Four Steps to Simplify Complexity

by Gabriyel Wong,

DATE: 2020-02-27

The secret to trump your complexity adversary. 

The complexity we face daily underscores an existential purpose many of us fail to grasp — our ability to continuously learn, grow, adapt and surmount. We react to complexities in life or threat stressors with the well known “fight, flight or freeze” response as behavioural science aptly describes it. Research¹ has shown that the “flight” response (also known as “escapism”) has probably deeper association with the human basic instinct which we could perhaps easily validate through a closer introspection of our own reactions to the frequent difficult situations we face.


Productivity advocates in our fast-moving societies argue the need for simplification without realising its difference with superficiality. The business world has no paucity of evidences where over-simplification is conveniently chosen as the de facto solution in place of taking on complexity as an opportunity for learning. Increased cycles of hiring and firing, endless corporate restructuring exercises in multi-national companies and governments’ coercive tactics against social networks are examples of such escapism behaviour when individuals and organisations face complexity. The consequence? Apparent long term impact on sustainability as seen in shorter corporate lifespans² and government office terms.


So how do we simplify complexity to thrive? Our thesis on complexity management surrounds a systematic four-step process which we believe would be useful to any individual and organisation.



Complexity exists in different forms and shapes. Scoping complexity means taking an approach to create an abstraction to within one’s intellectual ability to understand a system. For example, how do we predict weather? We would then ask ourselves which part of the world are we interested in? Where can we find information about the weather there? From a statistical perspective, starting small may lead us to understand similar trends that could govern the bigger system. Scoping a complex system helps us systematically develop knowledge about the universe we are interested in.


In order to effectuate scoping, we need to first be able to define the boundaries of the universe we want to invest our time to learn. For example, to understand increasing suicidal rates of a society we could start by studying the lifestyles of people from a certain age group. Similarly, to find an entry point for business in a promising industry, we can look at a part in the value chain where inefficiency exists and there are clear opportunities to disrupt.


Scoping requires not just drawing boundaries but thereafter identifying the three key elements: the Stakeholders, their Relationships and the Dynamics, that is, how the relationships change over time. From System Theory³, if we consider a universe to consist of multiple sub-systems, then each sub-system may be perceived as a stakeholder. Each stakeholder is influenced by external events contributed by other stakeholders and its own behaviour. Stakeholders are connected to one another to facilitate their interaction and these channels are known as relationships.


Stakeholders depending on their relationships with one another determines the collective output or response of the universe. Moreover, relationships are dynamic as they can change over time. This can happen through a different set of connectivity among stakeholders as well as the kind of information that is being transacted between stakeholders.


For example, how can we understand the intricacies of human cognition with regards to the subject of music? We learn from historical studies about outstanding musicians as key (stakeholders) and the impact of their work in terms of creating movements (relationships). This is usually intertwined with their personal lives and state of societal development (dynamics) such as the the existence of poverty or oppression.



The purpose of deconstructing a universe is to understand as deeply as possible the causality within and beyond the universe so that rules and patterns may be conceived. The deconstruction process consists to two very important elements.


Data Acquisition — We develop knowledge through meaningful information from both past and present. To take on a data or evidence-driven approach to tackle complex systems means we need reliable data to validate our conjectures.


Science and engineering are two disciplines that are governed by data. They form the cornerstone of our modern society through the industrial revolution and recently the Internet age. It would impossible for us to enjoy the comfort and speediness from air transport if there weren’t countless hours of engineering invested in designing, testing and retesting of prototypes in failed hypotheses. The converse is true when traditional businesses collapse in the face of the new digital world because of the lack of data to guide decisions and strategies.


Data Sanitisation — We will always be challenged by the irony of data’s availability versus its usability. Quantitative finance analysts use sophisticated software to glean petabytes of past and present data daily but only less than five percent globally can consistently beat the market.


Another strategy to acquire sanitised signals is through engaging in experiential learning⁴. The core concept of Learning-by-Doing in experiential learning is meaningful because only by being “in the system” would someone be able to derive knowledge relevant to various context.



The reconstruction process amalgamates insights to form a picture close to reality so that we can validate our hypothesis with a level of confidence. predict the outcome with some level of confidence. It involves identifying the causality between each stakeholder through the interconnected relationships and the outcome of the overall system. Often this leads to patterns that may either be observable or new hypotheses that require further validation as granularity increases. Two important concepts undergird the reconstruction process —


Learning Efficiency

Since complex systems are difficult to understand, efficient learning is critical because both time and resources are limited. Taking this from an investment perspective, it means maximizing learning with minimum resource used. To illustrate, businesses today struggle with over leveraging to scale their top-line. Exorbitant costs to hire and retain senior executes, elaborate marketing campaigns and lofty product ideas without robust market research sinks companies into the red with little learning value in the long run. Learning efficiently means the ability to know how to learn, track progress and continuously co-create in a culture that fosters that altogether.


Para-referential Thinking

Confirmation bias has a pernicious influence on our observation ability. This is a natural part of our cognition which we could perhaps intentionally train ourselves to overcome because the learning process relies heavily on the positive reinforcement. Similar to modern psychology concepts such as lateral thinking² which essentially is a technique of problem solving by approaching problems at diverse angles instead of concentrating on one approach and meta-cognition which may be casually referred to as “thinking about thinking” (or second order thinking), para-referential thinking is about “reading the picture from outside the universe”. It is the universe logic that governs the systems we are working on that lie beyond what we would usually consider.


To illustrate, in Clayton Christensen’s Theory of Innovation⁵ paper, he noticed a neighbourhood milkshake business tried hard to understand its customers’ preferences by running surveys and adding menu items but that did not go well. Through his serendipitous discovery he found out that people who queued for the milkshakes were not really going for the tastiest or value-for-money milkshakes. Instead there were several reasons: first, the Dads wanted to feel good buying their children milkshakes which is more of a culture thing. Second, people driving off with milkshakes were usually stuck in morning peak hour traffic. Compared to most food, milkshakes take longer to finish because they are thick and hence able to keep people occupied in their cars when they are stuck in traffic.


Looking from within the system in this example did not seem to be help even if the business owners spend more time and money to improve their menu. However, taking different perspectives beyond their universe such as observing the activities and lives of people can help decipher the hidden code and possibly turn the business around. In short, para-referential thinking amplifies existential but unapparent relationships so that we may discover guiding principles that govern complex systems.



By aggregating rules or validated hypotheses, we start to develop a more comprehensive knowledge of complex systems. However, most real world problems are dynamic. This means relevant and continuous optimisation is necessary to uncover gaps within a system so that learning can remain robust. For instance, to improve conversion rates on e-commerce websites gurus would try numerous techniques to optimize user experience only to miss the importance of understanding the customer’s mind which is key to solving the conversion problem.


Finally, expanding the reconstruction concept across the systems and universes mentioned before allows our knowledge to grow infinitely deep and wide. This creates a propulsion effect which in turn benefits our learning speed over time.



Complexities in life though myriad and varied in nature, present opportunities for us to learn and grow. The objective of the four-step process presented in this article is to provide a systematic scaffold for learning about managing complexity. It is intentionally directional than prescriptive, so that the practitioner cannot find an easy way out of complex issues to which true learning begins.


  1. Norman B. Schmidt, J. Anthony Richey, Michael J. Zvolensky, and Jon K. Maner |
  2. Scott D. Anthony, S. Patrick Viguerie, Evan I. Schwartz and John Van Landeghem |
  3. M.L. Rhodes |
  4. John Dewey |
  5. Clayton Christensen |

The Author
Gabriyel joined Reapra in June 2018 as Chief Research Officer. Prior to Reapra, Gabriyel held senior leadership roles with several global unicorns (Rocket Internet, Skyscanner) and listed corporates (Expedia, Media Nusantara Citra) overseeing product strategy, management and investment in Asia. He was the co-founder of Singapore’s first game technology research lab in NTU and was faculty member of the university’s Computer Engineering School. In 2011, Gabriyel co-founded his 3D technology start-up that sold visualization systems to Intel and Motorola. Gabriyel holds a PhD and Master’s degree in Electrical and Electronics Engineering from NTU and an MBA from University of Illinois (UC).