This post is the 3rd of several devoted to addressing the complex challenges of modelling the coronavirus as a public health issue. It is also about clarifying for a wider audience how and why such modelling is important, as well as the value and power of complex systems thinking and computational modelling for public health policy.
This post is the 2nd of several devoted to addressing the complex challenges of modelling the coronavirus as a public health issue. It is also about clarifying for a wider audience how and why such modelling is important, as well as the value and power of complex systems thinking and computational modelling for public health policy.
In response to these advances, the current post is meant to be the first of several addressing the complex challenges of modelling the coronavirus as a public health issue. It is also about clarifying for a wider audience how and why such modelling is important, as well as the value and power of complex systems thinking and computational modelling for public health policy. Still, this does not mean modelling will answer all of our questions; nor does it mean that all models are equally useful!
Pete Barbrook-Johnson, Alex Penn and Ben Shaw have begun a new CECAN case study working with DfT to support their work on ‘Mobility-as-a-Service’ (i.e. using data and software, such as apps, to manage and access transport services).
The Centre for the Evaluation of Complexity Across the Nexus (CECAN) has produced supplementary guidance for the 2020 revision of the Magenta Book. The Magenta Book, published by HM Treasury, is the official government guide about policy evaluation.
On 9th January 2020, Dione Hills (Tavistock Institute) and Pete Barbrook-Johnson (University of Surrey) delivered training on how to conduct evaluations in complex settings to a mixed group of practitioners and policy teams working on public health issues in London.
Alex Penn and Pete Barbrook-Johnson (CECAN Senior Research Fellows based at University of Surrey) and colleagues from Mott McDonald ran the first of three Participatory Systems Mapping workshops in the Eden river catchment in Cumbria on 15th January 2020.
Like the rest of the UK, Northern Ireland has experienced weak productivity growth since the 2008 Financial Crisis despite broader economic recovery. Consequently, boosting productivity has been a central goal of contemporary economic policy even to the extent that both UK and Northern Ireland industrial strategies have been described as productivity policies.
On 4th October 2019, CECAN held a briefing and workshop session with the Department for Business, Energy and Industrial Strategy (BEIS) on dealing with complexity in policy evaluation.
Pete Barbrook-Johnson, a UKRI Innovation Fellow hosted by CECAN, has published the first report from his 3-year fellowship. The report reviews innovative public-private partnerships working in the UK on food-energy-water- environment ‘nexus’ domains.
A demi-regularity, also known as a demi-reg, is a semi-predictable outcome pattern. The demi-reg concept has relevance to fundamental principles in realist evaluation (Pawson and Tilley, 1997) including the context-mechanism-outcome (CMO) configuration and middle-range theorizing.
Picking up on the CECAN webinar last year on the topic ‘How to evaluate – or commission – an evaluation when everything is messy’, Dione Hills (Tavistock Institute and CECAN Associate) was asked to give a key note speech last month at the Norwegian Evaluation Conference.
Alex Penn and Pete Barbrook-Johnson (Senior Research Fellows in CECAN) recently delivered a bespoke 1-day course on participatory systems mapping for researchers and policy makers in the new SIPHER project.
CECAN Fellow, Jayne Cox, has published a project report from her funded fellowship project entitled “How does the commissioning process inhibit the uptake of complexity-appropriate evaluation?”.
People attending CECAN training and events often want to hear about evaluations that ‘failed’ because the wrong (i.e. not complexity appropriate) methods or approach were used. Providing such examples is not easy: accounts of evaluations that ‘fail’ are rarely published, and when we have examples from our own practice, confidentiality – or embarrassment – can make it hard to talk about these in public.
Artists and academics and everyday people who care about the health of the planet have been doing the calculus for a long time. There is a new urgency to these calculations now, partly thanks to the School Strikes and Extinction Rebellion, and underpinned by a wave of action-oriented estimates about the state of the global ecosystem and its capacity to carry all life, including a human race projected to reach nearly 10 billion people by 2050 (UN 2017).
We and our colleagues often talk a good game when it comes to complexity. We want to understand nuance and context, and we know things are messier than we realise. Yet, we still struggle to grapple with complex issues and can feel frustrated with our lack of progress, or others’ seemingly foggy thinking.
The webinar views the challenge of creating complexity-informed evaluation by seeing it as a public management challenge. How can public management adopt a more complexity-informed approach? The session will outline an emerging complexity-informed approach to public management: the Human, Learning, Systems (HLS) approach.
Participation in defining elements or issues within a system is a critical requirement in defining that system adequately. In many social policy spaces, hearing the voices of those who are affected, and who have a lived knowledge of the issues, is foundational. This is equally important in defining the elements of a complex system to anchor evaluation.
This webinar will present an overview of the System Effects methodology, an approach developed to understand the user experience of complex systems. System Effects draws on the methodological approaches of soft systems thinking, fuzzy cognitive mapping, and graph theoretical analysis.
We know research impact unfolds in complex and unpredictable ways, so how on earth do we learn from and evaluate it? In this blog, I will take a look at some of the approaches we have been developing and using in CECAN – a research centre set up to tackle the issue of complexity in evaluation. I will explain how you can use these approaches to do a quick and effective evaluation of complex research impacts, helping you understand what works and why.
Public policy questions are often complex. Decisions rely on both evidence and values. Where should these values come from: politicians, public officials, independent experts, or the public?
This session briefly outlines the origins of theory of change, its purpose, and wide variety of uses. The main message is to show that theory of change as a tool, is instrumental in helping evaluators, intervention managers, and wider stakeholders, understand how change happens. In turn this contributes to better targeted and shaped interventions, and ultimately improved practice.
The field of complexity related research is growing apace, and CECAN has been at the forefront of exploring how research methods from this field can be incorporated into evaluation practice (given sufficient resources, expertise and willingness on the part of evaluation funders!).
Many policies are ‘complex’, that is, they have multiple objectives and multiple actors with possibly conflicting objectives, feedback loops, and policy outcomes that are dependent on the details of the policy history. Evaluating such policies is correspondingly difficult, because often the impact of the policy is unpredictable and the outcomes may be highly context dependent.
On 6th February, Vanna Aldin (Head of Analytics and Chief Economist at the Food Standards Agency) kindly presented a CECAN Webinar on ‘Risk Analysis at the Food Standards Agency post EU-Exit: The role of economics and social science in informing risk management’.
CECAN Ltd is pleased to be working with Defra to develop a Complexity Evaluation Framework that will equip Defra commissioners of evaluation with a checklist of core considerations to ensure that policy evaluations sufficiently consider the implications of complexity theory.
The rise of data science has opened up a number of opportunities for government policy planning and complex evaluation. Although both data science and evaluation involve using data to better understand a particular issue, the benefit of using data science for policy evaluation has still to be established.
There has recently been an upsurge of interest in what constitutes ‘Evaluative Thinking’ (ET). One frequently quoted definition of this term (Buckley 2015) refers to ET being “critical thinking applied to contexts of evaluation”[i]. This blog reflects on ways in which ‘evaluative thinking’ and the application of an understanding of complexity to evaluation can be mutually supportive.
Participation of stakeholders and citizens is increasingly recognized as a helpful and, in some circumstances, crucial ingredient when managing complex, adaptive socio-ecological systems. The scale, interconnectedness and ongoing dynamics of these systems require new approaches bringing together stakeholder knowledge and problem ownership with new complexity-appropriate tools. Such approaches have much to offer both ex-post evaluation and ex-ante policy appraisal.