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Economic Pessimism is wrong. You should be optimistic because of recombination

I was having a discussion with some colleagues  about Pikety’s book earlier this week. One particularly interesting argument someone eloquently (and forcefully)  advocated was that the real value of Pikety is not the argument about inequality. Instead, it is evidence that the period of post-war economic growth is an anomaly, and we should get used to permanent growth rates of 1-2% forever more.

This issue of economic pessimism goes to the heart of much of the current debates on economic policy. Is productivity growth limited? Do we face inevitable stagnation, which will force bitter, socially disruptive policy choices? Are the central banks just blowing hot air into a burst balloon?

Of course, you can argue that the current situation resembles the miserable dark period of the 17th century: on that score I’d highly recommend reading the great historian David Hackett Fisher’s The Great Wave: Price Revolutions and the Rhythm of History. There are previous instances of overpopulation, demographic headwinds, commodity price pressure, and the rising cost of basic goods like shelter and falling real standards of living.

But I’ve noted plenty of contrary evidence, like this the other week, or this.

And I think there is one overwhelming argument against techno-pessimism and its related fear of economic stagnation. Another fabulous recent book is W. Brian Arthur’s The Nature of Technology: What It Is and How It Evolves. Arthur argues that technology largely advances by recombination of existing technologies in new ways.  Technological recombination naturally creates new niches and new needs and new problems, which in turn call forth new solutions. (I’ll be talking about Arthur and the Sante Fe approach to systems and complexity a lot in the future.)

The more building blocks you have lying around for reassembly, the more creative technical progress is possible. And we have more such blocks lying around for reassembly than ever before in history, with better technologies (like full-text journal articles on the internet) for passing knowledge around.

That argues for expecting more technological changes, not less. I think there are profound challenges in the institutional and social adaptations needed to cope with economic evolution, but at root I am optimistic about economic growth. We’re not going back to the 1700s.

 

2017-05-11T17:32:43+00:00 June 11, 2014|Adaptation, Insight & Creativity, Technology|

“Strategies grow like weeds in a garden”. So do trades.

How much should you trust “gut feel” or “market instincts” when it comes to making decisions or trades or investments? How much should you make decisions through a rigorous, formal process using hard, quantified data instead? What can move the needle on performance?

In financial markets more mathematical approaches have been in the ascendant for the last twenty years, with older “gut feel” styles of trading increasingly left aside. Algorithms and linear models are much better at optimizing in specific situations than the most credentialed people are (as we’ve seen.) Since the 1940s business leaders have been content to have operational researchers (later known as quants) make decisions on things like inventory control or scheduling, or other well-defined problems.

But rigorous large-scale planning to make major decisions has generally turned out to be a disaster whenever it has been tried. It has generally been about as successful in large corporations as planning also turned out to be in the Soviet Union (for many of the same reasons). As one example, General Electric originated one of the main formal planning processes in the 1960s. The stock price then languished for a decade. One of the very first things Jack Welch did was to slash the planning process and planning staff.  Quantitative models (on the whole) performed extremely badly during the Great Financial Crisis. And hedge funds have increasing difficulty even matching market averages, let alone beating them.

What explains this? Why does careful modeling and rigor often work very well on the small scale, and catastrophically on large questions or longer runs of time? This obviously has massive application in financial markets as well, from understanding what “market instinct” is to seeing how central bank formal forecasting processes and risk management can fail.

Something has clearly been wrong with formalization. It may have worked wonders on the highly structured, repetitive tasks of the factory and clerical pool, but whatever that was got lost on its way to the executive suite.

I talked about Henry Mintzberg the other day. He pointed out that contrary to myth, most successful senior decision-makers are not rigorous or hyper-rational in planning, Quite the opposite. In the 1990s he wrote a book, The Rise and Fall of Strategic Planning, which tore into formal planning and strategic consulting (and where the quote above comes from.)

There were three huge problems, he said. First, planners assumed that analysis can provide synthesis or insight or creativity. Second, that hard quantitative data alone ought to be the heart of the planning process. Third, assuming the context for plans is stable, or predictable. All of them were just wrong. For example,

For data to be “hard” means that they can be documented unambiguously, which usually means that they have already been quantified. That way planners and managers can sit in their offices and be informed. No need to go out and meet the troops, or the customers, to find out how the products get bought or the wards get flight to what connects those strategies to that stock price; all that just wastes time.

The difficulty, he says, is that hard information is often limited in scope, “lacking richness and often failing to encompass important noneconomic and non-quantitiative factors.” Often hard information is too aggregated for effective use. It often arrives too late to be useful. And it is often surprisingly unreliable, concealing numerous biases and inaccuracies.

The hard data drive out the soft, while that holy ‘bottom line’ destroys people’s ability to think strategically. The Economist described this as “playing tennis by watching the scoreboard instead of the ball.” ..  Fed only abstractions, managers can construct nothing but hazy images, poorly focused snapshots that clarify nothing.

The performance of forecasting was also woeful, little better than the ancient Greek belief in the magic of the Delphic Oracle, and “done for superstitious reasons, and because of an obsession with control that becomes the illusion of control. ”

Of course, to create a new vision requires more than just soft data and commitment: it requires a mental capacity for synthesis, with imagination. Some managers simply lack these qualities – in our experience, often the very ones most inclined to rely on planning, as if the formal process will somehow make up for their own inadequacies. … Strategies grow initially like weeds in a garden: they are not cultivated like tomatoes in a hothouse.

Highly analytical approaches often suffered from “premature closure.”

.. the analyst tends to want to get on with the more structured step of evaluation alternatives and so tends to give scant attention to the less structured, more difficult, but generally more important step of diagnosing the issue and generating possible alternatives in the first place.

So what does strategy require?

We know that it must draw on all kinds of informational inputs, many of them non-quantifiable and accessible only to strategists who are connected to the details rather than detached from them. We know that the dynamics of the context have repeatedly defied any efforts to force the process into a predetermined schedule or onto a predetermined track. Strategies inevitably exhibit some emergent qualities, and even when largely deliberate, often appear less formally planned than informally visionary. And learning, in the form of fits and starts as well as discoveries based on serendipitous events and the recognition of unexcited patterns, inevitably plays a role, if not the key role in the development of all strategies that are novel. Accordingly, we know that the process requires insight, creativity and synthesis, the very thing that formalization discourages.

[my bold]

If all this is true (and there is plenty of evidence to back it up), what does it mean for formal analytic processes? How can it be reconciled with the claims of Meehl and Kahneman that statistical models hugely outperform human experts? I’ll look at that next.

Decision-makers need an “insight” map, not academic models

Have you noticed how much the business world increasingly talks about “insight’, but in vague, undefined and often murky ways?  The term is becoming ever more common as the perceived value of raw information goes down. What does it mean?

Insight is that flash of recognition when you see something in a fresh way. What seemed murky becomes clear. What seemed confusing now has regularities, or at least patterns. It is about recognition and intuitive understanding of what action needs to be taken.

That’s why I’ve been talking about research into decision-making recently. It isn’t because of academic interest. Decisions are a very practical matter, about specific situations rather than generalities. But for twenty years I saw the brightest policymakers and leading market players make decisions that went terribly wrong. And I noticed when they got things very right.  What made the difference between success and failure?

To answer that you need to recognize the patterns, and that means you need to look at grounded, empirical research.  Of course, experience is essential. But you need to learn the lessons from experience, too.

All research involves taking an abstract step back and trying to find patterns.  The trouble is most academic research in economics and finance is centered around models which seek to explain things in general terms.

But a model isn’t the only way to identify the important features in a situation (despite what many academic economists believe.) In fact, most problems confronting decision-makers are more like “how do I get from A to B” or “What are the major risks just ahead of me and how do I go round them?” For most real problems, a map  is more useful than an abstract model. You can see the lie of the land and where you need to go. You recognize and name the features of the landscape.  You know which direction to head next. That’s what you need if you want to go places.

How is thinking in terms of maps different? Maps retain more useful detail relating to particular purposes and tasks. They are specific about facts on the ground, but they have the right scale and representation of the problem. For example, you use a road map for driving from New York to Boston. It leaves out most of the detail of roads in urban subdivisions or farm tracks, but Interstate 95 is very clear. You use a nautical chart for taking a sailboat into Mystic Harbor.

Models offer generalized “explanation” based on a few easily quantified variables. But if you want to reach harbor safely you are better off with a chart showing the actual, very specific rocks in the channel, instead of a mathematical model of boats.

Maps can show the appropriate scale of detail for the task in hand. They can show shorter routes to your destination. They are less reliant on assumptions. They orient you on the landscape and let you know where you are, even when the outlook is foggy and unclear.  They are traditionally drawn by triangulating from different viewpoints rather than a single perspective.

So I’m looking at research on this blog which helps map the territory, and find the blindspots – the cliffs, the marshes, the six-lane interstates to your destination.  You need to see what people have already observed about the landscape. (The actual reports for clients don’t go into the research, just the results – the map itself, not the why. I just find it fascinating and love writing about it)

Decision-makers are like explorers. You can wander off into the desert yourself. But it helps if you have a map.  Insight means you’ve  recognized how to get to where you want to go.

 

 

2017-05-11T17:32:44+00:00 April 15, 2014|Decisions, Insight & Creativity, Maps, Quants and Models|