Yet another hypothesis for why the industrial revolution happened in Europe is that higher infectious disease levels elsewhere caused most cultures that might have produced technological development were more collectivist in order to reduce the spread of disease.
Collectivism may have inhibited scientific and technological innovation by discouraging trial-and-error learning and ideas which signal an absence of group loyalty.
collectivists make sharp distinctions between coalitional in-groups and out-groups, whereas among individualists the in-group/out-group distinction is typically weaker (Gelfand et al. 2004). A consequence is that collectivists are more wary of contact with foreigners
I suspect this effect is real but not strong enough to be the primary cause of the industrial revolution. It does, however, provide a good clue about why a relatively tropical region such as the Yangtze River Delta lagged behind more temperate England.
When I first heard and read about Seasteading, I thought it was mostly well thought out, but that it hadn’t reached its goal of providing a business plan that would support a small group of non-wealthy people to set up the first seastead in international waters.
Now Peter Thiel has donated $500,000 to fund a new organization called The Seasteading Institute. My intuition is that it will take somewhere between $2 million and $20 million of charitable contributions to reach the threshold of resources needed for a seastead to become viable in international waters. But the first big donation is typically harder for a nonprofit to get than subsequent donations, and the size of this initial donation (with only a rudimentary organization) suggests that there’s a good chance that more money can be raised once more specific plans are developed and more people indicate commitments to implement them.
Predictocracy (part 2)
Book review: Predictocracy: Market Mechanisms for Public and Private Decision Making by Michael Abramowicz (continued from prior post).
I’m puzzled by his claim that it’s easier to determine a good subsidy for a PM that predicts what subsidy we should use for a basic PM than it is to determine the a good subsidy for the basic PM. My intuition tells me that at least until traders become experienced with predicting effects of subsidies, the markets that are farther removed from familiar questions will be less predictable. Even with experience, for many of the book’s PMs it’s hard to see what measurable criteria could tell us whether one subsidy level is better than another. There will be some criteria that indicate severely mistaken subsidy levels (zero trading, or enough trading to produce bubbles). But if we try something more sophisticated, such as measuring how accurately PMs with various subsidy levels predict the results of court cases, I predict that we will find some range of subsidies above which increased subsidy produces tiny increases in correlations between PMs and actual trials. Even if we knew that the increased subsidy was producing a more just result, how would we evaluate the tradeoff between justice and the cost of the subsidy? And how would we tell whether the increased subsidy is producing a more just result, or whether the PMs were predicting the actual court cases more accurately by observing effects of factors irrelevant to justice (e.g. the weather on the day the verdict is decided)?
His proposal for self-resolving prediction markets (i.e. markets that predict markets recursively with no grounding in observed results) is bizarre. His arguments about why some of the obvious problems aren’t serious would be fascinating if they didn’t seem pointless due to his failure to address the probably fatal flaw of susceptibility to manipulation.
His description of why short-term PMs may be more resistant to bubbles than stock markets was discredited just as it was being printed. His example of deluded Green Party voters pushing their candidate’s price too high is a near-perfect match for what happened with Ron Paul contracts on Intrade. What Abramowicz missed is that traders betting against Paul needed to tie up a lot more money than traders betting for Paul. High volume futures markets have sophisticated margin rules which mostly eliminate this problem. I expect that low-volume PMs can do the same, but it isn’t easy and companies such as Intrade have only weak motivation to do this.
He suggests that PMs be used to minimize the harm resulting from legislative budget deadlocks by providing tentative funding to projects that PMs predict will receive funding. But if the existence of funding biases legislatures to continue that funding (which appears to be a strong bias, judging by how rare it is for a legislature to stop funding projects), then this proposal would fund many projects that wouldn’t otherwise be funded.
His proposals to use PMs to respond to disasters such as Katrina are poorly thought out. He claims “not much advanced planning of the particular subjects that the markets should cover would be needed”. This appears to underestimate the difficulty of writing unambiguous claims, the time required for traders to understand them, the risks that the agencies creating the PMs will bias the claim wording to the agencies’ advantage, etc. I’d have a lot more confidence in a few preplanned PM claims such as the expected travel times on key sections of roads used in evacuations.
I expect to have additional comments on Predictocracy later this month; they may be technical enough that I will only post the on the futarchy_discuss mailing list.
Book review: Predictocracy: Market Mechanisms for Public and Private Decision Making by Michael Abramowicz.
This had the potential to be an unusually great book, which makes its shortcomings rather frustrating. It is loaded with good ideas, but it’s often hard to distinguish the good ideas from the bad ideas, and the arguments for the good ideas aren’t as convincing as I hoped.
The book’s first paragraph provides a frustratingly half-right model of why markets produce better predictions than alternative institutions, involving a correlation between confidence (or sincerity) and correctness. If trader confidence was the main mechanism by which markets produce accurate predictions, I’d be pretty reluctant to believe the evidence that Abramowicz presents of their success. Sincerity is hard to measure, so I don’t know what to think of its effects. A layman reading this book would have trouble figuring out that the main force for accurate predictions is that the incentives alter traders’ reasoning so that it becomes more accurate.
The book brings a fresh perspective to an area where there are few enough perspectives that any new perspective is valuable when it’s not clearly wrong. He is occasionally clearer than others. For instance, his figure 4.1 enabled me to compare three scoring rules in a few seconds (I’d previously been unwilling to do the equivalent by reading equations).
He advocates some very fine-grained uses of prediction markets (PMs), which is a sharp contrast to my expectation that they are mainly valuable for important issues. Abramowicz has a very different intuition than I do about how much it costs to run a prediction market for an issue that people normally don’t find interesting. For instance, he wants to partly replace small claims court cases with prediction markets for individual cases. I’m fairly sure that obvious ways to do that would require market subsidies much larger than current court costs. The only way I can imagine PMs becoming an affordable substitute for small claims courts would be if most of the decisions involved were done by software. Even then it’s not obvious why one or more PM per court case would be better than a few more careful evaluations of whether to turn those decisions over to software.
He goes even further when proposing PMs to assess niceness, claiming that “just a few dollars’ worth of subsidy per person” would be adequate to assess peoples’ niceness. Assuming the PM requires human traders, that cost estimate seems several orders of magnitude too low (not to mention the problems with judging such PMs).
His idea of “the market web” seems like a potentially valuable idea for a new way of coordinating diverse decisions.
He convinced me that Predictocracy will solve a larger fraction of democracy’s problems than I initially expected, but I see little reason to believe that it will work as well as Futarchy will. I see important classes of systematic biases (e.g. the desire of politicians and bureaucrats to acquire more power than the rest of us should want) that Futarchy would reduce but which Predictocracy doesn’t appear to alter.
Abramowicz provides reasons to hope that predictions of government decisions 10+ years in the future will help remove partisan components of decisions and quirks of particular decision makers because uncertainty over who will make decisions at that time will cause PMs to average forecasts over several possible decision makers.
He claims evaluations used to judge a PM are likely to be less politicized than evaluations that directly affect policy because the evaluations are made after the PM has determined the policy. Interest groups will sometimes get around this by making credible commitments (at the time PMs are influencing the policy) to influence whoever judges the PM, but the costs of keeping those commitments after the policy has been decided will reduce that influence. I’m not as optimistic about this as Abramowicz is. I expect the effect to be real in some cases, but in many cases the evaluator will effectively be part of the interest group in question.
Book review: The Birth of Plenty : How the Prosperity of the Modern World was Created by William Bernstein.
This book contains many ideas about the causes of economic growth that are approximately right, but rarely backs them up with good arguments.
He starts by saying four institutions are needed to escape from a Malthusian trap: property rights (rule of law), reason (scientific methods), capital markets, and fast transportation/communication. But later when discussing why some countries were slow to develop, he adds ad hoc explanations (e.g. “excessive military expenditure” “reliably derails great nations”).
The biggest shortcoming of the book is that it ignores evidence that China provides a counter-example to his main claims. He doesn’t acknowledge expert claims that parts of China around 1800 had a degree of property rights and rule of law that was comparable to England at that time, nor does he discuss the recent dramatic Chinese takeoff that happened with a mediocre degree of property rights and rule of law.
He gives many hints about why those four institutions are helpful, but provides little evidence that any one is essential. About the closest he comes to providing rigorous evidence is a graph indicating how much of economic growth appears to be explained by a Rule-of-Law indicator. He follows that with a similar graph of how government spending levels explain economic growth, and claims the negative effect of government spending would be invisible without the computed trend line, but the rule-of-law trend is more impressive. I see those graphs differently. The most obvious trend is that government spending over about 15 to 18% (of GDP?) reduces growth, with no obvious pattern for lower spending levels. The most obvious trend in the rule-of-law graph is that low values on the rule-of-law indicator are associated with larger variations in economic growth, which is somewhat contrary to his claim that such values reliably prevent growth.
The section I found most valuable was the one describing reasons for thinking that 16th century Holland created the beginnings of the industrial revolution.
There are enough misleading or false statements in the book to convince me not to trust him. For example, he refers to eclipse prediction around 1700 as a spectacular change to what was previously a mystery. He appears unaware that eclipses had been predicted more than a millennium earlier.
He often digresses into anecdotes that have no apparent relevance. For example, he claims “a healthy market for government debt is, in fact, essential for funding business”. After giving two implausible theoretical reasons for that claim, he says it was “vividly demonstrated in the U.S.” in 1862, but then gives a description of how government bonds were sold, without mentioning anything about the effect on business.
His discussion of the possible trade-offs between inflation and unemployment makes a claim that increased unemployment caused more unhappiness than “an identical rise in inflation”. But inflation is measured in different units that unemployment. If we happened to measure inflation in percent per presidential election, the naive comparison would work much differently. (He is subtly misinterpreting a serious paper that is hard to fully explain to laymen).
His advice to undeveloped nations includes “before a nation builds roads … it must first train lawyers”, which makes me doubt his understanding of what causes the rule of law.
Book review: The Age of Turbulence: Adventures in a New World by Alan Greenspan.
The first half of this book provides a decent history of the past 40 years, with a few special insights such as descriptions of how most presidents in that period worked (he’s one of the least partisan people to have worked with most of them). The second half is a discussion of economics of rather mixed quality (both in terms of wisdom and ability to put the reader to sleep).
He comes across as a rather ordinary person whose private thoughts are little more interesting that his congressional testimony.
One of the strangest sections describes the problems he worried would result from a projected paydown of all federal government debt. He does claim to have been careful not to forget the possibility those forecasts could be mistaken. But his failure to mention ways that forecasts of Social Security deficits could be way off suggests he hasn’t learned much from that mistake.
He mentions a “conundrum” of falling long-term interest rates in 2004-2005, when he had expected that rising short-term rates would push up long-term rates. I find his main explanation rather weak (it involves technology induced job insecurity leading to lower inflation expectations). But he then goes on to describe a better explanation (but is vague about whether he believes it explains the conundrum): the massive savings increase caused largely by rapid growth in China. I suspect this is a powerful enough force that Deng Xiaoping deserves more credit than Greenspan for the results that inspired the label Maestro.
The book is often more notable for what it evades than what it says. It says nothing about his inflationary policies in 2003-2004 or his favorable comments about ARMs and how they contributed to the housing bubble.
He gives a brief explanation of how Ayn Rand converted him to an Objectivist by pointing out a flaw in his existing worldview, but he is vague about his drift away from Objectivism. His description of the 1995 government “shutdown” as a crisis is fairly strong evidence of a non-Randian worldview, but mostly he tries to avoid controversies between libertarianism and the policies of politicians he likes.
He often praises markets’ abilities to signal valuable information, yet when claiming that the invasion of Iraq was “about oil”, he neglects to mention the relevant market prices. Those prices appear to discredit his position (see Leigh, Wolfers and Zitzewitz’ paper What do Financial Markets Think of War in Iraq?).
He argues against new hedge fund regulations on the grounds that hedge funds change their positions faster than regulators can react. He is right about the regulations that he imagines, but it’s unfortunate that he stops there. The biggest financial problems involve positions that can’t be liquidated in a few weeks. It seem like it ought to be possible for accounting standards to provide better ways for institutions to communicate to their investors how leveraged they are and how sensitive their equity is to changes in important economic variables.
He argues against using econometric models to set Fed policy, citing real problems with measuring things like NAIRU and GDP, but if he was really interested in scientifically optimizing Fed policy, why didn’t he try to create models based on more relevant and timelier data (such as from the ISM?) the way he did when he had a job that depended on providing business with useful measures? Maybe he couldn’t have become Fed chairman if he had that kind of desire.
I listened to the cd version of this book because I got it as a present and listening to it while driving had essentially no cost. I wouldn’t have bought it or read the dead tree version.