The Loop You Keep Closed

An investment team has a bad year. They reconstruct it and find real faults: position sizing too aggressive into the drawdown, stop discipline that slipped, a correlation they had trusted that moved against them. So they tighten the sizing, formalise the stops, add the correlation to the risk dashboard. Every fix is sensible. Three years later, in a different market, the same shape of loss arrives again.

The puzzle is that there is nothing wrong with the team. Yet the second loss repeats the shape of the first, the one thing the review never touched, and there is reason to think sustained success makes that pattern worse, not better.

What kind of problem is this?

It is tempting to call it a competence problem, but that does not fit the evidence: the team corrected the errors it found. It is a learning problem, of a particular kind the word usually hides. There are two quite different things you can learn from a loss. You can run the existing strategy better, fixing the mistakes that happen within its rules. Or you can ask whether the strategy’s governing assumptions still fit the world it is trading in. The question is not why a few people are careless, but why capable people so often run their learning on the first track and so rarely on the second.

When a failure is a consistent pattern in which one level is examined and another is not, rather than a lack of effort, the problem has a definite shape: the architecture of learning. And the discipline that has studied it, beginning in the mid-1970s, is the theory of organisational learning.

So let’s borrow.

This is the multi-model move: recognise the shape of a problem, find the discipline that has thought rigorously about that shape, and import its frameworks deliberately rather than reinventing them from scratch.

Its central distinction comes from Chris Argyris and Donald Schön, who developed it across work culminating in their 1978 treatment of organisational learning, and spent the following decades studying what gets in its way.

Argyris and Schön drew a line between two kinds of error-correction. Single-loop learning detects a mismatch between intention and result and corrects it by adjusting behaviour, leaving the underlying goals and assumptions intact. Their own illustration was a thermostat: it senses the room is below the set temperature and turns on the heat, without asking whether the setting is right. Double-loop learning is the second question. It examines the governing variables themselves, the goals and assumptions that define what counts as an error, and is willing to change them.

In the cases Argyris and Schön studied, the obstacle was often not ignorance but defence. They separated two things: the espoused theory, the reasons a person gives for a decision, and the theory-in-use, the one their behaviour actually reveals. The two are often different, and the gap is usually unspoken, and may be hidden even from the person themselves. Under threat, people fall back on the same theory-in-use, which they called Model I: keep control, win rather than lose, hold difficult feelings down, and stay outwardly rational. Model I breeds defensive routines, the shared habits that spare people the discomfort of questioning their own assumptions. Certain questions become undiscussable, and then the fact that they are undiscussable becomes undiscussable too. The moves themselves can be quite deliberate, concealing, controlling, explaining things away, even while the pattern behind them stays out of sight. So the second loop ends up closed without anyone deciding to close it, by people who believe they are simply being rigorous.

The alternative they set out is Model II. The aim is not endless doubt, or shutting down anyone who argues a case, but good information, real choice, and genuine commitment to what is decided. People show the reasoning behind their views so others can test it, and they make their case while honestly inviting challenge to it. So double-loop learning depends not just on which questions get asked, but on whether the reasoning behind the answers can itself be examined.

Argyris’s most uncomfortable finding followed from this, in a provocatively titled 1991 essay, “Teaching Smart People How to Learn.” The people worst at double-loop learning, he argued, are often the most successful and the most expert. They have built careers on getting things right and have rarely failed, so they have had little practice at what double-loop learning asks: treating their own mistakes as information, not as a threat to a hard-won identity. Faced with evidence that a governing assumption was wrong, the highly capable do not always get curious. They can get defensive, pin the cause on something outside themselves, and shut the inquiry down so smoothly that the defence looks like analysis.

In an investment process you can tell the two loops apart, though not as neatly as you might hope. Single-loop learning is everything you do to run a strategy better without changing its assumptions: tuning parameters, tightening limits, refining execution and sizing. Double-loop learning asks the harder question, the one that closed Why Good Strategies Stop Working, aimed at the strategy itself: what does it assume, and does that still hold? The governing variables are wider than beliefs about the market regime. They include the firm’s risk appetite, the liquidity it has promised clients, the incentives it pays, and what it treats as success. Where the boundary falls also depends on where you stand: swapping out a failing strategy can be double-loop at the desk and still leave the firm’s mandate and incentives untouched.

Long-Term Capital Management is a telling case. Official reviews found that its risk framework, and its counterparties’, underestimated joint shocks and liquidity drying up; that positions which looked diversified carried the same convergence exposures across markets; and that extreme leverage amplified the losses and made a disorderly liquidation a threat to wider markets. That risk-management lesson is well established. A double-loop reading goes further, offered as a hypothesis: that all of this was clever fine-tuning inside a view of risk that may not have been challenged effectively enough.

Investing can intensify these defences, for two reasons that feed each other. The first is success itself. Double-loop learning takes practice at treating mistakes and surprises as information rather than as threats, and a strategy that has worked for years can reduce the pressure to build that practice, while convincing its owners they do not need it. The second is public commitment. A thesis is stated openly, defended to clients, and used to raise the money now behind it, so reopening it means admitting in public that it was wrong. The defence rarely shows up in a single decision. Averaging down is not defensive in itself; it may follow a rule set in advance that responds to the evidence. The defence lies in how the decision is justified: quietly raising the bar for evidence, explaining away the bad news after the event, or treating whoever points it out as disloyal. Diagnose the reasoning, not the trade.

It follows, first, that exhortation does not work. Telling capable people to be more open-minded is as empty as telling them to be less biased: defensive routines are not a bad attitude that a better one would cure, but a learned, social response to threat, strongest on the questions closest to a person’s own competence and commitment. You cannot will the second loop into running, and you cannot simply install it either. Argyris treated individual and organisational defences as reinforcing each other, so structure that lowers the cost of challenge is necessary but not enough on its own.

What structure can do is lower the personal cost of challenge: separate the team that runs a strategy from the group responsible for checking whether its assumptions still hold, so that asking is someone’s job rather than an act of disloyalty; make disagreement an assigned job, through pre-mortems and red teams; write the governing assumptions down ahead of time, so a review studies a document rather than a colleague. None of this, on its own, is double-loop learning. If the strategy owners never learn to show their own data and reasoning, and to revise their own theory-in-use, the challenge becomes one more ritual: one side prosecutes, the other defends, and the decision turns into a contest over authority rather than a shared test of the reasoning.

This changes the test of whether the second loop is running. It is not whether reviews overturn premises: a real inquiry can examine a governing assumption and decide it still holds, while an organisation can overturn assumptions for show without really inquiring at all. The test is whether the premise was genuinely open to revision: whether the assumptions and failure conditions were recorded before the result, evidence against it sought rather than just received, the reasoning opened to challenge, a dissenter able to change the decision, and the learning built into rules, limits and incentives. The discipline of holding a model loosely, which Why One Model Is Never Enough set as the goal of this series, is not a temperament admirable people can summon. It is a practice, one that has to be built into how decisions are actually made.

A strategy that has compounded for years can become one whose premise is hardest to examine and most costly to reopen, and the edge that lasts belongs to whoever can still reopen what success has closed.

Investing is only one place this appears. Aviation offers a useful institutional analogue. International standards make prevention the sole objective of accident investigation, require the investigation to remain separate from proceedings concerned with blame or liability, and require an authority independent of bodies that could compromise its objectivity. Its purpose is not to punish, though it is not literally blame-free.

The closing note is the one Argyris insisted on and that practitioners least want to hear. Awareness is not the cure. The professionals he studied could describe defensive routines in detail and went on producing them in the same session, because the routines act on the blind spot, not the conscious mind. Understanding the pattern does not exempt you from it, but it tells you where to build the structure, and the practice, that might on a good day force the question you would otherwise decline to ask.

The discipline is in asking: which loop is this running on, and is the premise allowed to be questioned?

References & Further Reading

1. Theory in Practice: Increasing Professional Effectiveness: Chris Argyris & Donald Schön

2. Double Loop Learning in Organizations: Chris Argyris

3. Organizational Learning: A Theory of Action Perspective: Chris Argyris & Donald Schön

4. Overcoming Organizational Defenses: Facilitating Organizational Learning: Chris Argyris

5. Teaching Smart People How to Learn: Chris Argyris

6. The Reflective Practitioner: How Professionals Think in Action: Donald Schön

7. The Fifth Discipline: The Art and Practice of the Learning Organization: Peter Senge