The first of these dynamics is what might be called the ‘guaranteed winner scam’. It works like this: select 65,536 people and tell them that you have developed a methodology that allows for 100 per cent accurate prediction of the winner of next weekend’s big football game. You split the group of 65,536 into equal halves and send one half a guaranteed prediction of victory for one team, and the other half a guaranteed win on the other team. You have ensured that your prediction will be viewed as correct by 32,768 people. Each week you can proceed in this fashion. By the time eight weeks have gone by there will be 256 people anxiously waiting for your next week’s selection because you have demonstrated remarkable predictive capabilities, having provided them with eight perfect picks. Presumably they will now be ready to pay a handsome price for the predictions you offer in week nine.The second,
. . . is the ‘hot hand fallacy’ which was coined to describe how people misinterpret random sequences, based on how they view the tendency of basketball players to be ‘streak shooters’ or have the ‘hot hand’ (Gilovich et al., 1985). The ‘hot hand fallacy’ holds that the probability in a random process of a ‘hit’ (i.e. a made basket or a successful hurricane landfall forecast) is higher after a ‘hit’ than the baseline probability.9 In other words, people often see patterns in random signals that they then use, incorrectly, to ascribe information about the future.In the paper I used the dynamics to explain why there is not likely to be convergence on the skill of hurricane landfall forecasts anytime soon. The existence of (essentially) an infinite number of models of hurricane landfalls coupled with the certainty that unfolding experience will closely approximate a subset of available models creates a context ripe for seeing spurious relationships and chasing randomness. However, the basic argument has much more general applicability.
On average, the probability of buying the prediction in Round 2 for people who received a correct prediction in Round 1 was 5 percentage points higher than those who previously received an incorrect prediction in Round 1 (P=0.046). The effect is monotonic and well-defined; probabilities of buying were 15 percentage points (P=0.000), 19 percentage points (P=0.000), and 28 percentage points (P=0.000) higher in Rounds 3, 4, and 5 . . .The authors identify two interesting results:
The first is that observations of a short streak of successful predictions of a truly random event are sufficient to generate a significant belief in the hot hand of an agent; the perception which also did not revert even in the final round of coin flip. By contrast, the equally unlikely streak of incorrect predictions also generated a relatively weak belief in the existent of an “unlucky” agent whose luck was perceived to be likely to revert before the game finishes; evidence which was reflected in an increase in the subject’s propensity to buy in the final round of coin flip.The study also looked at whether characteristics of the participants might be related to their behavior, finding: "there is no statistical evidence that some people are systematically more (or less) susceptible to the measured effects."
Consider, for example, Jewson et al. (2009) which presents a suite of 20 different models that lead to predictions of 2007–2012 landfall activity to be from more than 8 per cent below the 1900–2006 mean to 43 per cent above that mean, with 18 values falling in between. Over the next five years it is virtually certain that one or more of these models will have provided a prediction that will be more accurate than the long-term historical baseline (i.e. will be skilful). A broader review of the literature beyond this one paper would show an even wider range of predictions. The user of these predictions has no way of knowing whether the skill was the result of true predictive skill or just chance, given a very wide range of available predictions. And because the scientific community is constantly introducing new methods of prediction the ‘guaranteed winner scam’ can go on forever with little hope for certainty.8Such models are of far more than academic interest -- they guide hundreds of billions of dollars in investment and financial decisions related to insurance and resinsurance. What if such decisions rest on an intellectual house of cards? How would we know?
Plant scientists at Rothamsted Research, a complex of buildings and fields in Hertfordshire, UK, that prides itself on being the longest-running agricultural research station in the world, have spent years preparing for their latest experiment — which will attempt to prove the usefulness of a genetically modified (GM) wheat that emits an aphid alarm pheromone, potentially reducing aphid infestation.It would be a mistake to conclude that the protesters are in some way anti-science or fearful that the genetically modified crops might fail to work as advertised (though surely some protesters do have these views). Their main concern is that the crops will perform exactly as advertised, and lead to further gains in agricultural productivity.
Yet instead of looking forward to watching their crop grow, the Rothamsted scientists are nervously counting the days until 27 May, when protesters against GM crops have promised to turn up in force and destroy the experimental plots.
The protest group, it must be acknowledged, has a great name — Take the Flour Back. And it no doubt believes that it has the sympathy of the public. The reputation of GM crops and food in Britain, and in much of mainland Europe, has yet to recover from the battering it took in the late 1990s. In Germany, the routine destruction of crops by protesters has meant that scientists there simply don't bother to conduct GM experiments any more.
The Rothamsted scientists have also attempted to win over the public, with a media campaign that explains what they are trying to do and why. After the protesters announced their plans to “decontaminate” the research site, the scientists tried to engage with their opponents, and pleaded with them to “reconsider before it is too late, and before years of work to which we have devoted our lives are destroyed forever”. The researchers say that in this case they are the true environmentalists. The modified crop, if it works, would lower the demand for environmentally damaging insecticides.
Our current political system chooses to deal with world hunger through the model of “food security”, arguing that there is not enough food to go around and that we need techno-fixes to solve this. This approach ignores the fact that there is a global food surplus – many people just can’t afford to buy food. This problem is being amplified by land grabs- communities that used to grow food for themselves are being forced out of their ancestral homes, often by corporations expanding cash crop production.This is not the statement of a group concerned primarily with the potential unanticipated risks of GM crops to the environment or people, but rather, it is the manifesto of a group concerned that GM crops will perform exactly as intended.
The industrial food system throws away (in the journey from farms to traders, food processors and supermarkets), between a third and a half of all the food that it produces – enough to feed the world’s hungry six times over. (2)
Free trade policies imposed by the International Monetary Fund make it much harder for governments to protect small and family farmers from big multinationals. With the expansion of free-market capitalism, agricultural systems in many countries in the global south have become focused on producing cash crops for export to rich western nations. At the same time, their markets have been opened to food imports, including imports from US and EU companies at less than the cost of production. US farmers benefit from billions of dollars in subsidies which make up as much as 40% of US net farm income. This means they can afford to export their crops at well below production cost. (3) This is ruining the livelihoods of small farmers in the global south.
In the 70 years that have passed since Joseph Schumpeter coined the term "creative destruction," economists have struggled awkwardly with how to think about growth and innovation. Born of the low-growth agricultural economies of 18th Century Europe, the dismal science to this day remains focused on the question of how to most efficiently distribute scarce resources, not on how to create new ones -- this despite two centuries of rapid economic growth driven by disruptive technologies, from the steam engine to electricity to the Internet.Where does that "manna from heaven" originate? In pricing incentives, of course, derived from economic theory. But once you take a closer look at both practice and the theoretical origins, you find that economics explains far less than we've been led to believe.
There are some important, if qualified, exceptions. Sixty years ago, Nobelist Robert Solow and colleagues calculated that more than 80 percent of long-term growth derives from technological change. But neither Solow nor most other economists offered much explanation beyond that. Technological change was, in the words of one of Solow's contemporaries, "manna from heaven."
"[W]e can achieve US emissions reduction goals for 2020 and possibly even 2030 through deployment of existing technologies. . . Price goes up, demand goes down. Economists typically call it the 'law of demand'--one of the very few laws we've got."The theory is sound, its application is not -- a point that readers of this blog and The Climate Fix will well understand.
The arithmetic of economic and population growth, efficiency limits related to the rebound effect, as well as systemic issues call into question the hopes of decoupling economic growth from GHG growth. One should not deceive oneself into believing that such an evolutionary (and often reductionist) approach would be sufficient to cope with the complexities of climate change. “Green growth” proponents need to scrutinise the historical macro- (not micro-) economic evidence, in particular the arithmetic of economic and population growth, as well as the significant influence of the rebound effect.Such messages are not well-received by conventional economists. In their exchange, Wagner explains to Nordhaus and Shellenberger that economic theory trumps real world evidence, and this means that debate over such issues is not necessary:
The main points from climate economics are no longer up for debate: carbon is a pollutant; we need make polluters pay, either through a cap or a price. Virtually all economists agree--from Holtz-Eakin, Laffer and Mankiw on one side to Stiglitz, Sachs, and Krugman on the other.Like most debates on climate this one ends predictably, with Wagner appealing to the motives of Shellenberger and Nordhaus:
Once again, this one is not up for debate. You can argue that politically we can't get there, so we need to do other things in the short term, but it's not up for debate whether this is the economically correct solution.
[Y]our entire operation seems to be geared toward propagating contrarian-sounding views that once in a while get you some attention and get picked up by an editor somewhere, but otherwise are just that: contrarian for the sake of wanting to be different from the packSnore. But the larger point here is that there are articles of faith in the discipline of economics which are viewed as taboo to challenge, even when they fail to represent themselves in practice with the simplicity and elegance of theory.
Instead of "technology" Solow was really talking about "innovation." That innovation need not be understood because it was "manna from heaven" is characteristic of many arguments from conventional economists and is particularly endemic in the climate debate. From such a perspective, of course anyone asking about where innovation comes from -- other than from the magic of the invisible hand -- must either be ignorant or malign.Robert Solow, winner of the 1987 Nobel Memorial Prize in Economic Sciences, is famous for, in the recent words of a high-ranking State Department official, “showing that technological innovation was responsible for over 80 percent of economic growth in the United States between 1909 and 1949.”. . Typically, technical or technological progress isn’t explicitly defined by those invoking Solow, but people take it to mean new gadgets.
However, Solow meant something much broader. On the first page of “Technical Change and the Aggregate Production Function,” the second of his two major papers, he wrote: “I am using the phrase ‘technical change’ as a shorthand expression for any kind of shift in the production function. Thus slowdowns, speedups, improvements in the education of the labor force, and all sorts of things will appear as ‘technical change.’ ” But his willfully inclusive definition tends to be forgotten.
Solow was constructing a simple mathematical model of how economic growth takes place. On one side was output. On the other side was capital and labor. Classical economists going back to Adam Smith and David Ricardo had defined the “production function”—how much stuff you got out of the economy—in terms of capital and labor (as well as land). Solow’s point was that other factors besides capital, labor, and land were important. But he knew his limitations: He wasn’t clear on what those factors were. This is why he defined “technical change” as any kind of shift (the italics are his) in the production function. He wasn’t proving that technology was important, as economists in recent years have taken to saying he did. All Solow was saying is that the sources of economic growth are poorly understood.
The cautionary tale of Solow is emblematic of how economists get science and technology wrong. One economist creates a highly idealized mathematical model. The model’s creator is, as Solow was, honest about its limitations. But it quickly gets passed through the mill and acquires authority by means of citation. A few years after Solow’s paper came out, Kenneth Arrow, another Nobel Prize winner, would write that Solow proved the “overwhelming importance [of technological change in economic growth] relative to capital formation.” It’s a sort of idea laundering: Solow said that we don’t know where growth in economic output comes from and, for want of a better alternative, termed that missing information “technical change.” But his admission of ignorance morphed into a supposed proof that new technologies drive economic growth.Remarkably, in the 21st century, our policy debates reflect the fact that we do not have a good idea where innovation comes from, how it is directed and how we prepare for its inevitable downsides. Too often conventional economics presents an an obstacle to debating and discussing this topic.
Over the next century, global energy demand will double, and perhaps triple. But even were energy consumption to stay flat, significantly reducing emissions from today's levels will require the creation of disruptive new technologies. It's a task for which a doctrine focused on the efficient allocation of scarce resources could hardly be more ill-suited.Read the three essays discussed here in full at The Breakthrough Journal blog, at Bridges Trade BioRes review, and at Slate.
