Written by Alan Hudson and Parker Essick
We are not epidemiologists, or behavioral scientists. Neither can we claim any expertise in modeling the potential consequences of policy actions to inform policy design and implementation. But we do have lots of experience of exploring whether and how data and learning can support the design and implementation of policies to address complex challenges relating to corruption and the use of public resources (see our strategy and our recent submission to the World Bank on “data and development”). Data, theory and learning are part of our DNA. Maybe models should be too.
Complexity, adaptation and models
As we’ve adapted our work to the context of COVID-19 and sought to keep up to speed on the many things that have been written about COVID-19 and governance, we’ve been interested to see discussions about how to respond to the new challenges bring to the fore a number of issues that have been the focus of our work over the last few years. These include issues relating to the complex nature of the challenges, the need for adaptive responses, and the value and limits of models in informing the design of those responses.
First, much discussion has focused on the complex, systemic and globally interdependent nature of the challenges posed by COVID-19, and the value of a systems perspective in addressing those challenges. Pieces that have caught our attention include: COVID-19 means systems thinking is no longer optional, by Seth Reynolds; A systems perspective on the coronavirus: If the Wire was about COVID-19, what would the seasons be?, by Monalisa Salib; and, Why a systems response to COVID-19 is critical, by Olivia Leland.
Second, there have also been a number of interesting contributions that have focused on the need for adaptive and learning-centered responses. These include an excellent paper on adaptive leadership by Ben Ramalingam, Leni Wild and Matt Ferrari; Smart Containment with Active Learning: A Proposal for a Data-Responsive and Graded Approach to COVID-19, by Tahir Andrabi and colleagues; Seeing pandemics as complex adaptive problems, by Peter Harrington; and, A call for a new generation of COVID-19 models by Alex Engler.
Third, there have been many pieces that have explored the role that science, data and modeling can play in deciding amongst policy options, while also highlighting the complicated path which leads from science and data (whether that’s data about the past, or data derived from modeling the future), through values and politics, to policy, practice and results. Pieces that we have found particularly interesting include: Science isn’t a clear-cut pandemic guide, by Therese Raphael; There’s no such thing as just “following the science”, by Jana Bacevic; The truth about scientific models, by Sabine Hossenfelder; The mudfight over “wild-ass” COVID numbers is pathological, by Roger Pielke Jr.; Role of modelling in COVID-19 policy development, by Emma McBryde and colleagues; The problem of modelling: Public policy and the coronavirus, by Paul Collier; 10 Tips for Making Sense of COVID-19 Models for Decision-Making, by Elizabeth Stuart and colleagues; Sharing models for COVID-19: Guidance and tools, by Fionntánn O’Donnell; and, a super-interesting piece on Modeling the pandemic: Attuning models to their contexts, by Tim Rhodes and colleagues.
For those with more time, and enthusiasm to learn about modeling, we recommend Computational modelling: Technological futures, by the UK Government’s Office for Science, Council for Science and Technology. And for some additional insights into how models are, and might, be used in different parts of the world, see: South Africa’s use of COVID-19 modelling has been deeply flawed. Here’s why, by Seán Mfundza Muller; How to forecast outbreaks and pandemics: America needs the contagion equivalent of the national weather service, by Caitlin Rivers and Dylan George; Newsom Announces New COVID-19 Modeling Website, Open-Source Tools For ‘Citizen Scientists’; and, if you can get beyond the paywall, Our modelling must be the best as Britain comes out of lockdown, by Nigel Shadbolt.
Exploring the value and limits of modeling
Inspired by the attention given to models and modeling in relation to COVID-19, and struck by the absence of modeling approaches in the governance and development space with which we are most familiar, we thought it would be worth exploring the role that models can play in the design and implementation of public policies relating to complex, systemic and social challenges.
To this end, we plan to convene a small group of people who work on and around issues including data and evidence, adaptive development, modeling, and public policy, to explore some key questions:
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Potential for modeling impact of governance-related policies: How useful and feasible would it be to deploy models that enable the exploration of policy impacts, to inform the design and implementation of policies – or policy commitments in the Open Government Partnership process, for instance – to address governance-related challenges, such as those relating to corruption and the use of public resources?
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Lessons from other spheres and systems: What lessons and insights does the use of models to explore the possible impacts of different policies in different spheres and systems – for instance, in relation to economic systems, ecological dynamics, traffic flows, and epidemiological crises – hold for efforts to make use of models in relation to governance-related challenges?
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Models and their use: What sorts of models might best support progress along the pathway from data, through politics and policy, to impact, as regards challenges relating to governance, corruption and the use of public resources? What implications does the fact that the effectiveness of policy in these areas depends upon relationships, trust, legitimacy and compliance have for how such models would need to be developed and used?
Our discussions may conclude that modeling the potential impacts of different policies is not a useful or feasible way to go in the governance and development space. Or, that there is a wealth of experience about the value and challenges of modeling in relation to public policy that we need to get up to speed on. Or, that exploring the role that models might play in informing adaptive responses to complex social challenges – and governance-related challenges in particular – is a rich seam to explore.
We have an open mind.
If this piques your interest, please drop us a line. We’d love to learn along together!
¹ Models take many many forms. In this context, we are referring to computer-based dynamic representations of reality that combine theory and data to explore possible future scenarios by changing the parameters and assumptions which drive the model’s dynamics. We are particularly interested in models that include actors and behavior as key steps on the pathway from policy to impact, rather than ones that focus on relationships between more abstract variables.
First published by Global Integrity, 16 July 2020. The Global Integrity website has since closed; this text comes from the Internet Archive’s copy of https://www.globalintegrity.org/2020/07/16/modeling/.