Many actuarial modernization and transformation projects fail or have poor outcomes. They are often massively oversimplified. They focus on the desired result of implementing different technology. These projects are complex, chaotic and adaptive processes. They will succeed or fail based on the organization’s current culture and management’s understanding of the project’s potential to change its culture.
To understand these dynamics, we must take a deeper look at:
- The origins of cognitive bias
- The physics of information and computation
- The nature of organizations
- The anthropology of culture
Once you understand how these disciplines feed one another, you can understand the root sources of pain when transforming your organization.
The Origins of Cognitive Bias
The most crucial concept of cognitive science is that how a problem is framed dictates how the solution will be approached. The framing is so sensitive that it is affected by the order of words and analogies used. This is because our brains use fuzzy logic to create the heuristics required to simplify problems. The advantage of our mind’s heuristic approach is that we can find adequate solutions to massively complex issues with limited information. The disadvantage is that we operate under cognitive biases leading to functional solutions that are less than optimal. Prospect theory states that people regret losses more than they favor gains.1 People will be risk-averse when framing problems in terms of gains and risk-seeking when framing issues in terms of losses.
These and other cognitive biases have us framing the underlying mechanisms of project dynamics suboptimally or even incorrectly. Neoclassical economics is founded on many heuristics. As a result, we tend to frame business problems as far more linear than they really are. By linear, I mean thinking that if I put x in, I should get a simple, predictable result. But to show why this is not the case, I need to go back 13.7 billion years and give you a new reference frame for how project dynamics come about and the roots of their failure.
The Physics of Information and Computation
What is information? Information is not a thing but a physical ordering of things.2 Information has no meaning that the everyday use of the word implies. Only systems far from equilibrium and bursting with localized energy can create physical order within pockets of the universe, such as Earth. When energy is removed, entropy will turn order into disorder, the object will disintegrate and the physical ordering will be lost. Systems in equilibrium and complete disorder are dead.
Information must be encapsulated in solid objects, such as rocks, DNA or hard drives, to protect its physical order from destruction. Computation long preceded life because matter can compute through chemical reactions by changing unstable states into highly organized structures. Chemical reactions are primitive transistors.3
Fast-forward approximately 13 billion years. This simple chemical computation has created ever more complex and highly organized systems.4 For example, deciduous trees have evolved to transform sunlight, through photosynthesis, into the ability to compute seasons. Neuroscientist Suzana Herculano-Houzel explained how early primates evolved to learn how to cook food so they could eat more calories in far less time. This evolutionary leap allowed them to grow the largest prefrontal cortex in the animal kingdom and divert their attention to topics outside of just finding food.
Now fast-forward another 100,000 years or so. Those same primates have used their spare time and constructed economies that transform energy into physical objects such as computers, airplanes and insurance. Along with the computation from their knowledge and know-how, this energy creates a positive feedback loop of innovation. As more energy is applied to the out-of-equilibrium systems, more complex information and economies are created. As much as we like to think of our firms as inanimate machines, they are the essence of living organisms going through the evolutionary process.
The Nature of Organizations
How does an organization compute information? The smallest unit of computation within the organization is a person. It is natural to think it is a network or computer, but without a person feeding the data and programming the computer, it is just a boat anchor. Computers are simply the tools to help with computation and hide information from the death clutches of entropy. Unfortunately, a person can accumulate a maximum amount of knowledge and know-how, known as the person byte.5 To overcome this limitation, tribes evolved.
A tribe is nothing more than a network of people. A tribe was evolution’s way of creating parallel computing. By working together, everyone had a higher chance of survival because they could distribute the computation and diversify the knowledge and know-how. The larger and more diverse the network, the more they could accomplish. Just as people have the person byte limit, teams, business units and firms also have limits on their knowledge and know-how.
What is the source of the limits? Learning is an experiential and social activity, which leads to expertise being geographically concentrated around knowledge centers. The lumpiness of knowledge and know-how makes them difficult and time-consuming to accumulate, exacerbated by the complexity and depth of the topic. The expertise embedded in the network of people, teams and business units makes copying the success and expertise of a firm difficult. If a portion of the business is removed, then the knowledge and know-how leave with the people.
These network connections do not end at the boundaries of the firm. The entire economy is a network of people, teams, business units, firms, communities, cities, counties, states and countries. These network connections are not free. How do these different networks know how to cooperate and collaborate to achieve goals? How does society moderate the costs of collaboration? It creates cultures.
The Anthropology of Culture
What is culture? According to Dr. David White:6 “Culture is a reference system of how to operate in a group. Reference systems evolve from shared experiences and challenges faced by organizations. A reference system can be divided into three elements: shared dominant logic (SDL), practices and adaptations.
- SDLs are preconscious and pervasive mental representations for rationalizing, explaining and idealizing. They comprise the shared assumptions and what people deem as good.
- Practices are everyday habits, routines and processes the organization runs—SDL powers them.
- Adaptations are typical behaviors or attitudes that are reactions or compensations for dominant logics and practices.”
I need to put some color around these three definitions. SDLs are simple rules that guide how we preconsciously behave. It is essential to understand they are preconscious behavior, which means they come from the unthought knowns. Your thick employee handbook does not contain the logic. The unthought knowns are the operating systems running in the firm’s background. They are like Microsoft Windows running while I am building an Integrate model.
For example, the simple rules include “making financial numbers this quarter is top priority” or “upper management needs to weigh in on all major decisions.” Organizations can have many such rules. These rules are not additive, and their impacts on the organization are not linear. The effect is more like what is found in a chaotic system such as Conway’s Game of Life, in which simple rules may lead to complex and surprising behavior.
The SDLs are reinforced through repetition, practice and rewards. White states: “The more successful the organization, the more hardened the dominant logic. This is because reference systems tend to be ‘overlearned’ and reflexively applied in novel situations. This can be good for developing strong and cohesive cultures, especially in smaller organizations, but severely limiting when trying to transform an organization.” This overlearning and reinforcement make cultures sticky and hard to change. This tacit knowledge weaves itself into the overall knowledge and know-how of the organization’s network.
For instance, actuaries are rich in standards of practice, which is a prime example of what White calls professionalization. Actuarial science is one of only a handful of white-collar apprenticeships. We take exams for five, seven, 10 years—or more—to learn to become an actuary. The exams are a filtration process to find meticulous, risk-averse, intelligent people. This procedure is mission-critical for an industry that sells long-dated promises. At least back in my day, we spent most of our exam-taking journey working full-time. This process is purposefully designed to indoctrinate everyone into the culture and tradition.
Adaptation is the response to the logic set that combines complex corporate behavior. Due to our cognitive limitations, adaptation is not optimal. At best, it is more likely a local maximum sufficient to meet the demands of the environment based on current practices. For example, Equity Funding Corporation of America put 99 at the front of its fictitiously created insurance policy numbers. They went so far as to build a specialized computer system to manage the fraud. This scheme allowed them to profit from reinsurance, which I doubt was their initial intent. This billion-dollar bubble was an adaptation and evolution to conflicting SDLs and practices already in the company’s culture. I am not implying that adaptation automatically leads to fraud, but that fraud is an extreme response deriving from the same processes as non-fraud.
According to White, there are no such concepts as “one company with one culture,” that “culture starts from the top,” or “our values are our culture.” These myths oversimplify the complexity of cultural dynamics based on a dream that culture is an easily malleable, independent variable. Affecting culture is much less like turning a car than steering cattle in a long cattle drive in Wyoming.
Cultures emerge from reinforced processes and behaviors that evolved so groups of people could improve, adapt and survive. Actuaries, software engineers, risk managers and so on will have diverse cultures because they have different challenges and concerns. They will have formed different SDLs, practices and adaptations to their environments.
What is the Source of Failure?
Transformation and modernization projects aspire to introduce innovative technology that will change processes. They enable the organization to achieve new goals. The heuristic for success is to improve technology and organizational capabilities. This heuristic will focus their attention in the wrong direction, so the project will fail to meet its intended target. Beyond the modern technology, success hinges on developing new logic, practices and adaptations.
The more significant the transformation required, the greater the needed impact on the culture. The more powerful the culture change, the more likely the project is to fail. Cultures are like sticks—they can bend only so far before they snap. Significant transformations take the same amount of culture change and emotional discipline from the CEO down to the janitor.
Professionalization leads us to an interesting dilemma. Actuarial culture and, for that matter, organizational culture got insurance companies to where they are today. If the culture were not moderately successful, then the company would not still exist. But this is where Prospect theory emerges from the shadows. It is human nature not to want to lose the culture that enabled your success. Many people nonetheless thirst for the gains earned by moving in a new direction. Risk aversion further reinforces the stickiness of culture, especially for risk-averse professions and industries. Drawing from author Tony Robbins, you cannot become who you want to be by staying who you currently are. Our professionalization, coupled with our risk aversion, creates a double whammy. Practices appropriate to prior eras have a propensity to be locked in place. Oh, but it gets worse!
By the nature of transformation and modernization, knowledge and know-how are embedded in the current people, processes and systems. The knowledge and know-how must be migrated from the prior technology to modern technology. Just like your computer’s hard drive gets fragmented, so too do firms’ expertise as people change focus, move jobs or leave companies. The long-dated nature of our promises can severely exacerbate the issue. Human knowledge and know-how are not very compressible, unlike biological seeds and eggs. In a time-consuming defragmenting exercise, information, knowledge and know-how must be painstakingly moved, relearned and adapted for the new system. This transformation requires new practices, further exacerbating the shock to the culture. Oh, but it gets even worse!
The transformation process requires existing teams to change, recombine or communicate in new ways. This means their cultures will potentially clash. Lack of trust and bureaucracy are the most significant frictions to collaboration among networks. The direct evidence of this is when project managers vent that teams x, y and z cannot seem to work together. It is because they do not have a reference system to know how to work together.
I am not saying that having different teams collaborate is terrible—quite the contrary. The mixing and matching of teams’ knowledge and know-how allow firms to accomplish astonishing feats. But if you do not account for the feedback loop between the transformation process and the company culture, it is easy to overpromise and underdeliver.
The inconvenient truth is projects are adaptive processes. As different disciplines come together, their cultures collide, so they need to produce new logic models and processes that work for them. They must create ubiquitous languages so they can communicate. The process is like a stochastic search algorithm that bounces around all the state space trying to find a solution. This algorithm will likely find itself in undesirable states during the search. But through trial and error, the algorithm eventually will land on an acceptable solution. The solution will allow people to get into a flow. The larger the transformation desired, the larger the state space to search and the more time-consuming it is to find a result. Once in the flow and the solution is found, the results are far more predictable.
Actuarial transformation and modernization projects are challenging because they are not changing technology but changing culture. They are made even harder by the heuristics focused on outputs, which makes them feel like they are extensions of business as usual (BAU). If we approach them as BAU, this leads to frustration and makes projects feel like you are pushing a rope. For a given input, we want a predictable output. This expectation rarely meshes with reality. It frames the issues incorrectly and focuses our attention on solving the wrong problems. It is hard to steer the ship if we do not understand its dynamics.
I took you through a 13.7-billion-year journey so I could reframe the problem and build from the ground up. I demonstrated that there is physics to projects based on thermodynamics, information, networks and adaption. Until teams have learned to work together through trial and error, you cannot assume the expected outcomes of BAU.
Actuarial transformation and modernization projects fail because:
- As a possible byproduct of our lengthy training, actuaries may be slow to adapt practices to changing circumstances and technologies.
- Information, knowledge and know-how get fragmented or lost over time as people migrate. The recovery of these items requires defragmentation, which can be exceedingly difficult and time-consuming.
- As new complexities arise within projects, new teams will have to work together. They will need to search for new ways to communicate and collaborate, which means evolving their cultures. If this reality is not accounted for, it can lead to overpromising and underdelivering.
Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries or the respective authors’ employers.
- 1. Burton, Edwin T., and Sunit N. Shah. 2013. Behavioral Finance: Understanding the Social, Cognitive, and Economic Debates. Wiley. ↩
- 2. Hidalgo, Cesar. 2016. Why Information Grows: The Evolution of Order, from Atoms to Economies. Basic Books. ↩
- 3. Ibid. ↩
- 4. Supra note 2. ↩
- 5. Supra note 2. ↩
- 6. White, David G. 2021. Disrupting Corporate Culture: How Cognitive Science Alters Accepted Beliefs About Culture and Culture Change and Its Impact on Leaders and Change Agents. Routledge, Taylor Et Francis Group. ↩
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