Viewing the World Through Complex Adaptive Systems
Small differences can lead to large consequences or change outcomes. A popular example of this is that a butterfly could flap its wings in New York and the next day in Tokyo there will be rain instead of sunshine. This phenomenon is commonly known as the ‘Butterfly Effect’ and it highlights the relationship between minute conditions and ending outcomes within a system. Although interesting, the Butterfly Effect is only a piece in the puzzle of understanding our greater world. A larger piece to the puzzle, but by no means the complete picture, are Complex Adaptive Systems (CAS). If the Butterfly Effect represents the relationship in a system, then a CAS is the system itself.
Viewing the world through Complex Adaptive Systems enables us to understand the manner in which the world functions. In turn, better decisions can be made with more effective results. So, what is a CAS? According to an article by the Harvard Business Review, a CAS is composed of three main features, each leading into the other. The first feature requires independent agents that decide their own actions. The second feature requires those agents to interact with one another. Lastly, the third feature is a result of the first two features. The result is a system that behaves as its own agent but differently than the agents that compose it (Sullivan, 2011). A good example of this are ant colonies. Each colony has worker ants, nursing ants, construction ants, etc… each with their own ability to decide how they should behave. All the ants interact with one another and all these interactions lead to the behavior of a colony. The colony has cycles, periods of growth & contraction, and other behaviors. However, the ants do not decide when the colony should grow as they only interact with one another on a local level without having influence on the colony at a global level (Sullivan, 2011). This is only one example of a CAS but there are many systems like this in our world such as: eco-systems, corporations, hospitals, and financial markets, to name a few. The study of Complex Adaptive Systems is relatively young but important nonetheless. More technical features of CAS can be explored in systems such as healthcare and finance.
The healthcare system in its entirety can be seen as a CAS, with whole hospitals acting as agents and all hospitals together creating the healthcare system. If a snapshot of the healthcare system were to be taken and all data needed could be acquired, then it would be possible to make a model of the healthcare system. This model could have the ability to forecast the number of future patients or persons in need of a certain treatment. Vital and valuable information, no doubt. However, this model requires an immense amount of data and would only be valid for the snapshot in time the data was collected. As time progresses, new interactions enter the system which as we know from the Butterfly Effect, can cause significant changes in the system, revealing that a CAS may not be able to be modeled. A relevant example is the Covid Pandemic. A research article from the University of York notes this detail about CAS, in regards to the pandemic. The article notes that since CAS are so dependent on initial conditions it is important to always seek the most newly available data so that available solutions can be adjusted accordingly (Angeli, F., & Montefusco, 2020). In essence, the research article suggests by modeling the pandemic with stagnant information the insights from that model will become outdated quickly. Further, implying that constant new data will enhance the model to be more accurate, enabling effective and actionable insights. Overall, showing that CAS can be modeled to a certain extent but not perfectly due to their ever-changing nature.
The financial markets also highlight the importance of new data in representing its respective CAS. In fact, much of finance is dedicated to modeling how a certain market works and devising a strategy to profit from the model. Many financiers now know the markets are complex adaptive systems and plan accordingly. For example, they might create a model with the abundant data in finance and then devise a trading strategy. They would then test the strategy and if it works they would utilize it. Lastly, since they know they are in a CAS, they would understand that their strategy would only work for a certain amount of time before the system changes and the strategy is no longer valid (Jennings, 2020). This example shows the inability to perfectly model a CAS. In finance, there is an abundant amount of new data every second. Even with this large supply of relevant data, perfect models cannot be achieved. Due to the nature of a CAS, a small change in the market could eventually change the entire system and even with infinite data it may be impossible for a model to have the ability to account for this.
Complex Adaptive Systems exist everywhere in our world, largely without us fully understanding them. Even though we participate in them, we cannot accurately describe them in the long term. Despite this, the study of CAS will continue and improve our understanding of the world. So far, we know we can model a CAS and even draw insights from it with enough relevant data. However, we do not have the calculus to know when our model will become irrelevant regardless of the data available. Overtime, hopefully further study can help us to achieve greater understanding of minute details causing entire system changes. Lastly, viewing the world through CAS allows you to see how every part of a system is interconnected, and that no matter how small an event within the system, large and intricate impacts can be made.
Works Cited:
Tim Sullivan. September 2011. “Embracing Complexity”. Harvard Business Review. https://hbr.org/2011/09/embracing-complexity
Angeli, F., & Montefusco, A. (2020). Sensemaking and learning during the Covid-19 pandemic: A complex adaptive systems perspective on policy decision-making. World development, 136, 105106. https://doi.org/10.1016/j.worlddev.2020.105106
John Jennings. June 15th, 2020. “Why The Stock Market Doesn’t Make Any Sense”. Forbes. https://www.forbes.com/sites/johnjennings/2020/06/15/why-the-stock-market-doesnt-make-any-sense/?sh=19ee908d3eb3
