Mark draws out nicely the difference between stuff that’s complicated and stuff that’s complex.
Complicated is something like putting a man on the moon:
There was a clear objective – success was easily measured. The laws of physics pertained and sending a rocket to the moon did not change those laws. Effect can be predicted from cause. It is possible to make big, long-term plans and be reasonably certain of achieving them – London 2012, Channel Tunnel, Crossrail etc.
Complexity, however, is a different beast:
Dealing with something like poverty is complex. Poverty is hard to define meaningfully. It is difficult to measure improvement. It is subject to changeable human behaviour and action is persistently met with unintended re-action. Effect can be deduced from cause, but only in retrospect.
In complicated problems, Mark notes that the “build” mindset works, whereas in complex situations it’s the “grow” approach that works best.
The bit of Mark’s analysis that hit home for me was this:
The problem is not in plans, people or methods – it’s in mindset. Trying to build things that really need to be grown just won’t work – no matter how they are managed.
He also notes that we shouldn’t necessarily just abandon “programmatic approaches” but ensure we understand when complexity is at play and change our approach accordingly.
His thinking is mainly applied to IT infrastructure, but the applicability of this thought to social care or mental health is clear.
Both are significant systems with large amounts of money, buildings, providers, commissioners, wider stakeholders and, most importantly, users and families. Neither are particularly well-defined or have a particularly clear idea of what their end goal is (for mental health: better mental health of the population? More cost-effective services? More people with mental health problems in work? Less stigma? For social care: more money? More integration? Less integration? More care homes? More community provision?). And, goodness me, human behavior and action within the mental health and social care systems is changeable!
Indeed, I’ve always thought the mental health and social care systems are a bit like a Jackson Pollock painting:
To this end, it’s always been clear that the mental health and social care systems are complex. But plans to try and improve them have always tended to rely on top-down, “build” solutions.
Whilst equally not calling for the abandonment of good programmatic approaches in social care and mental health, what we do need to do is recognise the complexity at play and update our approaches accordingly. In these cases, such adaptations include: (1) distributing power amongst all stakeholders through all aspects of co-production; (2) putting people at the centre of their care and support, through personalised approaches and money more directly in their hands through Personal Budgets; and (3) creating a more equal playing field for a wider variety of providers based in across a range of settings.
If people like Mark are clear the “grow” mentality needed by complexity applies to the (supposedly) deterministic world it’s easy (but wrong) to assume government IT is, then it’s pretty obvious that the equivalent “grow” approach is needed for the future of the mental health and social care systems.
or How I learnt to love statistics and be an Honest Consultant
Chris Dillow at Stumbling and Mumbling often notes how remarkable it is that so few people know or understand Bayes’ Theorem. C.P. Snow wrote his famous “Two Cultures” essay drawing on his observation of how few people know about something as fundamentally important as the Second Law of Thermodynamics, and that this is the equivalent of not having heard about the works of Shakespeare.
I’ve often thought the same about the Normal Distribution, often called the Bell Curve after its distinctive shape.
The Normal Distribution is a result of probability theory that we all learnt in secondary school maths, and shows how we would expect a random range of related variables to be distributed.
In the Normal Distribution illustrated above, the average of all of the values of a given set of numbers (for example, people’s height) is in the middle. In any distribution, over 68% of all of these values will fall within one standard deviation – the amount of variation from the average. Over 95% of all values will be within two standard deviations of the average.
(Applying this example to women’s height, we find that the average height for women is 5ft4in with a standard deviation of 3in. Thus, 68% of women’s height will fall between 5ft1in and 5ft7in, and 95% of all women’s height will be between 4ft10in and 5ft10in.)
The Normal Distribution is a really useful way of thinking about where single instances fit into an overall picture.
How could we apply the Normal Distribution to the idea of people’s experiences of public services? Let’s think of the horizontal (x) axis as a quality continuum, with the very worst experience on the left, through the average in the middle, to the very best on the right; and let’s think of the vertical (y) axis as a frequency continuum, from the very rare at the bottom to the very often at the top.
Thinking in this way, what we see is that people’s experiences of public services follow a Normal Distribution. The vast majority of people’s experiences are average or thereabouts; only very rarely (i.e., more than two standard deviations away from the average, or around 0.1% of the time) do people have either the very best or the very worst experience of public services.
I think this observation is important for two main reasons.
The first is about how politics, policy and campaigning is conducted. Although most people’s experiences are average or thereabouts, the experiences and examples we hear most about are, almost by definition, unusual. They exist at either end of the Normal Distribution. So politicians often talk about the very best case scenario in the new idea they’re introducing or in the White Paper case studies that are cited. Similarly, organisations present the best examples of the work they’ve done, promoting these through various communications channels or capturing them in funding bids or contract tenders.
In the above diagram, politicians and organisations operate mainly at point P on the Normal Distribution.
At the other end, we hear of nightmare stories of people’s experiences of services in the headlines of newspapers, or of scare stories from campaigners which highlight the very worst impact of this or that policy change. Newspapers and campaigners operate at point N on the Normal Distribution.
(Of course, each respective group can swap which end of the Normal Distribution they operate at depending on what purpose they are seeking to serve; think of politicians talking about “Broken Britain”, for example.)
The second reason it’s important to think about the Normal Distribution of people’s experiences of public services is to note that, most often, the very rare is what drives most activity. Trying to prevent or minimise the very worst in public services is the realm of regulators, legal teams and complaints procedures; trying to promote the very best is the business of funding bids, think tank proposals, job applications etc.
And it’s this difference between the ends and the middle of the Normal Distribution that creates the problem in the space of people’s expectations of public services. The gap between what the Normal Distribution says our experience is most likely to be (95% of people will be within two standard deviations of average) and what we think our experience will be – the space of N and P represented by newspaper headlines and political rhetoric – leads to expectations that, in reality, can very rarely be met.
A politician gives a speech in the space of P in which they say how things are going to be much better for us all. But, across a whole population, only 0.13% are likely to feel that full impact; the rest will have average experiences whilst some will have terrible experiences, so that both groups feel the promise of the politician hasn’t been delivered.
A newspaper reports in the space of N of an appalling case that occurred because of this or that change. Many will think that this is more than typical than it is, despite only 0.13% of the relevant population having that experience and the vast majority having an average or thereabout experience.
A commissioner commissions a new service from a provider based on the promises in the space of P the provider gave in its funding proposal. The reality is that the service provided is average, with some great outcomes and some very poor ones. This is exactly what the Normal Distribution could have told the commissioner, but they remain disappointed because of their original expectations.
Where does this leave us? I think that understanding and using the Normal Distribution could help us have a more honest approach to what people can expect from public services.
In an area that is “good” at what is does, what we’re really saying is that people’s experiences are generally slightly better than average; the Normal Distribution of such a place would like this (blue line) compared to the normal Normal Distribution (black line).
In this area, slightly more people have a better experience, slightly fewer people have a poor experience, and a very small proportion of people still have extremely good or bad experiences. The net effect is that the average experience of all people in the area is slightly better than normal; effectively, the Normal Distribution has been slightly shifted to the right.
In an area that is “poor” at what it does, the Normal Distribution would look like this (blue line) compared to the normal Normal Distribution (black line):
In such a “poor” area, slightly more people have a poor experience, slightly fewer people have a good experience, and a very small proportion of people still have extremely good or bad experiences. The average experience of all people in the area is slightly worse than normal; effectively, the Normal Distribution has been slightly shifted to the left.
The subtitle of this post comes from my personal feeling that an Honest Consultant is one whose pitch to a potential client would entail a discussion about the Normal Distribution and how the results of their work will make things a little bit better than average for most people in an area.
Such a consultant is unlikely to be successful in their work. But, in understanding and using the Normal Distribution and what it tells us about people’s experiences of public services, the Honest Consultant is at least managing the expectations of the potential client and the public they represent. If in doing so this reduces the gap between what people expect and what they experience, and so increases people’s trust and understanding of what public services can or can’t do, then the Honest Consultant’s use of the Normal Distribution will have been worthwhile.
His face showed another kind of fatigue, the tormented weariness, the anger and the fear of a struggle against a thought, an idea – against something that cannot be grappled, that never rests – a shadow, a nothing, unconquerable and immortal, that preys upon life.
In conversation with a colleague they mentioned in passing they had been in their current role for 11 years. It was at that point I realised I had been working only for 9 years in total – I don’t celebrate my ten-year work anniversary until June 2015.
This provided a useful perspective and set off a series of interconnected, personal thoughts about where I have been, where I am and where I’m going.
Orson Welles, when asked about why he achieved what he did in making Citizen Kane at the age of 26, said it was due to arrogance and ignorance. He didn’t know what was and wasn’t achievable in film and so simply went about achieving what he wanted to.
There is a whole literature dedicated to age-achievement curves, broadly considering at what age significant contributions to different disciplines are made. In a paper on age and scientific genius, Jones, Reedy and Weinberg note that the median age of “great achievement” (typically Nobel prize-winning contributions or equivalent) is 37 in maths, 40 in physics, 43 in engineering, and 45 in surgery and psychology.
Exploring these differences in more detail, it is noted:
people who excel in abstract fields, like art or physics, tend to be younger than those who win prizes in fields that require more context, like history or medicine.
Even within abstract fields there are variations: theorists generally make their greatest contributions earlier than those who are “experimental” by just over 4.5 years.
There are basic reasons for these age-achievement curves:
The most obvious factor is education: Scientists spend ages 5 through 18 in school, and then ages 18 through 30ish getting their academic degrees. Then a few years of learning on the job… Meanwhile, scientific breakthroughs tend to be less common in old age because we invest less in learning as we get older, and our skills gradually become less relevant.
The most important conceptual work typically involve radical departures from existing paradigms, and the ability to identify and appreciate these radical departures may be greatest shortly after initial exposure to a paradigm, before it has been fully assimilated.
Is there an equivalent age-achievement relationship in public services and the public officials who run them? It is difficult to know because arguments can be made either way – for people being younger or older when they make/made a significant contribution – and I don’t think there’s a literature that has considered this question.
My feeling is that people are probably older when they make significant contributions to public services. Assuming it is possible to attribute changes to the effects of one particular individual, making that change happen requires things like seniority, the ability to persuade others, and having the chance to build a reputation over time – characteristics that come, mainly, with age.
What does this mean for how I feel now?
As a younger man I was in a hurry, partly because I’d been a late starter. Now I am much less so. The ignorance and arrogance of youth – the things I didn’t know I didn’t know – carried me so far; the ability and leeway to ask questions or offer challenges that other people didn’t was present. This, coupled with a strong work ethic, meant some progress on things I was involved with could be made.
Experiencing things for the first time in the world of work was a blessing. I had fresh eyes. There was no sense of the routine, no jaded feelings from having been here before. There was no chance to say: “I remember when…” or inclination to lament: “We’ve tried that before…”. By definition, the things I was then involved with and at what level I operated weren’t as sophisticated or complicated as they are now, and so lent themselves to a progress of sorts.
In the earlier stages there had been no seeing behind the curtain and realising the Emperor is at best only partially clothed. I was optimistic, not cynical. Cynicism or the (temporary) loss of hope or optimism is one of the hardest new realities to deal with. “Never doubt that a small group of thoughtful, committed people can change the world. Indeed, it is the only thing that ever has.” “All that is necessary for the triumph of evil is that good men do nothing.” “We must become the change we want to see in the world.” These are all true and yet none of them are true.
Work-wise the issues are more complex, more nuanced, more serious; less easy to solve, less easy to understand and less easy to address. The decision points become finer, the judgments more balanced, the action less direct and the influence more subtle. The implications are bigger – for people, for staff, for policy, practice and precedent. The room for mistakes is larger; the margin of success slimmer. It is said that political ideology most resembles a horseshoe; I’ve always found this interpretation compelling, and it may not take much to jump the gap, perhaps even before you realise you’ve done so.
And these changes in work happen at a time when there are changes in a personal life. Relationships, family, children, health, balance, perspective; a slowing down, a different pace, an accrual of experience; perhaps a sense of where the limit of your ability might lie; understanding what got you here won’t get you there.
What does this mean for how I feel about the future?
All of the above are phenomena that need time and space to consider. Taking this time, decompressing experience, so that it stretches out, feels important. In doing so there are more opportunities for reflection, to think about what something might mean and to think of implications in many directions. To think of why? as well as what? and how?
There are plenty of future times. To make the most of the fact things have happened or been tried before, of history, of documented experience, is a benefit. It’s a chance to learn, change what needs to be changed, keep what needs to be kept and work with the people who need to be worked with.
In taking time to truly know where I am now – of undertaking a personal appraisal – there is more chance to absorb experience and to fill in the gaps that ignorance and arrogance left behind, and of equipping myself for a more balanced future.
 – “Age and Scientific Genius” (pdf), National Bureau of Economic Research. A fascinating paper by Dean Simonton summarising what is known about age and outstanding achievement, including methodological questions, is available here (pdf).
 – For example, try easily finding the average age of all local authority chief executives.
There are many of us whose work is with or in the public sector who want and expect things to be better. Nevertheless, we sometimes find ourselves being sceptical about some solutions being put forward, and particularly the way in which they’re presented (such scepticism is often misinterpreted as a defence of the status quo – a point I’ve disabused here).
The word that sets off my own scepticism is “innovation”.
A series of recent posts on Arbitrary Constant have taken this starting point and brought together a series of reflections:
This final post draws out an explicit view on degrees of innovation, how these relate to other forms of change in public service reform, and along what lines these degrees develop.
The diagram below captures this view.
In this diagram we see how “innovation” leads to “best practice” leads to “improvement” leads to what should be “standard” in public services. We further see that moving in any one of three directions can increase these degrees: increasing Scale, increasing how well Known something is, or transferring practice across Sectors.
As I’ve repeatedly said, very little can truly be thought of as “innovative”. Having a more honest appraisal of the extent to which something is “new”, in my view, leads to a better understanding of the extent to which this “thing” might achieve change. This also provides us with a better understanding of the practical approaches, tools and techniques that might be useful to take the innovation from its current “degree” to the next, higher “degree”.
Of course, in no way do I expect this contribution on innovation to gain any sort of currency. I hope, though, that by sharing it it becomes a useful framework within which people may reflect on the variety of means being used to achieve the ends to which most of us aspire: improved public services.
As a reminder: the reason for this was because I think it’s useful to know the degree to which something is innovative or whether it is, for example, something that actually replicates practice in another area; or is something that is supposed to be done anyway; or perhaps something using a different mechanism to what is found in other places.
The suggested innovation scale has three axes: Scale (i.e. size), Known and Sector.
To help bring it to life, below are three examples of “innovation” and where I think they might lie respectively within the innovation scale.
Example 1: Using Twitter
This example is included to help orientate people within the innovation scale. Twitter, as a communications tool within public services, is used nearly everywhere (Scale), Known to virtually everyone and is used across all Sectors. Thus, to speak of Twitter as “innovative” in public services would seem, and is, a bit of a nonsense.
Example 2: Personal Budgets
Personal Budgets are very well Known about, especially in adult social care, and the principle of them is starting to be seen in other Sectors of public service, notably health and employment. Nevertheless, the Scale at which Personal Budgets exist still isn’t especially large. In this sense, then, Personal Budgets aren’t innovative; equally, something is happening such that they aren’t being adopted to the extent that it is hoped they would be.
Example 3: Alliance contracting
Alliance contracting is something that is relatively new in public services. It is only happening in a couple of places (Scale) and is relatively unKnown. What’s more, it’s primarily happening only in adult social care Sector at the moment. Thus, according to the suggested innovation scale, alliance contracting is innovative.
These examples hopefully give a flavour of how the innovation scale may be useful. In the next (and final) post on innovation, I’ll share an overall way of thinking about degrees and innovation and how it relates to other forms of change in public service reform.