Laura Berlinsky-Schine
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When you or your organization encounters an obstacle, what’s your approach for handling it? Do you use the accepted methodology, tackling the surface issue, rather than attempting to discover the root-cause? Or do you look at the big picture and question the policies and procedures that might be the underlying cause of the problem in the first place?

The former method is called single-loop learning, while the latter is double-loop learning. Double-loop learning involves questioning accepted norms and changing structures, not just looking for quick fixes. What does the theory entail, and how can it be put into practice? Here’s what it means and how it functions in the real world. 

What is double-loop learning theory?

Double-loop learning theory was created by Chris Argyris, an organizational trainer. It is a method of challenging people’s underlying assumptions about how to solve problems. Rather than continuing to do things the way they have always been done within an organization — the supposed “right way” of doing things — Argyris proposed that people take into account new experiences and evidence and incorporate them into our response to complex issues and situations. It involves learning from experience, rather than relying on the tried (and not necessarily true) methods.

Examples of double-loop learning

Example #1

Organization X has been using the same template for marketing plans for over a decade. When an employee (let’s call her Marsha) questions the method, her manager tells her that this the way they’ve always done it, and no one has complained so far. Meanwhile, competitors are trying different approaches to evolve with the times. Marsha questions the “tried-and-true” method and proposes an alternative, pointing out that competitors are refreshing their strategies. She also points to the previous quarters, in which sales have dropped. She suggests team members brainstorm and offer new ideas. 

Example #2

In the book That’s Not How We Do It Here!, John Kotter and Holger Rathgeber use an allegory of a meerkat clan facing deadly droughts and vulture attacks to help organizations thrive in the face of challenges and setbacks. In the community, the population has declined and the meerkats are facing internal tension about how to cope. Members of the clan propose suggestions, but the leaders tell them, “That’s not how we do it here.” One meerkat, Nadia, goes in search of alternative methods. 

Example #3

A facility is producing a product that keeps having the same defect. The workers must continuously correct the defect. However, they then begin to question the entire makeup of the product, considering alternative ways of creating and producing it in the first place. Once they question the underlying principles of manufacturing the item, they develop a new method of producing it from the ground up. 

What is single-loop learning?

Argyris called the common, go-to method of solving problems single-loop learning. He suggested that people tend to rely on the rules and how things have been done in the past to resolve issues when they encounter them and are resistant to developing new ways of approaching problems and challenges. 

This methodology, of course, doesn’t address underlying problems and is often ineffective, because it fails to account for an ever-changing world and market. Organizations and people who stick to singe-loop learning will inevitably fall behind. 

Examples

Example #1

Argyris used the classic example of the thermostat to explain his theory. The thermostat has just two responses to problems: when the room becomes too cold, it turns the furnace on; when it becomes too hot, it turns the furnace off. This can be thought of as a metaphor for organizations that don’t take the time to assess the rationale behind their polices and consider their efficacy. Instead, they stick to the policies that have always been in place and the way they’ve always done things because that’s how it is.

Example #2

A manager believes in her own skill and competence but doesn’t think her employees are capable of the level of work she demands. Rather than giving them challenging assignments, she micromanages their work and refuses to delegate. This results in decreased morale and an overworked manager, who is engaging in a vicious cycle of single-loop learning: she believes her employees to be incompetent, so rather than giving them the opportunity to prove her wrong, she continues to do things the way she’s always approached problems — by doing everything herself. 

Example #3

A teacher finds that the exam she gives her students every year generally results in poor performance across the board. She decides to modify the test, changing the format, rather than tackling the underlying problem: the strategies she is using to teach the material aren’t as effective as she thinks. She is attempting to address the problem using single-loop learning — looking at only the surface problem and not the underlying issues — rather than digging deeper to examine the root cause — the entire process of how she is teaching.

What is the difference between single- and double-loop learning?

Single-loop learning accepts given structures, policies and practices for addressing problems that arise. When issues do occur, people and organizations will take action but only working within the accepted way of handling them. It assumes that the procedures in place are sound and need no alteration. 

Meanwhile, double-loop learning does not accept underlying policies and procedures but questions entire structures and ways of approaching difficult situations. Rather than putting band-aid fixes on problems, as single-loop learning tends to do, double-loop learning asks people to reflect on how well things have been working and why these procedures are in place at all. Unlike single-loop learning, which retains the existing structure and looks for actions that resolve problems when they occur, double-loop learning questions the entire model.

Double-loop learning can be thought of as outside-the-box thinking, while single-loop learning stays within the confines on the box.

Admittedly, double-loop learning requires significantly more time and effort than single-loop learning, but the payoff is often worth the investment. When people and organizations take the time to reflect on their assumptions about the best way of handling problems and question policies that might have been in place for years, they can create stronger models that result in better productivity, efficiency and even profit.

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