When dealing with complex systems, we best look for leverage points.
Case study: Sugar tax
In 2018 the UK started taxing soft drinks containing sugar – known as the Soft Drinks Industry Levy (SDIL) or ‘sugar tax’. The SDIL was introduced as an anti-obesity policy.
But unlike most taxes that aim to change consumer behavior by encouraging consumers to switch to cheaper products, the SDIL was targeted at producers. It was designed to incentivize producers to reformulate soft drinks to lower-sugar recipes. The design had these key elements:
Manufacturers were given two years between the levy’s announcement and implementation to reformulate before it became active
A tiered structure targeted the highest-sugar brands and incentivized sugar reductions
No levy on soft drinks containing less than 5g of sugar per 100ml
Levy of 18p per litre on soft drinks containing 5g to 8g of sugar per 100ml
Levy of 24p per litre on soft drinks containing more than 8g of sugar per 100ml.
The result? More than 45,000 tonnes of sugar was removed from soft drinks in the UK following the tax introduced in 2018.
The sugar tax worked by identifying and modifying a key system parameter. As the tax was designed in tiers, higher levels of sugar content resulted in higher taxes. As such, the tiered design altered the incentives presented to manufacturers to make it more attractive to reformulate their products. Rather than directly persuading consumers to consume less sugar, this approach instead tries to gear the system dynamics of the market towards reductions in the sugar content of available products.
On decision-making: Seeing the whole system
If we are dealing with a simple problem or system, a linear process works well.
But if we are dealing with a complex system, our approach should change.
A complex adaptive system is a dynamic network of many agents who each act according to individual strategies or routines and have many connections with each other. They are constantly both acting and reacting to what others are doing, while also adapting to the environment they find themselves in. Because actors are so interrelated, changes are not linear or straightforward: Small changes can cascade into big consequences; equally, major efforts can produce little apparent change.
In such cases, we should be looking for leverage points. That is targeted changes that produce wider system effects towards the desirable direction.
For example, instead of focusing on the whole population, we could target ‘structurally influential individuals’ to induce artificial tipping points.
To translate this using the example of healthy nutrition, if even a subset of consumers decide to switch to a healthier version of a food product, this can have broader effects on a population’s health through the way the system realigns. It becomes marginally more profitable for suppliers to stock the healthier option, which then may change the mix of products available to consumers in general.
Question for you:
When was the last time you mistook a complex situation for a simple one?
What is one situation that can benefit from thinking of it as a system? How would you approach it?
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