Risk Management

Timing Might NOT Be Everything

October 18, 2011 By Wendi Leave a Comment

Recently when I’ve been in the middle of political discussions with friends or family, I’ve found myself on the side of an argument where I rather sadly concede that President Obama might not be the right person for the times. It occurs to me that this point of view might have less to do with the President and more to do with a middle-aged belief about what has limited or supported my personal accomplishments to-date – a belief that I’ve been fortunate to have predilections about what I do to make a living and a temperament about how I do it that have been well-suited to the particular state of the business world during my career lifetime (so far, at least). A feeling that timing is everything.

A few weeks ago, I attended a one day seminar on the topic of Forecasting in the Face of Risk and Uncertainty. Most of the people attending this event were involved in the financial markets and their main (and in most cases, only) interest is figuring out how to make money. Ironically, virtually all of the presenters (other than the final session panelists) were people who are mostly interested in thinking about BIG problems – scientists and academics – and relatively unconcerned about how to convert their ideas into money. The seminar’s major themes that interested me were these:

Risk = Hazard x Vulnerability
Risk should not be confused with Uncertainty. Risk implies that future events will occur with measurable probability. Uncertainty implies that the likelihood of future events is indefinite or incalculable.
It appears that uncertainty is increasing as systems become more complex and tightly coupled.
The more complex and tightly coupled the system, the greater the likelihood that there will be accidents. In these environments, adding more controls or safeguards in attempts to offset the likelihood of accidents will have the perverse effect of increasing the likelihood that accidents will occur because the controls increase both the complexity and tight coupling of the system.
But, simplicity is not necessarily the solution to complexity because simple systems are just as vulnerable to phase shifts or environmental disruptions. In fact, simple systems and complex, tightly coupled systems share one design feature – they tend to be optimized perfectly for a specific environment. If the environment changes even slightly in a way that makes it difficult for the system to adapt, the system is prone to collapse.

During the final session one of the panelists observed that the cockroach is a very successful creature – having survived massive disruptions in its external environment time and time again. Apparently, cockroach survival has been enhanced by its extreme sensitivity to puffs of wind (indicating a potential predator on the move). Puff of wind, cockroach senses it, and initiates evasive maneuvers (turns and moves in the opposite direction). However, cockroaches are not very well-designed insects. In fact, this panelist described them as a perennial runner up in the competition for best insect design. However, their less than perfect optimization for the environment has served them well in the long run. They haven’t gotten to dominate the planet at any given time, but they’ve outlasted many other perfectly adapted insects (and other life forms as well).

Optimizing for a particular set of circumstances (tightly coupled complex or simple system design) is analogous to being the right person at the right time at the right place (etc., etc.). In business settings we often talk about a process being well designed if it gets the right information to the right person at the right time. But what if all this rightness is wrong? What if it’s better in the long run to be almost right? What if the right time is too short a timeframe for rightness? What if timing is not everything?

Organizational innovation, it seems to me, is an attempt to survive (retain relevance) over the long term. But, some of the limitations that organizations place on themselves in order to manage the inherent risks and uncertainty that accompany innovation might inadvertently increase those risks and not effectively manage uncertainty. For example, companies believe that it is risk limiting to hew close to their existing markets – explore adjacencies where they can leverage existing resources and expertise. And, on some level, this makes sense – you’ve got all this stuff (resources, expertise, infrastructure, systems) available and you know how to use it to make money (or create value if you’re a non-profit or an institution with a mission that has a broader definition of value than a financial one). But what if by staying close to what you know and using similar or the same resources and expertise, you are adding complexity to your system and more tightly coupling success to the same set of value drivers and risk factors? What if what looks like you’re managing risk is actually creating more risk and giving you a false sense that you are prepared to handle uncertainty? Rather than having created a more robust and resilient organization, you have created one that is ever more sensitive to slight shifts in its environment and has a reduced capacity to adapt. So you get to be the perfectly right organization for the time, but you have not become the organization that has a better chance of being around for a long time.

So, how do organizations use innovation to help manage for the long term? First, I think we need to distinguish between the long term and forever. While this might seem obvious, I’m not sure that most of us really acknowledge that every major organizational system will cease to exist at some point – nothing is forever. Even serially successful innovative companies like Apple will not last forever. Among the many musings about the significance of Steve Jobs and Apple in the wake of his untimely, recent death, one rang most true to me because it seemed like a perfect restatement of Clayton Christensen’s theory of innovation from below.

In this article (some of it is excerpted below), Cliff Kuang makes the point that Steve Jobs understood design better than anyone. For a long time, personal computing devices were pretty ugly and hard to use – they were badly designed – but they were fast and powerful. And, for an equally long time, design was too costly to be delivered to or desired by more than the elite. (Market penetration of Macs was always a fraction of the PC.) But, not very long ago, the price point of delivering great design in computing devices fell within reach of the many and computing power was a given, no longer a differentiator. Today, great design has become a standard feature required of computing devices. So, it’s possible that the wave of great design as an innovation in technology has passed. That, more than anything else, may cause Apple to stumble going forward, but Apple has already figured out how to use innovation to manage for the long term. The company has survived failures (NeXT Cube, Lisa, Newton), customer unhappiness (the iPhone 4’s antennae), and now, the death of its visionary founder/leader.

So, what makes systems resilient? Just the sort of things that have been engineered out of lean organizations – slack and redundancy – the less than perfect design. I frequently hear executives bemoaning the “hobbies” that employees pursue as evidence of innovation gone wrong. But, I think that hobbies are the quintessential redundancies that pop up when there’s a bit of slack. They’re good and necessary for innovation. At the same time, organizations must also be willing to invest in some of the hobbies that appear to be scalable which requires leadership in the face of risk and uncertainty.

And what does it look like if you’re on the wrong side of a bet? Ask yourself if you’d like to be Reed Hastings of Netflix – a guy who is experiencing what looks like BIG failure at the level of the institution. Or if you’d have been happy being Steve Jobs when Apple decided to offer Lisa customers the option of trading in their purchase for a Mac as a mea culpa for having sold them an expensive product that the company was not going to support because it was a failure. Probably not. But, if you are almost right (rather than perfectly right) and if you have a resilient organization that can absorb shocks to the system, you probably have a much better chance of thriving over the long haul because timing is not everything, even if it helps.

Sources:

“Wind Direction Coding in the Cockroach Escape Response: Winner Does Not Take All,” Rafael Levi and Jeffrey M. Camhi, sourced on 10/12/11 at http://www.jneurosci.org/content/20/10/3814.full.pdf

“What Can Steve Jobs Still Teach Us?,” Cliff Kuang, Fast Company Newsletter, October 5 2011, sourced on 10/14/11 at http://www.fastcompany.com/design/2011/what-can-steve-jobs-still-teach-us

From What Can Steve Jobs Still Teach Us?:

A decisive factor that aided Steve Jobs was fortuitous timing. He came of age just in time to become a founding father of the personal-computer movement. And he was still young enough when he returned to Apple, in 1997, that his own instinctive sense of what a computer might become could be brought to life. In the 1980s and 1990s, computers were sold on their speed and technical capabilities. But by 2000, these features had largely become commoditized–it no longer mattered how fast a computer was when basic issues of usability and integration became paramount. What did speed matter if you didn’t know what all the menus meant, or if you were hit with pop-up errors every time you clicked your mouse?

Before 1997, Jobs was ahead of his time: The computers he made were overpriced for the market, because he thought that usability was more important than capability. But as computers reached maturity and became a staple in every home, his obsessions became more relevant to the market. Indeed, many of Apple’s recent signature products, such as the iPad or the iPhone, were ideas first conceived in the 1990s or even the 1980s–they had to bide their time.

Jobs is ahead of his time in other ways too: He has taught his entire organization to play in the span of product generations rather than product introductions. Apple designers say that now, each design they create has to be presented alongside a mock-up of how that design might evolve in the second or third generation. That should ensure Apple’s continued success for a long time, aided, of course, by the tremendous momentum that Jobs’s leadership has provided the company.

And for fun, it appears that cockroaches will NOT inherit the earth even though they are more resilient than humans.

Filed Under: Business Model, Innovation, Risk Management

The Value of Experience

March 1, 2011 By Wendi 1 Comment

I have a friend who has held several senior level executive positions at very large companies over the past 20 years. She is currently looking for a job and, times being what they are, she finds that unless her experience completely matches up against the new opportunity, she’s not considered as desirable a candidate as someone else whose experience is a more perfect fit. As a strategy and innovation consultant, I’m frequently asked to provide referrals for work that is the same as the work I’m proposing to do. In effect, there is an assumption that my previous success can be replicated if somehow the situation is close enough and if I do exactly the same thing again.

We treat experience as extremely valuable – as if it embodies deep knowledge and insight and is highly predictive of success in the future. We believe that because someone has managed a particular situation in the past successfully, he or she is a better bet to achieve success in the future when placed in a similar situation. But what if experience at best is irrelevant and at worst is misleading? What if how we understand correlation is deeply flawed? What if the truth wears off?

If we define experience as the proven ability to achieve a particular result under certain conditions, it is analogous to the scientific principle of replicability which is a foundation of the scientific method. In a scientific experiment, recreating the conditions under which a particular outcome has been observed is supposed to yield the predicted outcome. But, scientists are finding is that this is not the case. In many scientific experiments, the probability that predicted outcomes will occur actually decreases relative to the number of times the experiment is recreated increases. This is called the decline effect.

Many of the obvious culprits – regression to the mean, publication bias, selective reporting – do not provide a satisfactory explanation for the problem (which is a BIG one for science). After all, if a fact is not a fact – that is, something true that remains true – then what are these things that are being observed? What is the truth?
Regression to the mean is a statistical phenomenon that common wisdom captures in the phrase “the law of averages.” So, any experiment that shows a big effect at first might be expected to show a small effect another time and then bounce around until the effects cluster about an average. However, for many experiments with statistically solid data sets, the data doesn’t regress to a mean, the data thoroughly degrades, for which there is no satisfactory explanation.
Publication bias is the tendency of scientific journals (or almost any publication) to prefer to publish research that supports previously published research. After some period of time, the insight becomes orthodoxy, and then these same journals are more open to disconfirming research. While this explains some of the reasons why certain scientific facts become enshrined and then discredited, it doesn’t explain early positive results.

Selective reporting is the perceptual bias that humans bring to observation. An example of selective reporting can be found in an experiment where scientists look for physical symmetry, which is supposed to be a marker for a preferred mate, in animal subjects. If the scientist knows that the observed subject has successfully mated and is looking for symmetry that is expressed in something as minute as tail feathers or whiskers, there is a predisposition to see it. Experiments that test the efficacy of acupuncture provide another window into the problem of selective reporting. During a 30 year timeframe, experiments were conducted in Asia and the US/Europe. All of the experiments in Asia supported the effectiveness of acupuncture and only about 60% of the US/European experiments reached the same conclusion.
The most disturbing new information that I learned about the scientific method was not that scientists and scientific publications are biased or that judgment based on beliefs turns out to be more important than decisions based on a so-called solid set of facts, but that the notion of a threshold for statistical validity, that a phenomenon’s occurrence is not likely to be caused by chance – the 95% probability level – was originally set because it made “pencil and slide-rule calculations easier.” And much research chases after achieving results that meets this criterion so that they can be treated as facts.

If it turns out that facts are slippery and no more solid than beliefs, how do we understand the value of experience? Our contention that experience matters derives from the scientific model of replicability. But that model is not as firm a foundation as we may have hoped. If we can’t rely on the scientific construct of replicability – find someone whose experience matches up precisely against the current situation to better predict a successful outcome – what can we rely on? Turning to science for answers means a long wait, because science is only now beginning to grudgingly acknowledge that it has a problem. Even though it is much more challenging, I believe that we need to acknowledge that we are not sure what the value of experience actually is and that we might be as wrong about it as much scientific research is turning out to be.

Source: “The Truth Wears Off,” Jonah Lehrer, The New Yorker, December 13, 2010

Filed Under: Decision-making, Leadership, Risk Management Tagged With: experience, human, leaders

Sample Size of 1

March 1, 2010 By Wendi 1 Comment

Why do we cling to beliefs even when we know that they are wrong? That is the persistent question that thrummed in my thoughts as I read The Ascent of Money, Niall Ferguson’s financial history of the world. Ferguson’s book is a highly readable study of financial innovations that transformed economies, intertwined themselves with politics, and shaped world history. As the reader is taken on a tour of financial history, the characters and scenes change, but the potential for disruption continues unabated. Each innovation has unintended consequences which are sometimes more profound than the innovation itself. In almost every case, the inventor believes that he has a complete understanding of how the change that he initiates will play out. And, invariably, his understanding which is based on history is shown to be inadequate.

While the past may be prologue, it is not predictor of the future. And yet, we treat the future as potentially knowable and are endlessly startled when what happens seems to border on the impossible. Ferguson cites Frank Knight: “Again and again an event will occur that is ‘so entirely unique that there are no others or not a sufficient number to make it possible to tabulate enough like it to form a basis for any inference of value about any real probability.’”* We want to believe that the sample size on which we are basing our forecast of the future is sufficiently large and the events are sufficiently independent of one another to help us assess risk. But in most cases, the events occur in a sequence and are related to one another, reducing the sample size effectively to one. So we are left with uncertainty – that which is unknowable and unpredictable – but we cling to our belief that we are managing risk.

These notions are incredibly important because the universal language of business is finance and a key concern of finance is managing risk. Innovation – bringing something new into existence – is more than inherently risky, it is a profoundly uncertain exercise. That is why it makes more sense to invest resources in experimentation rather than developing detailed business cases based on historical data. Small, ideally scalable, experiments can be stopped if they fail or expanded if they succeed. Because it is very hard to predict in advance what will succeed at any given time. To believe that you can may be comforting, but it is wrong because the data that is used to support most business cases turns out to be based on a sample size of 1.

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A few weeks after writing this blog post, I was reading yet another analysis of financial risk. This time, it was The Economist’s special report in the February 13, 2010 issue. One of the pieces quoted John Maynard Keynes: “It is better to be roughly right than precisely wrong.” Somehow this idea seems related to the challenge of clinging to beliefs that we know to be wrong. Perhaps it’s because we also believe that we are roughly right at the same time. But being roughly right also implies that you are roughly wrong. However, we tend to discount the roughly wrong and inflate the roughly right. The article also quoted Chuck Prince, head of Citigroup in 2007, who described why the bank pursued activities that, as it turned out, were precisely wrong: “as long as the music is playing, you’ve got to get up and dance.” We have a very poor understanding of how to simultaneously hold the notions of roughly right and roughly wrong, which seems to lead us into situations where we are blind to precisely wrong.

* Frank H. Knight, Risk, Uncertainty and Profit (Boston, 1921)

Filed Under: Innovation, Risk Management