Machine, Platform, Crowd – Book Review

Andrew McAfee and Erik Brynjolfsson have done it again. ‘Machine, Platform, Crowd’ is a lucid and timely exploration of three powerful trends unleashed by the digital revolution. They describe the ‘three rebalancings’: first, as machine learning either complements or supplants human minds, secondly , as platforms drive the selection, production and distribution of products and services, and thirdly, as on-line crowds increasingly augment or surpass the core functions of companies.

Although the authors don’t put it this way, there are essentially three economic drivers of the ‘triple revolution’. First, the “free, perfect and instant economics of digital information goods in a time of pervasive networks” which means that “the marginal cost of an additional digital copy is (almost) zero”. Second, the positive network effects from the growth in user interaction. As they put it, “Networked goods can become more valuable as more people use them. The result is ‘demand side economies of scale’, giving an advantage to bigger networks”.

The third driver is the distinctive ability of multi-sided platforms to subsidise one or more set of users in order to incentivise the participation of other sets of users. Digital technology allows many platforms to offer free or discounted services which draw in consumers. This encourages ecosystem growth by attracting more producers or advertisers who pay for the privilege, in turn making it more attractive for other consumers and users.

The book is clearly structured into three sections discussing the title themes, with handy summaries at the end of each chapter. Although the three themes are given equal billing, in the end what dominates is the potential for platforms to capitalise on machine learning and crowd mobilisation. Chapter 7 observes that platforms compete on drawing in users’ contributions and curating them effectively, “but it becomes much more difficult to build a vibrant platform if at least two are already in place”. Network effects and the reluctance of consumers to switch or use more than one platform (or “multi-home”) allows successful platforms to dominate their markets. McAfee and Brynjolfsson are curiously reluctant to use the word, but tendency of platforms towards monopoly is clear. The potentially negative effects of this on competition and innovation are left unexplored, although they would probably need another book to do them justice.

The book also glosses over another distinctive feature of the growth of platform companies: many of today’s giants spent years losing money. Investors have tolerated this, placing faith in the platforms’ ability to turn rapid growth in their user base into longer term profits. They perceive market dominance arising from investing in network effects has long term value. The problem for incumbent companies facing digital disruption is how to secure similar investor indulgence in seeking to transform themselves. The authors argue that there is room for incumbent companies to co-exist with platforms, but they leave little room to doubt that the momentum is with the latter.

The authors end on an optimistic note, saying that “the next few decades should be better than any other […] so far”, although they are careful to describe this as “possibility and a goal”. Indeed, they are suitably humble throughout about making predictions of the disruptive trend that they describe. Amusingly, while the publishers describe the book as a “toolkit” MacAfee and Brynjolfsson say at the outset that they are not offering a “playbook” and that “we suspect that people who offer such a playbook are kidding either themselves or their readers. There is simply too much change and […] uncertainty”. Nevertheless, they have done us all a great service in explaining some of the powerful trends that will shape our future.

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How our mobiles could make us rich

Our mobiles could help us lead richer lives – this was the bold claim that I made at the recent Ignite conference in London. Based on my earlier post entitled Beyond Mobile Banking, I noted that people already say that their mobiles have improved their money management: according to ING’s International Survey last year, 71% of Europeans said so. Given the difficulty that most people have with financial decisions, mobiles offer enormous potential for further improvement. You can see the presentation here:  How our mobiles could make us rich – Ignite Event 06 03 2017

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Confessions of an Economic Forecaster


Economists’ forecasts are notoriously inaccurate. This fallibility is hardly a great surprise given that we live in an irretrievably uncertain world. So why do we keep doing it? What can we learn? Here are my eighteen secrets and myths about the economic forecasters’ art.

1. Worse than fallible

It’s not just that economic forecasts are prone to be wrong. It’s that they tend to let you down when you most need them. Paul Samuelson once said the stock market has “predicted nine out of the last five recessions”. By comparison, economic forecasters rarely predict any! They are typically caught out by shocks.

2. But decisions need forecasts

Economists are well aware of the fallibility of forecasts. But they have no option but to keep making them. People can’t live without them – all decisions are based on a view of the future. People also keep asking for them because they can provide a (spuriously) precise way of justifying decisions made for other reasons. And if things go wrong, they can blame the ‘expert’!

3. A deceptive air of certainty

In an uncertain world, forecasts, and the story behind them, provide us with comfort. But if we place too much faith in them they may lead us astray. Such overconfidence gives us an illusion of control. We can see this in investment projects that are often late or over-budget. Similarly, official forecasts, for example from governments or central banks, are routinely over-optimistic. They assume policy success, sometimes because they are designed to change people’s expectations and behaviour.

Forecasters often ignore the sage advice that “it’s better to be vaguely right rather than exactly wrong”. Having constructed models to predict the future, forecasters tend to forget that they are only approximations.

4. Charlatans rule

Confident dogmatic forecasters are provocative and often grab the headlines. Unfortunately, as Philip Tetlock has ably demonstrated, their strong but simple narratives are often way off the mark. The devil is often in the detail, so we should be wary of forecasts based on a single factor. It’s also a good discipline to try to attach a probability to your forecasts. Admitting uncertainty and being open about your degree of confidence in your prediction is not a sign of weakness, but helpful realism.

5. Try to be objective

We’re all biased, and our forecasts reflect our pre-conceptions. Forecasters need to acknowledge this, and aim for objective assessments. It is easy to pay too much attention to the most recent and most shocking news. Yet, the latest news may be misleading, and forecasters often gloss over the fact that newly published statistics can be revised dramatically. They have a natural human tendency to indulge in elegant, after-the-fact rationalisations. Hindsight is indeed a wonderful thing. But it’s all too tempting to succumb to selective memory, cherishing your successes while forgetting your mistakes.

6. The more the merrier

By making more forecasts, you have more chances to ‘win’ by getting something right! But more seriously, it also gives you more chances to learn. Rather than sticking with a forecast that is off track, it is important to revise your views in the light of new information. As John Maynard Keynes put it “when the facts change, I change my mind. What do you do, sir?”.

7. Unlike traders, forecasters do get marks for style

Financial market traders and investors are judged by the profits that they make. As a result, they are happy to get it right for the wrong reasons. But relying on luck is dangerous. It has a nasty habit of running out on you in the end.

By contrast, and contrary to popular opinion, forecasting is not just, or maybe even mainly, about accuracy. Aside from providing provocative or comforting stories, forecasts are about survival rather than getting it right. Repeatedly making small errors may not kill you, particularly if you learn from them or if they help you to avoid a catastrophic error. And accuracy is often relative, just as when faced with a charging bull, your survival depends not on outrunning the bull, but your neighbour.

8. It’s more about ‘why’ than ‘what’

Meaningful forecasts come with useful explanations. They should provide the users with a framework of thought and wisdom, rather than just knowledge and information. A common error is to assume that because two things are moving together that the two are related, let alone that one is causing the other: as scientists say, correlation is not causation. Wisdom comes from a deeper understanding of why things happen.

9. Past isn’t always prologue

The models used by forecasters are based on past causal relationships. People’s spending, for example, will typically closely reflect what is happening to their incomes. Many such relationships are reliable, with variables showing steady trends or cyclical patterns. But sadly, history doesn’t always repeat itself, or even rhyme. Things don’t always revert to their previous trends, and so what is considered ‘normal’ can be a moving target. Models live in the past, and die in the future.

Former British Prime Minister Harold MacMillan once said that his biggest fear was “Events dear boy, events”. So it is for forecasts. Unprecedented shocks have a habit of wrecking them. Economists make a crucial distinction between risk and uncertainty. Risk is where you know the odds, like playing roulette. Uncertainty is where you don’t. Worse still, you may have no idea that the game is about to change entirely; Former US Defense Secretary Donald Rumsfeld referred to this as the ‘unknown unknowns’. This is why forecasting is more of an art than a science.

10. Some things are more certain than others

The further out you look, the more uncertain things are, making forecasts even less reliable. This is why economic pundits on TV prefer to look smart by focusing on the short term. In fact, some forecasts of monthly indicators, which are released with a month or so’s delay, are more like ‘backcasts’, because anecdotal information helps to ‘predict’ last month’s outcome.

Apart from the time dimension, some variables are more volatile, or prone to extreme outcomes than others. For example, commodity prices and share prices are far more volatile than consumer prices. The importance of this is reinforced by the fact that the starting point for the forecast is often critical to the outcome. Economies, like the weather or biological systems, may be acutely sensitive to initial conditions. The phenomenon of one thing leading to another, or ‘path dependency’, makes forecasting especially difficult. In the metaphor of chaos theory, a butterfly flapping its wings may ultimately lead to a tornado far away.

11. Some things matter more than others

In wrestling with the complexity of the economy, the forecaster’s job is helped by the fact that some things matter more than others, and therefore deserve more attention. Sometimes this is obvious. Thus businesses focus on variables critical to the thresholds key to their decisions to invest, lend, hire, buy or sell. For example, the prospective price of oil may a critical determinant of the profitability of an oil rig.

However, sometimes we are prone to miss crucial risks to a forecast. We may fail to recognise our implicit assumptions, often because we mistakenly take things for granted. The global financial crisis that broke in 2008 at least partly stemmed from a widespread complacency that US house prices don’t fall.

12. Stories can help, but also hinder

One way of dealing with the myriad of possible futures is to construct scenarios. Rather than ‘betting the ranch’ on a single forecast, scenarios are stories constructed around the key drivers of the outlook in terms of their probability and impact. The stories give the users benchmarks for answering the question ”what kind of a world are we in?”. This can be a helpful way of thinking through potentially disruptive events beforehand, because people are liable to make bad decisions under stress. Contingency planning can allow ‘grace under fire’.

But in thinking about alternative scenarios, it’s important not to be blinded about other possibilities – the unknown unknowns. Multiple stories can confuse, and you have to make a choice in the end. The goal is to pick a ‘no regrets’ strategy that’s robust in variety of scenarios – but that’s easier said than done.

13. Don’t just forecast what you can measure

Scenarios are one way tackling important issues that don’t lend themselves to measurement. Economists often tend to ignore non-economic factors that are hard to put into their models. In so doing they often miss the ‘elephants in the room’. In particular, politics and institutions matter. In the long run they can be critical in determining whether countries succeed or fail. And even in the short term, the outcome of a divisive election, as we have been observing recently in the US, may be far more important to the outlook than a forensic analysis of the latest economic data.

14. More data isn’t always better

Data is vital to test theories in the real world. But that doesn’t mean that more data is always better. Data doesn’t speak for itself. Economic forecasters, embarrassed again by the global financial crisis, are in danger of being overshadowed by the new breed of ‘data scientists’. The emergence of Big Data and machine learning is opening up great new forecasting opportunities. In effect, massive computational capacity is being used to spot patterns through automated trial and error.

However, powerful new weapons are dangerous in the wrong hands. ‘Predictive analytics’ is in reality a tool for micro-economic forecasting. It’s not a ‘silver bullet’. More data and more variables gives more chances of correlations. The problem is that the proportion that are spurious and dangerously misleading rises even faster. As Nassim Taleb puts it, ‘Big Data’ brings ‘cherry-picking to an industrial level’. It is crucial to put the data into context, check its quality, understand the questions we are trying to answer, and to separate the signal from the noise. Identifying ‘why’ things are happening is vital for developing better forecasts.

But here the distinction between risk and uncertainty is crucial: more data in an uncertain world may create the impression that we are dealing with calculable risks. In such situations, simple rules of thumb, or ‘heuristics’, may outperform complex forecasting techniques. And they are clearly quicker and cheaper!

15. Magnitudes aren’t always necessary

For some purposes, we don’t need precise, quantified forecasts. Sometimes the direction is enough. That said, it also helps to get the timing right too! This is particularly true of questions that have binary outcomes. Wars and elections are won or lost. We are far more concerned with who wins an election than their margin of victory.

Such events are not easy to plug into economic models, and require different methods, with an emphasis on probabilities. Scenarios based on these contingent forecasts can then be produced. In some cases, some step changes, or what modellers call ‘structural breaks’, can be anticipated, such as preannounced policy shifts or regulatory changes.

16. Not all relationships are linear

Non-linear relationships create major problems for forecasters. They are difficult to model, and harder still to forecast. This is particularly troubling now because many of the emerging new digital technologies exhibit, or have the potential for, exponential, explosive growth. Minor initial errors in your forecasting model quickly lead to huge ones.

In this environment, Nassim Taleb, in his book ‘Antifragile’, advocates that we give up on forecasting. Instead, we should focus on non-predictive forms of decision-making, looking for choices that are not merely robust in the face of unexpected extreme changes, but actively benefit from the them. Sadly, this is easier said than done, and many businesses appear to be paralysed by uncertainty rather than energised by techno-phoria. That said, technological progress is not entirely unpredictable. Moore’s Law, which suggests that computer processing power doubles every two years, has proven to be remarkably durable.

17. Beware of market madness

Surveys of forecasts typically show that only few mavericks stick their necks out. Most forecasters prefer to stick close to the consensus of their peers. They seek safety in numbers, figuring that it is better to get it wrong in a crowd. This is just as well, because when it comes to the financial markets, they are typically wrong, often by a large margin.

Financial markets are not like other markets, because their prices are themselves forecasts. Financial asset prices reflect expected future returns and risk. According to the ‘efficient market hypothesis’, you cannot beat the wisdom of the crowd, because its collective intelligence already incorporates all available information. While it is certainly true that markets quickly incorporate incoming information, and it is hard, if not impossible to outperform them, this hypothesis does not stand up to scrutiny. And all too often, the wisdom of crowds is revealed to be madness, with extreme, unexpected outcomes. Investor beliefs about the future are loosely held, and prone to shifts from optimism to pessimism. This leads to booms and busts in market prices that leave consensus-minded forecasters trailing in their wake.

18. The ‘unusually uncertain’ cliché

Forecasters are prone to downplay the potential accuracy of their forecasts by saying ‘times are unusually uncertain’. It’s a cliché that is surprisingly rarely challenged. All eras have their own uncertainties. The current era is surely beset by potential volatility from digital technologies, political and social turmoil, unconventional monetary policies, financial fragilities, and regulatory change. But hindsight makes it easy to forget previous eras had their own uncertainties at the time – just think of the existential threats of the nuclear arms race during the Cold War. By deterring risk taking, the ‘unusual uncertainty’ cliché often equates to a pessimistic outlook. Yet we should not forget that the darkest hour comes before the dawn. Positive surprises do happen.

A version of this post appeared in the Q3 2016 edition of ING World

Posted in Big Data, Bond Markets, Economic Forecasting, Macro, Macroeconomics, Politics | Tagged , , , , , , , , , | 1 Comment

Forget the New Normal. A VoxEU column

Normality suggests that the crisis is behind us, that we again understand what’s happening, and that we can make predictions. It invites little sense of urgency to make radical policy adjustments. It tempts us into thinking policy ‘normalisation’ may be around the corner. But there are several reasons why the term ‘abnormal’ could more readily be applied. I revisit the “New Abnormal” in a column entitled “The New Normal That Never Was”. You can read it in full here.

cliffefig2_0 revised

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Driverless Cars – The Route to Richer, Happier Lives


Driverless cars are already a reality. There’s still work to be done on the technology, and big challenges confront their widespread adoption. But there can be little doubt that they promise to be a transformative technology. Apart from boosting output and productivity, driverless cars are set to make transport cheaper, faster, safer, cleaner, healthier, more social, and more convenient. So cities won’t just be smarter, but happier places too.

Tech leaders such as Google and Tesla suggest this could happen in the coming decade, which may be overoptimistic. But once the transition begins, the clamour to make cities not just smarter, but happier, will become irresistible.


Today, cars are used less than 5% of the time. Shared vehicles would vastly increase this: Google suggests to as much as 75% of the time. This means that less vehicles will be needed, with some experts estimating as much as a 90 percent reduction.

This would greatly reduce the cost of mobility, and eliminate much of the need for parking spaces, freeing up valuable space for other, more attractive uses. In the US, for example, it is reckoned that there are nearly four parking spaces per vehicle.

The savings would not end there. With human drivers being initially segregated on highways, and eventually banned from public roads, traffic management would become massively more efficient. Driverless cars would travel much closer together in ‘platoons’ in synchronised fashion, interacting seamlessly via vehicle-to-vehicle communications and road sensors.

The result is that road space would be used far more intensively and congestion reduced and potentially eliminated. One study suggested that road capacity would be at least doubled. Faster journeys would therefore free up huge amounts of time. Drivers in England spend an average of 4 ½ hours a week driving. Imagine if this were cut in half, for extra work, leisure or sleep. This would come on top of the time that former drivers would gain as passengers in transit, using their vehicles as a mobile lounge or office.

Transforming cities

Driverless cars will radically alter the geography of cities. With streets largely cleared of parked cars, journeys might be less confined to the traditional hub-and–spoke highway networks, allowing more flexible decisions on both home and work locations.

In principle, the reduced pain and cost of commuting is likely to prompt some people to commute longer distances. However, since this might threaten the economic benefits of driverless cars, some form of road charging may help to discourage this. This would be easy to implement, since shared vehicle journey charges would likely be at least partly distance-based. Taxing usage would also provide governments with an income stream to replace the revenues lost on parking fees and speeding fines.

Another convenience of driverless cars is that they may also offer passenger-less services such as deliveries. This could complement the drone delivery services already envisaged by Amazon. Just imagine if the driverless car that you ordered to take you to a party could pick up your shopping and gifts – or even your kids – on its way?

Sharing rather than owning vehicles will also allow users more choice. Commuting to work on your own (as most people do)? Then order a single person vehicle, a ‘personal pod’[1]. Taking the kids to the beach? Then order a large people carrier with cinema equipment to keep them entertained en route.

More sociable places

Driverless cars promise to make cities more sociable places. Reduced travel times would free up more time for leisure. Enhanced mobility for non-drivers such young, elderly or disabled people would boost their social lives. Freed from driving, vehicle occupants would be free to interact with each other and, via mobile technology, with their friends elsewhere.

Moreover, particularly since driverless cars are likely to be electric, or hydrogen, powered, they will offer enormous environmental benefits to cities. According to some estimates, autonomous vehicles could reduce air pollution by an average 90 percent. This in turn will add to the benefits to city dwellers’ health and longevity. Aside from reduced pollution, the sharp reduction in of the number of road accidents, over 90 percent of which are down to human error, will address one of the prime sources of injury and death. According to the World Health Organisation, 1.2 million people are killed on the roads every year.

The reduction in accidents would cut the need for heavy safety protection, adding further to the tendency for driverless cars to be smaller and lighter. Combined with the dramatic efficiency gains in usage, energy and resource utilisation would plunge, reinforcing the environmental benefits.

So the economic, environmental and social impact of driverless cars promises to be transformational. In economic terms, they represent a massive productivity boost. Output will benefit from quicker journeys and the increased scope for working while travelling. The reduced cost of travelling, insurance, repair and the freeing up road and parking space will increase the spending power of both consumers and businesses. And the magnitude of this could be huge. In the US, the direct costs of owning and running cars accounts for over 12% of the cost of living. ING’s initial estimate is that over two-thirds of this, 8%, could be eventually be eliminated by driverless cars.[2]

Impact of driverless cars on CPI

The reshaping of the urban infrastructure will also entail substantial new investment. The near-gridlocked cities in emerging economies such as India and China stand to make huge gains in both mobility and activity

Beyond the monetary

Yet the benefits of the driverless car revolution will go well beyond the monetary. Research shows that commuting is one of the biggest sources of stress. According to Professor Daniel Kahneman “commuting is the worst part of the day, and policies that can make commuting shorter and more convenient would be a straightforward way to reduce minor but widespread suffering”. Studies show that people with the longest commutes have the lowest satisfaction with life, particularly if they are driving. So by making journeys quicker and more enjoyable driverless cars will make cities more liveable.

Just hype?

But wait. Isn’t all this talk of driverless cars becoming the norm in a few years just tech hype? Driverless cars still have a lot to learn about life on city streets, populated by unpredictable humans who are not just driving, but walking and cycling. They still have to figure out to deal with bad weather and icy roads. And even if we assume that these technological challenges are met, there are other barriers before driverless cars take over from human-driven vehicles.

On the legal front, while accidents will become progressively rarer as pesky human drivers gradually depart from the roads, issues of liability and data privacy will need to be resolved. Socially, there will be challenges from the loss of jobs, starting with taxi and truck drivers. Then there is the question of the interaction with public transport and infrastructure. While driverless vehicles may supplant bus services, will they complement or replace train services?

Moreover, traditional auto manufacturers will struggle with this new disruptive technology. Some will find it hard to transition from merely making cars to becoming mobility and experience providers. They may try to cling on to the idea that people will still want to own their own cars, even if they are not driving them.

Indeed, the ingrained car-owning culture may take many years to fade. But the compelling logic of sharing will ultimately prevail. As the economic, social and environmental benefits of driverless cars come to be recognised, the speed with which they overtake human-driven vehicles is bound to accelerate.




[1] If this became popular, this would reduce congestion further. Another alternative would have a similar effect would be ride sharing, or pooling. The success of Uberpool, whereby people share rides, and also the costs, indicates the potential.

[2] This is even before factoring in potential savings in health care and real estate (parking) costs.

Posted in Autonomous Vehicles, Driverless Cars, Economic Growth, Inflation, Innovation, Macro, Productivity, Real Estate, Sharing Economy, Technology | Leave a comment

Looking for Trouble

How politics could fuel downside risks to global markets

It is easy to find downside risks to the outlook for the global economy and markets. Worse still, a recent ING report, entitled ‘Looking For Trouble’, highlights doubts about the ability of economic policy to revive economic growth were a recession to strike. In a follow-up narrated presentation, I focus on the role that politics might play. In particular, there is potential for a damaging feedback loop between negative economic and financial market shocks and the rise of populist politics.

Looking for trouble PP20160427 10.08 - chart 3

An important part of the feedback loop between politics and economics comes through shifts in economic policy. Populists are challenging some or all of the neo-liberal orthodoxies on fiscal restraint, monetary laxity and trade and market liberalisation.

Looking for trouble PP20160427 10.08 - chart 2

Yet even if the mainstream politicians hold off the populist insurgency, it is worrying that policy-makers have failed to identify, or are unwilling to use, the necessary policy tools to counter a recession were it to strike. On the monetary front, doubts are growing as to how much further they could push on quantitative easing or negative interest rates. For now, mainstream politicians resist calls for more support from fiscal policy. But setbacks in the financial markets, or in the voting booths, may change this.

A significant downturn, even if milder than the ‘Great Recession’ of 2008-09, could open the door to yet another reappraisal of macro-economic policy. Politicians could revisit debt-financed public investment programmes, taking advantage of low or negative interest rates. Even ‘helicopter money’, whereby the stimulus is funded by newly-created money, could turn from theory into reality. While this may seem a long shot for 2017, more such radical moves are likely when the next downturn inevitably arrives.

Posted in Brexit, Economic Growth, Macro, Macroeconomics, Politics, US Election | Tagged , , , , , , , , , | Leave a comment

Savers are Negative on Negative Rates

In recent weeks market turmoil has put negative interest rates firmly on to the centrals banks’ agenda. The Bank of Japan sprang a surprise by following the European Central Bank in moving into negative territory, and there is even talk that the US Federal Reserve might be forced to reverse course and follow suit.

Negative rates, negative reactions

Did you save less with lower interest rates?

This poses a challenge to banks over whether or not to pass on the cuts to retail customers. Late last year ING surveyed around 13,000 consumers in Europe, the US and Australia to ask how they might react if rates went negative. A remarkable 77% said that they would take money out of their savings accounts. While a few would spend more, this would be offset by almost as many saving more. Yet most said that they would either switch into riskier investments or hoard cash ‘in a safe place’. Better news, then, for safe makers than for banks and central banks.

Click here for the full report.

Posted in EMU, Macro, Macroeconomics | Tagged , , , , , , , , , | 9 Comments