{"id":822,"date":"2026-10-08T06:59:50","date_gmt":"2026-10-08T04:59:50","guid":{"rendered":"https:\/\/www.mariaelenaluccerini.com\/?p=822"},"modified":"2026-11-13T10:02:23","modified_gmt":"2026-11-13T09:02:23","slug":"double-loop-learning-recurring-problems","status":"publish","type":"post","link":"https:\/\/www.mariaelenaluccerini.com\/en\/articles\/double-loop-learning-recurring-problems\/","title":{"rendered":"Why do the same problems keep coming back? Single- and double-loop learning"},"content":{"rendered":"\n
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ARTICLES<\/a> \u00b7 ORGANISATIONAL LEARNING<\/p>\n\n\n\n

Why do the same problems keep coming back<\/span>? Single- and double-loop learning<\/h1>\n\n\n\n

A model for understanding why the same problems keep recurring and how people and organisations can learn to question the assumptions that guide action.<\/em><\/p>\n\n\n\n

by Maria Elena Luccerini<\/a><\/p>\n<\/div>\n\n\n\n

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In organisations, we often address a problem, see a temporary improvement and find ourselves back in the same situation a few weeks or months later. A check is added, a procedure is changed, another meeting is called. The error is corrected, but the mechanism producing it remains untouched<\/strong>.<\/p>\n\n\n\n

This is the difference between changing an answer and changing how we arrive at answers; between asking only what we should do better and also asking which beliefs, implicit rules and objectives are shaping what we do<\/strong>.<\/p>\n\n\n\n

Chris Argyris and Donald Sch\u00f6n<\/strong> described these two levels through the concepts of single-loop learning and double-loop learning<\/strong>. The first corrects action while leaving underlying criteria unchanged; the second questions those criteria too, making it possible to redesign action from a different understanding.<\/p>\n\n\n\n

They are not two alternatives from which to choose once and for all. Single-loop learning is needed to regulate, improve and keep everyday work reliable. Double-loop learning becomes crucial when the same problems return, the context has changed or familiar solutions no longer deliver the expected results<\/strong>.<\/p>\n\n\n\n

A theory of organisational learning that remains relevant<\/h2>\n\n\n\n

Double-loop learning emerged in the 1970s within the theory of action developed by Chris Argyris and Donald Sch\u00f6n<\/strong>. For these scholars, learning is not simply acquiring a new idea or understanding a problem intellectually. Learning takes place when a gap between intentions and results is identified and corrected through action.<\/p>\n\n\n\n

This distinction matters. We may take part in a retrospective, recognise a mistake and develop a very clear interpretation, but if we continue to act in the same way at work, learning has not yet changed our actions.<\/p>\n\n\n\n

A 2023 systematic review by Mercedes-Victoria Auqui-Caceres<\/strong> and Andrea Furlan<\/strong>, based on 128 studies published between 1974 and 2021, shows that the concept is frequently cited but often used in a limited way. Double-loop learning requires both cognitive change (in assumptions, beliefs or guiding values) and observable changes in behaviour<\/strong>. If only the way we describe the problem changes, or a behaviour changes without examining the premises behind it, genuine double-loop learning has not yet taken place.<\/p>\n\n\n\n

Two different ways of correcting an error<\/h2>\n\n\n\n

In single-loop learning, when a result does not match what we expected, we look for a more effective course of action without changing the underlying objectives and rules. This is how a thermostat works: it detects a difference from the set temperature and switches the heating on or off.<\/p>\n\n\n\n

In double-loop learning, a further question comes into play: is the set temperature still right?<\/strong> The system does not simply correct the difference; it can reconsider the criterion used to assess it.<\/p>\n\n\n\n

<\/th>Single-loop learning<\/th>Double-loop learning<\/th><\/tr><\/thead>
Central question<\/th>How do we correct the gap?<\/td>What are we taking for granted?<\/td><\/tr>
What changes<\/th>Actions, techniques or procedures<\/td>Assumptions, guiding values and objectives, followed by actions<\/td><\/tr>
When it is useful<\/th>When objectives and rules remain valid<\/td>When problems recur or the context has changed<\/td><\/tr>
Example<\/th>Adding a check to a process<\/td>Asking why the process needs that check and what conditions are contributing to the error<\/td><\/tr><\/tbody><\/table>
Table adapted from Chris Argyris\u2019s model.<\/figcaption><\/figure>\n\n\n\n

Consider a project that is continually delayed. A single-loop response might add checks, reset deadlines or introduce a new planning tool. These actions may help, but they might not be enough. Double-loop learning opens up different questions: Are priorities genuinely shared? Do people have the authority they need to make decisions? Are the objectives of the functions involved aligned, or are they working against one another? What information is not being shared, and why?<\/p>\n\n\n\n

The point is not always to search for a deeper cause. It is to understand what level the problem sits at<\/strong>. If the direction remains right, improving the action may be enough. If the assumptions keep producing the same result, changing procedures alone will only bring temporary corrections.<\/p>\n\n\n\n

What we say we do and what we actually do<\/h2>\n\n\n\n

To understand the assumptions guiding action, Argyris and Sch\u00f6n<\/strong> distinguish between espoused theory and theory-in-use<\/strong>. The first consists of the values and principles we say we follow. The second can be inferred by observing how we act in real situations.<\/p>\n\n\n\n

An organisation may say it values collaboration while rewarding only individual results. A manager may say they want to delegate while continuing to ask for approval at every step. A team may say that discussion matters, then become defensive whenever someone raises uncomfortable information.<\/p>\n\n\n\n

This gap is not necessarily hypocrisy. Often it is unconscious. People sincerely believe they behave according to their stated values, while under pressure they fall back on different rules. That is why theory-in-use cannot be uncovered simply by asking people what they think they do: it has to be reconstructed from observable events, conversations, decisions and consequences.<\/strong><\/p>\n\n\n\n

Double-loop learning begins when the gap between intentions and actions becomes something a group can explore without immediately turning it into an accusation. In that space, the group can recognise implicit assumptions, examine them and design alternatives.<\/p>\n\n\n\n

The real obstacle: how we reason under pressure<\/h2>\n\n\n\n

According to Argyris<\/strong>, when situations feel threatening or embarrassing, people tend to act according to what he calls Model I<\/strong>: they try to maintain control, maximise the chance of being right, minimise the expression of negative feelings and appear rational. They defend their position, make judgements and attribute causes, while leaving the assumptions behind their reasoning implicit and difficult to test.<\/p>\n\n\n\n

The result is paradoxical. In trying to protect ourselves, others or the relationship, we create misunderstanding, rigidity and self-reinforcing patterns. People become highly skilled at producing behaviours that undermine the very outcomes they want<\/strong>: a dynamic Argyris called skilled incompetence<\/em>.<\/p>\n\n\n\n

Model II<\/strong>, associated with double-loop learning, does not require people to abandon their position or share every decision. It calls for valid information, a combination of advocacy and inquiry, explicit reasoning that can be tested, and responsibility for choices. The central shift is from unilateral control to making our reasoning open to challenge.<\/strong><\/p>\n\n\n\n

Defensive routines: protecting ourselves at the cost of learning<\/h2>\n\n\n\n

When Model I behaviours recur, they can become defensive routines: actions, practices or policies that avoid embarrassment and threat while also preventing people from understanding and addressing their causes. They are automatic, learned and often socially accepted. For that reason, they can outlast the people involved: someone leaves the organisation, someone else joins, and the same dynamic continues.<\/p>\n\n\n\n

A two-year ethnographic action research study, published in 2026 by Mercedes-Victoria Auqui-Caceres and Andrea Furlan, observed three recurring patterns:<\/p>\n\n\n\n