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.