[Case study] Why psychology makes "must do purposes" your only option

"Must-do purposes" help innovators overcome not just rational resistance but the Goodwin Curve too.

[Case study] Why psychology makes "must do purposes" your only option
People take a bit to see the benefits of changes | Photo by Dimitry B / Unsplash


Efforts to make cities car-free illustrate why innovators must ensure that the purposes/ agendas of their work are truly "must do's:"

Innovation work actually needs "must-do" purposes for two reasons.

It's not just because it's rational for people to reject changes (that might be for the greater good but hurt them personally).

It's also because human psychology, especially Loss Aversion, causes people to fear the downsides of changes more than they get excited for the accompanying upsides.

To make a change stick, you then first need to make it happen despite this gut-driven opposition and then make it through the "Goodwin Curve," the waves of post-implementation opposition, until the "new way" becomes a fresh status quo, and people have personally experienced its benefit.

The story

The BBC's India Bourke reports on the experience that cities across the U.S. and Europe (e.g., Ljubljana, London, New York, and Paris) had with attempting to reduce or remove cars from downtowns.

(Thanks as ever to the folks at Future Crunch, one of the best general newsletters out there, for pointing me to this story.)

At first, such efforts often face vitriol from outraged citizens or outside vigilantes, some of it pragmatic and fundamentally rational but much of it emotion-laden ranting about "out-of-control governments ripping away free people's rights." But city administrators and advocates of lower reliance on cars have learned that such opposition is both temporary and predictable.

Psychology's "Prospect Theory," including the idea of "loss aversion, have taught us that people worry more about what they lose than in than what they might gain from changes.

What makes the evolution of this loss aversion predictable is the "Goodwin Curve." Named for the University College London professor who discovered it,

"the [Goodwin] curve (or dip) charts how public support for road pricing schemes tend to begin well, with recognition of the need for intervention.
That support then falls away as more specific details are released ahead of enforcement, only to rise again after implementation.

[Researchers haven't been able to] find a single example of a traffic-reduction measure that's been in place for more than two years that's then gone on to be removed because of a lack of public support."

And so it has come to pass. City after city has seen car reduction schemes become popular with citizens as time passed and people simply got used to it and got to experience the related benefits.

Advocates have even learned to hack this behavior, conducting lightweight, short MVP-style experiments to give people a flavor of the new experience pre-implementation, to accelerate acceptance.

The only issue left for cities is to ensure that the low point of their Goodwin Curve and public support does not coincide with election times, which would lock in a temporary setback.

[Source: BBC via Future Crunch]

The point for doing credible innovation work

As already mentioned, the experience with the Goodwin Curve highlights another reason that innovators must not just accept any project, certainly not as originally proposed. Projects must be framed in ways that make them "must do's."

Must do purposes are critical not just for intellectual reasons (like the risk of changes to harm current business) but also for psychological ones (like Prospect Theory).

But I also found this case study delightful for a second, unexpected reason: Cities are using "MVP-style" experiments not merely to test, learn, and iterate their proposed solution but also to prime and iterate on user Desirability.

This points out first that Desirability is not fixed. It can be shaped, independent of the solution. And second, it reminds us that lightweight testing has multiple uses. Should cities (in this case) also use their MVP testing actually to improve their solutions? Of course. But it would leave benefits on the table, or asphalt 😁, if innovators didn't also use the MVP to shape Desirability.



Find the full story here (External link)

Bourke, India. "From London to New York: Can quitting cars be popular?" (Jan. 22, 2024). BBC. Accessed Feb. 2, 2024.

Cities in the U.S. and Europe:

Governor of New York State

Further reading

On Prospect Theory (incl. loss aversion): Wikipedia. Accessed Feb. 2, 2024.

On The Goodwin Curve: Goodwin, Phil. "The gestation process for road pricing schemes." (Jun. 1, 2006). The National Academies of Sciences, Engineering, and Medicine. ISSN: 0962-6220. Accessed Feb. 2, 2024.


Photo "Black & White Pedestrian Lane" by Dimitry B on Unsplash


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