Photo by Michael Dziedzic on Unsplash

By Fatima Keaik, Behavioural Science Consultant at UNDP Kuwait and
Roxani Roushas, Innovation Analyst at UNDP Regional Hub for the Arab States

Behavioural science (BeSci) has gained huge momentum over the past decade across the spectrum of sustainable development challenges. From a vast array of BeSci experiments conducted by UNDP Country Offices around the world, we have learned a great deal about where and how behavioural approaches can add most value. Increasingly, we are using BeSci not as a stand-alone solution or an end in itself, but as one of several complementary approaches to navigating the complexity that is sustainable development.

The focus of behavioural practice so far has been very much on how cognitive biases influence human decisions and behaviour. Biases (i.e. systematic “shortcuts”, patterns or errors in decision-making) have been documented in psychology for much longer than that, and have been the pièce de résistance of nudging — which is essentially about bringing small adjustments to decision-making processes that help to overcome those biases, sometimes with disproportionately high impact. To borrow from Nobel Prize winners Esther Duflo and Abhijit Banerjee, known for their evidence-based, experimental approaches to poverty reduction, ‘it is possible to make very significant progress against the biggest problem in the world through the accumulation of a set of small steps, each well thought out, carefully tested, and judiciously implemented.’

Daniel Kahneman, Cass Sunstein, and Olivier Sibony, in their new bestselling book “Noise” have now introduced us to interesting nuances between a bias (which is the average of error and is found within individuals) and noise (which is the variability of error and is seen across individuals). While bias manifests in a particular direction at group level (e.g. resulting in particular groups of people being consistently discriminated against), noise means decision-making outcomes are scattered, even where they would be expected to be uniform. 

For instance, the authors speak of harmful noise among judges, forecasters, recruiters, and doctors, such as the fact that two doctors, or even the same doctor, may provide entirely different diagnoses or treatments for the same condition. In short, similar people with similar backgrounds and information can end up with dramatically different decisions and judgements. Yet, they are often under an "illusion of agreement", i.e. unaware of the fact that their judgement differs from that of their peers. When aggregated across an organization, unwanted noise can add up to ineffectiveness, or misalignment with objectives. In times of crisis, such as during a pandemic, the inability to harmonise decision-making based on evidence, particularly where evidence itself is noisy, can become dangerous. 

Sources of noise can be:

  • the absence of decision-making rules or processes, 

  • decision-makers having different interpretations of those rules (resulting in e.g. one judge passing a harsher sentence than another judge, for the same crime), or 

  • mental and emotional states leading e.g. a recruiter to assess an interviewee more generously on one day than another. 

In most cases, bias and noise are likely to co-exist. Bias can at least in some cases be easier to “see” or diagnose because of its directionality (decisions consistently go in a particular direction). However noise is typically easier to measure, given it merely requires capturing the variability of decisions against each other. For measurement purposes it doesn’t matter how much each individual decision deviates from an “ideal”; if decisions would be expected to be quite uniform but actually go in all directions, it’s sufficient to measure how scattered they are from each other.

Building on its existing efforts in the Arab States, UNDP is particularly interested in how the idea of noise reduction can contribute to public sector effectiveness in support of sustainable development. In Kuwait, UNDP has enabled the establishment of KPAL, a government think-tank that is now applying behavioural science to public policy areas. In 2020, an ideathon UNDP co-hosted with the Kuwait Government brought together civil servants from across the region to develop solutions for public sector effectiveness by leveraging behavioural science and design thinking. So what new entry points could the concept of noise reduction provide in our efforts on enhancing public sector effectiveness using behavioural science? Inspired by Kahneman et al, we see opportunities to: 

  • Conduct “noise audits” to measure variability in outcomes linked to public administration and service delivery - where is noisy decision-making hindering sustainable development outcomes?

  • Explore how algorithms and digital tools can bring more discipline to decision-making processes through automation. While these can reduce noise, it’s also important to design such tools in a way that they don’t exacerbate bias (by embedding bias into the underlying rules) or close the space for creativity. 

  • Identify cases where capturing “noise” and avoiding groupthink can be helpful in surfacing innovative ideas and more diverse perspectives. 

As we continue nudging more sustainable individual behaviours, let us also start listening for noise, where it matters.

Before you go… Listen in on the O Behave! Podcast on which we recently discussed Behavioural Science & the SDGs with Ogilvy’s Kimberly Richter.

References:

 

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