Structured Analytic Techniques for Intelligence Analysis

This is the title of the 3rd edition of the same, by Randolph H. Pherson and Richards J. Heuer Jr, published by Sage in 2019 

It is not cheap book, so I am not encouraging its purchase, but I am encouraging the perusal of its contents via the contents list and via Amazon’s “Look inside” facility.

Why so? The challenges facing intelligence analysts are especially difficult, so any methods used to address these may be of wider interest. These are spelled out in the Foreword, as follows:


This report is of interest in a number of ways:

  1. To what extent are the challenges faced similar/different to those of evaluations of publicly visible interventions?
  2. How different is the tool set, and the categorisation of the contents of that set?
  3. How much research has gone into the development and testing of this tool set?

The challenges

Some of these challenges are also faced by evaluation teams working in more overt and less antagonistic settings, albeit to a lesser degree.  For example, what will work in future in a slightly different settings (1), missing and ambiguous evidence (2), and with clients and other stakeholders who may intentionally or unintentionally not disclose or actually mislead (3) , and whose recommendations can affect peoples lives, positively and negatively (4).

The contents of the tool set

My first impression is that this book casts its net much wider than the average evaluation text (if there is such a thing). The families of methods include team working, organising, exploring, diagnosing, reframing, foresight, decision support, and more. Secondly, there are quite a few methods within these families I had not heard of before, including Bowtie analysis, opportunities incubator, morphological analysis, premortem analysis, deception detection and inconsistencies finder. The last two are of particular interest. Hopefully they are more than just a method brand name.

Research and testing

Worth looking at, alongside this publication, is this 17 page paper by Artner, S., Girven, R., & Bruce, J. (2016). Assessing the Value of Structured Analytic Techniques in the U.S. Intelligence Community. RAND Corporation. Its key findings are summarised as follows:

    • The U.S. Intelligence Community does not systematically evaluate the effectiveness of structured analytic techniques, despite their increased use.
    • One promising method of assessing these techniques would be to initiate qualitative reviews of their contribution in bodies of intelligence production on a variety of topics, in addition to interviews with authors, managers,  and consumers.
    • A RAND pilot study found that intelligence publications using these techniques generally addressed a broader range of potential outcomes and implications than did other analyses.
    • Quantitative assessments correlating the use of structured techniques to measures of analytic quality, along with controlled experiments using these techniques,  could provide a fuller picture of their contribution to intelligence analysis.

See also Chang, W., & Berdini, E. (2017). Restructuring Structured Analytic Techniques in Intelligence.  For an interesting in-depth analysis of bias risks and how the are managed and possibly mismanaged. Here is the abstract:

Structured analytic techniques (SATs) are intended to improve intelligence analysis by checking the two canonical sources of error: systematic biases and random noise. Although both goals are achievable, no one knows how close the current generation of SATs comes to achieving either of them. We identify two root problems: (1) SATs treat bipolar biases as unipolar. As a result, we lack metrics for gauging possible over-shooting—and have no way of knowing when SATs that focus on suppressing one bias (e.g., over-confidence) are triggering the opposing bias (e.g., under-confidence); (2) SATs tacitly assume that problem decomposition (e.g., breaking reasoning into rows and columns of matrices corresponding to hypotheses and evidence) is a sound means of reducing noise in assessments. But no one has ever actually tested whether decomposition is adding or subtracting noise from the analytic process—and there are good reasons for suspecting that decomposition will, on balance, degrade the reliability of analytic judgment. The central shortcoming is that SATs have not been subject to sustained scientific of the sort that could reveal when they are helping or harming the cause of delivering accurate assessments of the world to the policy community.

Both sound like serious critiques, but compared to what? There are probably plenty of evaluation methods where the same criticism could be applied – no one has subjected them to serious evaluation.

“The Checklist Manifesto”, another perspective on managing the problem of extreme complexity

The Checklist Manifesto by Atul Gwande, 2009

Atul differentiates two types of problems that we face when dealing with extreme complexity. One is that of ignorance, there is a lot we simply don’t know. Unpredictability is a facet of complexity that many writers on the subject of complexity have given plenty of attention to, along with possible ways of managing that unpredictability. The other problem that Atul identifies is that of ineptitude. This is our inability to make good use of knowledge that is already available. He gives many examples where complex bodies of knowledge already exist that can make a big difference to people’s lives, notably in the field of medicine. But because of the very scale of those bodies of knowledge the reality is that people often are not cable of making full use of it and sometimes the consequences are disastrous. This facet of complexity is not something I’ve seen given very much attention to in the literature on complexity, at least that which I have come across. So I read this book with great interest, an interest magnified no doubt by my previous interest in, and experiments with, the use of weighted checklists, which are documented elsewhere on this website.

Another distinction that he makes is between task checklists and communication checklists. The first are all about avoiding dumb mistakes, forgetting to do things we should know that have to be done. The second is about coping with unexpected events, and the necessary characteristics of how we should cope by communicating relevant information to relevant people. He gives some interesting examples from the (big) building industry, where given the complexity of modern construction activities, and the extensive use of task checklists,  there are still inevitably various unexpected hitches which have to be responded to effectively, without jeopardising the progress or safety of the construction process.

Some selected quotes:

  • Checklists helped ensure a higher standard of baseline performance.
  • Medicine has become the art of managing extreme complexity  – and a test of whether such extreme complexity can, in fact, be humanely mastered”
  • Team work may just be hard in certain lines of work. Under conditions of extreme complexity, we inevitably rely on a division of tasks and  expertise…But the evidence suggests that we need them to see their job not just as performing their isolated  set of tasks well, but also helping the group get the best possible results
  • It is common to misconceived power checklists function in complex lines of work. They are not comprehensive how to guides whether for building a skyscraper or getting a plane out of trouble. They are quick and simple tools aimed to buttress the skills of expert professionals. And by remaining swift and usable and resolutely modest, they are saving thousands upon thousands of lives.
  • When you are making a checklist, you have a number of key decisions. You must define a clear pause point at which the checklist is supposed to be used (unless the moment is obvious, like when a warning light goes on or an engine fails) you must decide whether you want a do-confirm checklist or read-do checklist. With a do-confirm checklist team members perform their jobs from memory and experience, often separately. But then they stop. They paused to run the checklist and confirm that everything that was supposed to be done was done. With the read-do checklist, on the other hand, people carry out the task as they check them off, it’s more like a recipe. So for any new checklist created from scratch, you have to pick the type that makes the most sense of the situation.
  • We are obsessed in medicine with having great components – the best drugs, the best devices, the best specialists – but paid little attention to how to make them fit together well. Berwisk notes how wrongheaded this approach is ‘anyone who understands systems will know immediately that optimising part is not a great route to system excellent ‘he says.

I could go on, but I would rather keep reading the book… :-)

 

Calling Bullshit: THE ART OF SKEPTICISM IN A DATA-DRIVEN WORLD

Reviews

Wired review article

Guardian review article

Forbes review article

Kirkus Review article

Podcast Interview with the authors here

ABOUT CALLING BULLSHIT (=publisher blurb)
“Bullshit isn’t what it used to be. Now, two science professors give us the tools to dismantle misinformation and think clearly in a world of fake news and bad data.

Misinformation, disinformation, and fake news abound and it’s increasingly difficult to know what’s true. Our media environment has become hyperpartisan. Science is conducted by press release. Startup culture elevates bullshit to high art. We are fairly well equipped to spot the sort of old-school bullshit that is based in fancy rhetoric and weasel words, but most of us don’t feel qualified to challenge the avalanche of new-school bullshit presented in the language of math, science, or statistics. In Calling Bullshit, Professors Carl Bergstrom and Jevin West give us a set of powerful tools to cut through the most intimidating data.

You don’t need a lot of technical expertise to call out problems with data. Are the numbers or results too good or too dramatic to be true? Is the claim comparing like with like? Is it confirming your personal bias? Drawing on a deep well of expertise in statistics and computational biology, Bergstrom and West exuberantly unpack examples of selection bias and muddled data visualization, distinguish between correlation and causation, and examine the susceptibility of science to modern bullshit.

We have always needed people who call bullshit when necessary, whether within a circle of friends, a community of scholars, or the citizenry of a nation. Now that bullshit has evolved, we need to relearn the art of skepticism.”

Evaluation Failures: 22 Tales of Mistakes Made and Lessons Learned

Edited by: Kylie Hutchinson – Community Solutions, Vancouver, Canada. 2018 Published by Sage. https://us.sagepub.com/en-us/nam/evaluation-failures/book260109

But $30 for 184-page paperback is going to limit its appeal! The electronic version is similarly expensive, more like the cost of a hardback. Fortunately, two example chapters (1 and 8) are available as free pdfs, see below. Reading those two chapters makes me think the rest of the book would also be well worthwhile reading. It is not ofter you see anything written at length about evaluation failures. Perhaps we should set up an online-confessional, where we can line up to anonymously confess our un/professional sins. I will certainly be one of those needing to join such a queue! :)

PART I. MANAGE THE EVALUATION
Chapter 2. The Scope Creep Train Wreck: How Responsive Evaluation Can Go Off the Rails
Chapter 3. The Buffalo Jump: Lessons After the Fall
Chapter 4. Evaluator Self-Evaluation: When Self-Flagellation Is Not Enough
PART II. ENGAGE STAKEHOLDERS
Chapter 5. That Alien Feeling: Engaging All Stakeholders in the Universe
Chapter 6. Seeds of Failure: How the Evaluation of a West African
Chapter 7. I Didn’t Know I Would Be a Tightrope Walker Someday: Balancing Evaluator Responsiveness and Independence
PART III. BUILD EVALUATION CAPACITY
Chapter 9. Stars in Our Eyes: What Happens When Things Are Too Good to Be True
PART IV. DESCRIBE THE PROGRAM
Chapter 10. A “Failed” Logic Model: How I Learned to Connect With All Stakeholders
Chapter 11. Lost Without You: A Lesson in System Mapping and Engaging Stakeholders
PART V. FOCUS THE EVALUATION DESIGN
Chapter 12. You Got to Know When to Hold ’Em: An Evaluation That Went From Bad to Worse
Chapter 13. The Evaluation From Hell: When Evaluators and Clients Don’t Quite Fit
PART VI. GATHER CREDIBLE EVIDENCE
Chapter 14. The Best Laid Plans of Mice and Evaluators: Dealing With Data Collection Surprises in the Field
Chapter 15. Are You My Amigo, or My Chero? The Importance of Cultural Competence in Data Collection and Evaluation
Chapter 16. OMG, Why Can’t We Get the Data? A Lesson in Managing Evaluation Expectations
Chapter 17. No, Actually, This Project Has to Stop Now: Learning When to Pull the Plug
Chapter 18. Missing in Action: How Assumptions, Language, History, and Soft Skills Influenced a Cross-Cultural Participatory Evaluation
PART VII. JUSTIFY CONCLUSIONS
Chapter 19. “This Is Highly Illogical”: How a Spock Evaluator Learns That Context and Mixed Methods Are Everything
Chapter 20. The Ripple That Became a Splash: The Importance of Context and Why I Now Do Data Parties
Chapter 21. The Voldemort Evaluation: How I Learned to Survive Organizational Dysfunction, Confusion, and Distrust
PART VIII. REPORT AND ENSURE USE
Chapter 22. The Only Way Out Is Through
Conclusion

 

 

 

The Power of Experiments: Decision Making in a Data-Driven World


By Michael Luca and Max H. Bazerman, March 2020. Published by MIT Press

How organizations—including Google, StubHub, Airbnb, and Facebook—learn from experiments in a data-driven world.

Abstract

Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you’ve probably been an unwitting participant in a variety of experiments—also known as randomized controlled trials—designed to test the impact of changes to an experience or product. Once an esoteric tool for academic research, the randomized controlled trial has gone mainstream—and is becoming an important part of the managerial toolkit. In The Power of Experiments: Decision-Making in a Data Driven World, Michael Luca and Max Bazerman explore the value of experiments and the ways in which they can improve organizational decisions. Drawing on real world experiments and case studies, Luca and Bazerman show that going by gut is no longer enough—successful leaders need frameworks for moving between data and decisions. Experiments can save companies money—eBay, for example, discovered how to cut $50 million from its yearly advertising budget without losing customers. Experiments can also bring to light something previously ignored, as when Airbnb was forced to confront rampant discrimination by its hosts. The Power of Experiments introduces readers to the topic of experimentation and the managerial challenges that surround them. Looking at experiments in the tech sector and beyond, this book offers lessons and best practices for making the most of experiments.

In The Power of Experiments: Decision-Making in a Data Driven World, Michael Luca and Max Bazerman explore the value of experiments, and the ways in which they can improve organizational decisions. Drawing on real world experiments and case studies, Luca and Bazerman show that going by gut is no longer enough—successful leaders need frameworks for moving between data and decisions. Experiments can save companies money—eBay, for example, discovered how to cut $50 million from its yearly advertising budget without losing customers. Experiments can also bring to light something previously ignored, as when Airbnb was forced to confront rampant discrimination by its hosts.

The Power of Experiments introduces readers to the topic of experimentation and the managerial challenges that surround them. Looking at experiments in the tech sector and beyond, this book offers lessons and best practices for making the most of experiments.

See also a World bank blog review by David McKenzie

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

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

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?

 

 

The Book of Why: The New Science of Cause and Effect

by Judea Pearl, Allen Lane, May 2018

Publisher blurb: “‘Correlation does not imply causation.’ This mantra was invoked by scientists for decades in order to avoid taking positions as to whether one thing caused another, such as smoking and cancer and carbon dioxide and global warming. But today, that taboo is dead. The causal revolution, sparked by world-renowned computer scientist Judea Pearl and his colleagues, has cut through a century of confusion and placed cause and effect on a firm scientific basis. Now, Pearl and science journalist Dana Mackenzie explain causal thinking to general readers for the first time, showing how it allows us to explore the world that is and the worlds that could have been. It is the essence of human and artificial intelligence. And just as Pearl’s discoveries have enabled machines to think better, The Book of Why explains how we can think better.

Introduction: Mind over data (pdf copy)

Chapter 1: The Ladder of Causation (pdf copy)

Reviews: None found yet, but they will be listed here when found

2020 05 20: New material from Judea Pearl – “On this page I plan to post viewgraphs and homeworks that I currently use in teaching CAUSALITY. So far, we have covered the following chapters”:

http://bayes.cs.ucla.edu/BOOK-2K/viewgraphs.html