Tag Archives: Prediction Markets

Pandemic Futarchy Design

Researchers … use … feeds from a global network of students, staff and alumni to construct a “stringency index” that boils down to a single number how strictly governments in 160 countries are locking down their economies and societies to contain the spread of the virus. … plans to include state-by-state measures of stringency in the U.S. … latest version … draws on 17 indicators to determine the stringency of the government response. (More)

Not that I hear anyone eagerly clamoring to try, but let me sketch out how one would use decision markets to set pandemic policy. Just to plant another flag on how widely they could be usefully applied, if only enough folks cared about effective policy.

As you may recall, a decision market is a speculative (i.e., betting) market on a key outcome of a decision, conditional on which discrete decision is made. To apply these to the current pandemic, we need to pick

  1. key ex-post-measurable outcome(s) of interest,
  2. likely-enough key decisions which could substantially influence those outcomes,
  3. participants capable of learning a bit about how decisions related to outcomes,
  4. sponsors who care enough about informing these decisions, and
  5. legal jurisdictions that may allow such markets.

Regarding participants, sponsors, and permission, it makes sense to be opportunistic. Seek any sponsors interested in relevant questions, any participants you can get to trade on them, and any jurisdiction that let you want to do. Alas I have no sponsor leads.

For key decisions, we could consider using bills before legislatures, administrative rulings, or election results. But there are a great many of these, we don’t get much warnings about many, and most have little overall impact. So I’d prefer to aggregate decisions, and summarize policy via three key choice metrics per region:
Lockdown Strictness. As described in the quote above, some have created metrics on lockdown strictness across jurisdictions. Such metrics could be supplemented by cell-phone based data on trips outside the home.
Testing Volume. The number of tests per unit time, perhaps separated into the main test types, and perhaps also into accuracy classes.
Tracing Volume. The number of full-time equivalent tracers working to trace who infected whom. Perhaps supplemented by the % of local folks use apps that report their travels to tracing authorities.

Yes, worse pandemic outcomes will likely cause more lockdown, tests, and tracing. But one could look at outcomes that happen after decisions. Such as how average future outcomes depend on the decisions made this month or quarter.

For key outcomes, the obvious options are deaths and economic growth.

For deaths, we can avoid testing problems by looking at total deaths, or equivalently “excess” deaths relative to prior years. It helps to note the ages of deaths, which can be combined with local mortality tables to estimate life-years lost. Even better, if possible, note the co-morbidities of those who died, to better estimate life-years lost. And even more better, have estimates of the relative quality of those life-years.

For economic growth, just take standard measures of regional income or GDP, and integrate them many years into the future, using an appropriate discount factor. Assuming that the temporary disruption from a pandemic is over within say 10 years, one could end the bets after say ten years, projecting the last few years of regional income out into the indefinite future.

As usual, there will be a tradeoff here re how far to go in accounting for these many complexities. I’d be happy to just see measures of life years lost related to lockdown strictness, perhaps broken into three discrete categories of strictness. But I’d of course be even happier to include economic growth as an outcome, and tests and tracing as decisions. Either aggregate all outcomes into one overall measure (using values of life years), or have different markets estimate different outcomes. For decisions, either separate markets for each type of decision. Or, ideally, combinatorial markets looking at all possible combinations of outcomes, decisions, and regions.

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Subpoena Futures

A subpoena … is a writ issued by a government agency, most often a court, to compel testimony by a witness or production of evidence under a penalty for failure. … party being subpoenaed has the right to object to the issuance of the subpoena, if it is for an improper purpose, such as subpoenaing records that have no relevance to the proceedings, or subpoenaing persons who would have no evidence to present, or subpoenaing records or testimony that is confidential or privileged. (More)

Parties may obtain discovery regarding any nonprivileged matter that is relevant to any party’s claim or defense and proportional to the needs of the case, considering the importance of the issues at stake in the action, the amount in controversy, the parties’ relative access to relevant information, the parties’ resources, the importance of the discovery in resolving the issues, and whether the burden or expense of the proposed discovery outweighs its likely benefit. (More)

Exceptions are quite limited: self-incrimination, illegally-obtained info, and privileges of spouses, priests, doctors, diplomats, and lawyers. The remarkable fact is that the law has little general respect for privacy. Unless you can invoke one of these specific privileges, you must publicly report to the court any info that it thinks sufficiently relevant to a current court case. You simply have no general right to or expectation of privacy re stuff a court wants to know. Courts don’t even compensate you for your costs to collect evidence or appear to testify.

And yet. Consider what I wrote March 5:

The straightforward legal remedy for [pandemic] externalities is to let people sue others for infecting them. In the past this remedy has seemed inadequate for two reasons:

1. It has often been expensive and hard to learn and prove who infected who, and
2. … most folks just can’t pay large legal debts.
The vouching system directly solves (2), … And the key to (1) is ensuring that the right info is collected and saved.

First, consider some new rules that would limit people’s freedoms in some ways. Imagine people were required to keep an RFID tag (or visible QR code) on their person when not at home, and also to save a sample of their spit or skin once a week? Then phones could remember a history of the tags of people near that phone, and lawsuits could subpoena to get surveillance records of possible infection events, and to see if spit/skin samples on nearby dates contain a particular pathogen, and its genetic code if present. We might also adopt a gambled lawsuit system to make it easier to sue for small harms. (More)

Here, to help law deal with pandemics, I was tempted to propose specific rules re info that people must collect and preserve. Yet if courts can get any info they think relevant, why is there ever a problem with courts lacking info to deal with key harms, such as pandemic infection?

The answer is that current law allows a huge exception to its subpoena power. Courts can force you to reveal info that you have already collected, on paper, a computer, in your head, or in your physical objects. But you usually have no obligation to collect and save info now that the court might want later. As a result, many people and orgs go out of their way to not save incriminating info. For example, firms do key discussions verbally, not recorded, rather than via email. Thus you have no obligation to save spit samples or detailed records of where your phone goes, to help with future pandemic infection lawsuits.

This seems a huge and inconsistent loophole. I could understand if the law wanted to respect a more general right to privacy. Then the court might weigh the value of some info in helping court cases against the social harm from forcing its publication via a subpoena. As a result, it might sometimes block a subpoena even when the info collected would be relevant to a court case.

But I can’t see a reason to eagerly insist on access to info that seems relevant to a court case, and yet put no effort into inducing people to collect and preserve such info beforehand. So I propose that we create a legal process by which legal judgements are made on, if collected and saved, how likely some info would be to be subpoenaed, and how valuable it would be in that case.

When info would be valuable enough if collected and saved, then the court should require this. I don’t have a strong opinion on who exactly should bring a suit asking that such info be saved, or who should represent the many who would have to save that info. But one obvious system that occurs to me is to just have courts usually make ex post estimates of info value by the end of each court case, and then use “subpoena futures” prediction markets to make an estimate of that value ahead of time. (And make it legal and cheap to start such markets.)

So, if a subpoena futures market on a type of info estimates its expected court value to be above a standard threshold, then by law that info must be collected and saved. These prediction markets needn’t be huge in number, if they could estimate the average value of such info collect over a large group, which would then justify requiring that entire group collect the info. Such as everyone in an area who might infect others with a pandemic. If some subgroup wanted to claim that such info wasn’t less valuable regarding them, and so they should be excused, why they’d have to create different prediction markets to justify their different estimates.

For example, when a pandemic appears, if those who might infect others are likely vouched, then those who might be infected would want to require that first group to collect and save info that could be used later to prove who infected who. So they’d create prediction markets on the likely court value of spit samples and phone location records, and use market estimates to get courts to require the collection of that info.

Compared to my prior suggestion of just having the law directly require that such info be collected, this subpoena futures approach seems more flexible and general. What other harms that we do each other could be better addressed by lawsuits if we could require that relevant info be collected and saved?

(Btw, courts need not estimate info value in money terms. They might instead express the value of each piece of info in terms of its multiple of a “min info unit”, i.e., the value of info where they’d be just on the border of allowing it to be subpoenaed for a particular case.)

Added 7a: As mentioned in this comment, we now have this related legal concept:

Spoliation of evidence is the intentional, reckless, or negligent withholding, hiding, altering, fabricating, or destroying of evidence relevant to a legal proceeding …The spoliation inference is a negative evidentiary inference that a finder of fact can draw from a party’s destruction of a document or thing that is relevant to an ongoing or reasonably foreseeable civil or criminal proceeding.

My proposal can be seen as expanding this concept to allow a much weaker standard of “foreseeable”. And instead of allowing a presumption at trial, we just require the evidence to actually be collected.

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Decision Theory Remains Neglected

Back in ’84, when I first started to work at Lockheed Missiles & Space Company, I recall a manager complaining that their US government customer would not accept using decision theory to estimate the optimal thickness of missile walls; they insisted instead on using a crude heuristic expressed in terms of standard deviations of noise. Complex decision theory methods were okay to use for more detailed choices, but not for the biggest ones.

In his excellent 2010 book How to Measure Anything, Douglas W. Hubbard reports that this pattern is common:

Many organizations employ fairly sophisticated risk analysis methods on particular problems; … But those very same organizations do not routinely apply those same sophisticated risk analysis methods to much bigger decisions with more uncertainty and more potential for loss. …

If an organization uses quantitative risk analysis at all, it is usually for routine operational decisions. The largest, most risky decisions get the least amount of proper risk analysis. … Almost all of the most sophisticated risk analysis is applied to less risky operational decisions while the riskiest decisions—mergers, IT portfolios, big research and development initiatives, and the like—receive virtually none.

In fact, while standard decision theory has long been extremely well understood and accepted by academics, most orgs find a wide array of excuses to avoid using it to make key decisions:

For many decision makers, it is simply a habit to default to labeling something as intangible [=unmeasurable] … committees were categorically rejecting any investment where the benefits were “soft.” … In some cases decision makers effectively treat this alleged intangible as a “must have” … I have known managers who simply presume the superiority of their intuition over any quantitative model …

What they seem to take away from these experiences is that to use the methods from statistics one needs a lot of data, that the precise equations don’t deal with messy real-world decisions where we don’t have all of the data, or that one needs a PhD in statistics to use any statistics at all. … I have at times heard that “more advanced” measurements like controlled experiments should be avoided because upper management won’t understand them. … they opt not to engage in a smaller study—even though the costs might be very reasonable—because such a study would have more error than a larger one. …

Measurements can even be perceived as “dehumanizing” an issue. There is often a sense of righteous indignation when someone attempts to measure touchy topics, such as the value of an endangered species or even a human life. … has spent much time refuting objections he encounters—like the alleged “ethical” concerns of “treating a patient like a number” or that statistics aren’t “holistic” enough or the belief that their years of experience are preferable to simple statistical abstractions. … I’ve heard the same objections—sometimes word-for-word—from some managers and policy makers. …

There is a tendency among professionals in every field to perceive their field as unique in terms of the burden of uncertainty. The conversation generally goes something like this: “Unlike other industries, in our industry every problem is unique and unpredictable,” or “Problems in my field have too many factors to allow for quantification,” and so on. …

Resistance to valuing a human life may be part of a fear of numbers in general. Perhaps for these people, a show of righteous indignation is part of a defense mechanism. Perhaps they feel their “innumeracy” doesn’t matter as much if quantification itself is unimportant, or even offensive, especially on issues like these.

Apparently most for-profit firms could make substantially more profits if only they’d use simple decision theory to analyze key decisions. Execs’ usual excuse is that key parameters are unmeasurable, but Hubbard argues convincingly that this is just not true. He suggests that execs seek to excuse poor math abilities, but that seems implausible as an explanation to me.

I say that their motives are more political: execs and their allies gain more by using other more flexible decision making frameworks for key decisions, frameworks with more wiggle room to help them justify whatever decision happens to favor them politically. Decision theory, in contrast, threatens to more strongly recommend a particular hard-to-predict decision in each case. As execs gain when the orgs under them are more efficient, they don’t mind decision theory being used down there. But they don’t want it up at their level and above, for decisions that say if they and their allies win or lose.

I think I saw the same sort of effect when trying to get firms to consider prediction markets; those were okay for small decisions, but for big ones they preferred estimates made by more flexible methods. This overall view is, I think, also strongly supported by the excellent book Moral Mazes by Robert Jackall, which goes into great detail on the many ways that execs play political games while pretending to promote overall org efficiency.

If I ever did a book on The Elephant At The Office: Hidden Motives At Work, this would be a chapter.

Below the fold are many quotes from How to Measure Anything:

Continue reading "Decision Theory Remains Neglected" »

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Socialism Via Futarchy

On Bryan’s recommendation, I just read Niemietz’s Socialism: The Failed Idea That Never Dies, which credibly argues that two dozen socialism experiments over the last century have consistently failed, with roughly this pattern:

The not-real-socialism defence is only ever invoked retrospectively, namely, when a socialist experiment has already been widely discredited. As long as a socialist experiment is in its prime, almost nobody disputes its socialist credentials. On the contrary: practically all socialist regimes have gone through honeymoon periods, during which they were enthusiastically praised and held up as role models by plenty of prominent Western intellectuals. (More)

Noteworthy results from the latest experiment:

The number of worker-run cooperatives increased from fewer than 1,000 when Chávez was first elected to well over 30,000 in less than a decade. By the end of Chávez’s second term, cooperatives accounted for about 8% of Venezuela’s GDP and 14% of its workforce … It soon became clear … that many cooperatives were behaving like capitalist enterprises, seeking to maximize their net revenue … For example, rather than supplying their products to local markets … export them to other countries where they can sell them at higher prices … Also, many cooperatives have refrained from accepting new members. … As Chávez himself said: … if we are 20 in a cooperative, we are going to work for the benefit of us 20, and that is merely capitalism. Cooperatives need to be impelled towards socialism.’ (More)

Even after so many very expensive experiments, they still apparently have only have the vaguest idea of what detailed arrangements might actually achieve what they want. It seems they have mainly waited until an allied group gained control somewhere, and then tried a few random variations that resonate with local supporters.

There still seems to be great passion in the world for further socialism experiments, but it seems hard to hold much hope if they continue with this pattern. While I’m not personally very inspired by the socialist vision, I do like for people to get what they want, and that includes people who want socialism. So I’m taking the time to think about how to help them get it.

Which induces me to consider variations on futarchy to help to achieve socialism. If you recall, futarchy is a form of governance wherein market speculators choose policies to maximize an ex-post-measured welfare measure. The thicker are these markets (perhaps via subsidies), the stronger are the incentives for speculators to learn what is actually effective in achieving that welfare. This seems a good match, if what socialism most needs now is less a good system and more a good learning environment in which to search for good systems.

The big question for futarchy-based socialism is: what are the ex-post-measurable outcomes that indicate a successful socialism? That is, how would you know one when you saw it? Obviously you’d want to include some basic consumption measures, like G.D.P., but if that’s all you maximize there’s no obvious reason why the result will be especially socialist. You might include risk-aversion over consumption, which punishes inequality to some degree, but again it isn’t obvious that risk-aversion greatly favors socialism. Even more directly and strong punishing inequality and emphasizing the poor doesn’t obviously favor any more socialism than we see in high-redistribution low-regulation capitalist Nordic “social democracies”.

Consider:

Socialism is … characterised by social ownership of the means of production and workers’ self-management of enterprise … Social ownership can be public, collective or cooperative ownership, or citizen ownership of equity. (More)

What all socialism has in common … is … bottom-up governance of society based on local assemblies which elect delegates that share their peoples’ living conditions, can be overridden, answer to and are replaceable by them, who can federate into councils and repeat the process for larger areas and amounts of people. (More; see also)

It seems that to many a central concept of socialism is each person having a high a degree control (also called “ownership”) over their world, including both their immediate world and the larger economic/political world. This is not just control to enable one to achieve high consumption, but also control over one’s workplace, and probably even more control than is required for these purposes. In this view, successful socialism is a world of busybodies with strong abilities to get into each others’ business.

To promote socialism then, we might try a futarchy whose welfare measure includes not just measures of consumption, but also of control.

For example, one measure of control would ask random people to try to induce particular random changes in their world. The stronger the correlation between actual changes afterward and the changes that we randomly assigned them, the more we’d say that people in this world had a lot of control over it. But we’d need to find some widely-accepted weights that say which possible changes count for how much, and we’d need ways to get people to actually try to change their world in the ways we assign them. These seems hard to achieve. Also, this would probably find near zero control for larger social structures, no matter how things are arranged. And we’d need to find ways to prevent this world from suddenly becoming more plastic to support test changes, while less supporting non-test changes.

Also, I worry that simple-minded measures of individual control might induce many decisions to be made via big xor trees. Such trees would seem to let anyone who controls inputs to any leaf of the tree determine the root as well. Though of course in practice not being able to predict the other inputs means you can’t actually usefully control the output. But can we formally define average individual control in a way that doesn’t promote such xor trees?

Probably the simplest solution is to just survey people about their sense of control over their world. You might want to emphasize people who’ve recently visited other worlds, so they can reasonably compare their world to others. And you’d want to limit the abilities of local authorities to force people to give desired survey answers, such as via the threat of retaliation. If a strong central government were part of a socialist society, that may also make it difficult to measure consumption. Such governments have been known to try to distort consumption stats to make themselves look good.

One solution to these problems would be to rely on capitalist foreigners, and on travel to visit them, for both market speculators and welfare measurement.

That is, let random citizens (perhaps whole families) of the socialist society be extracted periodically and made to visit a capitalist foreign land. During that foreign visit, they can be privately interviewed about both their sense of control and their consumption levels, and they can be offered the chance to stay in that foreign land. (Via offers with varying degrees of attractiveness.) Stats on what they said and on who chose to stay could then be used to estimate the welfare of that society, without allowing that socialist government to retaliate via knowing who said what. Foreign speculators could also pay to talk privately to these visitors, to help inform their market speculation choices.

In this scenario, this socialist society would, to help it more quickly learn what works best, commit to delegating to these capitalist foreigners the measurement of its welfare and substantial participation in their speculative governance markets. Of course people at home within this socialist society could also be allowed to speculate in these markets, and to contribute to stats read by foreigners. But this approach avoids extreme corruption problems by making sure that foreigners can speculate, and measure welfare, in ways that are outside of the control of a perhaps powerful socialist government.

Of course if this approach eventually settled on a stable solution for making a good socialist society, they might want to drop this external futarchy run by foreigners to become entirely self-governing. That would make sense if and when full self-governance became more important than faster learning about how to make socialism work.

And that’s as far as I’ve thought for now. Of course if sufficient interest were expressed in this concept, I could put in some more thought.

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What Info Is Verifiable?

For econ topics where info is relevant, including key areas of mechanism design, and law & econ, we often make use of a key distinction: verifiable versus unverifiable info. For example, we might say that whether it rains in your city tomorrow is verifiable, but whether you feel discouraged tomorrow is not verifiable. 

Verifiable info can much more easily be the basis of a contract or a legal decision. You can insure yourself against rain, but not discouragement, because insurance contracts can refer to the rain, and courts can enforce those contract terms. And as courts can also enforce bets about rain, prediction markets can incentivize accurate forecasts on rain. Without that, you have to resort to the sort of mechanisms I discussed in my last post. 

Often, traffic police can officially pull over a car only if they have a verifiable reason to think some wrong has been done, but not if they just have a hunch. In the blockchain world, things that are directly visible on the blockchain are seen as verifiable, and thus can be included in smart contracts. However, blockchain folks struggle to make “oracles” that might allow other info to be verifiable, including most info that ordinary courts now consider to be verifiable. 

Wikipedia is a powerful source of organized info, but only info that is pretty directly verifiable, via cites to other sources. The larger world of media and academia can say many more things, via its looser and more inclusive concepts of “verifiable”. Of course once something is said in those worlds, it can then be said on Wikipedia via citing those other sources.

I’m eager to reform many social institutions more in the direction of paying for results. But these efforts are limited by the kinds of results that can be verified, and thus become the basis of pay-for-results contracts. In mechanism design, it is well known that it is much easier to design mechanisms that get people to reveal and act on verifiable info. So the long term potential for dramatic institution gains may depend crucially on how much info can be made verifiable. The coming hypocralypse may result from the potential to make widely available info into verifiable info. More direct mind-reading tech might have a similar effect. 

Given all this reliance on the concept of verifiability, it is worth noting that verifiability seems to be a social construct. Info exists in the universe, and the universe may even be made out of info, but this concept of verifiability seems to be more about when you can get people to agree on a piece of info. When you can reliably ask many difference sources and they will all confidently tell you the same answer, we tend to treat that as verifiable. (Verifiability is related to whether info is “common knowledge” or “common belief”, but the concepts don’t seem to be quite the same.)

It is a deep and difficult question what actually makes info verifiable. Sometimes when we ask the same question to many people, they will coordinate to tell us the answer that we or someone wants to hear, or will punish them for contradicting. But at other times when we ask many people the same question, it seems like their best strategy is just to look directly at the “truth” and report that. Perhaps because they find it too hard to coordinate, or because implicit threats are weak or ambiguous. 

The question of what is verifiable opens an important meta question: how can can we verify claims of verifiability? For example, a totalitarian regime might well insist not only that everyone agree that the regime is fair and kind, a force for good, but that they agree that these facts are clear and verifiable. Most any community with a dogma may be tempted to claim not only that their dogma is true, but also that it is verifiable. This can allow such dogma to be the basis for settling contract disputes or other court rulings, such as re crimes of sedition or treason.

I don’t have a clear theory or hypothesis to offer here, but while this was in my head I wanted to highlight the importance of this topic, and its apparent openness to investigation. While I have no current plans to study this, it seems quite amenable to study now, at least by folks who understand enough of both game theory and a wide range of social phenomena.  

Added 3Dec: Here is a recent paper on how easy mechanisms get when info is verifiable.

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Occam’s Policy Razor

Nine experiments provide support for promiscuous condemnation: the general tendency to assume that ambiguous actions are immoral. Both cognitive and functional arguments support the idea of promiscuous condemnation. (More)

The world is full of inefficient policies. But why, when many can can simply and clearly explain why such policies are inefficient? The following concrete example suggests a simple explanation:

Logically, it doesn’t seem cruel to offer someone an extra option, if you don’t thereby change their other options. Two thirds of poll respondents agree re this prisoner case. However, 94% also think that the world media would roast any nation who did this, and they’d get away with it. And I agree with these poll respondents in both cases.

Most of the audience of that world media would not be paying close attention, and would not care greatly about truth. They would instead make a quick and shallow calculation: will many find this accusation innately plausible and incendiary enough to stick, and would I like that? If they answer is yes, they add their pitchforks to the mob. That’s the sort of thing I’ve seen with internet mobs lately, and also with prior media mobs.

As most of the world is eager to call the United States an evil empire driven by evil intent, any concrete U.S. support for torture might plausibly be taken as evidence for such evil intent, at least to observers who aren’t paying much attention. So even those who know that in such cases allowing torture can be better policy would avoid supporting it. Add in large U.S. mobs who are also not paying attention, and who might like to accuse U.S. powers of ill intent, and we get our situation where almost no one is willing to seriously suggest that we offer torture substitutes for prison. Even though that would help.

Similar theories can explain many other inefficient policies, such as laws against prostitution, gambling, and recreational drugs. We might know that such policies are ineffective and harmful, and yet not be able to bring ourselves to publicly support ending such bans, for fear of being accused of bad intent. This account might even explain policies to punish the rich, big business, and foreigners. The more that contrary policies could be spun to distracted observers as showing evil intent, the more likely such inefficient policies are to be adopted.

Is there any solution? Consider the example of Congress creating a commission to recommend which U.S. military bases to close, where afterward Congress could only approve or reject the whole list, without making changes. While bills to close individual bases would have been met with fierce difficult-to-overcome opposition, this way to package base closings into a bundle allowed Congress to actually close many inefficient bases.

Also consider how a nation can resist international pressure to imprison one disliked person, or to censor one disliked book. In the first case the nation may plead “we follow a general rule of law, and our law has not yet convicted this person”, while in the second case the nation may plead “We have adopted a general policy of free speech, which limits our ability to ban individual books.”

I see a pattern here: simpler policy spaces, with fewer degrees of freedom, are safer from bias, corruption, special-pleading, and selfish lobbying. A political system choosing from a smaller space of possible policies that will then apply to a large range of situations seems to make more efficient choices.

Think of this as Occam’s Policy Razor. In science, Occam’s Theory Razor says to pick the simplest theory that can fit the data. Doing this can help fractious scientific communities to avoid bias and favoritism in theory choice. Similarly, Occam’s Policy Razor says to limit policy choices to the smallest space of policies which can address the key problems for which policies are needed. More complexity to address complex situation details is mostly not worth the risk. This policy razor may help fractious political communities to avoid bias and favoritism in policy choice.

Yes, I haven’t formalized this much, and this is still a pretty sloppy analysis. And yes, there are in fact many strong criticisms of Occum’s Razor in science. Even so, it feels like there may be something to this. And futarchy seems to me a good example of this principle. In a futarchy with a simple value function based on basic outcomes like population, health, and wealth, then voting on values but betting on beliefs would probably mostly legalize things like prostitution, gambling, recreational drugs, immigration, and big business. It would probably even let prisoners pick torture.

Today we resist world mob disapproval regarding particular people we don’t jail, or particular books we don’t ban, by saying “Look we have worked out general systems to deal with such things, and it isn’t safe for us to give some folks discretion to make exceptions just because a mob somewhere yells”. Under futarchy, we might similarly resist world disapproval of our prostitution, etc. legalization by saying:

Look, we have chosen a simple general system to deal with such things, and we can’t trust giving folks discretion make policy exceptions just because talking heads somewhere scowl. So far our system hasn’t banned those things, and if you don’t like that outcome then participate in our simple general system, to see if you can get your desired changes by working through channels.

By limiting ourselves to simple general choices, we might also tend to make more efficient choices, to our overall benefit.

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10% Less Democracy

My GMU econ colleague Garett Jones has a book coming out in February: 10% Less Democracy: Why You Should Trust Elites a Little More and the Masses a Little Less. I just read it, and found it so engaging that I’ll respond now, even though Jones’ publisher surely prefers book publicity nearer its publication date.

Regarding to the vast space of possible governments, it seems to me that Jones uses “more democratic” to describe situations closer to a 100% democracy ideal, wherein all citizens have an equal say and can vote directly on all government choices, with government able to control all other choices. In this framing, anything that makes it harder for voters to simply and directly choose the options they understand and prefer makes a system less democratic.

That includes electing representatives instead of directly voting on policy, and also logrolling, divided government, and other complexities that make it harder for citizens to tell what is going on and to assign responsibility. It includes any limits on who can vote, and any ties to outsiders that limit internal discretion, like treaties with other nations or selling debt to bondholders. And it includes longer terms for the elected, and more indirection, such as when politicians appoint other officials instead of directly electing those other officials.

By these standards, our current system obviously deviates greatly from a fully democratic ideal, and Jones approves of most of these deviations, especially ones that result in longer term views and in more informed voters and officials. And he’d like to move modestly further in such less-democracy directions, though not too far, as he accepts that strong autocrats tend much more to kill their citizens, allow famines, and create more economic growth volatility (though similar average levels of war and growth). Jones musters a lot of data in support of his modestly-cut-democracy view.

I did a few surveys yesterday which suggest that overall my Twitter followers find the existing degree of democracy pretty close to their ideal, though a majority would also prefer a reduction. So, for them, Jones’ position doesn’t seem at all controversial:

In the past I’ve tended to think about all this in terms of principal-agent problems. It doesn’t always make sense to make all decisions yourself, if you can instead consult an agent who does or could know more than you. But you must be careful to keep such agents under sufficient control. So if they are careful, voters may reasonably gain by delegating to experts. However, the reason I found Jones’ book so engaging is that I found a lot of the data Jones presented to be challenging to understand from this principal-agent view. (And also, it was a pleasure to engage such fundamental issues.)

For example, politicians with longer terms but without safe districts act at the end of their term more like politicians who have shorter terms. They pass fewer bills, make more pork projects, more trade protection, more labor market regulation, more environmental reforms, have optimistic budget forecasts, and support fewer currency devaluations. Apparently, voters don’t remember much of what politicians do beyond the last years or so.

Cities with appointed (vs elected) city treasurers pay 0.7% lower interest rates. Central bankers who are more independent produce lower inflation and fewer financial crises, at no overall cost to unemployment or real growth rates. Elected judges give more awards to in-state folks at the expense of out of state folks, and their legal opinions are less often cited as precedent. Nations with more independent judges have stronger property rights, less red tape to start a business, fewer employment regulations, and less government ownership of banks.

In general, elected regulators allow utilities to pass fewer costs on to customers, resulting in both lower prices but also in less investment and worse service. Electric utilities regulated by elected officials have lower consumer prices, pay higher interest rates, and more blackouts. Elected telecom regulators oversee lower capacity services, and independent telecom regulators gave in less to demands by government telecom organizations.

Jones is inspired by these examples to support Alan Blinder’s proposal to create an independent central-bank-like expert body to set tax policy, with Congress deciding only broad parameters like total take, progressively, and corporate fraction.

Some of these patterns can be understood in terms of commitment problems. When there is a temptation for politicians to renege on prior commitments, it can help to let them commit via choosing appointees who are out of their control at the crucial moments of temptation. Commitment problems seem especially important for city treasurers, central bankers, and utility regulators. And law court decisions are a classic commitment problem.

These results can also be somewhat understood in terms of the advantages of retrospective relative to prospective voting, and of aggregation in retrospective voting. That is, if voters are impatient and can better judge how their life has gone in the past than they can judge the effects of policies on the future, then voters can be better off when politicians are judged more on their past accomplishments, which happens more with longer terms. And if voters find it hard to attribute responsibility to specific officials, it can be better if they they focus on electing fewer bigger politicians (like mayors) who appoint more other officials.

However, I’m not sure that commitment problems and retrospective voting actually account for most of these patterns. Jones’ book subtitle talks instead about trusting elites, and do note that there is a much more widespread pattern of governments authorizing high status experts in each area to decide key results in their area, including who are to be considered the next generation of experts.

Consider how much we defer to military experts on defense, police on crime, medical experts on health, academics on research, lawyers on law, etc. Yes, in principle we could punish them if past outcomes in their area were bad, but we rarely do this. And professional licensing is a more general policy by which government authorizes control by the high status people in each area. These policies seem less like clever indirect ways to commit or to enable retrospective voting, and more like a simple status effect, wherein voters and politicians want to be seen as respecting and not opposing those high in status.

While all these examples that Jones didn’t include seem to be examples of less democracy, they seem to me to less clearly support his position that this kind of less democracy is good. Excess professional licensing does a lot of harm. The military seems to overemphasize things that high status leaders like more, like fighter planes and aircraft carriers. Medicine seems to overemphasize high status doctors over other medical professionals. Education and research seems to overemphasize the topics by which academics gain the highest status. Law seems overly complex and to overemphasize the need for expensive lawyers. And so on.

Compared to arguing over specific policies, I very much appreciate Jones calling our attention to larger more general issues regarding the design of our political system. But I prefer to generalize even further, via something like futarchy. I can support futarchy without needing opinions on whether tax policy should be run by a panel of independent experts, nor even whether it is in general better or worse to let high status experts in each area control those areas. As long as we use some reasonable (broad retrospective) national welfare measure, with futarchy I could instead trust a general mechanism to make good choices about such things.

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Ways to Choose A Futarchy Welfare Measure

In my futarchy proposal, I suggest a big change in how we aggregate info re our policy choices, but not in how we decide what outcomes we are trying to achieve. My reason: one can better evaluate proposals that do not change everything, but instead only change a bounded part of our world. So I described choosing a “national welfare function” the way we now choose most things, via a legislature that continually passes bills to edit and update a current version. And then I described a new way to estimate what policy actions might best increase that welfare. (I also outline an agenda mechanism for choosing which policy proposals to evaluate when.)

In this post, I want to consider other ways to choose a welfare function. I’ll limit myself here to the task of how to choose a function that makes tradeoffs between available measured quantities. I won’t discuss here how to choose the set of available measured quantities (e.g, GDP, population, unemployment) to which such functions can refer. Options include:

1) As I said above, the most conservative option is to have an elected legislature vote on edits to an existing explicit function. Because that’s the sort of thing we do now.

2) A simple, if radical, option is to use a “market value” of the nation. Make all citizenships tradeable, and add up the market value of all such citizenships. Add that to the value of all the nation’s real estate, and any other national assets with market prices. With this measure, the nation would act like an apartment complex, maxing the net rents that it can charge, minus its expenses. (A related option is to use a simple 0 or 1 measure of whether the nation survives in some sufficient form over some very long timescale.)

3) A somewhat more complex option would be to define a simple measure over possible packages of measured quantities, then repeatedly pick two random packages (via that measure) and ask a random citizen which package they prefer. Then fit a function that tries predict current choices. (Like they do in machine learning.) Maybe slant the random picks toward the subspaces where citizen choice tests will add the most info to improve the current best fit function.

4) An option that requires a lot of complexity from each ciziten is to require each citizen to submit a welfare function over the measured quantities. Use some standard aggregation method to combine these into a single function. (For example, require each function to map to the [0,1] interval and just add them all together.) Of course many organizations would offer citizens help constructing their functions, so they wouldn’t have to do much work if they didn’t want to. Citizens who submit expensive-to-compute functions should pay for the extra computational that they induce.

5) Ralph Merkle (of Merkle-tree fame) proposed  that “each citizen each year report a number in [0,1] saying how well their life has gone that year”, with the sum of those numbers being the welfare measure.

I’m sure there must be other interesting options, and I’ll add them here if I hear of some. Whatcha got?

A common issue with all these mechanisms is that, under futarchy, every time a bill is considered, those who trade on it acquire assets specified in terms of the then-current national welfare measure. So the more often that the official welfare measure changes, the more different kinds of assets are in circulation. These assets last until all the future measures that they refer to are measured. This is a reason to limit how often the official measure changes.

Inspired by a conversation with Teddy Collins.

Added 22Aug: Some polls on this choice:


The status quo approach is the most popular option, followed by market value and then fitting random picks.

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Decision Markets for Monetary Policy

The goals of monetary policy are to promote maximum employment, stable prices and moderate long-term interest rates. By implementing effective monetary policy, the Fed can maintain stable prices, thereby supporting conditions for long-term economic growth and maximum employment. (more)

Caltech, where I got my PhD in social science, doesn’t have specialists in macroeconomics, and they don’t teach the subject to grad students. They just don’t respect the area enough, they told me. And I haven’t gone out of my way to make up this deficit in my background; other areas have seemed more interesting. So I mostly try not to have or express opinions on macroeconomics

I periodically hear arguments for NGDP Targeting, such as from Scott Sumner, who at one point titles his argument “How Prediction Markets Can Improve Monetary Policy: A Case Study.” But as far as I can tell, while this proposal does use market prices in some ways, it depends more on specific macroeconomic beliefs than a prediction markets approach needs to. 

These specific beliefs may be well supported beliefs, I don’t know. But, I think it is worth pointing out that if we are willing to consider radical changes, we could instead switch to an approach that depends less on particular macroeconomic beliefs: decision markets. Monetary policy seems an especially good case to apply decision markets because they clearly have two required features: 1) A clear set of discrete decision options, where it is clear afterward which option was taken, 2) A reasonably strong consensus on measurable outcomes that such decisions are trying to increase. 

That is, monetary policy consists of clear public and discrete choices, such as on short term interest rates. Call each discrete choice option C. And it is widely agreed that the point of this policy is to promote long term growth, in part via moderating the business cycle. So some weighted average of real growth, inflation, unemployment, and perhaps a few more after-the-fact business cycle indicators, over the next decade or two seems a sufficient summary of the desired outcome. Let’s call this summary outcome O.  

So monetary policy just needs to pick a standard metric O that will be known in a decade or two, estimate E[O|C] for each choice C under consideration, and compare these estimates. And this is exactly the sort of thing that decisions markets can do well. There are some subtitles about how exactly to do it best. But many variations should work pretty well. 

For example, I doubt it matters that much how exactly we weight the contributions to O. And to cut off skepticism on causality, we could use a 1% chance of making each discrete choice randomly, and have decision market estimates be conditional on that random choice. Suffering a 1% randomness seems a pretty low cost to cut off skepticism.

For more, see the section “Monetary Policy Example” in my paper Shall We Vote on Values, But Bet on Beliefs?

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Understandable Social Systems

Brennan and Magness’ book Cracks in the Ivory Tower: The Moral Mess of Higher Education reviews many ways that colleges overpromise, and fail to deliver. It confirms (with Caplan’s Case Against Education) a picture wherein ordinary people are pretty clueless about a big institution in their lives. This cluelessness also seems to apply to many other life areas, such as medicine, charity, politics, etc. In each area, most people don’t seem to understand very basic things, like what exactly is the product, and what are the incentives of professionals?

That is, we each live in many complex social systems, such as political, transport, medical, religious, food, and school systems. Due to our poor understanding of such systems, we have low abilities to make intelligent personal choices about them, and even worse abilities to usefully contribute to efforts to reform them. This suggests a key criteria for evaluating social systems: understandability.

When we don’t understand our social systems, we can be seen as having little agency regarding them. They are like the weather; they exist, and may be good or bad, but we are too ignorant to do much about them. If a situation is bad, we can’t work to make it better. Some elites might have agency re such institutions, but not the rest of us. So a key question is: can we reform or create social institutions that are more understandable, to allow ordinary people to have more agency regarding the institutions in their lives?

One possible solution is to use meta-institutions, like academia, news media, or government regulators, that we may better understand and trust. We might, for example, support a particular reform to our medical system based on the recommendation of an academic institution. Our understanding of academia as a meta-institution could give us agency, even when we were ignorant of the institutions of medicine.

As an analogy, imagine that someone visits a wild life refuge. If this visitor does not understand the plants and animals in this area, they might reasonably fear the consequences of interacting with any given plant or animal, or of entering any given region. In contrast, when accompanied by a tour guide who can advise on what is safe versus dangerous, they might relax. But only if they have good reason to think this guide roughly shares their interests.  If your guide is a nephew who inherits your fortune if you die, you may be much less relaxed.

So here’s a key question: is there, at some level of abstraction, a key understandable institution by which we can usefully choose and influence many other parts of our social world? If we think we understand this meta institution well enough to trust it, that could give us substantial agency regarding key large scale features of our social worlds. For example, we could add our weight to particular reform efforts, because we had good reasons to expect such reforms to on average help.

Alas, academia, news media, and government regulators all seem too complex and opaque to serve in this key meta role. But three other widely used and simpler social mechanisms may be better candidates.

  1. Go with the majority. Buy the product that most other people buy, use the social habits that most others use, and have everyone vote on key big decisions. When some people know what’s best, and others abstain or pick randomly, then the majority will pick what’s best. Yes, there are many topic areas where people don’t abstain or pick randomly when they don’t know what’s best. But if we can roughly guess which are the problematic topics, then in other areas we may gain at least rough agency by going with the crowd.
  2. Follow prestige. Humans have rich ancient intuitive mechanisms for coordinating on who we find impressive. These mechanisms actually scale pretty well, allowing us to form consensus on the relative prestige of nations, professions, schools, cities, etc., and via these proxies, of individuals. Related ancient mechanisms let us form consensus on elite opinion, i.e., on what prestigious people tend to think on any given topic. Yes, elites are biased toward themselves, and to express opinions that make them seem impressive. Still, we can do worse than to follow our best.
  3. Embrace Winners. Nations, cities, firms, professions, teams, media, clubs, lovers, etc. often compete, in the sense that some grow at the expense of others that shrink or disappear. Often they compete for our personal support. And often we see judge that the competition is roughly “fair” and open to many potential competitors. In such cases, we may embrace the winners. For example, we may try many competitors, and stick with those we like best. Or we may go with the lowest price offer, if we can control well enough for quality variations.

Each of these big three mechanisms has limits, but they do seem to satisfy the requirement that they are very simple and many ordinary people can at least roughly understand why they work, and where they run into problems. Together they may cover a pretty wide range of cases. In addition, we can augment them with many other approaches. For example, we can just expose ourselves to choices and follow our intuitions on which are best. We can follow choices by those we know and trust well, those who seem to know more about a topic, and those who seem more honest in their evaluations. Together all these tricks may give us substantial agency re the social institutions in our lives.

Yet those examples of how badly most people misunderstand school, medicine, etc. suggest there is vast room for improvement. And so I look for ways to do better. Not just at designing institutions that actually work, in the sense of producing efficiency, equity, generality, robustness, evolvability, etc. Not just at designing meta-institutions with these features. And not just at gaining the support of majorities or elites, or at winning many fair competitions in the world. I seek meta-mechanisms that can also be simple and clear enough to their advantages be understandable to many ordinary people.

This is the context in which I’d like you to see my highest hopes for prediction markets. I offer them not just as mechanisms that actually work, producing and aggregating info at a low cost. After all, there may be other complex and subtle mechanisms that experts expect to achieve similar or even somewhat better results. But the problem in that case is that ordinary people may wonder how well they can trust such expert judgements.

No, I’m interested in the potential for prediction markets to serve as a simple understandable meta-institution, on par with and at the level of going with the majority, following prestige, and embracing winners. Simple enough that many ordinary people can directly understand why they should work well in many applications, and also to understand roughly where their limitations lie. Yes, not everyone can understand this, but maybe most everyone could know and trust someone who does understand.

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