Tag Archives: Prediction Markets

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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Space Fund

At a space conference this last weekend, I was inspired to ponder the key problem I see regarding space colonization: how to recruit the great passion among so many to support and participate somehow in the topic today, while avoiding the vast waste that most likely results when that passion is directed to greatly premature near term projects.

Someday humans will colonize Antartica, the top of the Himalaya mountains, and the bottom of Earth oceans. But this won’t happen until these colonies are in the ballpark of cost-effective relative to more familiar locations. Quirky preferences or religious devotion can make a modest difference, but can’t overcome huge cost differences.

The same applies to colonization of space, a place much harder to colonize. While extra passion and quirky preferences can make a modest difference, mostly space colonization just can’t happen until near when it would be feasible given more ordinary motives. Efforts spent well before that time are mostly wasted, unless they are especially well targeted toward easing later efforts when such colonization is nearly feasible.

Here’s my decision-market idea for tying current passion to useful future efforts:

  1. Create a space fund that passively reinvests its assets to grow over a long period, a fund to which anyone can donate,
  2. Define an ex-post measure of successful space colonization. For example, LNYD = Log of number N people living in space for at least Y years by date D.
  3. For a modest fee, let anyone at anytime submit a proposal for how to spend the entire space fund. Any proposal is fair game, including transferring all of this fund to a new fund managed a new way.
  4. Create financial assets $LNYD that pay in proportional to this measure LNYD. (This may require setting a min & max value for the measure.) Let people trade these assets for cash, creating a LNYD market price.
  5. Each proposal submission is evaluated via a LNYD-based decision market. That is, for each proposal, on a particular unique pre-announced date, market speculators may trade LNYD assets for cash, in trades that are called off if (or if not) this proposal is approved. If the LNYD price difference between approval and non-approval is clearly positive, the proposal is approved. (The price difference threshold used here should reflect the fact that this system should reject a great many proposals, and approve only one.)

Under this system, people today who want to feel involved with space colonization can do so in three ways: 1) donate to the space fund, 2) develop and submit proposals for approval, or 3) trade in the markets that decide if to approve proposals. Later, when space colonization is nearly feasible, so that money spent can actually make a difference, these decision markets should make good choices about when and how to spend this fund to best create maximal colonization, according to the initially- chosen measure.

That’s the basic idea. Now here’s a variation, designed to avoid incentives for sabotage. When a donor donates $2 to the space fund, $1 goes into the fund, and this donor gets back a $LNYD asset whose value is guaranteed to fall within [$0,$1]. They can then trade this $LNYD asset in the decision markets. The remaining $(1-LNYD) asset is put into in a new space fund tied to a new goal defined regarding some date D’ after date D. In this system, only this new fund holds the $(1-LNYD) assets that might tempt a holder to sabotage the space colonization effort.

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Speculator-Chosen Immigrants

On immigration, the big political tug-o-war axis today is: more or less immigrants. But if you want tug the rope sideways, both to oppose polarization and to have a better chance of adding value, you might do better to focus on a perpendicular axis. Such as transferable citizenship, crime liability insurance for immigrants, or the topic of this post: who exactly to admit.

Even if we disagree on how many immigrants we want, we should agree that we want better immigrants. For example, good immigrants pay lots of taxes, volunteer to help their communities, don’t greatly harm our political or social equilibria, are not criminals, and impose fewer burdens on government benefit systems. Yes, we may disagree on the relative weights to assign to such features, but these disagreements seem relatively modest; there’s plenty of room here to work together to make better choices.

Note that, for the foreseeable future, we aren’t likely to approve for immigration more than a small fraction of all the outsiders who’d be willing to apply, if we were likely to accept them. So as a practical matter our efforts to pick candidates should focus on estimating well at the high tail of the distribution, for the candidates most likely to be best.

Note also that while a better way to select immigrants might induce us to accept more immigrants, those who are wary of this outcome tend to feel risk averse about such changes. Thus we should be looking for ways to pick immigrants that seem especially good at assuring skeptics that any one person is a good candidate.

To achieve all this, I suggest that we look at the prices of new financial assets that we can create to track the net tax revenue from each immigrant, conditional on their being admitted. Let me explain.

For every immigrant that we admit, the government could track how much that person pays in taxes each year, and also how much the government spends on that person via benefits whose costs can be measured individually. We could probably assign individual costs for schools, Medicare and Medicaid, prison, etc. For types of costs or benefits that can’t be measured individually, we’d could attribute to each immigrant some average value across citizens of their location and demographic type. When there are doubts, let us err in the direction of estimating higher costs, so that our measures are biased against immigrants adding value.

Okay, so now we have a conservative net financial value number for each immigrant for each year, a number that can be positive or negative. From these numbers we can create financial assets that pay annual dividends proportional to these numbers. If we let many people trade such assets, their market prices should give us decent estimates of the current present financial value of this stream of future revenue. And if we allow trading in such assets regarding people who apply to immigrate, with those trades being conditional on that person being admitted and coming, then such prices would estimate the net financial value of an immigration candidate conditional on their immigrating.

We could then admit the candidates for whom such estimates are highest; using a high threshold could ensure a high confidence that each immigrant is a net financial advantage. Those who are skeptical about particular immigrants, or about immigration in general, could insure themselves against bad immigration choices via trades in these markets, trades from which they expect to profit if their skepticism is accurate.

As usual, there are some subtitles to consider. For example, traders must be given some info on each candidate, and market estimates are more accurate the more info that traders are given. While I see no obvious legal requirement to do so, candidates could be assured some privacy. Immigration skeptics, however, might want to limit such privacy, to better ensure that each immigrant is a net gain.

Once immigrants become citizens, they of course have stronger privacy rights. While the government-calculated dividend values on them each year would reveal some info, there’s no need to reveal details of how that number was computed. To cut info revealed further, we might even wait and pay dividends as a single lump every five years.

In principle, a trader might acquire a large enough net negative stake in a particular immigrant that they have an incentive to hurt that immigrant, or at least to hurt that immigrant’s chances of achieving high net value. We might thus want to limit the size of negative stakes, at least after the immigrant comes, and among traders with opaque abilities to cause such harms.

The fact that net financial revenue can be both positive and negative complicates the asset creation. We might add some large constant to the financial numbers, to ensure that dividends paid have a positive sign. Or we might create two assets, one that pays dividends for the positive amounts, and one that pays for the negative amounts.

Some groups of candidates, such as a church, family, or firm, might be worth more if admitted as a unit together. We might then have trades on packages of assets for a whole group of candidates, trades conditional on their all being admitted as a unit. With a high enough estimated value of the group, we might then just admit such groups as units, even when we have doubts about individual members.

And that’s it, another pull-the-rope-sideways proposal designed to improve policy on a hot-button topic without taking a side on topic’s main dispute. Whether you want more or fewer immigrants, you should want better immigrants.

Added 1p 25Mar: If we could design individual measures of cultural assimilation and impact on cultural change, and assign dollar values to those measures, then we could include them in this proposed system.

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Can We Trust Deliberation Priests?

In Science, academic “deliberation” experts offer a fix for our political ills:

Citizens to express their views … overabundance [of] … has been accompanied by marked decline in civility and argumentative complexity. Uncivil behavior by elites and pathological mass communication reinforce each other. How do we break this vicious cycle? …

All survey research … obtains evidence only about the capacity of the individual in isolation to reason about politics. … [But] even if people are bad solitary reasoners, they can be good group problem-solvers … Deliberative experimentation has generated empirical research that refutes many of the more pessimistic claims about the citizenry’s ability to make sound judgments.

Great huh? But there’s a catch:

Especially when deliberative processes are well-arranged: when they include the provision of balanced information, expert testimony, and oversight by a facilitator … These effects are not necessarily easy to achieve; good deliberation takes time and effort. Many positive effects are demonstrated most easily in face-to-face assemblies and gatherings, which can be expensive and logistically challenging at scale. Careful institutional design involv[es] participant diversity, facilitation, and civility norms …

A major improvement … might involve a randomly selected citizens’ panel deliberating a referendum question and then publicizing its assessments for and against a measure … problem is not social media per se but how it is implemented and organized. Algorithms for ranking sources that recognize that social media is a political sphere and not merely a social one could help. …

It is important to remain vigilant against incentives for governments to use them as symbolic cover for business as usual, or for well-financed lobby groups to subvert their operation and sideline their recommendations. These problems are recognized and in many cases overcome by deliberative practitioners and practice. … The prospects for benign deployment are good to the degree that deliberative scholars and practitioners have established relationships with political leaders and publics—as opposed to being turned to in desperation in a crisis.

So ordinary people are capable of fair and thoughtful deliberation, but only via expensive processes carefully managed in detail by, and designed well in advance by, proper deliberation experts with “established relationships with political leaders and publics.” That is, these experts must be free to pick the “balance” of info, experts, and participants included, and even who speaks when how, and these experts must be treated with proper respect and deference by the public and by political authorities.

No, they aren’t offering a simple well-tested mechanism (e.g., an auction) that we can apply elsewhere with great confidence that the deployed mechanism is the same as the one that they tested. Because what they tested instead was a mechanism with a lot of “knobs” that need context-specific turning; they tested the result of having particular experts use a lot of discretion to make particular political and info choices in particular contexts. They say that went well, and their academic peer reviewers (mostly the same people) agreed. So we shouldn’t worry that such experts would become corrupted if we gave them a lot more power.

This sure sounds like a priesthood to me. If we greatly empower and trust a deliberation priesthood, presumably overseen by these 20 high priest authors and their associates, they promise to create events wherein ordinary people talk much more reasonably, outputting policy recommendations that we could then all defer to with more confidence. At least if we trust them.

In contrast, I’ve been suggesting that we empower and trust prediction markets on key policy outcomes. We’ve tested such mechanisms a lot, including in contexts with strong incentives to corrupt them, and these mechanisms have far fewer knobs that must be set by experts with discretion. Which seems more trustworthy to me.

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Replication Markets Team Seeks Journal Partners for Replication Trial

An open letter, from myself and a few colleagues:

Recent attempts to systematically replicate samples of published experiments in the social and behavioral sciences have revealed disappointingly low rates of replication. Many parties are discussing a wide range of options to address this problem.

Surveys and prediction markets have been shown to predict, at rates substantially better than random, which experiments will replicate. This suggests a simple strategy by which academic journals could increase the rate at which their published articles replicate. For each relevant submitted article, create a prediction market estimating its chance of replication, and use that estimate as one factor in deciding whether to publish that article.

We the Replication Markets Team seek academic journals to join us in a test of this strategy. We have been selected for an upcoming DARPA program to create prediction markets for several thousand scientific replication experiments, many of which could be based on articles submitted to your journal. Each market would predict the chance of an experiment replicating. Of the already-published experiments in the pool, approximately one in ten will be sampled randomly for replication. (Whether submitted papers could be included in the replication pool depends on other teams in the program.) Our past markets have averaged 70% accuracy, and the work is listed at the Science Prediction Market Project page, and has been published in Science, PNAS, and Royal Society Open Science.

While details are open to negotiation, our initial concept is that your journal would tell potential authors that you are favorably inclined toward experiment article submissions that are posted at our public archive of submitted articles. By posting their article, authors declare that they have submitted their article to some participating journal, though they need not say which one. You tell us when you get a qualifying submission, we quickly tell you the estimated chance of replication, and later you tell us of your final publication decision.

At this point in time we seek only an expression of substantial interest that we can take to DARPA and other teams. Details that may later be negotiated include what exactly counts as a replication, whether archived papers reveal author names, how fast we respond with our replication estimates, what fraction of your articles we actually attempt to replicate, and whether you privately give us any other quality indicators obtained in your reviews to assist in our statistical analysis.

Please RSVP to: Angela Cochran, PM acochran@replicationmarkets.com 571 225 1450

Sincerely, the Replication Markets Team

Thomas Pfeiffer (Massey University)
Yiling Chen, Yang Liu, and Haifeng Xu (Harvard University)
Anna Dreber Almenberg & Magnus Johannesson (Stockholm School of Economics)
Robin Hanson & Kathryn Laskey (George Mason University)

Added 2p: We plan to forecast ~8,000 replications over 3 years, ~2,000 within the first 15 months.  Of these, ~5-10% will be selected for an actual replication attempt.

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Toward An Honest Consensus

Star Trek original series featured a smart computer that mostly only answered questions; humans made key decisions. Near the start of Nick Chater’s book The Mind Is Flat, which I recently started, he said early AI visions were based on the idea of asking humans questions, and then coding their answers into a computer, which might then answer the same range of questions when asked. But to the surprise of most, typical human beliefs turned out to be much too unstable, unreliable, incoherent, and just plain absent to make this work. So AI research turned to other approaches.

Which makes sense. But I’m still inspired by that ancient vision of an explicit accessible shared repository of what we all know, even if that isn’t based on AI. This is the vision that to varying degrees inspired encyclopedias, libraries, internet search engines, prediction markets, and now, virtual assistants. How can we all coordinate to create and update an accessible shared consensus on important topics?

Yes, today our world contains many social institutions that, while serving other functions, also function to create and update a shared consensus. While we don’t all agree with such consensus, it is available as a decent first estimate for those who do not specialize in a topic, facilitating an intellectual division of labor.

For example: search engines, academia, news media, encyclopedias, courts/agencies, consultants, speculative markets, and polls/elections. In many of these institutions, one can ask questions, find closest existing answers, induce the creation of new answers, induce elaboration or updates of older answers, induce resolution of apparent inconsistencies between existing answers, and challenge existing answers with proposed replacements. Allowed questions often include meta questions such as origins of, translations of, confidence in, and expected future changes in, other questions.

These existing institutions, however, often seem weak and haphazard. They often offer poor and biased incentives, use different methods for rather similar topics, leave a lot of huge holes where no decent consensus is offered, and tolerate many inconsistencies in the answers provided by different parts. Which raises the obvious question: can we understand the advantages and disadvantages of existing methods in different contexts well enough to suggest which ones we should use more or less where, or to design better variations, ones that offer stronger incentives, lower costs, and wider scope and integration?

Of course computers could contribute to such new institutions, but they needn’t be the only or even main parts. And of course the idea here is to come up with design candidates to test first at small scales, scaling up only when results look promising. Design candidates will seem more promising if we can at least imagine using them more widely, and if they are based on theories that plausibly explain failings of existing institutions. And of course I’m not talking about pressuring people to follow a consensus, just to make a consensus available to those who want to use it.

As usual, a design proposal should roughly describe what acts each participant can do when, what they each know about what others have done, and what payoffs they each get for the main possible outcomes of typical actions. All in a way that is physically, computationally, and financially feasible. Of course we’d like a story about why equilibria of such a system are likely to produce accurate answers fast and at low cost, relative to other possible systems. And we may need to also satisfy hidden motives, the unacknowledged reasons for why people actually like existing institutions.

I have lots of ideas for proposals I’d like the world to consider here. But I realized that perhaps I’ve neglected calling attention to the problem itself. So I’ve written this post in the hope of inspiring some of you with a challenge: can you help design (or test) new robust ways to create and update a social consensus?

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Choose: Allies or Accuracy

Imagine that person A tells you something flattering or unflattering about person B. All else equal, this should move your opinion of B in the direction of A’s claim. But how far? If you care mainly about accuracy, you’ll want to take into account base rates on claimers A and targets B, as well as more specific specific signs on the accuracy of A regarding B.

But what if you care mainly about seeming loyal to your allies? Well if A is more of your ally than is B, as suggested by your listening now to A, then you’ll be more inclined to just believe A, no matter what. Perhaps if other allies give a different opinion, you’ll have to decide which of your allies to back. But if not, trying to be accurate on B mainly risks seeming disloyal to A and you’re other allies.

It seems that humans tend to just believe gossip like this, mostly ignoring signs of accuracy:

The trustworthiness of person-related information … can vary considerably, as in the case of gossip, rumors, lies, or “fake news.” …. Social–emotional information about the (im)moral behavior of previously unknown persons was verbally presented as trustworthy fact (e.g., “He bullied his apprentice”) or marked as untrustworthy gossip (by adding, e.g., allegedly), using verbal qualifiers that are frequently used in conversations, news, and social media to indicate the questionable trustworthiness of the information and as a precaution against wrong accusations. In Experiment 1, spontaneous likability, deliberate person judgments, and electrophysiological measures of emotional person evaluation were strongly influenced by negative information yet remarkably unaffected by the trustworthiness of the information. Experiment 2 replicated these findings and extended them to positive information. Our findings demonstrate a tendency for strong emotional evaluations and person judgments even when they are knowingly based on unclear evidence. (more; HT Rolf Degen)

I’ve toyed with the idea of independent juries to deal with Twitter mobs. Pay a random jury a modest amount to 1) read a fuller context and background on the participants, 2) talk a bit among themselves, and then 3) choose which side they declare as more reasonable. Sure sometimes the jury would hang, but often they could give a voice of reason that might otherwise be drown out by loud participants. I’d have been willing to pay for this a few times. And once juries became a standard thing, we could lower costs via making prediction markets on jury verdicts if a case were randomly choose for jury evaluation.

But alas, I’m skeptical that most would care much about what an independent jury is estimated to say, or even about what it actually says. For that, they’d have to care more about truth than about showing support for allies.

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Can Foundational Physics Be Saved?

Thirty-four years ago I left physics with a Masters degree, to start a nine year stint doing AI/CS at Lockheed and NASA, followed by 25 years in economics. I loved physics theory, and given how far physics had advanced over the previous two 34 year periods, I expected to be giving up many chances for glory. But though I didn’t entirely leave (I’ve since published two physics journal articles), I’ve felt like I dodged a bullet overall; physics theory has progressed far less in the last 34 years, mainly because data dried up:

One experiment after the other is returning null results: No new particles, no new dimensions, no new symmetries. Sure, there are some anomalies in the data here and there, and maybe one of them will turn out to be real news. But experimentalists are just poking in the dark. They have no clue where new physics may be to find. And their colleagues in theory development are of no help.

In her new book Lost in Math, theoretical physicist Sabine Hossenfelder describes just how bad things have become. Previously, physics foundations theorists were disciplined by a strong norm of respecting the theories that best fit the data. But with less data, theorists have turned to mainly judging proposed theories via various standards of “beauty” which advocates claim to have inferred from past patterns of success with data. Except that these standards (and their inferences) are mostly informal, change over time, differ greatly between individuals and schools of thought, and tend to label as “ugly” our actual best theories so far.

Yes, when data is truly scarce, theory must suggest where to look, and so we must choose somehow among as-yet-untested theories. The worry is that we may be choosing badly:

During experiments, the LHC creates about a billion proton-proton collisions per second. … The events are filtered in real time and discarded unless an algorithm marks them as interesting. From a billion events, this “trigger mechanism” keeps only one hundred to two hundred selected ones. … That CERN has spent the last ten years deleting data that hold the key to new fundamental physics is what I would call the nightmare scenario.

One bad sign is that physicists have consistently, confidently, and falsely told each other and the public that big basic progress was coming soon: Continue reading "Can Foundational Physics Be Saved?" »

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How To Fund Prestige Science

How can we best promote scientific research? (I’ll use “science” broadly in this post.) In the usual formulation of the problem, we have money and status that we could distribute, and they have time and ability that they might apply. They know more than we do, but we aren’t sure who is how good, and they may care more about money and status than about achieving useful research. So we can’t just give things to anyone who claims they would use it to do useful science. What can we do? We actually have many options. Continue reading "How To Fund Prestige Science" »

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