9 days ago

From Dodgy Data to Dodgy Policy – Mrs May’s Immigration Targets

The TotalData™ value chain is about the flow from raw data to business decisions (including evidence-based policy decisions).

In this post, I want to talk about an interesting example of a flawed data-driven policy. The UK Prime Minister, Theresa May, is determined to reduce the number of international students visiting the UK. This conflicts with the advice she is getting from nearly everyone, including her own ministers.

As @Skapinker explains in the Financial Times, there are a number of mis-steps in this case.

  • Distorted data collection. Mrs May’s policy is supported by raw data indicating the number of students that return to their country of origin. These are estimated measurements, based on daytime and evening surveys taken at UK airports. Therefore students travelling on late-night flights to such countries as China, Nigeria, Hong Kong, Saudi Arabia and Singapore are systematically excluded from the data.
  • Disputed data definition. Most British people do not regard international students as immigrants. But as May stubbornly repeated to a parliamentary committee in December 2016, she insists on using an international definition of migration, which includes any students that stay for more than 12 months.
  • Conflating measurement with target. Mrs May told the committee that “the target figures are calculated from the overall migration figures, and students are in the overall migration figures because it is an international definition of migration”. But as Yvette Cooper pointed out “The figures are different from the target. … You choose what to target.”
  • Refusal to correct baseline. Sometimes the easiest way to achieve a goal is to move the goalposts. Some people are quick to use this tactic, while others instinctively resist change. Mrs May is in the latter camp, and appears to regard any adjustment of the baseline as backsliding and morally suspect.

If you work with enterprise data, you may recognize these anti-patterns.


David Runciman, Do your homework (London Review of Books Vol. 39 No. 6, 16 March 2017)

Michael Skapinker, Theresa May’s clampdown on international students is a mystery (Financial Times, 15 March 2017)

International students and the net migration target: Should students be taken out? (Migration Observatory, 25 Jun 2015)

Oral evidence: The Prime Minister (House of Commons HC 833, 20 December 2016) 


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16 days ago

Inspector Sands to Platform Nine and Three Quarters

Last week was not a good one for the platform business. Uber continues to receive bad publicity on multiple fronts, as noted in my post on Uber’s Defeat Device and Denial of Service (March 2017). And on Tuesday, a fat-fingered system admin at AWS managed to take out a significant chunk of the largest platform on the planet, seriously degrading online retail in the Northern Virginia (US-EAST-1) Region. According to one estimate, performance at over half of the top internet retailers was hit by 20 percent or more, and some websites were completely down.

What have we learned from this? Yahoo Finance tells us not to worry.

“The good news: Amazon has addressed the issue, and is working to ensure nothing similar happens again. … Let’s just hope … that Amazon doesn’t experience any further issues in the near future.”

Other commentators are not so optimistic. For Computer Weekly, this incident

“highlights the risk of running critical systems in the public cloud. Even the most sophisticated cloud IT infrastructure is not infallible.”

So perhaps one lesson is not to trust platforms. Or at least not to practice wilful blindness when your chosen platform or cloud provider represents a single point of failure.

One of the myths of cloud, according to Aidan Finn,

“is that you get disaster recovery by default from your cloud vendor (such as Microsoft and Amazon). Everything in the cloud is a utility, and every utility has a price. If you want it, you need to pay for it and deploy it, and this includes a scenario in which a data center burns down and you need to recover. If you didn’t design in and deploy a disaster recovery solution, you’re as cooked as the servers in the smoky data center.”

Interestingly, Amazon itself was relatively unaffected by Tuesday’s problem. This may have been because they split their deployment across multiple geographical zones. However, as Brian Guy points out, there are significant costs involved in multi-region deployment, as well as data protection issues. He also notes that this question is not (yet) addressed by Amazon’s architectural guidelines for AWS users, known as the Well-Architected Framework.

Amazon recently added another pillar to the Well-Architected Framework, namely operational excellence. This includes such practices as performing operations with code: in other words, automating operations as much as possible. Did someone say Fat Finger?


Abel Avram, The AWS Well-Architected Framework Adds Operational Excellence (InfoQ, 25 Nov 2016)

Julie Bort, The massive AWS outage hurt 54 of the top 100 internet retailers — but not Amazon (Business Insider, 1 March 2017)

Aidan Finn, How to Avoid an AWS-Style Outage in Azure (Petri, 6 March 2017)

Brian Guy, Analysis: Rethinking cloud architecture after the outage of Amazon Web Services (GeekWire, 5 March 2017)

Daniel Howley, Why you should still trust Amazon Web Services even though it took down the internet (Yahoo Finance, 6 March 2017)

Chris Mellor, Tuesday’s AWS S3-izure exposes Amazon-sized internet bottleneck (The Register, 1 March 2017)

Shaun Nichols, Amazon S3-izure cause: Half the web vanished because an AWS bod fat-fingered a command (The Register, 2 March 2017)

Cliff Saran, AWS outage shows vulnerability of cloud disaster recovery (Computer Weekly, 6 March 2017)

20 days ago

Uber’s Defeat Device and Denial of Service

Perhaps you already know about Distributed Denial of Service (DDOS). In this post, I’m going to talk about something quite different, which we might call Centralized Denial of Service.

This week we learned that Uber had developed a defeat device called Greyball – a fake Uber app whose purpose was to frustrate investigations by regulators and law enforcement, especially designed for those cities where regulators were suspicious of the Uber model.

In 2014, Erich England, a code enforcement inspector in Portland, Oregon, tried to hail an Uber car downtown in a sting operation against the company. However, Uber recognized that Mr England was a regulator, and cancelled his booking. 

It turns out that Uber had developed algorithms to be suspicious of such people. According to the New York Times, grounds for suspicion included trips to and from law enforcement offices, or credit cards associated with selected public agencies. (Presumably there were a number of false positives generated by excessive suspicion or Überverdacht.)

But as Adrienne Lafrance points out, if a digital service provider can deny service to regulators (or people it suspects to be regulators), it can also deny service on other grounds. She talks to Ethan Zuckerman, the director of the Center for Civic Media at MIT, who observes that

“Greyballing police may primarily raise the concern that Uber is obstructing justice, but Greyballing for other reasons—a bias against Muslims, for instance—would be illegal and discriminatory, and it would be very difficult to make the case it was going on.”

One might also imagine Uber trying to discriminate against people with extreme political opinions, and defending this in terms of the safety of their drivers. Or discriminating against people with special needs, such as wheelchair users.

Typically, people who are subject to discrimination have less choice of service providers, and a degraded service overall. But if there is a defacto monopoly, which is of course where Uber wishes to end up in as many cities as possible, then its denial of service is centralized and more extreme. Once you have been banned by Uber, and once Uber has driven all the other forms of public transport out of existence, you have no choice but to walk.


Mike Isaac, How Uber Deceives the Authorities Worldwide (New York Times, 3 March 2017)

Adrienne LaFrance, Uber’s Secret Program Raises Questions About Discrimination (The Atlantic, 3 March 2017)

24 days ago

Decision-Making Models

In my previous discussion of the ACPO national decision model (May 2014), I promised to return to the methodological question, namely what theories of decision-making would be relevant to NDM and any other decision models. I have just happened upon a doctoral thesis by Maxwell Mclean looking at the decision-making by coroners, which analyses local variation in coronial outcomes at three decision-making stages: whether to report the death, whether to advance to inquest, and the choice of inquest conclusion.

Mclean notes that there is no decision-making model for coroners equivalent to the police national decision model and focussed on standards and consistency of outcome. He finds other examples of decision-making models in nursing (Lewinson and Truglio-Londrigan, 2008; Husted and Husted, 1995; Jasper, Rosser and Mooney, 2013); social work (O’Sullivan, 2011; Taylor, 2010); and probation work (Carter, 1967; Rosecrance, 1985). However, several of these are descriptive models rather than normative models.

Within the professions mentioned by Mclean, I found a lot more work on evidence-based nursing as well as some interesting international discussions on decision-making within offender supervision. Looking further afield, I was interested to find an article about a decision-making model in the US Army, but this turned out to be merely a polemical article by a former Navy Seal advocating the use of Design Thinking.

Rosecrance introduces an interesting concept of the Ball Park, where a professional decision is influenced by the anticipated reaction of a more senior professional. For example, the decisions of a probation officer are not solely designed to achieve the desired outcomes for the client, but also designed to meet the approval of (1) judges, (2) prosecuting attorneys, and (3) probation supervisors. When a recommendation seems likely to meet the approval of these three entities, it is said to be “in the ball park”. The “ball park” concept is also used in sales negotiations, and this hints at the idea that the focus here is on “selling” (or at least defending) the decision rather than just making it.

Coming back to the police, this frames the NDM not just as a way of making the best decision but also avoiding censure if anything goes wrong. See my post on the National Decision Model and Lessons Learned (February 2017).


Miranda Boone and Martine Evans, Offender supervision and decision-making in Europe (Offender Supervision in Europe: Decision-Making and Supervision Working Group, 2013)

Jeff Boss, The Army’s New Decision-Making Model (Forbes, 8 August 2014)

Carter, R.M. (1967). The presentence report and the decision making process. Journal of
research in crime and delinquency. 4 203-211.

Jasper, M., Rosser, M., Mooney, G. (Eds.) (2013). Professional Development, Reflection
and Decision-Making in Nursing and Health Care (2nd ed.). Swansea: Wiley Blackwell.

Husted, G.L. and Husted, I.H. (1995). Ethical decision-making in nursing (2nd ed.). St
Louis: Mosby.

Lewenson, S.B. and Truglio-Londrigan, M. (2008). Decision-Making in Nursing, thoughtful approaches for practice. London: Jones and Bartlett Publishers International.

Maxwell Mclean, The Coroner in England and Wales; Coronial Decision-­Making and Local Variation in Case Outcomes (Doctoral Thesis, University of Huddersfield, 2015)

O’Sullivan, T. (2011). Decision making in social work (2nd ed.). Basingstoke: Palgrave
Macmillan

Rosecrance, J. (1985). The Probation Officers’ Search for Credibility: Ball Park
Recommendations. Journal of research in crime and delinquency. 31, (4) 539-554.

Mooi Standing, Perceptions of clinical decision-making: a matrix model (May 2010). This appears to be a chapter from Mooi Standing (ed) Clinical Judgement and Decision-Making in Nursing and Inter-professional Healthcare (McGraw Hill, 2010)

Taylor, B. (2010). Professional Decision-Making in Social Work. Exeter: Learning Matters.

Carl Thompson et al, Nurses, information use, and clinical decision making—the real world potential for evidence-based decisions in nursing (Evidence-Based Nursing Vol 7 No 3, July 2004) http://dx.doi.org/10.1136/ebn.7.3.68

Related posts
National Decision Model (May 2014)
National Decision Model and Lessons Learned (Feb 2017)

Updated 4 March 2017

26 days ago

National Decision Model and Lessons Learned

The appointment of Cressida Dick as the first female commissioner of the Metropolitan Police has been criticized in some quarters because of her involvement in the fatal shooting of Jean Charles de Menezes in 2005.

Dick was the “gold commander” who instructed armed officers to “stop” de Menezes. At the time, however, armed officers were following a new set of police guidelines known as Operation Kratos. In the context of these guidelines, Dick’s orders were interpreted as shoot-to-kill. At the Old Bailey in 2007, Dick denied that this had been her intention.

As Mary Dejevsky argues, the de Menezes case provides a lasting reminder of what can go wrong

“whether because the overall atmosphere has not been properly appraised, because the orders given were not precise enough, or simply because insufficient account has been taken of the human factor”.

The National Decision Model, which was introduced a few years after this incident, provides a framework that should (at least in theory) prevent this kind of miscommunication. See my post on the National Decision Model (May 2014). Perhaps this is one of the areas where “lessons have been learned”. Or perhaps not.

Iain Gould is a solicitor. One of his clients was involved in an incident in 2013 that resulted in his being tasered. The Independent Police Complaints Commission (IPCC) attributed this escalation, in part, to a failure to follow the National Decision Model.

“I would question whether PC B gave enough emphasis to the first element of the National Decision Model, which is to communicate. … More effort should have been made, in line with the National Decision Model, to engage Mr S in dialogue.”

The IPCC commissioned a report in 2015, which contains some analysis of the National Decision Model, and some recommendations for its improved use. There are two versions of the report:


    The Guardian view on the Met police: changing, but too slowly (23 February 2017)

    Duncan Campbell et al, Leaks raise sharp questions about police tactics (Guardian, 17 August 2005)

    Mary Dejevsky, Can Cressida Dick win over the public? Yes, if she’s learned from her mistakes (Guardian, 23 February 2017)

    Iain Gould, Is Police Taser Policy Working? (11 May 2016)

    Martin Hoscik, Sadiq Khan says ‘My heart goes out to the de Menezes family’ but insists Cressida Dick is the right choice to protect London (MayorWatch, 25 February 25, 2017)

    Maxwell Mclean, The Coroner in England and Wales; Coronial Decision-­Making and Local Variation in Case Outcomes (Doctoral Thesis, University of Huddersfield, 2015)

    Wail Qasim, Lessons Learned (LRB Blog, 27 February 2017)


    Related blogpost

    1 month, 21 days ago

    The Art of the New Deal – Trump and Intelligence

    In his 1967 book on Organizational Intelligence, Harold Wilensky praised President Roosevelt for maintaining a state of creative tension in the US administration. Wilensky reckoned that this enabled FDR to get a more accurate and rounded account of what was going on, and gave him some protection against the self-delusion of each department.

    (In FDR’s time, of course, it was considered entirely normal for an administration to be staffed by a bunch of white men with similar education. And yet even they managed to achieve some diversity of perspective.)

    Early reports of Donald Trump’s administration suggest an unconscious echo of the FDR style. Or perhaps a much earlier pattern.

    At the center of it all has been a cast of characters jockeying for Trump’s ear, creating a struggle for power that has manifested in a mix of chaos, leaks and uncertainty. The Trump White House already bears more resemblance to the court of a Renaissance king than to most prior administrations as favorites come and go, counselors quarrel over favor and policy decisions are often made by whim or without consultation. (Guardian, 4 Feb 2017)

    But it is difficult to see this as “creative tension” resulting in an “accurate and rounded” view.

    “Trump thinks he’s invincible,” says Hemmings, who doubts whether his advisors will ever question or criticise him. “Usually leaders choose the people around them to keep them in check, and Trump needs people to temper his hotheadedness and aggression. Instead, he’s picked advisors who worship him.” (Independent, 2 Feb 2017)

    Wilensky’s book also discusses the dangers of a doctrine of secrecy.

    Secrets belong to a small assortment of individuals, and inevitably become hostage to private agendas. As Harold Wilensky wrote “The more secrecy, the smaller the intelligent audience, the less systematic the distribution and indexing of research, the greater the anonymity of authorship, and the more intolerant the attitude toward deviant views.” (Gladwell 2010)

    And secrecy seems to a key element of the Trump-Bannon modus operandi.

    “These executive orders were very rushed and drafted by a very tight-knit group of individuals who did not run it by the people who have to execute the policy. And because that’s the case, they probably didn’t think of or care about how this would be executed in the real world,” said another congressional source familiar with the situation. “No one was given a heads-up and no one had a chance to weigh in on it.” (Politico 30 Jan 2017)

    But perhaps in reaction to the Bannonite doctrine of secrecy, there has been a flood of leaks from inside the administration. Chris Cillizza suggests two possible explanations – either these leaks are intended to influence Trump himself (because he doesn’t take anything seriously unless he hears it from his favourite media channels) or conversely they are intended as a kind of whistle-blowing.

    Marx thought that history repeated itself. (Alarmingly, Trump’s Counselor Steve Bannon adheres to the same view.) So are we into tragedy or farce here?


    Rachael Bade, Jake Sherman and Josh Dawsey, Hill staffers secretly worked on Trump’s immigration order (Politico, 30 Jan 2017)

    Chris Cillizza, The leaks coming out of the Trump White House cast the president as a clueless child (Washington Post, 26 January 2017), The leaks coming out of the Trump White House right now are totally bananas (Washington Post, 2 Feb 2017)

    Malcolm Gladwell, Pandora’s Briefcase (New Yorker, 10 May 2010)

    Rachel Hosie, The deeper reason we should be worried Donald Trump hung up on Australia PM Malcolm Turnbull (Independent, 2 Feb 2017)

    Linette Lopez, Steve Bannon’s obsession with a dark theory of history should be worrisome (Business Insider, 2 Feb 2017) HT @BryanAppleyard

    Carmen Medina, What is your Stupification Point? (6 May 2010)

    Joseph Rago, History Repeats as Farce, Then as 2016 (Wall Street Journal, 4 November 2016) paywall

    Sabrina Siddiqui and Ben Jacobs, Trump’s courtiers bring chaotic and capricious style to White House (Guardian, 4 February 2017)

    Related posts

    Puzzles and Mysteries (January 2010)
    Enemies of Intelligence (May 2010)
    Delusion and Diversity (October 2012)

    2 months, 24 days ago

    The Unexpected Happens

    When Complex Event Processing (CEP) emerged around ten years ago, one of the early applications was real-time risk management. In the financial sector, there was growing recognition for the need for real-time visibility – continuous calibration of positions – in order to keep pace with the emerging importance of algorithmic trading. This is now relatively well-established in banking and trading sectors; Chemitiganti argues that the insurance industry now faces similar requirements.

    In 2008, Chris Martins, then Marketing Director for CEP firm Apama, suggested considering CEP as a prospective “dog whisperer” that can help manage the risk of the technology “dog” biting its master.

    But “dog bites master” works in both directions. In the case of Eliot Spitzer, the dog that bit its master was the anti money-laundering software that he had used against others.

    And in the case of algorithmic trading, it seems we can no longer be sure who is master – whether black swan events are the inevitable and emergent result of excessive complexity, or whether hostile agents are engaged in a black swan breeding programme.  One of the first CEP insiders to raise this concern was John Bates, first as CTO at Apama and subsequently with Software AG. (He now works for a subsidiary of SAP.)

    from Dark Pools by Scott Patterson

    And in 2015, Bates wrote that “high-speed trading algorithms are an alluring target for cyber thieves”.

    So if technology is capable of both generating unexpected events and amplifying hostile attacks, are we being naive to imagine we use the same technology to protect ourselves?

    Perhaps, but I believe there are some productive lines of development, as I’ve discussed previously on this blog and elsewhere.

    1. Organizational intelligence – not relying either on human intelligence alone or on artificial intelligence alone, but looking for establishing sociotechnical systems that allow people and algorithms to collaborate effectively.

    2. Algorithmic biodiversity – maintaining multiple algorithms, developed by different teams using different datasets, in order to detect additional weak signals and generate “second opinions”.


    John Bates, Algorithmic Terrorism (Apama, 4 August 2010). To Catch an Algo Thief (Huffington Post, 26 Feb 2015)

    John Borland, The Technology That Toppled Eliot Spitzer (MIT Technology Review, 19 March 2008) via Adam Shostack, Algorithms for the War on the Unexpected (19 March 2008)

    Vamsi Chemitiganti, Why the Insurance Industry Needs to Learn from Banking’s Risk Management Nightmares.. (10 September 2016)

    Theo Hildyard, Pillar #6 of Market Surveillance 2.0: Known and unknown threats (Trading Mesh, 2 April 2015)

    Neil Johnson et al, Financial black swans driven by ultrafast machine ecology (arXiv:1202.1448 [physics.soc-ph], 7 Feb 2012)

    Chris Martins, CEP and Real-Time Risk – “The Dog Whisperer” (Apama, 21 March 2008)

    Scott Patterson, Dark Pools – The Rise of A. I. Trading Machines and the Looming Threat to Wall Street (Random House, 2013). See review by David Leinweber, Are Algorithmic Monsters Threatening The Global Financial System? (Forbes, 11 July 2012)

    Richard Veryard, Building Organizational Intelligence (LeanPub, 2012)

    Related Posts

    The Shelf-Life of Algorithms (October 2016)

    3 months, 22 days ago

    Uber Mathematics 2

    Aside from the discussion of Uber as a two-sided platform, addressed in my post on Uber Mathematics (Nov 2016), there is also a discussion of Uber’s overall growth strategy and profitability. @izakaminska has been writing a series of critical articles on FT Alphaville.

    Uber is wildly unprofitable, suggest that prices will rise once they’ve succeeded at monopolizing the industry: https://t.co/m3HB3q5YZV pic.twitter.com/taXcHfD2g5

    — Justin Wolfers (@JustinWolfers) December 1, 2016

    There are a few different issues that need to be teased apart here. Firstly, there is the fact that Uber is continually launching its service in more cities and countries. Nobody should expect the service in a new city to be instantly profitable. The total figures that Kaminska has obtained raise further questions – whether some cities are more profitable for Uber than others, whether there is a repeating pattern of investment returns as a city service moves from loss-making into profit. Like many companies in rapid growth phase, Uber has managed to convince its investors that they are funding growth into something that has good prospects of becoming profitable.

    Profitability in Silicon Valley seems to be predicated on monopoly, as argued by Peter Thiel, leveraging network effects to establish barriers to entry. This is related to the concept of a retail destination – establishing the illusion that there is only one place to go. Kaminska quotes an opinion by Piccioni and Kantorovich, to the effect that it wouldn’t take much to set up a rival to Uber, but this opinion needs to be weighed against the fact that Uber has already seen off a number of competitors, including Sidecar. Sidecar was funded by Richard Branson, who asserted that he was not putting his money into a “winner-takes-all market”. It now looks as if he was mistaken, as Om Malik (writing in the New Yorker) respectfully points out.

    But is Uber economically sustainable even as a monopoly? Kaminska has raised a number of  questions about the underlying business model, including the increasing need for capital investment which could erode margins further. Meanwhile, Uber will almost certainly leverage its cheapness and popularity with passengers to push for further deregulation. So the survival of this model may depend not only on a continual supply of innocent investors and innocent drivers, but also innocent politicians who fall for the deregulation agenda.


    Philip Boxer, Managing over the Whole Governance Cycle (April 2006)

    Izabella Kaminska, Why Uber’s capital costs will creep ever higher (FT Alphaville, 3 June 2016). Myth-busting Uber’s valuation (FT Alphaville, 1 December 2016). The taxi unicorn’s new clothes (FT Alphaville, 13 September 2016) FREE – REGISTRATION REQUIRED

    Om Malik, In Silicon Valley Now, It’s Almost Always Winner Takes All (New Yorker,
    30 December 2015)

    Brian Piccioni and Paul Kantorovich, On Unicorns, Disruption, And Cheap Rides (BCA, 30 August 2016) BCA CLIENTS ONLY

    Peter Sims, Why Peter Thiel is Dead Wrong About Monopolies (Medium, 16 September 2014)

    Peter Thiel, Competition Is for Losers (Wall Street Journal, 12 September 2014)

    Related Posts Uber Mathematics (Nov 2016) Uber Mathematics 3 (Dec 2016)

    4 months, 15 days ago

    Steering The Enterprise of Brexit

    Two contrasting approaches to Brexit from architectural thought leaders.

    Dan Onions offers an eleven-step decision plan based on his DASH method, showing the interrelated decisions to be taken on Brexit as a DASH output map.

    A decision plan for Brexit (Dan Onions)
    A stakeholder map for Brexit (Dan Onions)


    Let me now contrast Dan’s approach with Simon Wardley’s. Simon had been making a general point about strategy and execution on Twitter.

    In 25 years in business, I’ve never seen a problem caused by “poor execution”. It is always crap strategy looking for someone else to blame.

    — swardley (@swardley) April 29, 2016

    Knowing Simon’s views on Brexit, I asked whether he would apply the same principle to the UK Government’s project to exit the European Union.

    ah @richardveryard #Brexit could be an opportunity, it depends upon steps taken. Alas, in complex environments it can’t be pre-determined.

    — swardley (@swardley) November 8, 2016

    which goes back to first rule of strategy @richardveryard. it’s iterative. You consider as much as you can and adapt as you play the game.

    — swardley (@swardley) November 8, 2016

    It’s best summed up for me @richardveryard in this diagram pic.twitter.com/k8j8yciXsa

    — swardley (@swardley) November 8, 2016

    @swardley Nice picture. So how do you address strategic governance, given that #Brexit was supposedly about sovereignty and control.

    — Richard Veryard (@richardveryard) November 8, 2016

    @richardveryard : structure / governance is under doctrine (e.g. small autonomous teams, use appropriate methods, remove bias & duplication)

    — swardley (@swardley) November 8, 2016

    @richardveryard : whereas choice / decision / direction is under leadership.

    — swardley (@swardley) November 8, 2016

    @swardley You are answering in abstractions. How do you answer the concrete questions of sovereignty and control in relation to #Brexit?

    — Richard Veryard (@richardveryard) November 8, 2016

    Simon’s diagram revolves around purpose. OODA is a single loop, and the purpose is typically unproblematic. This reflects the UK government’s perspective on Brexit, in which the purpose is assumed to be simply realising the Will of the People. The Prime Minister regards all interpretation, choice, decision and direction as falling under her control as leader. And according to the Prime Minister’s doctrine, attempts by other stakeholders (such as Parliament or the Judiciary) to exert any governance over the process is tantamount to frustrating the Will of the People.

    Whereas Dan’s notion is explicitly pluralist – trying to negotiate something acceptable to a broad range of stakeholders with different concerns. He characterizes the challenge as complex and nebulous. Even this characterization would be regarded as subversive by orthodox Brexiteers. It is depressing to compare Dan’s careful planning with Government insouciance.

    Elsewhere, Simon has acknowledged that “acting upon your strategic choices (the why of movement) can also ultimately change your goal (the why of purpose)”. Many years ago, I wrote something on what I called Third-Order Requirements Engineering, which suggested that changing the requirements goal led to a change in identity – if your beliefs and desires have changed, then in a sense you also have changed. This is a subtlety that is lost on most conventional stakeholder management approaches. It will be fascinating to see how the Brexit constituency (or for that matter the Trump constituency) evolves over time, especially as they discover the truth of George Bernard Shaw’s remark.

    “There are two tragedies in life. One is to lose your heart’s desire. The other is to gain it.”


    Dan Onions, An 11 step Decision Plan for Brexit (6 November 2016)

    Richard Veryard, Third Order Requirements Engineering (SlideShare)

    Based on R.A. Veryard and J.E. Dobson, ‘Third Order Requirements Engineering: Vision and Identity’, in Proceedings of REFSQ 95, Second International Workshop on Requirements Engineering, (Jyvaskyla, Finland: June 12-13, 1995)

    Simon Wardley, On Being Lost (August 2016)

    Related Posts: VPEC-T and Pluralism (June 2010)