Some of my key takeouts from #BX2018 .
I'll try to consolidate these threads into a more structured and coherent article when I get some time/recovered fully from the jet lag. Let me know what you find interesting/useful. (Thread 1 of 2) 🖌️🎨: @simonbankscrea8
I'll try to consolidate these threads into a more structured and coherent article when I get some time/recovered fully from the jet lag. Let me know what you find interesting/useful. (Thread 1 of 2) 🖌️🎨: @simonbankscrea8
Shifting and holding practitioners' focus on the challenges, as opposed to starting with novel research topics or solutions, was put forward as a way to better align the incentives of all stakeholders involved in policy/product design process.
Others, such as Dr Martin Parkinson, pushed this perspective further, arguing that BI has a crucial role to play in all the modern day challenges we see starting to play out in society - energy security, city congestion, obesity, mobile addiction, antimicrobial resistance, etc.
Linked to this, @dilipsoman made a nice point about decomposing challenges into their precise behaviours. We need to have a concrete understanding of what actions we are trying change. This is how we tackle complex problems.
**Moving towards an ethical framework that the community can agree on**
The ethics of nudging - @CassSunstein proposed a 'Bill of Rights for Nudging', which covered five general principles that anyone in the business of nudging should consider. 📷: @kateglazebrook
The ethics of nudging - @CassSunstein proposed a 'Bill of Rights for Nudging', which covered five general principles that anyone in the business of nudging should consider. 📷: @kateglazebrook
A real positive was some recent data presented by Cass Sunstein, suggesting that citizen approvals for nudging (that allign with people's interests) are between 70-80%.
The real challenge here will be in understand people's values and interests, as well as how they might vary across cultural groups and locations. Using a smart cost/benefit analysis tool may be useful to lean on here.
The ethics of experimentation (RCT"s+) - lots of work to do here, however, teams like @beta_gov_au are really leading the charge with pre-registration + transparent results (even when nudges aren't effective). Hopefully, other leaders will follow suit. behaviouraleconomics.pmc.gov.au/projects
The ethics of not experimenting - A lot of confusion still exists around 'when' it is ethical to experiment. The core of this exists because practitioners believe/feel/are expected to 'know' whether the treatment (nudge) is more effective.
However, their confidence in that 'knowing' is the issue - there are a whole set of cognitive glitches that may lead decision-makers to overestimate their ability to predict the outcomes of interventions/policies - e.g the 'Scared Straight' campaign. vice.com/en_us/article/…
**The challenges with taking nudges to scale**
A common realisation across both the public and private sector speakers and attendees was the challenge of taking nudges to scale.
A common realisation across both the public and private sector speakers and attendees was the challenge of taking nudges to scale.
There have been many successful trials, with lots of good evidence to back them up, yet many are still sitting in the queue, creating this massive knowledge-action gap.
A number of reasons for this surfaced during the breakout session dedicated to the topic - these include stakeholder buy-in, current decision-making systems, timeliness, as well as implementation (last mile) failures.
To narrow down these factors into a simple, actionable framework, Rory Gallagher of @B_I_Tweets proposed SCALE (Sponsorship, Costs, Accountability, Logistics, Evidence).
In addition, the frontline need to be dealt with carefully - their buy-in and execution of the interventions can have a large impact of the fidelity of an intervention. As @robynmildon explained- 'it can really cost a lot for poor implementation'.
**The next era of nudging. Thinking about the prescription side differently**
Keynote @CassSunstein used a nice analogy - 'our GPS (e.g Google Maps) is the perfect nudge - is facilitates navigability for the end user, freeing up cognition to focus on more valuable tasks'.
Keynote @CassSunstein used a nice analogy - 'our GPS (e.g Google Maps) is the perfect nudge - is facilitates navigability for the end user, freeing up cognition to focus on more valuable tasks'.
The freeing up of a people's limited cognitive resources (so they can focus on more important tasks) seems to be a truely great benefit of defaults, choice automation, but also more convention interventions, like cash deposits.
Linked to this, Anuj Shah made a great case for prompting the disadvantaged, who are living with scarcity (see @m_sendhil s great book), at specific points when they might be getting little a bit of slack. Or better yet, help create the slack. guilfordjournals.com/doi/10.1521/so… …
There are however areas where defaults and automation may be detrimental - for example where we want people to learn from their choices. In these situations, active choice is a much more effective approach.
The example showcases a challenging but important hurdle for improving impact - mapping out their boundary/working conditions (the personal and situational factors under which a particular nudge is likely to be more effective).
Machine learning and large-scale digital experimentation can add a lot of value here, allowing us to narrow in on subgroups where a nudge has been particularly effective, as well as where it may have backfired. This allows for much more targeted roll outs.
The points mentioned above elude to an exciting new era of nudges, that are more nuanced and heterogeneous in their nature, improving our understanding of what nudges work, where/when they work and why.
ML + data science aren't only tools that complement BI. As @chiaravara persuasively argues, we should not forget about brain science when thinking about BI(behavioralscientist.org/brains-behind-… …). Integrating more design thinking also seems to be valuable, especially at the diagnostic phase.
