are essential tools in studying complex economics.
Firstly, there is the principle of fundamental uncertainty. It states that all decisions involve results that take place in the future. Whether those results take place 5 seconds from the decision or 5 years from the decision, there is always a degree of uncertainty. So most people cannot predict the economic consequences of their decisions with full accuracy. Take, for example, an investor who has poured money into novel tech product. The investor has many questions which she can never answer at the time of her investment. How well will this new product work? How popular will it become? How and to what extent will it be regulated? Of course, uncertainty does not keep people from making decisions, but unlike the assumptions of many neoclassical models, the agents’ decisions can never be deductively rational. If the agent faces a problem that is not well defined and does not have all the necessary information pertaining to this decision, she cannot make a deductively rational decision. She may make an intelligent or foolish decision given her circumstances, but she can never be deductively rational.
A decision can also be swayed according to the context in which it is being made. This context not only varies with the specific decision, but also with the individual. For example, the emotional state of the agent is one strong example of a context. Another example is the individual’s experiences. Going back to the example of the stock market, traders use past experiences to create “internal models” in order to imagine the ramifications of their choices. These models are continually updated as the person goes through more experiences. Some parts of their model may be thrown out and other parts could be added in. But all of them stem from the fundamental uncertainty of economic decision making.
A second reason for nonequilibrium is the continuous impact of technology. In Economics 101, an improvement in technology is seen as a positive “exogenous shock,” and the supply curve is shifted upward. But complexity economics argues that a change in technology is not a one-time disruption. Its effects are continually succeeding. That is, imagine a staircase, in which each development in tech brings you to each higher step. For example, take the vacuum tube. It allowed for the creation of radio, which created a demand for broadcasting technologies. The vacuum tube was also an integral part of early computers. Once computers were created, it spurred the demand for digital storage, computing languages and so on. Tech constantly generates more tech, yet again highlighting the self-reinforcing and cyclical nature of complex systems.
The revolution of complexity economics is a marked departure from neoclassical thinking because it asks a completely different question: How might individuals react to a pattern they create together, and how might that pattern alter itself as a result? That question proved very hard to answer for early economists, so they tweaked the question to: what type of individual actions are consistent with certain patterns? Which of those patterns result in equilibrium? These questions can be analyzed rigorously with mathematics, but they fail to completely account for the complex interactions between the economy’s agents. Therefore, general equilibrium models may not capture the ever-transient state of the economy in beautiful mathematical equations.
In review, complexity economics provides an answer to this original problem by portraying the economy as an ongoing computation made up of parts to the system and to each other. The system responds by changing accordingly, leading to more changes on the individual level. By using these insights, economists may find a more accurate way to talk about the economy.
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