Known unknowns: How to communicate certainly in an uncertain world

“From the speed of global warming to the likelihood of developing cancer, we must grasp uncertainty to understand the world. Here’s how to know your unknowns” By Anne Marthe van der Bles, New Scientist, 3rd July 2019

https://www.newscientist.com/article/mg24332372-600-known-unknowns-how-to-communicate-certainly-in-an-uncertain-world/

The above reminds me of the philosophers’ demands in The Hitchhikers Guide to the Galaxy: “We demand rigidly defined areas of doubt and uncertainty!” The philosophers were representatives of the Amalgamated Union of Philosophers, Sages, Luminaries and Other Thinking Persons and they wanted the universe’s second-best computer (Deep Thought) turned off, because of a demarcation dispute/.  It turns out, according to the above paper, that their demands were not so unreasonable after all :-)

Navigation by Judgment: Why and When Top Down Management of Foreign Aid Doesn’t Work

Errors arising from too much or too little control can be seen or unseen. When control is too little, errors are more likely to be seen. People do things they should not have done. When control is too much, errors are likely to be unseen, people don’t do things they should have done. Given this asymmetry, and other things being equal, there is a bias towards too much control

Honig, Dan. 2018. Navigation by Judgment: Why and When Top Down Management of Foreign Aid Doesn’t Work. Oxford, New York: Oxford University Press.

Contents

Preface
Acknowledgments
Part I: The What, Why, and When of Navigation by Judgment
Chapter 1. Introduction – The Management of Foreign Aid
Chapter 2. When to Let Go: The Costs and Benefits of Navigation by Judgment
Chapter 3. Agents – Who Does the Judging?
Chapter 4. Authorizing Environments & the Perils of Legitimacy Seeking
Part II: How Does Navigation by Judgment Fare in Practice?
Chapter 5. How to Know What Works Better, When: Data, Methods, and Empirical Operationalization
Chapter 6. Journey Without Maps – Environmental Unpredictability and Navigation Strategy
Chapter 7. Tailoring Management to Suit the Task – Project Verifiability and Navigation Strategy
Part III: Implications
Chapter 8. Delegation and Control Revisited
Chapter 9. Conclusion – Implications for the Aid Industry & Beyond
Appendices
Appendix I: Data Collection
Appendix II: Additional Econometric Analysis
Bibliography

YouTube presentation by the author: https://www.youtube.com/watch?reload=9&v=bdjeoBFY9Ss

Snippet from video: Errors arising from too much or too little control can be seen or unseen. When control is too little, errors are more likely to be seen. People do things they should not have done. When control is too much, errors are likely to be unseen, people don’t do things they should have done. Given this asymmetry, and other things being equal, there is a bias towards too much control

Book review: By Duncan Green in his 2018 From Poverty to Power blog

Publishers blurb:

Foreign aid organizations collectively spend hundreds of billions of dollars annually, with mixed results. Part of the problem in these endeavors lies in their execution. When should foreign aid organizations empower actors on the front lines of delivery to guide aid interventions, and when should distant headquarters lead?

In Navigation by Judgment, Dan Honig argues that high-quality implementation of foreign aid programs often requires contextual information that cannot be seen by those in distant headquarters. Tight controls and a focus on reaching pre-set measurable targets often prevent front-line workers from using skill, local knowledge, and creativity to solve problems in ways that maximize the impact of foreign aid. Drawing on a novel database of over 14,000 discrete development projects across nine aid agencies and eight paired case studies of development projects, Honig concludes that aid agencies will often benefit from giving field agents the authority to use their own judgments to guide aid delivery. This “navigation by judgment” is particularly valuable when environments are unpredictable and when accomplishing an aid program’s goals is hard to accurately measure.

Highlighting a crucial obstacle for effective global aid, Navigation by Judgment shows that the management of aid projects matters for aid effectiveness

Linked Democracy Foundations, Tools, and Applications

Poblet, Marta, Pompeu Casanovas, and Víctor Rodríguez-Doncel. 2019. Linked Democracy: Foundations, Tools, and Applications. SpringerBriefs in Law. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-13363-4. Available in PDF form online

“It is only by mobilizing knowledge that is widely dispersed
across a genuinely diverse community that a free society can
hope to outperform its rivals while remaining true to its
values”

(Ober 2008, 5) cited on page v

Chapter 1 Introduction to Linked Data Abstract This chapter presents Linked Data, a new form of distributed data on the web which is especially suitable to be manipulated by machines and to share knowledge. By adopting the linked data publication paradigm, anybody can publish data on the web, relate it to data resources published by others and run artificial intelligence algorithms in a smooth manner. Open linked data resources may democratize the future access to knowledge by the mass of internet users, either directly or mediated through algorithms. Governments have enthusiastically adopted these ideas, which is in harmony with the broader open data movement.

Chapter 2 Deliberative and Epistemic Approaches to Democracy Abstract Deliberative and epistemic approaches to democracy are two important dimensions of contemporary democratic theory. This chapter studies these dimensions in the emerging ecosystem of civic and political participation tools, and appraises their collective value in a new distinct concept: linked democracy. Linked democracy is the distributed, technology-supported collective decision-making process, where data, information and knowledge are connected and shared by citizens online. Innovation and learning are two key elements of Athenian democracies which can be facilitated by the new digital technologies, and a cross-disciplinary research involving computational scientists and democratic theorists can lead to new theoretical insights of democracy

Chapter 3 Multilayered Linked Democracy An infinite amount of knowledge is waiting to be unearthed. —Hess and Ostrom (2007) Abstract Although confidence in democracy to tackle societal problems is falling, new civic participation tools are appearing supported by modern ICT technologies. These tools implicitly assume different views on democracy and citizenship which have not been fully analysed, but their main fault is their isolated operation in non-communicated silos. We can conceive public knowledge, like in Karl Popper’s World 3, as distributed and connected in different layers and by different connectors, much as it happens with the information in the web or the data in the linked data cloud. The interaction between people, technology and data is still to be defined before alternative institutions are founded, but the so called linked democracy should rest on different layers of interaction: linked data, linked platforms and linked ecosystems; a robust connectivity between democratic institutions is fundamental in order to enhance the way knowledge circulates and collective decisions are made.

Chapter 4 Towards a Linked Democracy Model Abstract In this chapter we lay out the properties of participatory ecosystems as linked democracy ecosystems. The goal is to provide a conceptual roadmap that helps us to ground the theoretical foundations for a meso-level, institutional theory of democracy. The identification of the basic properties of a linked democracy eco-system draws from different empirical examples that, to some extent, exhibit some of these properties. We then correlate these properties with Ostrom’s design principles for the management of common-pool resources (as generalised to groups cooperating and coordinating to achieve shared goals) to open up the question of how linked democracy ecosystems can be governed

Chapter 5 Legal Linked Data Ecosystems and the Rule of Law Abstract This chapter introduces the notions of meta-rule of law and socio-legal ecosystems to both foster and regulate linked democracy. It explores the way of stimulating innovative regulations and building a regulatory quadrant for the rule of law. The chapter summarises briefly (i) the notions of responsive, better and smart regulation; (ii) requirements for legal interchange languages (legal interoperability); (iii) and cognitive ecology approaches. It shows how the protections of the substantive rule of law can be embedded into the semantic languages of the web of data and reflects on the conditions that make possible their enactment and implementation as a socio-legal ecosystem. The chapter suggests in the end a reusable multi-levelled meta-model and four notions of legal validity: positive, composite, formal, and ecological.

Chapter 6 Conclusion Communication technologies have permeated almost every aspect of modern life, shaping a densely connected society where information flows follow complex patterns on a worldwide scale. The World Wide Web created a global space of information, with its network of documents linked through hyperlinks. And a new network is woven, the Web of Data, with linked machine-readable data resources that enable new forms of computation and more solidly grounded knowledge. Parliamentary debates, legislation, information on political parties or political programs are starting to be offered as linked data in rhizomatic structures, creating new opportunities for electronic government, electronic democracy or political deliberation. Nobody could foresee that individuals, corporations and government institutions alike would participate …(continues)

THE MODEL THINKER What You Need to Know to Make Data Work for You

by Scott E. Page. Published by Basic Books, 2018

Book review by Carol Wells “Page proposes a “many-model paradigm,” where we apply several mathematical models to a single problem. The idea is to replicate “the wisdom of the crowd” which, in groups like juries, has shown us that input from many sources tends to be more accurate, complete, and nuanced than input from a single source”

Contents:

Chapter 1 – The Many-Model Thinker
Chapter 2 – Why Model?
Chapter 3 – The Science of Many Models
Chapter 4 – Modeling Human Actors
Chapter 5 – Normal Distributions: The Bell Curve
Chapter 6 – Power-Law Distributions: Long Tails
Chapter 7 – Linear Models
Chapter 8 – Concavity and Convexity
Chapter 9 – Models of Value and Power
Chapter 10 – Network Models
Chapter 11 – Broadcast, Diffusion, and Contagion
Chapter 12 – Entropy: Modeling Uncertainty
Chapter 13 – Random Walks
Chapter 14 – Path Dependence
Chapter 15 – Local Interaction Models
Chapter 16 – Lyapunov Functions and Equilibria
Chapter 17 – Markov Models
Chapter 18 – Systems Dynamics Models
Chapter 19 – Threshold Models with Feedbacks
Chapter 20 – Spatial and Hedonic Choice
Chapter 21 – Game Theory Models Times Three
Chapter 22 – Models of Cooperation
Chapter 23 – Collective Action Problems
Chapter 24 – Mechanism Design
Chapter 25 – Signaling Models
Chapter 26 – Models of Learning
Chapter 27 – Multi-Armed Bandit Problems
Chapter 28 – Rugged-Landscape Models
Chapter 29 – Opioids, Inequality, and Humility

From his Coursera course, which the book builds on: “We live in a complex world with diverse people, firms, and governments whose behaviors aggregate to produce novel, unexpected phenomena. We see political uprisings, market crashes, and a never-ending array of social trends. How do we make sense of it? Models. Evidence shows that people who think with models consistently outperform those who don’t. And, moreover, people who think with lots of models outperform people who use only one. Why do models make us better thinkers? Models help us to better organize information – to make sense of that fire hose or hairball of data (choose your metaphor) available on the Internet. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures. In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians. The models covered in this class provide a foundation for future social science classes, whether they be in economics, political science, business, or sociology. Mastering this material will give you a huge leg up in advanced courses. They also help you in life. Here’s how the course will work. For each model, I present a short, easily digestible overview lecture. Then, I’ll dig deeper. I’ll go into the technical details of the model. Those technical lectures won’t require calculus but be prepared for some algebra. For all the lectures, I’ll offer some questions and we’ll have quizzes and even a final exam. If you decide to do the deep dive, and take all the quizzes and the exam, you’ll receive a Course Certificate. If you just decide to follow along for the introductory lectures to gain some exposure that’s fine too. It’s all free. And it’s all here to help make you a better thinker!”

Some of his online videos on Coursera

Other videos

Participatory modelling and mental models

These are the topics covered by two papers I have come across today, courtesy of Peter Barbrook-Johnson, of Surrey University. Both papers provide good overviews of their respective fields.

Moon, K., Adams, V. M., Dickinson, H., Guerrero, A. M., Biggs, D., Craven, L., … Ross, H. (2019). Mental models for conservation research and practice. Conservation Letters, 1–11.

Abstract: Conservation practice requires an understanding of complex social-ecological processes of a system and the different meanings and values that people attach to them. Mental models research offers a suite of methods that can be used to reveal these understandings and how they might affect conservation outcomes. Mental models are representations in people’s minds of how parts of the world work. We seek to demonstrate their value to conservation and assist practitioners and researchers in navigating the choices of methods available to elicit them. We begin by explaining some of the dominant applications of mental models in conservation: revealing individual assumptions about a system, developing a stakeholder-based model of the system, and creating a shared pathway to conservation. We then provide a framework to “walk through” the stepwise decisions in mental models research, with a focus on diagram based methods. Finally, we discuss some of the limitations of mental models research and application that are important to consider. This work extends the use of mental models research in improving our ability to understand social-ecological systems, creating a powerful set of tools to inform and shape conservation initiatives.

PDF copy here

Voinov, A. (2018). Tools and methods in participatory modeling: Selecting the right tool for the job. Environmental Modelling and Software, 109, 232–255.

Abstract: Various tools and methods are used in participatory modelling, at di?erent stages of the process and for di?erent purposes. The diversity of tools and methods can create challenges for stakeholders and modelers when selecting the ones most appropriate for their projects. We o?er a systematic overview, assessment, and categorization of methods to assist modelers and stakeholders with their choices and decisions. Most available literature provides little justi?cation or information on the reasons for the use of particular methods or tools in a given study. In most of the cases, it seems that the prior experience and skills of the modelers had a dominant e?ect on the selection of the methods used. While we have not found any real evidence of this approach being wrong, we do think that putting more thought into the method selection process and choosing the most appropriate method for the project can produce better results. Based
on expert opinion and a survey of modelers engaged in participatory processes, we o?er practical guidelines to improve decisions about method selection at di?erent stages of the participatory modeling process

PDF copy here

Subjective measures in humanitarian analysis

A note for ACAPS, by Aldo Benini, A. (2018). PDF available at https://www.acaps.org/sites/acaps/files/resources/files/20180115_acaps_technical_note_subjective_measures_full_report.pdf

Purpose and motivation

This note seeks to sensitize analysts to the growing momentum of subjective methods and measures around, and eventually inside, the humanitarian field. It clarifies the nature of subjective measures and their place in humanitarian needs assessments. It weighs their strengths and challenges. It discusses, in considerable depth, a small number of instruments and methods that are ready, or have good potential, for humanitarian analysis.

Post World War II culture and society have seen an acceleration of subjectivity in all institutional realms, although at variable paces. The sciences responded with considerable lag. They have created new methodologies – “mixed methods” (quantitative and qualitative), “subjective measures”, self-assessments of all kinds – that claim an equal playing field with distant, mechanical objectivity. For the period 2000-2012, using the search term “subjective measure”, Google Scholar returns around 600 references per year; for the period 2013 – fall 2017, the figure quintuples to 3,000. Since 2012, the United Nations has been publishing the annual World Happiness Report; its first edition discusses validity and reliability of subjective measures at length.

Closer to the humanitarian domain, poverty measurement has increasingly appreciated subjective data. Humanitarian analysis is at the initial stages of feeling the change. Adding “AND humanitarian” to the above search term produces 8 references per year for the first period, and 40 for the second – a trickle, but undeniably an increase. Other searches confirm the intuition that something is happening below the surface; for instance, “mixed method  AND humanitarian” returns 110 per year in the first, and 640 in the second period – a growth similar to that of “subjective measures”.

Still in some quarters subjectivity remains suspect. Language matters. Some collaborations on subjective measures have preferred billing them as “experience-based measures”. Who doubts experience? It is good salesmanship, but we stay with “subjective” unless the official name of the measure contains “experience”.

What follows 

We proceed as follows: In the foundational part, we discuss the nature of, motivation for, and reservations against, subjective measures. We provide illustrations from poverty measurement and from food insecurity studies. In the second part, we present three tools – scales, vignettes and hypothetical questions – with generic pointers as well as with specific case studies. We conclude with recommendations and by noting instruments that we have not covered, but which are likely to grow more important in years to come

Rick Davies comment: High recommended!

Reflecting the Past, Shaping the Future: Making AI Work for International Development

USAID, September 2018. 98 pages. Available as PDF

Rick Davies comment: A very good overview, balanced, informative, with examples. Worth reading from beginning to end.

Contents

Introduction
Roadmap: How to use this document
Machine learning: Where we are and where we might be going
• ML and AI: What are they?
• How ML works: The basics
• Applications in development
• Case study: Data-driven agronomy and machine learning
at the International Center for Tropical Agriculture
• Case study: Harambee Youth Employment Accelerator
Machine learning: What can go wrong?
• Invisible minorities
• Predicting the wrong thing
• Bundling assistance and surveillance
• Malicious use
• Uneven failures and why they matter
How people influence the design and use of ML tools
• Reviewing data: How it can make all the difference
• Model-building: Why the details matter
• Integrating into practice: It’s not just “Plug and Play”
Action suggestions: What development practitioners can do today
• Advocate for your problem
• Bring context to the fore
• Invest in relationships
• Critically assess ML tools
Looking forward: How to cultivate fair & inclusive ML for the future
Quick reference: Guiding questions
Appendix: Peering under the hood [ gives more details on specific machine learning algorithms]

See also the associated USAID blog posting and maybe also  How can machine learning and artificial intelligence be used in development interventions and impact evaluations?

 

 

Bayesian belief networks – Their use in humanitarian scenarios An invitation to explorers

By Aldo Benini. July 2018. Available here as a pdf

Summary

This is an invitation for humanitarian data analysts and others –  assessment, policy and advocacy specialists, response planners and grant writers – to enhance the reach and quality of scenarios by means of so-called Bayesian belief networks. Belief networks are a powerful technique for structuring scenarios in a qualitative as well as quantitative approach. Modern software, with elegant graphical user interfaces, makes for rapid learning, convenient drafting, effortless calculation and compelling presentation in workshops, reports and Web pages.

In recent years, scenario development in humanitarian analysis has grown. Until now, however, the community has hardly ever tried out belief networks, in contrast to the natural disaster and ecological communities. This note offers a small demonstration. We build a simple belief network using information currently (mid-July 2018) available on a recent violent crisis in Nigeria. We produce and discuss several possible scenarios for the next three months, computing probabilities of two humanitarian outcomes.

Figure 1: Belief network with probability bar charts (segment)

We conclude with reflections on the contributions of belief networks to humanitarian scenario building and elsewhere. While much speaks for this technique, the growth of competence, the uses in workshops and the interpretation of graphs and statistics need to be fostered cautiously, with consideration for the real-world complexity and for the doubts that stakeholders may harbor about quantitative approaches. This note is in its first draft. It needs to be revised, possibly by several authors, in order to connect to progress in humanitarian scenario methodologies, expert judgment and workshop didactics

RD Comment: See also the comment and links provided below by Simon Henderson on his experience (with IOD/PARC) of trialing the use of Bayesian belief networks

Representing Theories of Change: Technical Challenges with Evaluation Consequences

A CEDIL Inception Paper, by Rick Davies. August 2018.  A pdf copy is available here 

 

Abstract: This paper looks at the technical issues associated with the representation of Theories of Change and the implications of design choices for the evaluability of those theories. The focus is on the description of connections between events rather than the events themselves, because this is seen as a widespread design weakness. Using examples and evidence from Internet sources six structural problems are described along with their consequences for evaluation.

The paper then outlines a range of different ways of addressing these problems which could be used by programme designers, implementers and evaluators. The paper concludes with some caution speculating on why the design problems are so endemic but also pointing a way forward. Four strands of work are identified that CEDIL and DFID could invest in to develop solutions identified in the paper.

Table of Contents

What is a theory of change?
What is the problem?
A summary of the problems….
And a word in defence….
Six possible ways forward
Why so little progress?
Implications for CEDIL and DFID
References

Postscript: Michael Bamberger’s 2018 07 13 comments on this paper

I think this is an extremely useful and well-documented paper.  Framing the discussion around the 6 problems, and the possible ways forward is a good way to organize the presentation.  The documentation and links that you present will be greatly appreciated, as well as the graphical illustrations of the different approaches.
Without getting into too much detail, the following are a few general thoughts on this very useful paper:
  1. A criticism of many TOCs is that they only describe how a program will achieve its intended objectives and they do not address th challenges of identifying and monitoring potential unintended and often undesired, outcomes (UOs)  While some UOs could not have been anticipated, many others could, and these should perhaps be built into the model.  For example, there is an extensive literature documenting negative consequences for women of political and economic empowerment, often including increased domestic violence.  So these could be built into the TOC, but in many cases they are not.
  2. Many, but certainly not all, TOCs do not adequately address the challenges of emergence the fact that the environment in which the program operates; the political and organizational arrangements; and the characteristics of the target population and how they respond to the program are all likely to change significantly during the life of the project.  Many TOCs implicitly assume that the project and its environment remain relatively stable throughout the project lifetime.  Of course, many of the models you describe do not assume a stable environment, but it might be useful to flag the challenges of emergence. Many agencies are starting to become interested in agile project management to address the emergence challenge.
  3. Given the increasing recognition that most evaluation approaches do not adequately address complexity, and the interest in complexity-responsive evaluation approaches, you might like to focus more directly on how TOCs can address complexity.  Complexity is, of course, implicit in much of your discussion, but it might b useful to highlight the term.
  4. Do you think it would be useful to include a section on how big data and data analytics can strengthen the ability to develop more sophisticated TOCs.  Many agencies may feel that many of the techniques you mention would not be feasible with the kinds of data they collect and their current analytical tools.
  5. Related to the previous point, it might be useful to include a brief discussion of how accessible the quite sophisticated methods that you discuss would be to many evaluation offices.  What kinds of expertise would be required?  where would the data come from? how much would it cost.  You don’t ned to go into too much detail but many readers would like guidance on which approaches are likely to be accessible to which kinds of agency.
  6. Your discussion of “Why so little progress?” is critical.  It is my impression that among the agencies with whom I have worked,  while many evaluations pay lip-service to TOC, the full potential of the approach is very often not utilized.  Often the TOC is constructed at the start of a project with major inputs from an external consultant.  The framework is then rarely consulted again until the final evaluation report is being written, and there are even fewer instances where it is regularly tested, updated and revised.  There are of course many exceptions, and I am sure experience may be different with other kinds of agencies.  However, I think that many implementing agencies (and many donors) have very limited expectations concerning what they hope TOC will contribute.  There is probably very little appetite among many implementing agencies (as opposed to a few funding agencies such as DFID) for more refined models.
  7. Among agencies where this is the case, it will be necessary to demonstrate the value-added of investing time and resources in more refined TOCs.  So it might be useful to expand the discussion of the very practical, as opposed to the broader theoretical, justifications for investing in the existing TOC.
  8. In addition to the above considerations, many evaluators tend to be quite conservative in their choice of methodologies and they are often reluctant to adopt new methodologies – particularly if these use approaches with which they are not familiar.  New approaches, such as some of those you describe can also be seen as threatening if they might undermine the status of the evaluation professional as expert in his/her field.

Participatory approaches to the development of a Theory of Change: Beginnings of a list

Background

There have been quite a few generic guidance documents written on the use of Theories of Change. These are not the main focus of this list. Nevertheless, here are those I have come across:

Klein, M (2018) Theory of Change Quality Audit, at https://changeroo.com/toc-academy/posts/expert-toc-quality-audit-academy

UNDG (2017) Theory of Change – UNDAF Companion Guidance, UNDG.  https://undg.org/wp-content/uploads/2017/06/Theory-of-Change-UNDAF-Companion-Pieces.pdf

Van Es M, Guijt I and Vogel I (2015) Theory of Change Thinking in Practice. HIVOS. http://www.theoryofchange.nl/sites/default/files/resource/hivos_Theory of Change_guidelines_final_nov_2015.pdf.

Valters C (2015) Theories of Change: Time for a radical approach to learning in development. ODI. https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/9835.pdf.

Rogers P (2014) Theory of Change. Methodological Briefs Impact Evaluation No. 2. UNICEF.
http://devinfolive.info/impact_evaluation/img/downloads/Theory_of_Change_ENG.pdf.

Vogel I (2012) Review of the use of ‘Theory of Change’ in international development. Review Report for DFID.
http://www.dfid.gov.uk/r4d/pdf/outputs/mis_spc/DFID_Theory of Change_Review_VogelV7.pdf

Vogel I (2012) ESPA guide to working with Theory of Change for research projects. LTS/ITAD for ESPA. http://www.espa.ac.uk/files/espa/ESPA-Theory-of-Change-Manual-FINAL.pdf

Stein, D., & Valters, C. (2012). Understanding Theory in Change in International Development. The Asia Foundation. http://www2.lse.ac.uk/internationalDevelopment/research/JSRP/downloads/JSRP1.SteinValters.pdf

James, C. (2011, September). Theory of Change Review. A Report Commissioned by Comic Relief. http://www.theoryofchange.org/pdf/James_Theory of Change.pdf

UNDAF (UNDG, 2017).

Participatory approaches to ToC construction

Burbaugh B, Seibel M and Archibald T (2017) Using a Participatory Approach to Investigate a Leadership Program’s Theory of Change. Journal of Leadership Education 16(1): 192–205.

Katherine Austin-Evelyn and Erin Williams  (2016) Mapping Change for Girls, One Post-It Note at a Time. Blog posting

Breuer E, Lee L, De Silva M, et al. (2016) Using theory of change to design and evaluate public health interventions: a systematic review. Implementation science: IS 11: 63. DOI: 10.1186/s13012-016-0422-6. Recommended

Breuer E, De Silva MJ, Fekadu A, et al. (2014) Using workshops to develop theories of change in five low and middle-income countries: lessons from the programme for improving mental health care (PRIME). International Journal of Mental Health Systems 8: 15. DOI: 10.1186/1752-4458-8-15.

De Silva MJ, Breuer E, Lee L, et al. (2014) Theory of Change: a theory-driven approach to enhance the Medical Research Council’s framework for complex interventions. Trials 15: 267. DOI: 10.1186/1745-6215-15-267.