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Volume 24, Issue 26;   June 26, 2024: Additive bias…or Not: I

Additive bias…or Not: I


When we alter existing systems to enhance them, we tend to favor adding components even when subtracting might be better. This effect has been attributed to a cognitive bias known as additive bias. But other forces more important might be afoot.
A well-festooned utility pole

A well-festooned utility pole. This sort of thing develops bit-by-bit, rather than by intention. Each maintenance team does what it's ordered to do. It isn't given the resources required to straighten out the accumulated mess.

Additive bias is a cognitive bias. It's the tendency to identify or favor approaches to problem solving that add components to an existing solution rather than approaches that reduce the number of components. For example, when rewriting a definition of the additive bias to enhance its clarity, we would tend to produce revisions that have more words than did the original version.

Experiments that demonstrate additive bias, and the cognitive science literature about additive bias, agree: what matters is how the number of components N in the unaltered system compares to the number of components N' in the altered system. [Adams 2021][Stewart 2021][Neroni 2024] Because of the additive bias, people tend to favor solutions in which N' is greater than N.

In artificial experiment conditions, we observe that when people alter an existing system to meet new requirements, they tend to favor alterations that increase the number of system components as compared to alterations that reduce the number of system components.

The real world

The artificial Although additive bias is a real phenomenon,
in the real world, there are many possible
alternative explanations for asset bloat
conditions of experiments leave little room for explanation of the results beyond the additive bias. I therefore have no doubt that it's a real effect. But in the conditions we find in the wild, there is an abundance of alternative explanations for the effects analogous to what we observe in the experiments that reveal an additive bias.

In the real world, we do observe a phenomenon we might call asset bloat — bloating of the assets that we subject to repeated incidents of maintenance or extension. And asset bloat could well be the result of additive bias. But are other explanations possible?

In what follows, I propose two possible phenomena that could lead to effects that would appear to be due to additive bias in a real-life situation, but which are unrelated to additive bias. Specifically, the situation is one in which a team has been tasked with extending the capabilities of a software system.

A capability extension scenario

The system in question is a portion of a familiar software product such as a word processor or spreadsheet application. The application has many commands, but in this scenario we are being asked to extend the capabilities of one class of these commands. We must do so in a way that avoids changing any existing capabilities. The task of the engineers is to add this capability to all commands that need it.

Two non-technical phenomena that can lead to asset bloat

Although additive bias can lead to asset bloat, other phenomena can do so as well. Let's begin with two effects that have roots in organizational politics.

As engineers go about their work of adding the new capability to the 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. But in addition, there are nontechnical concerns that can be just as important, and which can affect the result in ways that can appear to be the result of additive bias. Here are examples of two such phenomena.

Rolling their own
When engineers identify an incumbent facility in the application that can provide support for what they're creating, they need not add to the system their own version of that facility. If they take that approach, they can save time and effort, and avoid adding components to the system. But they need assurance from the "owners" of that incumbent facility (a) that they can rely upon it and (b) that their intended use is and will be supported by the owners — that their use is consistent with the system architecture.
Sometimes, the owners do cooperate. They're willing to support this unintended use. But in other cases, the owners are unable to cooperate. They might have future plans that conflict with the use now being contemplated. The owners might not reject our engineers' request, because their plans aren't yet fully accepted by Governance, but they decline to support our engineers' request within the time window the engineers require. And so our engineers are compelled to "roll their own" version of what already exists. In this way, they build something that looks like the result of additive bias, but which is actually a result of unresolved political conflict.
Avoiding offense
In some cases, the new capability renders an incumbent capability unnecessary or duplicative. When this happens, responsible engineers would initiate a debate about the question of removing the incumbent capability. But if they know that advocates of the incumbent capability might take offense at such a debate, or if those advocates are politically powerful, the engineers are less likely to raise the issue.
The incumbent capability then remains in place. The end result might seem to be consistent with the result of an additive bias, but it is not. It's a result of politics.

Last words

Next time I'll examine two more mechanisms that can produce results consistent with additive bias. These mechanisms also appear to lead to asset bloat, but they actually arise from attempts to limit the total effort invested.  Additive bias…or Not: II Next issue in this series  Go to top Top  Next issue: Additive bias…or Not: II  Next Issue

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Comprehensive list of all citations from all editions of Point Lookout
[Adams 2021]
Gabrielle S. Adams, Benjamin A. Converse, Andrew H. Hales, and Leidy E. Klotz. "People systematically overlook subtractive changes," Nature 592:7853, (2021), pp. 258-261. Available here. Retrieved 4 June 2024. Back
[Stewart 2021]
Shea Stewart. "Overlooking Subtraction," University of Mississippi News 3842, A.pril 29, 2021. Available here. Retrieved 4 June 2024 Back
[Neroni 2024]
Maria Adriana Neroni, Nathan Crilly and Maria Antonella Brandimonte. "Unveiling the associative mechanisms underlying the additive bias: using an Implicit Association Test to gain insight into people's preference for additive actions," Manuscript, accepted version. Available here. Retrieved 4 June 2024 Back

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