Chapter 4. Ashby’s Law of Requisite Variety

Marcus Guest
6 min readSep 22, 2021

Pal’chinskii’s first principle[1] was: Increase your chances of success by seeking out and experimenting with a variety of ideas and, a few decades later a leading British scientist, W. Ross Ashby, echoed this principle in “An Introduction to Cybernetics.”[⁠2] It what would become known as the ‘first law’ of cybernetics (the science of communication and control in living and mechanical systems) ‘Ashby’s Law of Requisite Variety’ stated that: If a system is to adapt to change its control mechanism must have at least as much variety as that found in the external environment. In other words, if the world around you is changing faster than your ability to respond to it, then you’re in trouble.

If we want to understand ‘Ashby’s Law’ picture a thermostat monitoring a room’s temperature. When room temperature reaches a set level the thermostat either switches the room’s heating system on or off in order to keep the temperature at a desired level. But now imagine the thermostat only checks room temperature once a day, switching the heating system on one day and only switching it off the next when it checks room temperature again. The result is that the room would likely be too hot one day (as the heating system is on all day) and and too cold the next (as the heating system has been switched off all day). In this example the room’s control mechanism (the thermostat) doesn’t have a requisite variety of information flowing in — it’s monitoring the room’s temperature far too infrequently and, therefore, responding far too slowly to changes in conditions.

The control mechanism in organisations is management who choose what to focus on, recruit teams, set incentives, create processes and invest in the technologies needed to deliver results. Yet, unlike thermostats that are engineered to control ordered mechanical systems, managers in organisations are fallible humans trying to guide unpredictable living systems. And many managers, with the natural human aversion to uncertainty, [⁠3] try to turn their organisations into ‘well-oiled machines’ in order to make them easier to control and run: New, disruptive ideas are ignored in favour of imitating “best practices⁠”;[4] outputs are measured against Key Performance Indicators (KPIs) set at the beginning of the year; and contrarian people (the mavericks) are overlooked or eased out in favour of recruiting and promoting people who are considered a better ‘cultural fit’. These managers are treating their organisations the way they wished they were (as predictable machines) rather than how they really are (unpredictable living organisms).

Treating a living organic system as an ordered predicable machines helps many managers create the comforting sense of certainty and control they crave. But this also creates a critical deficiency. When new disruptive ideas — often coming from the mavericks, those at the edges of the system — are routinely left unexplored organisations fail to develop the ‘requisite variety’ of options they need to adapt when a shock (a game-changing new technology, a global crash, a pandemic) hits their system. When the wider landscape shifts suddenly organisations that have only ever relied on one way of doing things quickly fall into trouble as they have developed no alternatives ways to respond. Their only hope is for everything to go “back to normal”. But hope is rarely a viable strategy.

Adaptivity Intelligence (AQ)

Organisations that lack a requisite variety of responses to changes have ‘low-AQ’ (Adaptivity Intelligence). They are “dead players”[5] ‘incapable of working off-script and doing new things’. Organisations that have cultivated a requisite variety of responses (by experimenting with new ideas and learning) have ‘high-AQ’. They are “live players” ‘able to do things they have not done before’. When told that something is “best practice” dead players ask: Who did this? What was the result? Can you do this for us? But live players ask: Why is this best practice? Who was this best practice for? Do those same conditions apply to us today? Rather than unthinkingly copying someone else’s ‘past practice’ live players experiment with a variety of ideas to discover what might be valuable (or not) for them. Then, rather than scrambling to respond when the situation suddenly changes, they’re able to choose from a variety of options they have been exploring and have experience with. And they can do this because they follow Pal’chinskii’s first principle of increasing their chances of success by seeking out and experimenting with a variety of ideas (ahead of time).

Live players also embrace Pal’chinskii’s second principle: Failure is inevitable, so do things on a small enough scale that it’s survivable. They don’t go all in on one big bang change (ex, a digital transformation or ‘going Agile’). They know major projects are often not finished before another shift in conditions occurs, requiring them to adapt again. Live players therefore explore multiple ideas on a small-enough scale that they can be completed (and combined together later) or that won’t bring down the house if they do fail. By adopting this safe-to-fail ⁠[6] approach live players are able to experiment with contradictory ideas and learn quickly what works for them (or what doesn’t). They develop, ahead of time, the requisite variety of responses needed to respond quickly and effectively to change and therefore, relative to dead players, thrive in times of uncertainty.

Fig. 9: Ashby’s Law of Requisite Variety and Adaptivity Intelligence (AQ)

Adapted from ‘Complexity and Organization–Environment Relations: Revisiting Ashby’s Law of Requisite Variety’. Boisot and McKelvey (2011)

If organisations want to survive and thrive in uncertain times their ‘control mechanisms’ (managers) need to become more like the thermostat continually checking room temperature, rather than the one only checking it infrequently (as many managers do with the annual planning and budget cycle). This requires managers to open themselves up to greater inflows of information and bing more willing to experiment with the new. But many managers choose not to do this because more information also means more ‘noise’ as information is complex, ambiguous, unreliable or incomplete. But this is where Pal’chinskii’s third principle comes in. Managers don’t need to analyse all the data and try to predict the future. They can simply experiment with ideas that might work and then quickly select what’s working in the local context by developing effective feedback loops between decision-makers and those closest to the action. This approach requires attracting talent with varied backgrounds, experiences and ways of thinking to increase the range of ideas the organisation experiments with; then getting closer to end users to see which ideas delight or disappoint them, enabling managers to learn in real-time what they should do more of (or less of). This is how organisations learn and adapt as they go — increasing their AQ by developing a requisite variety of responses. Let’s have a look at how a company did this in the next chapter.

1 See Chapter 3

2 An Introduction to Cybernetics. W. Ross Ashby (1956)

3 On human aversion to uncertainty see Chapter 1

4 On the futility of copying “best practices” see the Introduction


6 This is the approach advocated in the Cynefin Framework when dealing with complex situations: Probe-Sense-Respond, which privileges launching experiments (probes) to learn what works or doesn’t in a particular context. Probes that have positive outcomes are amplified, while probes with negative outcomes are dampened (with learnings fed into new probes).



Marcus Guest

Govern the state by being straightforward; And wage war by being crafty. — Laozi, Tao Te Ching