How Cognitive Biases Make Technical Debt

Adopting a program for rationally managing technical debt is a complex undertaking. But the heart of the problem must certainly include how we decide whether and when to deal with technical debt. Researchers have demonstrated that decisions are not always rational. They can be affected by any of hundreds of cognitive biases. Cognitive biases underlie our human tendency to make systematic judgment errors based on thought-related factors rather than evidence. We review selected biases and suggest how to mitigate their effects on technical debt management programs to avoid catastrophes and create stunning successes.

As the total burden of technical debt increases, the importance of technical debt management increases. With respect to our growing portfolios of technical debt, an intriguing question arises. Given our impressive capacity for retiring technical debt, why do we so consistently allow our technical debt to grow to such extreme levels? Even more puzzling is the continued growth of technical debt in organizations that are actively trying to control it. It seems only reasonable to suggest that psychology might provide some answers to these questions.

The essential message of this program is twofold. First, technical debt forms or persists as a result of decisions people make throughout the organization. If we continue to make decisions as we always have, technical debt will continue accumulating, even if we install new engineering procedures. Second, the decisions we make are affected by cognitive biases. These biases might account, in part, for our disappointing performance in controlling the growth of technical debt. By understanding the cognitive biases most likely to lead to formation or persistence of technical debt, we can manage their effects to gain some control over the debt.

The cognitive biases we examine

How Cognitive Biases Make Technical Debt

Le Penseur (The Thinker) in the Musée Rodin in Paris. How we think is at the root of much of the problem we have with technical debt. This program explores the connection between how we think and the formation and persistence of technical debt. When we devise initiatives to control technical debt, we would be wise to consider the possibility that adjusting our thought processes might play a critical role in solving the problem.

The program examines a specific set of cognitive biases that can affect the size and complexity of the technical debt portfolio. For each one, we explain how it works to prevent control of the technical debt portfolio. We then suggest techniques for mitigating its effects.

Below are brief summaries of three of the cognitive biases that can affect an organization's efforts to control technical debt. We look at up to 15 cognitive biases, depending on the time available.

Loss aversion
Loss aversion is the tendency to favor options that avoid losses in preference to options that lead to equivalent or even greater gains.
Retiring technical debt usually entails deferring revenue in the short term. Some decision makers can perceive that effect as a loss. They might therefore tend to favor deferring technical debt retirement projects more than objective evidence would justify.
The ambiguity effect
The ambiguity effect 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. We do this even if the expected value of that more ambiguous outcome exceeds the expected value of the less ambiguous outcome.
Often we must choose between allocating resources to technical debt retirement and allocating resources to new capability development. If the benefits of new capability development seem less ambiguous, we tend to favor new capability development more than objective evidence would justify.
The availability heuristic
The availability heuristic is a method humans use to evaluate the validity or effectiveness of decisions, concepts, methods, or propositions. According to the heuristic, if we recognize the item as familiar, or related to something familiar, we're more likely to regard it as valid or workable.
In most organizations, technical debt retirement projects are less familiar to decision makers than are maintenance or development projects. On that ground alone technical debt retirement project proposals are at a disadvantage. But there are additional reasons why we tend to favor other work, and we discuss those as well.

In addition to these three cognitive biases, we also discuss a selection of the following: the endowment effect, the halo effect, the reification error, confirmation bias, the resilience error, absence blindness, unrealistic optimism, selection bias, the planning fallacy, the fundamental attribution error, the Dunning-Kruger effect, and the IKEA effect.

Learning model

We usually think of technical debt management skills as rather technical — free of emotional content. We hold this belief even though we know that our most difficult situations can be highly charged. Despite our most sincere beliefs, taking a project organization to the next level of performance does require learning to apply technical debt management skills even in situations of high emotional content. That's why this program uses a learning model that differs from the one often used for technical content.

Our learning model is partly experiential, which makes the material accessible even during moments of stress. Using a mix of presentation, simulation, group discussion, and metaphorical team problems, we make available to participants the resources they need to make new, more constructive choices even in tense situations.

Target audience

The target audience consists of two groups. Group A includes people who work on products or technical infrastructure that can carry technical debt. People in these roles are project sponsors, program managers, project managers, business analysts, architects, and project team members. Group B includes people who make or influence decisions about organizational policy and resource allocation across multiple projects. Examples include executives, functional managers, policymakers, and strategists. All experience levels can benefit.

Personal take-aways depend on part of the target audience to which one belongs. Although all of us are subject to cognitive biases, the biases most relevant to our work depend on the nature of the work and on our interactions with others. Some of these biases we explore are more likely to affect Group A; some are more likely to affect Group B.

Program duration

Available formats range from a one-hour survey to two days. The longer formats provide optimum value and include simulations of organizations as they grapple with the challenges of managing technical debt.

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