Category: Economics

Theories of Leisure, Past and Future

img_0028I am at leisure. Specifically—and many may not regard this as leisure—I just ran 17.71 miles in Yosemite Valley. I dropped the car along the road near the 41 junction and then just started running. I went south for a while, then circled back to Bridalveil Falls (lightly flowing), then up to the Glacier Point loop, then back down to El Capitan, then up to Yosemite Falls (not flowing). Lunch was at the Village and then I tracked down the car again.

Now, then, I am at leisure. The barman has set me up with a martini. I have a Fresno Fig flatbread on the way: goat cheese, bacon, arugula, and the critical figs. I am showered all the way down to between my toes. The late afternoon light is filtering through a mild haze onto the muddy belly of the lake. There must be bass out there somewhere. Let the bass live. Let them be at leisure.

A must-read on this topic is Derek Thompson’s Atlantic article, The Free-Time Paradox in America. I don’t agree with the thesis, though. It’s not really a paradox. It’s just an unknown. You should read Derek’s original, but I will comment briefly on some of his points. He argues that John Maynard Keynes forecast a reduction in work requirements by the 21st Century. Mechanization would take the drudgery out of most things and we would get to 15 hour work weeks with the management of our leisure time an increasing burden on us.

The present didn’t work out that way.

Instead, educated high-earners work ever harder. The only leisure class is the non-college-educated male youth who don’t work much these days but instead play video games (75% of their spare time) and are happier than when more of them worked. Derek rolls up several theories about why this might be the case. First, maybe it’s because the industry jobs disappeared and young men don’t like to work in retail and health care. Second, perhaps it’s because the wealthy workers are trying to keep up with the Joneses, though not exactly in the way that the Thorstein Veblen imagined it. Instead of conspicuous consumption, it is conspicuous activity. Finally, maybe it’s because work and leisure have blurred too much; entertaining ourselves on our smartphones is just too close to responding to an email from work.

I agree partly with the suggestion that economic productivity can be a very high level of creative action that is implicit in some of Derek’s commentary. Is there really much difference between landscape design and watercolor painting? Both require an understanding of materials and methods that result in an aesthetic outcome, though the former has more of a pragmatic impact than the latter. Is this a significant deviation from past economic efforts? Perhaps. The modern startup doesn’t have the dark satanic mills of the past, and is based, generally, on technological advances that are intellectually interesting. Sometimes this was the case historically, but not consistently.

Ultimately, what constitutes leisure activities rather than productive activities is inherently blurry. I suppose the golfing set might claim otherwise, but I do work-related thinking while running, and may intertwine writing efforts with other actions without harm to either of them. Leisure is fungible.

The future of leisure is similarly fungible. We can guess that virtual gaming will be even more compelling than existing gaming options, pulling young men and others even further away from engagement with the traditional economic sphere. Yet, even here there are opportunities: toolkits for virtual world design, the designs themselves, monetizing the experiences in compelling ways. Even my Yosemite experience can be virtualized. Fly drones around, mapping and imagining every square inch in ultra-4K resolution. Yes, drones are currently illegal in National Parks, but they could be used by licensed content producers, I’m guessing. Then everyone could fly, run, hike, walk, boat, and swim this little, leisurely experience.

I am at leisure.

Subtly Motivating Reasoning

larson-sheepContinuing on with the general theme of motivated reasoning, there are some rather interesting results reported in New Republic, here. Specifically, Ian Anson from University of Maryland, Baltimore County, found that political partisans reinforced their perspectives on the state of the U.S. economy more strongly when they were given “just the facts” rather than a strong partisan statement combined with the facts. Even when the partisan statements aligned with their own partisan perspectives, the effect held.

The author concludes that people, in constructing their views of the causal drivers of the economy, believe that they are unbiased in their understanding of the underlying mechanisms. The barefaced partisan statements interrupt that construction process, perhaps, or at least distract from it. Dr. Anson points out that subtly manufacturing consent therefore makes for better partisan fellow travelers.

There are a number of theories concerning how meanings must get incorporated into our semantic systems, and whether the idea of meaning itself is as good or worse than simply discussing reference. More, we can rate or gauge the uncertainty we must have concerning complex systems. They seem to form a hierarchy, with actors in our daily lives and the motivations of those we have long histories with in the mostly-predictable camp. Next we may have good knowledge about a field or area of interest that we have been trained in. When this framework has a scientific basis, we also rate our knowledge as largely reliable, but we also know the limits of that knowledge. It is in predictive futures and large-scale policy that we become subject to the difficulty of integrating complex signals into a cohesive framework. The partisans supply factoids and surround them with causal reasoning. We weigh those against alternatives and hold them as tentative. But then we have to exist in a political life, as well, and it’s not enough to just proclaim our man or woman or party as great and worthy of our vote and love, we must also justify that consideration.

I speculate now that it may be possible to wage war against partisan bias by employing the exact methods described as effective by Dr. Anson. Specifically, if in any given presentation of economic data there was one fact presented that appeared to undermine the partisan position otherwise described by the data, would it lead to a general weakening of the mental model in the reader’s head? For instance, compare the following two paragraphs:

The unemployment rate has decreased from a peak of 10% in 2009 to 4.7% in June of 2016. This rate doesn’t reflect the broader, U-6, rate of nearly 10% that includes the underemployed and others who are not seeking work. Wages have been down or stagnant over the same period.

Versus:

The unemployment rate has decreased from a peak of 10% in 2009 to 4.7% in June of 2016. This rate doesn’t reflect the broader, U-6, rate of nearly 10% that includes the underemployed and others who are not seeking work. Wages have been down or stagnant over the same period even while consumer confidence and spending has risen to an 11-month high.

The second paragraph adds an accurate but upbeat and contradictory signal to the more subtle gloom of the first paragraph. Of course, partisan hacks will naturally avoid doing this kind of thing. Marketers and salespeople don’t let the negative signals creep in if they can avoid it, but I would guess that a subtle contradiction embedded in the signal would disrupt the conspiracy theorists and the bullshit artists alike.

The Retiring Mind, Part III: Autonomy

Retiring Mind IIIRobert Gordon’s book on the end of industrial revolutions recently came out. I’ve been arguing for a while that the coming robot apocalypse might be Industrial Revolution IV. But the Dismal Science continues to point out uncomfortable facts in opposition to my suggestion.

So I had to test the beginning of the end (or the beginning of the beginning?) when my Tesla P90D with autosteer, summon mode, automatic parking, and ludicrous mode arrived to take the place of my three-year-old P85:

Intelligence Augmentation and a Frictionless Economy

Speed SkatingThe ever-present Tom Davenport weighs in in the Harvard Business Review on the topic of artificial intelligence (AI) and its impact on knowledge workers of the future. The theme is intelligence augmentation (IA) where knowledge workers improve their productivity and create new business opportunities using technology. And those new opportunities don’t displace others, per se, but introduce new efficiencies. This was also captured in the New York Times in a round-up of the role of talent and service marketplaces that reduce the costs of acquiring skills and services, creating more efficient and disintermediating sources of friction in economic interactions.

I’ve noticed the proliferation of services for connecting home improvement contractors to customers lately, and have benefited from them in several renovation/construction projects I have ongoing. Meanwhile, Amazon Prime has absorbed an increasingly large portion of our shopping, even cutting out Whole Foods runs, with often next day deliveries. Between pricing transparency and removing barriers (delivery costs, long delays, searching for reliable contractors), the economic impacts might be large enough to be considered a revolution, though perhaps a consumer revolution rather than a worker productivity one.

Here’s the concluding paragraph from an IEEE article I just wrote that will appear in the San Francisco Chronicle in the near future:

One of the most interesting risks also carries with it the potential for enhanced reward. Don’t they always? That is, some economists see economic productivity largely stabilizing if not stagnating.  Industrial revolutions driven by steam engines, electrification, telephony, and even connected computing led to radical reshaping our economy in the past and leaps in the productivity of workers, but there is no clear candidate for those kinds of changes in the near future. Big data feeding into more intelligent systems may be the driver for the next economic wave, though revolutions are always messier than anyone expected.

But maybe it will be simpler and less messy than I imagine, just intelligence augmentation helping with our daily engagement with a frictionless economy.

The Rise and Triumph of the Bayesian Toolshed

Bayes LawIn Asimov’s Foundation, psychohistory is the mathematical treatment of history, sociology, and psychology to predict the future of human populations. Asimov was inspired by Gibbon’s Decline and Fall of the Roman Empire that postulated that Roman society was weakened by Christianity’s focus on the afterlife and lacked the pagan attachment to Rome as an ideal that needed defending. Psychohistory detects seeds of ideas and social movements that are predictive of the end of the galactic empire, creating foundations to preserve human knowledge against a coming Dark Age.

Applying statistics and mathematical analysis to human choices is a core feature of economics, but Richard Carrier’s massive tome, On the Historicity of Jesus: Why We Might Have Reason for Doubt, may be one of the first comprehensive applications to historical analysis (following his other related work). Amusingly, Carrier’s thesis dovetails with Gibbon’s own suggestion, though there is a certain irony to a civilization dying because of a fictional being.

Carrier’s methods use Bayesian analysis to approach a complex historical problem that has a remarkably impoverished collection of source material. First century A.D. (C.E. if you like; I agree with Carrier that any baggage about the convention is irrelevant) sources are simply non-existent or sufficiently contradictory that the background knowledge of paradoxography (tall tales), rampant messianism, and general political happenings at the time lead to a likelihood that Jesus was made up. Carrier constructs the argument around equivalence classes of prior events that then reduce or strengthen the evidential materials (a posteriori). And he does this without ablating the richness of the background information. Indeed, his presentation and analysis of works like Inanna’s Descent into the Underworld and its relationship to the Ascension of Isaiah are both didactic and beautiful in capturing the way ancient minds seem to have worked.

We’ve come a long way from Gibbon’s era where we now have mathematical tools directly influencing historical arguments. The notion of inference and probability has always played a role in history, but perhaps never so directly. All around us we have the sharpening of our argumentation, whether in policymaking, in history, or in law.  Even the arts and humanities are increasingly impacted by scientific and technological change and the metaphors that emerge from it. Perhaps not a Cathedral of Computation, but modestly at least a new toolshed.

Inequality and Big Data Revolutions

industrial-revolutionsI had some interesting new talking points in my Rock Stars of Big Data talk this week. On the same day, MIT Technology Review published Technology and Inequality by David Rotman that surveys the link between a growing wealth divide and technological change. Part of my motivating argument for Big Data is that intelligent systems are likely the next industrial revolution via Paul Krugman of Nobel Prize and New York Times fame. Krugman builds on Robert Gordon’s analysis of past industrial revolutions that reached some dire conclusions about slowing economic growth in America. The consequences of intelligent systems on everyday life will have enormous impact and will disrupt everything from low-wage workers through to knowledge workers. And how does Big Data lead to that disruption?

Krugman’s optimism was built on the presumption that the brittleness of intelligent systems so far can be overcome by more and more data. There are some examples where we are seeing incremental improvements due to data volumes. For instance, having larger sample corpora to use for modeling spoken language enhances automatic speech recognition. Google Translate builds on work that I had the privilege to be involved with in the 1990s that used “parallel texts” (essentially line-by-line translations) to build automatic translation systems based on phrasal lookup. The more examples of how things are translated, the better the system gets. But what else improves with Big Data? Maybe instrumenting many cars and crowdsourcing driving behaviors through city streets would provide the best data-driven approach to self-driving cars. Maybe instrumenting individuals will help us overcome some of things we do effortlessly that are strangely difficult to automate like folding towels and understanding complex visual scenes.

But regardless of the methods, the consequences need to be considered. Our current fascination with Big Data may not lead to Industrial Revolution 4 in five years or twenty, but unless there is some magical barrier that we are not aware of, IR4 seems to be inevitable. And the impacts will perhaps be more profound than the past revolutions because, unlike those transitions, the direct displacement of workers is a key component of the IR4 plan. In Rotman’s article, Thomas Piketty’s r > g is invoked to explain the excess return on capital (r) versus economic growth rate (g) and how that leads to a concentration of wealth among the richest members of our society, creating a barbell distribution of economic opportunities where the middle class has been dismantled due to (per Gordon) the equalization of labor costs through outsourcing to low-cost nations. But at least there remains a left bell to that barbell in that it is largely impossible to eliminate the services jobs that are critical to retail, restaurant, logistics, health care, and a raft of other economic sectors.

All that changes in IR4 and the barbell turns into the hammer from the Olympic hammer throw as the owners of the capital take over the entire cost structure for a huge range of economic activities. The middle may not initially be gone, however, as maintenance of the machinery will require a skilled workforce. Even this will be a point of Big Data optimization, however, as predictive maintenance and self-healing systems optimize against their failure modes over usage cycles.

So let’s go back to Gordon’s pessimism (economics is, after all, the “dismal science”). What headwinds and tailwinds are left in IR4? Perhaps the most cogent is the recommended use of redistributive methods for accelerating educational opportunities while reducing the debt load of American students. The other areas that are discussed include unlimited immigration to try to offset hours per capita declines due to retirement and demographic effects, but Gordon’s application of this is not necessarily valid in IR4 where low-skilled immigration would cease because of a lack of economic opportunities and even higher-skilled workers might find themselves displaced.

One lesson learned from past industrial revolutions is that they created more opportunities than worker displacements. Steam power displaced animal labor and the workers needed to shoe and train and feed those animals. Diesel trains displaced steam engine builders and mechanics. Cars and aircraft displaced trains. But in each case there were new jobs that accompanied the shift. We might be equally optimistic about IR4, speculating about robot trainers and knowledge engineers, massive extraction industries and materials production, or enhanced creative and entertainment systems like Michael Crichton’s dystopian Westworld of the early 70s. Is this enough to buffer against the headwind of the loss of the service sector? Perhaps, but it will not come without enormous global disruption.