The new toolkit crosses local, central and international government action. It has many of the elements of the previous framework but also covers new ground. The most obvious is that we have changed the horizontal axis to better reflect the way government works in practice. This has meant including a number of new areas namely, influencing, engaging, designing, developing, resourcing, delivering and controlling (or managing). The vertical axis still follows the same logic from ‘softer’ more collaborative power at the top, down to more formal government power at the bottom of the axis. The update includes many familiar things from nudging behaviour to convening power and also adds new areas like deliberative approaches such as citizen juries. This is the framework for Policy Lab's new Government as a System toolkit. The new Government as a System toolkit framework. When looking across the whole system, it now has 56 distinct actions. Of course this isn’t an exhaustive set of options, you could create more and more detail as there is always more complexity and nuance that can be found in government. Importantly, we want policymakers to be considering how multiple levers are used together to address complex problems.

In this chapter we explore three approaches to ensuring that an effective intervention does lead to impact: they are scaling, dissemination, and knowledge translation. Each pathway can increase your impact - i.e., desired behaviour and societal change - but approach this goal from different directions and with emphasis on different activities and outputs. We will introduce you to the three approaches before deep diving into when and how to apply each approach.

On the whole, however, these behavioral interventions have been somewhat underwhelming, exposing an inherent brittleness that comes from three common “errors of projection” in current behavioral design methodology: projected stability, which insufficiently plans for the fact that interventions often function within inherently unstable systems; projected persistence, which neglects to account for changes in those system conditions over time; and projected value, which assumes that definitions of success are universally shared across contexts. Borrowing from strategic design and futures thinking, a new proposed strategic foresight model—behavioral planning—can help practitioners better address these system-level, anticipatory, and contextual weaknesses by more systematically identifying potential forces that may impact behavioral interventions before they have been implemented. Behavioral planning will help designers more effectively elicit signals indicating the emergence of forces that may deform behavioral interventions in emergent COVID-19 contexts, and promote “roughly right” directional solutions at earlier stages in solution development to better address system shifts.

I propose a four-stage model below that balances an understanding that each part is essential with the need to break it down into units of work that can be spread across internal teams and external vendors when necessary. But be warned: each handoff increases the potential for loss, particularly when there is an incomplete understanding of the adjoining stages. A tightly integrated process managed by people who understand the end-to-end process will always have the greatest likelihood of creating meaningful behavior change; that we can name the parts should not detract from the need for a whole. Behavioral Strategy: the defining of a desired behavioral outcome, with population, motivation, limitations, behavior, and measurement all clearly demarcated. Plain version: figuring out what “works” and “worth doing” mean in behavioral terms by collaborating with stakeholders. Behavioral Insights: the discovery of observations about the pressures that create current behaviors, both quantitative and qualitative. Plain version: figure out why people would want to do the behavior and why they aren’t already by talking to them individually and observing their behavior at scale. Behavioral Design: the design of proposed interventions, based on behavioral insights, that may create the pre-defined behavioral outcome. Plain version: design products, processes, etc. to make the behavior more likely. Behavioral Impact Evaluation: the piloting (often but not always using randomized controlled trials) of behavioral interventions to evaluate to what extent they modify the existing rates of the pre-defined behavioral outcomes. Plain version: figure out whether the products, processes, etc. actually make the behavior more likely. Behavioral Science: combining all four of those processes. Plain version: behavior as an outcome, science as a process.

summary of key points of book

"This is the question that Todd Rogers and I explore in our paper, “Persistence: How Treatment Effects Persist After Interventions Stop”, published in Policy Insights from the Behavioral and Brain Sciences. We propose a framework for understanding how and when interventions may lead to persistent behavior change. Specifically, we identify four “pathways”, or features of interventions, that may explain why some interventions are successful at generating persistent behavior changes. These pathways include (1) habit formation, (2) changing what or how people think, (3) changing future costs, and (4) external reinforcement"