When we decide we want to improve our health, transform our business or launch a new product, we develop a plan and execute it. As described in Why We Struggle with Complexity, we often apply logic and analogies of similar circumstances to make sense of these complex challenges and to determine what to do. We know logic (if this, then that) and analogy (like that, except this) decision making can fail miserably with complexity.
Logic becomes ineffective when there are too many variables or when variables are unknown or unpredictable. Analogies becomes ineffective when the context, people and variables are too different. If you failed to achieve a complex goal after hard work and developing the required knowledge, it is likely due to oversimplifying complexity with logic and analogy. You were likely impacted by one or more of these factors:
- Too many impacting variables for logic and analogy reasoning methods
- Unknown impacting variables that are difficult to identify
- Uncertain impacting variables difficult to predict how they will act, react, or interact
- Unpredictable outcomes due to change and the combination of the variables
Managing complexity is not new to scientists, inventors, scholars, and system engineers. They learned they need to perfect the process of:
- Understanding and predicting the complex system
- developing strategies that achieve the mission while addressing the complex system
- iterations of adjusting the strategy and model of the complex system
With complexity, a definitive answer based on logic or analogy is not possible. The logic and analogy reasoning that we were taught in school, is no match for too many, unknown, uncertain, and unpredictable variables. The best strategy comes from many iterations until the mission is achieved. Here is a closer look into how they manage complexity.
Scientists such as biologists, ecologists, meteorologists, psychologists, and sociologist model how numerous elements (variables) will act, react, and interact (complex system). Meteorologist use these predictive models forecast rain, wind, temperature and even hurricanes. When their weather predictions miss their mark, they make incremental adjustments to their models of the complex system, change and strategies. This results in a continuous cycle of improvement in forecasting.
Inventor Thomas Edison built a model for managing the complexity of invention. His model included a Menlo Park research laboratory, investor funding and teams of people with diverse domain knowledge. He used this invention framework to make over 3,000 incremental adjustments to his lightbulb creation. He had to address the complexity of finding the right balance of low production costs, durable filaments, brightness, and low energy consumption. While Edison did not invent the lightbulb, he did invent a bulb that fit within his “seven-point program” to be competitive with gaslighting. The seven points included “the parallel electric circuit; the durable, high-resistance light; the improved electrical generator; underground copper wiring; devices to keep the electrical current constant no matter the location of the bulb; safety fuses, insulating materials and usage meters; and lighting fixtures with switches to turn them on and off (1). Since electric power distribution did not exist, Edison had to manufacturer most of the devices. His first light bulb became commercially available in 1879 and he introduced the first electrical power plant and distribution system in New York in 1882 to create demand for his innovation.
Hedge fund firm Bridgewater Associates built a model to predict financial markets and to test their investment theses. To ensure as many plausible viewpoints are considered about the model’s impacting elements (complex system) and investment thesis (strategy), company founder Ray Dalio instituted a corporate framework of meritocracy. Bridgewater hiring strategy ensures a wide diversity of domain knowledge and meritocracy strategy solicits the best viewpoints including from the new hires just out of college. They document the success of employee theories, inputs, and persuasiveness to understand their decision-making process. They then make incremental adjustments to their predictive models, decision making process and investment theses based on what is learned from their multi-billion dollars of investments. Bridgewater used their predictive modeling and culture of meritocracy to grow to manage $150 billion of capital and one of a few organizations correctly predicting the 2008 Great Recession.
The system engineers of Amazon, Apple, Facebook, and Google show us how to thrive with managing complexity within frameworks with thousands, or millions of incremental improvements. It would be hard to imagine how we managed our lives with the early versions of Amazon (1994), Apple iPhone (2007), Facebook (2004) and Google (1998). These companies have become industry leaders in managing complexity within their frameworks with the use of Artificial Intelligence based on data science and deep learning algorithms. Google search analyzes “short clicks” versus their “long clicks” to continuously make incremental improvements to their solution. If users click on the first search result and does not come back (“short clicks”), the were likely satisfied. If users click on the first search result, spends time there (“long click”), then clicks and become satisfied with the second result, the deep learning neural network may make slight adjustments to the weighting of some of the hundreds of search signals and sub-signals.
Managing complexity is not something most people are taught in school, that is unless they enroll in advanced college degree programs. Master’s and post-doctorate degree programs study complex systems, such as biology, ecology, economics, meteorology, political science, psychology, or sociology. While these degree programs teach the complexity of their subject domains, they have little time to study the fundamentals of complex systems or how to address the complexity that fills our lives.
To manage complexity, scientists, inventors, scholars, and system engineers use frameworks to understand their complex systems and predictive modeling of their strategies. The use iterations of incremental improvements to their frameworks and predictive models as they learn. They determine the most effective strategies (forecasts, innovations, theories, and products) through iterations of trial and error, learning and adjusting their frameworks and models until they get it right.
To achieve complex goals, we can apply some of the same complex system and iteration thinking principles used by experts. We learned logic & analogy methods in school in a formal way. We learned complex system and iteration methods in life in an informal way while achieving our complex goals. We accomplish complex goals by making sense of the complex system, developing an effective strategy and iterations of “learning by doing” until we achieve our mission.
| Logic & Analogy | Complex System & Iteration |
| Few variables | Many variables |
| Known variables | Known & unknown variables |
| Certain variables | Certain & uncertain variables |
| Rules & formulas | Frameworks and models |
To be successful achieving complex goals to improve our health, transform our businesses or launching a new product, we need to be efficient and effective assessing the situation (complex system) and developing a plan (strategy) to achieve it. Complex system and iteration thinking can help get you there. To create frameworks of complex goals (complex systems), strategies and to manage iteration like experts, it requires understanding the eight essentials to managing complexity.
(1) Josephson, Matthew, Edison: A Biography. McGraw-Hill, 1959, p229
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