Last updated on July 10th, 2021 at 08:49 am
Technical debt arises in enterprise assets through the effects of two classes of drivers: obsolescence and decision-making. When technologies advance, or new technologies arise, or laws or regulations evolve. Existing assets or assets under development can sometimes be left behind. That’s how obsolescence produces technical debt. Debt driven mainly by decision-making is more difficult to describe. But anything that biases decisions away from strictly rational results presents risk. Three cognitive biases likely have strong effects on technical debt formation and persistence.
Cognitive biases affect decision-makingDecision-making produces technical debt as the people of the enterprise make choices in design, development, and resource acquisition or allocation. Typically, both obsolescence and decision-making contribute to producing technical debt, though either obsolescence or decision-making might be more important than the other in any given instance.
Managing debt driven principally by obsolescence isn’t difficult, but I’ll leave that topic for another time. For now, let’s focus on decision-making. Already widely accepted is the contribution of engineering decisions to technical debt formation. Indeed, many believe (in my view, incorrectly) that all or most technical debt arises from faulty decisions by engineers. Some engineering decisions are indeed faulty. But the current scale of technical debt is so large that faulty engineering is unlikely to account for it all. Investigating how resource allocation decisions might contribute to technical debt formation is certainly worthwhile.
In this post, I offer three examples illustrating how resource allocation decisions might contribute to technical debt formation and persistence. Each example shows how people make faulty decisions while believing they’re proceeddecision makering objectively and rationally. In each case, what causes the problem is a phenomenon called cognitive bias, though each example in this post illustrates the action of a different cognitive bias.
Amos Tversky and Daniel Kahneman were the first to identify the cognitive bias known as loss aversion [Kahneman 1984]. It’s the tendency to favor options that avoid losses in preference to options that lead to equivalent or even greater gains. A decision maker affected by loss aversion bias might conclude that not losing $5 is better than finding $5 or even $10. In this way, loss aversion skews decisions in favor of options that enable the enterprise to protect or enhance existing revenue streams. And it skews decisions in this way even if those decisions cause increases in operating expenses. This bias has effect even if the increases in operating expenses exceed the value of whatever revenue that decision protected.
Short term effects of loss aversion
Retiring technical debt usually entails deferring revenue in the short term, for two classes of reasons. First, we must turn the attention of some part of the engineering organization to debt retirement. Assuming that they would have been working on maintaining or enhancing existing products or services, this redirection can lead to reducing or deferring revenue. Second, during the debt retirement operation, some work might require short-term interruptions of revenue streams while the work is underway.
Thus, debt retirement efforts often do reduce revenue—or reduce revenue increases—in the short term. Some decision makers can perceive that effect as a loss.
Long term effects of loss aversion
The long-term effects of debt retirement can be gains, and those gains can be considerable. Typically, by retiring an asset’s technical debt, we reduce the difficulty (read: time required, effort, cost, and risk) of future maintenance and enhancement efforts involving the asset. We also reduce the probability of debt contagion.
Since these long-term effects of debt retirement are ongoing, their impact on the enterprise can be significant. But unless one is familiar with dealing with the consequences of technical debt, recognizing the value of retiring technical debt can be difficult. When loss aversion is in play, intuitive comparisons of the effects of (a) a short-term revenue loss or delay to (b) a long-term benefit of debt retirement favor development and maintenance over retiring technical debt.
Insulating decisions about debt retirement from the effects of loss aversion bias requires objective mathematical modeling of revenue losses and operating cost benefits for all options under consideration. Those models must also account for uncertainty, which makes them inherently ambiguous. And that leads us to consider our next cognitive bias, the ambiguity effect.
The ambiguity effect
The ambiguity effect is a cognitive bias that causes us to prefer options for which the probability of a desirable outcome is relatively better known, over options for which the probability of a desirable outcome is less well known, even if the expected value of that more ambiguous outcome exceeds the expected value of the less ambiguous outcome. The effect was first described by Daniel Ellsberg [Ellsberg 1961].
Consider a choice between allocating resources to new development and allocating resources to technical debt retirement. In most enterprises, decision makers are familiar with new development projects. Likewise, project champions, project sponsors, and project managers are also familiar with new development projects. All parties are less familiar with debt retirement. It’s reasonable to suppose that when confronted with such a choice, decision makers are likely to see debt retirement as carrying with it a probability of positive outcome that is less well known than the probability of a positive outcome for the new development project.
Because of the ambiguity effect, resource allocation decisions are likely to be biased against technical debt retirement, and in favor of maintenance or new development.
But there’s more. Most projects, of any kind, encounter trouble from time to time. When that happens, the urge to reallocate organizational resources can be powerful. Troubled projects might receive more resources if they’re viewed as important to the organization. If so, those resources often come from other projects. The ambiguity effect biases these resource reallocation decisions in a way analogous to initial resource allocation decisions, as described above. In other words, because of the ambiguity effect, when projects encounter trouble, debt retirement projects are less likely to be able to retain previously allocated resources than are maintenance or new development projects.
The availability heuristic
The availability heuristic is a method humans use to evaluate the validity or effectiveness of decisions, concepts, methods, or propositions [Tversky 1973]. According to the heuristic, if we recognize the item being evaluated as familiar, or related to something with which we are familiar, we’re more likely to regard it as valid or workable. And when making comparisons between two alternative decisions, concepts, methods, or propositions, we’re likely to assess more favorably the decision, concept, method, or proposition with which we’re more familiar, all other things being equal.
In organizations where decision makers have more experience evaluating maintenance or development project proposals than they have with technical debt retirement proposals, the availability heuristic acts to reduce the relative assessed favorability of technical debt retirement proposals. It does this in three ways.
Technical debt retirement projects are less familiar
First, in most organizations, technical debt retirement projects are less familiar to decision makers than are maintenance or development projects. On that ground alone the technical debt retirement project proposals are at a disadvantage.
The effects of retiring technical debt are less obvious
But the second effect of the availability heuristic is more important. To grasp the value of working on an asset, we must understand how it will affect the asset’s users. Likewise, to grasp the value of a technical debt retirement project, we must understand how technical debt hampers the enterprise. We must also understand how retiring the technical debt might confer advantages in terms of future engineering efforts. Usually, understanding the consequences of maintenance or development projects is more “available” to decision makers than is understanding the consequences of technical debt retirement projects. Even more dramatic is the difference between understanding the consequences of not funding a maintenance or development project and the consequences of not funding a technical debt retirement project.
Much of the benefit of technical debt retirement is indirect
That is, although there is some direct benefit in terms of the assets from which the debt has been retired, the most dramatic benefits are manifested in projects that follow the debt retirement project, and which depend on the assets that have been relieved of debt. Sometimes, those follow-on projects are known at the time decision makers are considering funding the debt retirement project. Sometimes those follow-on projects have yet to be specified or even recognized. In either case, they are less “available” to decision makers because those follow-on projects are indirect beneficiaries.
These three effects of the availability heuristic cause decisions about resource allocations to tend to favor maintenance or development projects over debt retirement projects.
Mitigating the risks of these three cognitive biases
Over time, as everyone becomes more familiar with technical debt retirement projects, these effects may wane somewhat. But waiting for that to happen isn’t exactly what one might call risk mitigation. For one thing, familiarity grows only if one is motivated and pays attention. As busy as are decision makers in modern organizations, depending on them to actively enhance their own familiarity with technical debt retirement projects is probably not the safest course.
An effective program of actively mitigating the risks of these three cognitive biases probably should focus on four areas.
Do what you can to increase decision maker familiarity with the concept of technical debt, and with the consequences of carrying existing technical debt. Conventional presentation-based training will help, but interactive, experiential training is far more effective. Participants must actually experience the consequences of technical debt in a well-designed and professionally facilitated simulation of a problem-solving task. A faithful simulation would include estimation, changing and ambiguous requirements, and team composition volatility.
Retrospectives are also known as after-action reviews, post mortems, debriefings, or lessons-learned sessions. They’re meetings that review processes that have just completed pieces of work [Kerth 2001]. Typically, only project team members attend. To maintain psychological safety and to encourage truth telling, enterprise decision makers and supervisors don’t attend, unless the organizational culture includes appropriate safeguards. In any case, a section of the retrospective dedicated to investigating the causes and consequences of technical debt can ensure capture of relevant knowledge and experience.
Mathematical modeling practice
Mathematical modeling is one path to creating a more objective foundation for decisions. It’s essential for improving estimation quality. Also helpful are high quality effort data and metrics data related to the formation and lifetime of technical debt. Reviews of estimates and projections during retrospectives can help improve their quality over time.
Determining the effects of risk mitigation failure provides important guidance for corrective action in risk mitigation. Developing metrics that reveal these failures is therefore essential to managing cognitive bias risk. I’ll be suggesting some valuable metrics in a future post.
These three cognitive biases are by no means the only cognitive biases that can affect the formation or persistence of technical debt. Of the more than 200 identified cognitive biases, those most likely to be relevant are those that affect decision-making. Watch this space for links to posts about additional cognitive biases and their affects on technical debt formation or persistence.
Other posts relating to cognitive biases
- “Undercounting nonexistent debt items”
- “Confirmation bias and technical debt”
- “Unrealistic optimism: the planning fallacy and the n-person prisoner’s dilemma”
[Ellsberg 1961] Daniel Ellsberg. "Risk, ambiguity, and the Savage axioms." The quarterly journal of economics, 643-669, 1961.
Available: here; Retrieved: August 17, 2018.
[Kahneman 1984] Daniel Kahneman, Amos Tversky, and Michael S. Pallak. “Choices, values, and frames,” American Psychologist 39:4, 341-350, 1984.
Available: here; Retrieved: August 8, 2017
[Kerth 2001] Norman L. Kerth. Project Retrospectives: A Handbook for Team Reviews. New York: Dorset House, 2001.
[Tversky 1973] Amos Tversky and Daniel Kahneman. "Availability: A heuristic for judging frequency and probability." Cognitive Psychology 5:2, 207-232, 1973.
Available: here; Retrieved: August 9, 2018.
Other posts in this thread
- Managing technical debt
- Leverage points for technical debt management
- Undercounting nonexistent debt items
- Crowdsourcing debt identification
- Demodularization can help control technical debt
- Legacy debt incurred intentionally
- Metrics for technical debt management: the basics
- Accounting for technical debt
- The resilience error and technical debt
- Synergy between the reification error and confirmation bias
- Retiring technical debt can be a wicked problem
- Retiring technical debt can be a super wicked problem
- Degrees of wickedness
- Three cognitive biases