Additive bias…or Not: II

Additive bias is a cognitive bias that affects our ability to assess the value of candidate solutions to problems. Specifically, it causes us to favor additive solutions — solutions that add components and complexity to solutions we already have. Likewise, it causes us to assess as less valuable subtractive solutions — solutions that have fewer components and which are less complex than solutions already in hand. For more about additive bias, see "Additive bias…or Not: I," Point Lookout for June 26, 2024.

As noted in my earlier post, additive bias is only one of many possible sources of overly complicated problem solutions. In that post, I posed a realistic scenario in which the additive bias could play a role. I also provided two descriptions of mechanisms in which organizational politics could cause people to favor additive solutions over subtractive solutions.

In this post I describe two other mechanisms unrelated to additive bias, and which can produce overly complicated problem solutions. These two mechanisms are based on assessments of engineers as to the cost of implementing the problem solution. In this scenario I suggest how an engineer could judge that an additive solution might be more easily implemented than a subtractive solution, even though the subtractive solution might appear to be a simpler result.

Two technical phenomena that can lead to asset bloat

Although additive bias can lead to asset bloat, other phenomena can do so as well. Here are two effects that have roots in technology.

As engineers go about their work of adding a capability to an application, they have the usual technical concerns. The must identify what parts of the code need alteration, what parts need to be added, and what parts need to be removed.

The cost of understanding

Under schedule and budget pressure, engineers must decide how much to invest in understanding how the system now works. Unless the engineers were personally involved in developing the system as it now stands, that task — understanding the system — typically involves study, research, and possibly interviews of the authors of the existing system, if they're available. In some cases, a lower-cost approach involves creating whatever support facility the new changes require, rather than studying the system to determine whether those capabilities are already present in some form, or if so, how to use them.

The end result, in some cases, is capability duplication that can appear to be the result of additive bias. In reality though, the engineers who do this will have found a way to meet the objective with even less work than would have been required without the duplication of capability. What appears to be additive is actually subtractive from the viewpoint of the engineers doing the work.

Revalidation

If our engineers decide to modify module M of the existing system, they might later be required to re-validate M as part of their work. Revalidation can require execution of test suites and evaluation of test results. To perform these operations, some portion of the engineering effort must be allocated to understanding the validation process, possibly extending the validation process, and performing the validation.

This portion of the effort can act as a deterrent to engineering teams that are considering approaches that involve modifying existing components of the system. To avoid revalidation, some teams elect alternative approaches. Alternative approaches often involve adding new elements that are required only because of the decision not to alter any components that would then require revalidation. The end result then can appear to be consistent with additive bias when it's actually the result of avoiding approaches that would necessitate revalidation.

It should be noted that in software engineering, at least, approaches that exploit inheritance and polymorphism can limit the need for revalidation.

Last words

These past two posts have provided four examples of mechanisms that cause system maintainers to choose to add components to the systems they maintain. They add these components even though they recognize that alternative subtractive approaches can yield superior outcomes. I hope that those who argue that these additions are evidence for the effects of additive bias can provide quantitative analysis to support their positions.   First issue in this series   Top    Next Issue

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