Now that the emotion around the UK Referendum has (hopefully) died down a bit, its time to look at why the (apparently) surprising Brexit result occurred. Based on experience during the referendum campaigns and reading quite a lot afterwards, there were 3 major causes that stood out.
A Mismatch of Belief Systems
In essence, the Remain camp believed an argument based on the EU as a desirable, stable platform going forward, plus the moral and economic benefits of it's precepts, was persuasive. Added to that they clearly felt that Establishment voices of authority would persuade the undecided, and if that didn't work "project Fear" - promising Doom if the UK left the EU, would persuade the floating voters - as they believed it did in the Scottish Independence referendum. (By the way, I think this was false - my recollection was that, at the end, the "Remain in UK" camp in the Scottish referendum made a lot of concessions to the Scots in the last days as Fear wasn't really working out).
Also, if you are going to paint the EU as a stable, desirable organisation to rest your campaign on, don't allow it to show itself as sclerotic, near-bankrupt and prone to random acts of mass terror during the election campaign.
The Leave camp, on the other hand, believed that a lot of people in the UK were suspicious of the EU, thought it was in a lot of trouble, or at least didn't see any tangible benefit from it, and were very also hostile to the "open borders" migration into the UK for a wide variety of reasons, true and false. Also, the Establishment, via a plethora of scandals, post 2007 austerity, and cultural remoteness was not nearly as trusted as Remain believed. It's also hard to believe that Remain thought that hectoring the British would ever work, its not for nothing the nation is depicted as having a "Bulldog spirit" - maybe it is more obvious to a non-Brit like me, but in general the British are as stubborn as it gets, so episodes like President Obama telling them they will be "back of the queue" for trade deal for example was bound to backfire.
Attitude - Hubris and Nemesis, and polls
Before the campaign, Remain was supposed to win, very comfortably, according to the polls. Bookies were offering 1/5 odds on a Leave win. In my view that influenced the campaigns, in that Remain was initially overconfident whereas Leave knew they were in for a real scrap, and as it became clearer that Remain was not going to be a slam dunk then panic set in, whereas Leave grew in confidence. This was exacerbated by the much larger risks to the senior people involved in the incumbent Remain camp.
By the way - a quick word on the polls - we tracked the UK 2010 and 2015 elections using our social media analytics systems, and found in both cases a stronger bias to the liberal, metropolitan viewpoint than the actual election results displayed. This we believe is due to 3 main reasons:
- Conservatives by nature are more likely to be online technology laggards and/or talk less about things, (but they do vote)
- The Liberal Metropolitans have better access to the Media, in general (many work in it, or are connected to it) so their voice is amplified
- A significant number of people do not have the courage of their convictions when they feel they have socially "frowned on" views (like anti immigration for example), and this is a poll-altering feature
This means there is a tangible "hidden residuum" voting for the socially uncool side, whatever that is, and its probably higher if the uncool side is seen as personally beneficial.
The System Dynamics of a disaster
However, the biggest failure of the Remain campaign was to not address the situation dynamically as it progressed. The (simplified) System Dynamic diagram above tries to capture this. In essence, Remain's arguments, exaggerated by Project Fear logic, tended to over-egg the risks (or at least be perceived to do so) and that led to an increasing resistance to the message and it gave a foothold to pro Leave media to start to land some telling blows. The Remain response was to double down on Project Fear, with increasingly exaggerated claims of Doom which allowed Leave to both lampoon Remain's claims and headroom to make even more exaggerated claims of its own. Cycle this through a few times, increase the hectoring volume, and more and more people just switched off to the messages of Doom. As it became clear Leave was gaining, they started to gain in confidence, getting that all important momentum - undecided people like winners. Then Remain visibly panicked, and output became more and more unbelievable (we eventually got to Brexit starting World War 3). By the time the Conservative government, who had largely run the Remain show, started to criticize Labour for not doing enough, it was clear Remain were mortally wounded.
What should Remain have done instead?
This is a simplified model - there are some sub-loops and unique events not shown, but in essence they fit in this overall model. Our experience of System Dynamic models is it is these high level models that often give the major insights, the detail is often bedevilled with layers of assumptions, where anything can happen with small changes. At any rate, the simple model would suggest 5 main actions:
(i) Make sure the ingoing assumptions are all valid, and defendable. GIGO, as they say.
(ii) Beware Hubris - assume the gap is less than you think, especially if you believe you have the "moral" advantage as quite a sizeable % - 5 to 10% in our view - are just keeping quiet as they dont want to be seen as "the Other"
(iii) Project Fear won't win it for you, and ramping it up certainly won't.
(iv) Break the circles if they are going against you. It was quite clear some time before the end that there was a vicious circle emerging for Remain, and they should have broken the circle and tried to understand the actual root causes of why people were not responding, and change their approach. In this case it was incorrect ingoing assumptions about the attarctiveness of their message. Ditto their opponents started to see a Virtuous circle emerge
(v) A corollary to (iv) - have other loops going, so if the main one doesn't work one can switch over. One of the major issues, left until far too late, was for Remain to actually deal with the concerns of the Leavers on a practical, policy basis.
Can we Trump this model?
But the main point of making such a model is to see if it is predictive. So we plan to turn it onto the US election, where our hypothesis is that fairly similar dynamics are taking place. So, what we have been doing is following the US election with our systems for a few months to try and calibrate the US systemically, and now the Presidential candidates are anointed we have a simple 2 horse system - and a 2 horse dynamic model to test out on it.
There are differences which we will have to set up for, but it should be an interesting experiment (and a decent test of our social analytics systems)
Succinctly put in The Atlantic
- (and summarised here), when faced with saying "Yes" to something new, risky, etc, research shows:
“If you are a manager, if you commit a false positive, you are going to embarrass yourself, and potentially ruin your career.” Managers, he says, are terrified of committing false positives, meaning saying something will be a hit when in fact it will flop.
False negatives, by contrast, present little costs. “If you reject a great idea,” Grant said, “most of the time, no one will ever know.” (One exception to this: Pity the editors who rejected Harry Potter.) Managers like to make safe bets and don’t mind the invisible losses.
In other words, one is seldom tarred with the results of saying No, and the best way to proect yourself when saying Yes is to only say Yes to "defendable" ideas (worked before, never get fired for buying IBM etc etc)
So, how to avoid this and let innovation flow? The paper argue that peers in an area are the best at judging someone else's work in the space, not managers. (Possibly...but they also say Science advances one dead scientist at a time...)
Anyhow, in most companies, Managers have the Yes/No role and Peers are seldom on tap, so how to get them out of the game theory bind above? The best way is to let Managers act like peers, in an experiment, Justin Berg found that:
"...asked managers to spend five minutes brainstorming about their own ideas before they judged other people’s ideas.” [That] "was enough to open their minds. Because when they came in to select ideas, they were looking for reasons to say no. Get them into a brainstorming mindset first, and now they’re not thinking evaluatively, they’re thinking creatively.”
All very interesting, but if the organisation still kicks you for False Positives, not sure this gets us anywhere further
Pokemon Go has become quite the thing
, but regular readers of this blog will know we are fascinated by one thing in the main - yes, where is all that user data going?
In less than a week Niantic (a Google spinout*) created one of the largest personal location databases in history, as well as other personal data, storage and camera co-ords. Here's what it does (thanks to USA today
For Android users, the game can access both the precise and general locations of the device as well as its camera – permissions inherently necessary to play the game. The game can also access users’ USB storage, contacts, network connections and more.
For iPhone users, the game can access users’ location, camera and photos. Many iOS users log in through their Google account, which initially granted the app full access. This meant, per Google, that the app “can see and modify nearly all information in your Google Account” including Gmail, Google Drive, Google Maps and more. [This was fixed yesterday, but many hundreds of thousands people had already signed up]
Niantic can also share the data it collects with Pokémon Co., which is partially owned by longstanding videogame and console maker Nintendo Co. More interesting is, as the WSJ notes
, the choice of what data to collect - they didn't need it all for the service to work, yet chose to collect it.
Here's what the Terms & Conditions say:
“We may disclose any information about you (or your authorized child) that is in our possession or control to government or law enforcement officials or private parties as we, in our sole discretion, believe necessary or appropriate,” the agreement states
Fairly standard deal for the Social Nets, but the amount of location data this system has is like no other, As USA Today points out, what is yet to become clear is the business model, so its hard to work out what they will do with the data. However, the company has said in the past that they will licence out the technology to other companies, which raises the prospect of the virtualspace being filled with virtual objects, and hordes of people chasing them.
There are already reports of 3rd parties using the data for their own ends (mugging, hassling people by making their homes in-game locations, etc) so this could get quite inconvenient..
Update - this last point has started to get serious, as the Grauniad notes, it's not clear who owns your space in cyberspace
Washington DC’s Holocaust Museum has asked Pokémon Go players to stay away: the museum was designated a Pokéstop, where players can pick up items like Pokéballs and revives, forcing its communications director to point out that playing a game inside a memorial to victims of Nazism is “extremely inappropriate”.
This will probably become a major issue, as it looks like it will open all the same issues as owning the air above your property, or the oil underneath. In general, if history is any guide, there will be abuse of other people's space by overenthusiastic or unscrupulous operators, followed by a clear need for regulation.
* Both Nintendo and Google are investors in Niantic, which started within Google before spinning off into a stand-alone company last year.
The Wisdom of Crowds showed us that a group of people were wiser than a group of experts - and then came the caveats. Firstly, it was shown that all the Wise people hae to decide independently of each other.
Now, it seems that small is better - Santa Fe Institute
New research by SFI Professor Mirta Galesic and her colleagues from the Max Planck Institute for Human Development in Berlin suggests that larger crowds do not always produce wiser decisions. In fact, when it comes to qualitative decisions such as “which candidate will win the election” or “which diagnosis fits the patient’s symptoms,” moderately-sized "crowds," around five to seven randomly selected members are likely to outperform larger ones. In the real world, these moderately-sized crowds manifest as physician teams making medical diagnoses; top bank officials forecasting unemployment, economic growth, or inflation; and panels of election forecasters predicting political wins.
Sounds suspiciously like a group of experts again, though there is the random selection requirement again. It also depends what the question is:
Where previous research on collective intelligence deals mainly with decisions of how much or how many, the current study applies to this-or-that decisions under a majority vote. The researchers mathematically modeled group accuracy under different group sizes and combinations of task difficulties. They found that in situations similar to a real world expert panel, where group members encounter a combination of mostly easy tasks peppered with more difficult ones, small groups proved more accurate than larger ones. This effect is independent of other influences on group accuracy, such as following an opinion leader or having group discussions before voting.
What about voting as a means of determining the majority opinion of a populace?
"These results, of course, do not mean that we should abandon large scale referendums like Brexit and national elections,” Galesic adds. “Choices between different policies and candidates often do not have a 'right' and a 'wrong' answer: different people simply prefer different things, and the outcomes of these decisions are complex, with a spectrum of consequences. It is important to account for everyone's opinion about the general direction in which they want their country to go -- including underrepresented groups.
“But when it comes to decisions with a more clear 'right' and 'wrong' answer -- where everyone can, at least after the fact, agree that one course of action was better than the other -- then moderately sized groups of experts can often be better than larger groups or individuals,” she says.
Not sure where this leaves us practically though - for some problems it's better to have smaller groups, others larger, depending on the question.
The first fatal crash
of a "self driving car" is pushing a lot of questions to the surface, on a whole lot of levels.
Is the Technology up to it yet?
Firstly, its is clear now that the technology is not yet ready for wide scale deployment - a camera probably should not be the prime mode of sensing (night and difficult lighting conditions, lens obstructed) and the current radar is clearly sub-optimal - there are a lot of potential obstacles below car roof height.
Was it tested enough?
These cars have done many millions of miles, and are said to be less risky than driving you own car (it has twice the miles/death ratio of average motoring - but the well heeled Tesla driving demographic is far from average). Another key question is what sort of miles? Has it been pushed hard, beyond the envelope, by test drivers. These are not "far-beyond the edge" driving conditions. It almost smacks of software industry culture - push a Beta product out there, let the customers find the bugs. But bugs in heavy, powerful, fast mechatronic devices can kill.
It was billed last year as the "arrival of your autopilot". Problem is, some people believed it. Fortunately, not too many people can afford them, and as mentioned above most of those who bought it are well heeled - less envelope pushers as a % of drivers. But there are some, posting videos of his experiments with hands free driving on YouTube (the driver concerned was one such)
The inevitable outcome - Regulation - is probably a Good Thing right now
As the WSJ explains
, despite misgivings regulators were persuaded to stay their hand:
Auto-safety regulators, meanwhile, were relatively silent on the technology even though many experts viewed Tesla’s program as the most aggressive self-driving system on U.S. roads. The National Highway Traffic Safety Administration, embroiled in managing a sharp increase in safety recalls, including tens of millions of rupture-prone air bags, lacks authority to approve or disapprove of the advanced technology or meaningfully slow its deployment.
Instead, car-safety regulators were forced to wait until a major mishap before significantly addressing Tesla’s Autopilot system. The May 7 fatal crash in Florida that killed 40-year-old Joshua Brown when his Tesla Model S drove under the trailer of an 18-wheel semi truck turning in front of the car offers NHTSA officials their first significant chance to flex regulatory muscle.
Now they will. And despite AI-Car supporters' cries of "men walking with red flags in front of cars" it is a necessary, and in the medum run a good step for the self driving car industry to get it a lot more right first.
What will kill the AI-Car industry stone dead is if it kills a few more people.
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