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Explain The Sacred

The following are 45 correlates that I’ve collected of things called “sacred”. I invite any of you to offer a theory of the sacred that explains as many of these as you can, as simply as you can. (And to suggests edits of this list.)

  1. Sacred things are highly (or lowly) valued. We politely revere, respect, & prioritize them.
  2. We revere sacred beliefs as well as acts. We feel dirty when thoughts go near illicit ones.
  3. Sacred is big, powerful, extraordinary. We fear, submit, & see it as larger than ourselves.
  4. Sacred things matter for our health, luck, and other outcomes we care about.
  5. We want the sacred “for itself”, rather than as a means to get other things.
  6. Sacred things are either more homogenous, or more unique, whichever is better.
  7. Sacred induces strong emotions: e.g., awe, joy, serenity, devotion, repulsion, & fear.
  8. We get emotionally attached to the sacred; our stance toward it is part of our identity.
  9. We desire to connect with the sacred, and to be more associated with it.
  10. To approach the sacred, we use self-control to purify ourselves, sacrifice, & commit. 
  11. We enjoy sacrificing for the sacred, to purify, & respect sacred, including via odd beliefs. 
  12. Sacred brings us comfort & consolation in hard times; losing it can feel devastating. 
  13. We affirm & learn sacred via mythic stories & accounts of how we & it fit in a universe.
  14. We have rules regarding how to approach sacred stuff, in part to protect us.
  15. The sacred isn’t for use by commoners, or for common purposes. 
  16. Shared views about the sacred bind, define, and distinguish social groups.
  17. Shared rituals & festivals bind & emotionally charge us, & help us to see sacred.
  18. We want associates to share our view of and attachment to the sacred.
  19. We get offended when others seem to deny our sacred views, and respond vigorously.
  20. We feel more equal to each other regarding sacred things; status matters less there.
  21. Either everyone (e.g. love) or very few (e.g. medicine) are entitled to opinions on sacred.
  22. Charismatic leaders motivate, get acceptance in part via appeals, connections to sacred. 
  23. Experts of the sacred are more prestigious & trusted, get more job security.
  24. Sacrificing for the sacred is seen as pro-social.
  25. Sacred things are sharply set apart and distinguished from the ordinary, mundane.
  26. Sacred things do not fit well with our animal natures, such as self-interest, competition.
  27. We dislike mixing sacred and mundane things together.
  28. We dislike money prices of sacred, & trades that get more mundane via less sacred.
  29. We dislike for-profit orgs of the sacred, relative to non-profits or government agencies. 
  30. Sacred things feel less limited by physics, & can seem to have unlimited possibilities.
  31. Sacred things really matter, fill deepest needs, complete us, make us pure, make all one.
  32. Sacred things last longer, and decay or break less. Sometimes eternal and unchanging.
  33. Sacred things are purer and cleaner, and closer to the ultimate core of existence.
  34. Sacred things have fewer random coincidences; their patterns mean something.
  35. Sacred things have fewer value conflicts with each other; you can have them all at once.
  36. It is harder to judge the relative value of sacred things, compared to mundane things.
  37. We understand the sacred poorly using cognitive rational analysis, or numbers.
  38. We understand the sacred better using intuition, flow, and creativity.
  39. How sacred things seem is less misleading; you can more trust their appearances.
  40. The sacred is mysterious, unlikely and even inconsistent. Who are we to question it?
  41. Sacred makes us stand outside ourselves, feel ecstasy, transcendence, different reality.
  42. We do not make or control the sacred, it makes and transforms us.
  43. Stuff (objects, dates, people, words, sounds) that touches the sacred gets sacred itself.
  44. We connect to sacred themes better via contact with sacred stuff.
  45. Over time, things that we often connect to tend to become sacred via nostalgia.

My attempt is described here.

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New Tax Career Agent Test

If that taxpayer approved, the taxes that he or she pays to the government could be diverted, and instead delivered to a “tax career agent“, who would have, in an auction, won and paid for the right to get such diverted payments from that particular taxpayer. For the government, this would like borrowing, i.e., a way to convert future tax payments into current revenue. This agent would now have incentives to advise and promote this taxpayer, but would have no unusual powers to influence this taxpayer’s behavior.

Previously I used a poll to estimate that career agents today who get 10% of client wages as a result raise those wages by 1.5% on average, suggesting that tax career agents (TCAs) who got ~20% of income might raise those same wages by 3%. But as this effect might be smaller for random workers, and as worker welfare gains would be less than wage gains, I estimated that TCAs raise worker welfare by ~1% on average, which at a real interest rate of 2% suggests a ~$20T present value to the world from adopting TCAs.

In my last post, I sketched a simple experiment design to test the TCA concept: give N random people TCAs, and track their income changes compared to N others who don’t. If TCAs raised wages by 0.3% per year, then given the usual random noise in wage changes, a ten year experiment with N=7000 seems sufficient, but an upper bound cost on this is ~$32M. Which is crazy cheap (~ a part in a million!) relative to TCA social value, but in our broken world we probably need something cheaper.

Here is my new concept: create a TCA for each worker, but get two auction prices per worker, one price if the TCA is active, i.e., free to promote and advise that worker, and a different price if the TCA is instead passive, i.e., prevented from helping this worker. Then randomly pick if the worker gets an active or passive TCA, and use the appropriate bids and prices to pick and charge the new TCA.

If there is sufficient competition in the bidding, then the difference between those two prices is a direct market estimate of how much bidders expect an active TCA to raise worker wages, minus the effort they expect an active TCA expect to put in to make this happen. This estimate is available per worker, and immediately at the experiment start. So even an N=100 experiment at a TCA expense cost of ~$1M for could give valuable data!

In addition to getting TCAs to estimate worker wage increases minus TCA costs, we might also want to get workers to estimate their welfare gains. And we could do this by putting workers into pairs, only one of which gets an active TCA, and making them bid against each other to see who gets that active TCA. Bids should give direct estimates of worker value (i.e., increased wages minus extra effort or inconvenience) if the winning bidder pays the lower bid price. These worker value estimates are also available per worker, and immediately at experiment start. And the extra revenue from worker bids cuts the cost of the experiment.

TCAs and workers would have strong incentives to make good estimates, but their estimates would still be based on pretty limited information. To get better informed estimates, it would help to spread this experiment out across time, and give later participants as much info as possible about earlier participant outcomes. The more time that elapses between the first and last TCA auctions, the more later participants will know, but the longer it will take to learn results from this experiment. Note that such a sequential approach also allows the experiment to better manage its expenses in the face of an initially uncertain costs per worker participant.

Here is a more detailed design based on the above concepts. Offer random workers a sufficient compensation (1% tax rebate?) so that most who are invited agree to participate for Y years. (If Y is short, pick post-college-age workers, so their choice of more schooling is less of an issue.) Participants allow substantial info on them, including their taxes, to be revealed to experimenters and other participants. Match participants into pairs who seem as similar as possible, then auction off these pairs one at a time in sequence over many years, showing all qualified bidders info on outcomes for all prior participants.

Each worker in each pair is asked for the bid B they would pay for a higher chance to be assigned the active, as opposed to passive, TCA for Y years. In addition, each pair auction has eight TCA auction prices, each qualified TCA bidder can bid on any or all of these eight prices, and the highest price wins each price auction, paying the second highest among its submitted prices. To prevent collusion within worker pairs, workers are given little info on their pair partners until they have set their bids.

The eight prices come from all combinations of three binary factors. First, there are the two workers, who will differ somewhat in their info. Second, there are different prices to become an active or passive TCA for Y years. Third, there are different prices depending on if the worker submitted the higher or lower bid to get the active TCA. Worker bids are kept secret until all eight TCA auction prices are set. Then the worker who bid more gets a 2/3 chance of being assigned the active TCA, and a 1/3 chance of being assigned the passive TCA. Given a bid B, we can estimate their added value V of having an active agent via V = 3B.

Note that at a 2% discount rate, the present value of 20% of the median US wage of $31K is ~$450K, 1% of which is ~$4.5K, implying a bid of ~$1.5K, an amount most workers can afford to pay.

This experimental design seems sufficient to extract key info re TCAs at a low cost. But it still needs more work. For example, we need tax experts to think about which parts of typical tax returns to include or not in TCA payments. We need finance experts to think about how to get sufficient numbers of competing TCA bidders, and how the experiment can hold and invest auction assets deposited, to minimize the costs and risks associated with paying off all TCAs as promised. We need labor experts to think about what worker info is sufficient to inform TCA bids. And we need legal experts to figure out how we can do all this within existing law. Any such experts want to help?

Added 20Nov: A similar test could be applied to my Buy Health proposal. For each possible patient get auction participants to bid on their price to provide health and life insurance separately, where different orgs provide the different types and aren’t allowed to coordinate, or as a bundle from a single org that can coordinate. See the per-patient estimated difference in death risk and medical spending.

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Testing Tax Career Agents

Agents who are paid a larger fraction of their client’s income have a stronger incentive to promote and advise those clients. Thus the tax career agent idea makes more sense for governments that tax a larger fraction of citizen income. So they make less sense for city or state governments. But a national government may not be willing to try it without seeing results from a smaller experiment. So how could we make a smaller experiment to test the concept?

The big advantage of tax career agents is that they are basically free to create. As the government already sits in that role, all it has to do is transfer that role to someone else, at almost no net cost to anyone. But to test the idea of tax career agents, we don’t have to rely on the fact that such agents are free for governments to create; we can pay extra to create such agents privately, just for the test.

To create a test tax career agent regarding client C, we could hold an auction to see who is willing to pay the most to, every year for the next Y years, be paid an amount equal to what C pays that year in income taxes. If agent A were a real tax career agent, the money paid to A would come from what C actually pays their government in taxes. But for the purpose of an experiment, this money paid to A could instead from the budget of the experiment. This alternate payment source should not matter much for A and C behavior.

So auction winners would first give large auction win payments to the experiment, after which the experiment would commit to paying them back each year when their clients pay taxes. In the meantime, the experiment would hold and invest these assets. As the experiment should be able to invest as well as agents, auction competition should induce the net cost of this experiment to be mainly the time and effort costs that agents expect to make advising and promoting clients.

In a prior post, I estimated that an agent A who got 20% of client C’s wages would increase those wages by 1-3%. As agents wouldn’t on average put in more efforts than they get paid for those efforts, that gives us an upper bound to the financial size of agent efforts. Thus if N clients with average income I each get a test tax career agent for Y years, the auction revenue to be collected and invested would be ~20%*Y*N*I, and the cost to create these agents would be ~1-3%*Y*N*I. (For Y large, these amounts are lower due to time discounting.) Note that much of this “cost” is actually a transfer to clients, who we expect to enjoy higher incomes.

Of course we’d want to track a similar-sized control group of N clients who didn’t get test tax career agents. And if we wanted to give experimental subjects the choice of if to create such an agent, then if only a fraction V volunteer to get such an agent, we’d want to track ~N/V workers who were offered the choice, and another ~N/V control workers not offered the choice. Note that we’d also need funds to manage the experiment, to collect data on participants, and to analyze the results.

And that’s a simple outline of the experiment design, including a rough estimate of its cost. In the U.S., 3% of $31K median income over ten years is $9.3K, which for N=1000 comes to $9.3M. This cost would of course be less for lower-income workers. Any want to do an analysis of what size N we’d want given to see significant results given this expected effect size?

Added 21Oct: Christopher McDonald did a Sample Size Analysis for Tax Career Agent Experiment. Assuming tax career agents improve wages on average by 0.3% per year, he finds that you’d need 7000 subjects over a ten year experiment to get a true-positive probability of 80% and a false-positive probability of 5%. So applying the same estimates as above, that gives an upper-bound cost of ~$30M. In my next post on this, I’ll outline a cheaper experiment design.

Added 12Nov: I just realized that I’d previously mis-calculated the wage rise to be 1-3%, instead of 10-30%. A smaller experiment would of course be required to see such a larger effect.

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Replace G.P.A. With G.P.C.?

Most schools assign each student a “grade point average”, i.e., a number that averages over many teacher evaluations of that student. Many schools also assign each teacher an “average student evaluation”, i.e., a number that averages over many student evaluations of that teacher. Many workplaces similarly post evaluations which average worker performance ratings across different tasks. And sport leagues often post rankings of teams, which average over team performance across many contests.

A lot rides on such metrics, even though they are simple aggregates over contests of varying difficulty, which creates incentives for players to “game” these metrics. For example, students seek to take, and teachers seek to teach, easy/fun classes; workers seek to do easy tasks, and sport teams seek to play easy opponents.

Yet we have long known of a better way, one I described briefly in 2001: stat-model-based summary evaluations.

For example, imagine that a college took all of their student grade transcripts as data, and from that made a best-fit statistical linear regression model. Such a model would predict the grade of each student in each class by using a linear combination of features of each class, such as subject, location, time of day and week, and also “fixed effects” for dates, professors, and especially students. That is, the regression formula would include a term in its sum for each student, a term that is a coefficient for that student, times one or zero depending on if that datum is about a grade for that student.

Such a fixed effects regression coefficient regarding a student should effectively correct for whether the student took easy or hard majors, classes, profs, times of day, year of degree, etc. Furthermore, standard stat methods would give us a “standard error” uncertainty range for this coefficient, so that we are not fooled into thinking we know this parameter more precisely than we do.

Thus a “grade point coefficient”, i.e., a G.P.C., should do better than a G.P.A. as a measure of the overall quality of each student. And the more that potential employers, grad schools, etc. focused on G.P.C.s instead of G.P.A.s, the less incentive students would have to search out easy classes, profs, etc. We could do the same for student evaluations of professors, and the more we relied on prof fixed effects to judge profs, then the less incentives they would have to teach easy classes, or to give students As to bribe them to give high evaluations.

The general idea is simple: fit performance data to a statistical model that estimates each performance outcome as a function of the various context parameters that one would expect to influence performance, plus a parameter representing the quality of each contestant. Then use those contestant parameter estimates as our best estimates of contestant quality. Such statistical models are pretty easy to construct, and most universities contain hundreds of people who are up to this task. And once such models are made and listened to, then contestants should focus more on improving their quality, and less on trying to game the evaluation metric.

Yes, as new data comes in, the models would get adjusted, meaning that contestant estimates would change a little over time, even after a contestant stopped having new performances. Yes, there will be questions of how many context parameters to include in such a model, but there are standard stat tools for addressing such questions. Yes, even after using such tools, there will remain some degrees of freedom regarding the types and functional forms of the model, and how best to encode key relevant factors. And yes, authorities can and would use those remaining degrees of freedom to get evaluation results more in their preferred directions.

But even so, this should be a huge improvement over the status quo. Instead of students looking for easy classes to get easier As, they’d focus instead on improving their overall abilities.

To prove this concept, all we need is one grad student (or exceptional undergrad) with stat training willing to try it, and one university willing to give that student access to their student transcripts (or student evals of profs). Once the models constructed passed some sanity tests, we’d try to get that university to let its students put their G.P.C.s onto their student transcripts. Then we’d try to get the larger world to care about G.P.C.s. So, who wants to try this?

P.S. I’ve posted previously on how broken are many of our eval systems, and how a better entry-level job eval system could allow such jobs to compete with college.

Added: This paper and this paper shows in detail how to do the stats.

One could get more than one useful number per student by adding terms that interact the student fixed effect terms with other features of classes. That second paper shows a two number system is more informative, but is rejected because “gains realized with the two-component index are offset by the additional complexity involved in explaining the two-component index to students, employers, college administrators and faculty.”

One might allow students to experiment with classes in new subjects by including a term that encodes such cases. One might include terms for race, gender, age, etc. of students, though I’d prefer transcripts to show student GPCs with and without such terms.

Added 17Oct: This book by Valen Johnson considers in detail models like those I describe above, wherein the performance of a student in a class is a linear combination of a student term, a class term, and an error. Except that sometimes instead of estimating a grade point, they instead estimate discrete grades, using several terms per class to describe the underlying parameter cutoffs between different discrete grades.

The student term sets an “adjusted GPA” and Johnson proposes to “allow students to optionally report adjusted GPAs on their transcripts.” He reports that when he attempted but failed to get Duke to do this in 1996, this was the biggest issue:

When the achievement index was considered for use as a mechanism to adjust GPAs for students at Duke, instructors who regularly assigned uniformly high grades quickly realized that the achievement index adjustment will make their grades irrelevant in the calculation of student GPAs. Worse still, many students notice the same thing. To thwart the adoption of the achievement index, these high-grading instructors and their student benefactors adopted the position that an A represented an objective assessment of student performance. An A was an A was an A. For them, it represented “excellent” performance on some well-defined but unobservable scale. Indeed, by the end of the debate, several literary theorists had finally identified an objective piece of text: a student grade. (p.222)

Apparently Johnson and others have long tried but failed to get schools to adopt GPCs and variations on them.

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More Academic Prestige Futures

Academia functions to (A) create and confer prestige to associated researchers, students, firms, cities, and nations, (B) preserve and teach what we know on many general abstract topics, and (C) add to what we know over the long run. (Here “know” includes topics where we are uncertain, and practices we can’t express declaratively.)

Most of us see (C) as academia’s most important social function, and many of us see lots of room for improvement there. Alas, while we have identified many plausible ways to improve this (C) function, academia has known about these for decades, and has done little. The problem seems less a lack of knowledge, and more a lack of incentives.

You might think the key is to convince the patrons who fund academia to change their funding methods, and to make funding contingent on adopting other fixes. After all, this should induce more of the (C) that we presume that patrons seek. Problem is, just like all the other parties involved, patron motives also focus on more on function (A) than on (C). That is, state, firm, and philanthropic patrons of academia mainly seek to buy what academia’s other customers, e.g., students and media, also buy: (A) prestige by association with credentialed impressiveness.

Thus offering better ways to fund (C) doesn’t help much. In fact, history actually moved in the other direction. From 1600 to 1800, science was mainly funded via prizes and infrastructure support. But then prestigious scientific societies pushed to replace prizes with grants. Grants give scientists more discretion, but are worse for (C). Scientists won, however; now grants are standard, and prizes rare.

But I still see a possible route to reform here, based on the fact that academics usually deny that their prestige is arbitrary, to be respected only because others respect it. Academics instead usually justify their prestige in function (A) as proxies for the ends of function (B,C). That is, academics tend to say that your best way to promote the preservation, teaching, and increase of our abstract knowledge is to just support academics according to their current academic prestige.

Today, academic prestige of individuals is largely estimated informally by gossip, based on the perceived prestiges of particular topics, institutions, journals, funding sources, conferences, etc. And such gossip estimates the prestige of each of these other things similarly, based on the prestige of their associations. This whole process takes an enormous amount of time and energy, but even so it attends far more to getting everyone to agree on prestige estimates, than to whether those estimates are really deserved.

Academics typically say that such sacred an end as intellectual progress is so hard to predict or control that it is arrogant of people like you to think you can see how to promote such things in any other way than to just give your money to the academics designated as prestigious by to this process, and let them decide what to do with it. And most of us have in fact accepted this story, as this is in fact what we mostly do.

Thus one way that we could hope to challenge the current academic equilibrium is to create better clearly-visible estimates of who or what contributes how much to these sacred ends. If academics came to accept another metric as offering more accurate estimates than what they now get from existing prestige processes, then that should pressure them into adjusting their prestige ratings to better match these new estimates. Which should then result in their assigning publications, jobs, grants etc. in ways that better promote such ends. Which should thus improve intellectual progress, perhaps by large amounts.

And as I outlined in my last post, we could actually create such new better estimates of who deserves academic prestige, via creating complex impact futures markets. Pay distant future historians (e.g., in a century or two) to judge then which of our academic projects (e.g., papers) actually better achieved adding to what we know. (Or also achieved preserving and teaching what we know.) Also create betting markets today that estimate those future judgments, and suggest to today’s academics and their customers that these are our best estimates of who and what deserve academic prestige. (Citations being lognormal suggests this system’s key assumptions are a decent approximation.)

These market prices would no doubt correlate greatly with the usual academic prestige ratings, but any substantial persistent deviations would raise a question: if, in assigning jobs, publications, grants, etc., you academics think you know better than these markets prices who is most likely to deserve academic prestige, why aren’t you or your many devoted fans trading in those markets to make the profits you think you see? If such folks were in fact trading heavily, but were resisted by outsiders with contrary strong opinions, that would look better than if they weren’t even bothering to trade on their supposed superior insight.

Academics seeking higher market estimates about they and their projects would be tempted to trade to push up those prices, even though their private info didn’t justify such a move. Other traders would expect this, and push prices back down. These forces would create liquidity in these markets, and subsidize trading overall.

Via this approach, we might reform academia to better achieve intellectual progress. So who wants to make this happen?

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Intellectual Prestige Futures

As there’s been an uptick of interest in prediction markets lately, in the next few posts I will give updated versions of some of my favorite prediction market project proposals. I don’t own these ideas, and I’d be happy for anyone to pursue any of them, with or without my help. And as my first reason to consider prediction markets was to reform academia, let’s start with that.

Back in 2014, I restated my prior proposals that research patrons subsidize markets, either on relatively specific results likely to be clearly resolved, such as the mass of the electron neutrino, or on simple abstract statements to be judged by a distant future consensus, conditional on such a consensus existing. Combinatorial markets connecting abstract questions to more specific ones could transfer their subsidizes to those the latter topics.

However, I fear that this concept tries too hard to achieve what academics and their customers say they want, intellectual progress, relative to what they more really want, namely affiliation with credentialed impressiveness. This other priority better explains the usual behaviors of academics and their main customers, namely students, journalists, and patrons. (For example, it was a bad sign when few journals showed interest in using prediction market estimates of which of their submissions were likely to replicate.) So while I still think the above proposal could work, if patrons cared enough, let me now offer a design better oriented to what everyone cares more about.

I’d say what academics and their customers want more is a way to say which academics are “good”. Today, we mostly use recent indicators of endorsement by other academics, such as publications, institutional affiliations, research funding, speaking invitations, etc. But we claim, usually sincerely, to be seeking indicators of long term useful intellectual impact. That is, we want to associate with the intellectuals about whom we have high and trustworthy shared estimates of the difference that their work will make in the long run toward valuable intellectual progress.

A simple way to do this would be to create markets in assets on individuals, where each asset pays as a function of a retrospective evaluation of that individual, an evaluation made in the distant future via detailed historical analysis. By subsidizing market makers who trade in such assets, we could today have trustworthy estimates to use when deciding which individuals among us we should consider for institutional affiliations, funding, speaking invitations, etc. (It should be easy for trade on assets that merge many individuals with particular features, such as Ph.Ds from a particular school.)

Once we had a shared perception that these are in fact our best available estimates, academics would prefer them over less reliable estimates such as publications, funding, etc. As the value of an individual’s work is probably non-linear in their rank, it might make sense to have people trade assets which pay as a related non-linear function of their rank. This could properly favor someone with a low median rank but high variance in that rank over someone else with a higher median but lower variance.

Why wait to evaluate? Yes, distant future evaluators would know our world less well. But they would know much better which lines of thought ended up being fruitful in a long run, and they’d have more advanced tech to help them study intellectual connections and lineages. Furthermore, compound interest would give us access to a lot more of their time. For example, at the 7% post-inflation average return of the S&P500 1871-2021, one dollar becomes one million dollars in 204 years. (At least if the taxman stays aside.)

Furthermore, such distant evaluations might only be done on a random fraction, say one percent, of individuals, with market estimates being conditional on such a future evaluation being made. And as it is likely cheaper to evaluate people who worked on related topics, it would make sense to randomly pick large sets of related individuals to evaluate together.

Okay, but having ample resources to support evaluations by future historians isn’t enough; we also need to get clear on the evaluation criteria they are to apply. First, we might just ask them to sort a sample of intellectuals relative to each other, instead of trying to judge their overall quality on some absolute scale. Second, we might ask them to focus on an individual’s contributions to helping the world figure out what is true on important topics; being influential but pushing in the wrong directions might count against them. Third, to correct for problems caused by scholars who play organizational politics, I’d rather ask future historians to rate how influential an individual should have been, if others had been a bit more fair in choosing to whom to listen.

The proposal I’ve sketched so far is relatively simple, but I fear it looks too stark; forcing academics to admit more than they’d like that the main thing they care about is their relative ranking. Thus we might prefer to pay a mild complexity cost to focus instead on having future historians rate particular works by intellectuals, such as their journal articles or books. We could ask future historians to rate such works in such a way that the total value of each intellectual was reasonably approximated by the sum of the values of each of their work’s.

Under this system, intellectuals could more comfortably focus on arguing about the the total future impact of each work. Derivatives could be created to predict the total value of all the works by an individual, to use when choosing between individuals. But everyone could claim that is just a side issue, not their main focus.

To pursue this project concept, a good first step would be to fund teams of historians to try to rank the works of intellectuals from several centuries ago. Compare the results of different historian teams assigned to the same task, and have teams seek evaluation methods that can be both reliable and also get at the key questions of actual (or counterfactual) impact on the progress that matters. Then figure out which kinds of historians are best suited to applying such methods, and which funding methods best induce them to do such work in a cost-effective manner.

With such methods in hand, we could with more confidence set up markets to forecast the impact of particular current intellectuals and their works. We’d probably want to start with particular academic fields, and then use success there to persuade other fields to follow their example. This seems easier the higher the prestige of the initial academic fields, and the more open are they all to using new methods.

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Make Law Like Couches, Not Cars

Action movies often show fights in complex environments like factories, ships, kitchens, warehouses, or construction sites. In such cases, whomever knows more details of that environment can have big advantages in the fight. However, when people instead try to arrange a “fair fight”, they usually choose simple environments that combatants can know similarly well, like an empty flat walled square or circle.

Lawyers often fill their offices with big shelves full of law books. As if to say “Law is a vast complex machine you wouldn’t want to mess with without access to an engineer like me who knows all its details.” Or more relevant, “The arena of law is as complex a place for a fight as is a factory or kitchen. You don’t want to fight there without a warrior like me who knows all those complex details.”

However, the essence of law is for a judge to hear A complain about B, and then to issue a ruling to reward or punish A or B. And the main point of such law is to induce better behavior via shared expectations of such rulings. For that purpose, what matters about the law is those shared prior expectations; further legal detail beyond that has little social value.

Actually, added legal complexity and detail can hurt, by tempting people to learn more legal details in order to gain strategic advantages. Just as warriors fighting in a kitchen would need to learn kitchen details, people with possible legal conflicts can need to learn about arbitrary legal details, or to hire lawyers who learn them, even if those details do little to help guide prior actions by A or B.

Imagine that two people will hold a verbal debate in some physical space. Law without needless details is like the debaters sitting on a simple couch to do their debate. Such a couch has little other structure besides that which is needed to coordinate their locations and orientations. Which is good.

Now imagine a couch with lots of little pockets holding weapons or controls to make the couch poke people, change shape, get hot, or make noises. Something like a car. If you were to be in a debate on such a complex couch, you’d want to invest in learning those details. For example, you might be able to poke your opponent out of view at just the right moment. Even though that is a social waste.

Is a minimal couch-like law possible? Consider juries. Imagine there is little formal law, so that juries can rule most any way they choose. In this case legal expectations are just expectations over jury rulings. So if A and B know the community from which jurors are chosen well enough, then they know that they have shared legal expectations. And they know that there’s not much either of them can do to gain more info on that. Their law is a couch, not a car.

Of course it is not enough just to have shared legal expectations; one also wants those expectations to do well at taking into account situation details known to both A and B. Thus one problem with a simple jury system is that random juries many not know important situation details that are known by both A and B. So each pair A and B might prefer that a case between be judged by a jury chosen from a community closer to them, so that this jury knows more of their shared context.

But you also couldn’t pick jurors who are too closely connected to A and B, as these might not be willing to function as independent jurors. So, for example, if A and B are both in the movie industry, it might make sense to give them a jury from the movie industry, who could then understand movie practices. But maybe not jurors who are currently working on the very same movie as they.

150 years ago, the US had something closer to this simple jury system, as stated laws were few and vague, juries made most decisions, lawyers were cheap and less often needed, and plea bargaining wasn’t yet much of a thing. Since then, US law has accumulated far more detail. Yet little of this detail seems to be an adaptation to a more complex world; most is just random. And we must pay lawyers who learn this detail if we hope to win at court.

Worse, regulations greatly restrict who can be a lawyer, slower more expensive legal processes add to our costs, and few of us have sufficient assets to pay if we lose. Thus US law has rotted in a great many ways. When will we notice that, and consider big changes?

By the way, one feature that we might want in a legal system is an ability ask it for prior approval for behavior. “Would it be legal or not-negligent if I did it this way?” And you might hope that a very detailed legal system could at least offer this advantage over a simple jury-based version. You’d just look up the relevant detailed law. But in fact our very complex detailed legal system doesn’t offer this feature. You just can’t ask what acts might be legal; you can only do stuff and find out later if you are punished.

Added 11a: Jury decisions can vary. To reduce the impact of that in particular parties, we could  have the consequences for them be set by prediction markets on jury decisions. Those market predictions would be far more consistent across cases.

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Want Your Complaint Heard?

People like to complain; social media is full of it. But such complaints seem less than fully satisfying, perhaps because we usually complain to third parties. Maybe what we really want is to know that the target of our complaint heard and understood it. If so, let’s make that possible.

Imagine that you feel a complaint coming on. So you go to YouHurtMe.Com, and navigate down a hierarchy of possible complaint target groups, to reach specific options like “White people who think they aren’t racist”, “Women who think they are too good for a man like me”, or “Students who grade grub to improve a B+”. You could pick larger encompassing target groups, or define some even more specific targets.

Once you find your target group, you next pick your specific complaint, such as “You actually are racist”, “You aren’t as good as you think”, or “Be grateful for your B+”. If you don’t see your complaint listed there, you can add one. Once you’ve declared yourself a complainer of this type, you can browse some essays expressing that complaint, and vote on which essay looks best.

Or you might add your own new essay for consideration. But your essay must be civil, and include at least one multiple-choice comprehension test question at the end.

Targets of complaints can also come to the website. They may sincerely want to hear complaints made against people like them, and want to show those who make such complaints that they’ve heard and understood them. The system asks each new visitor some questions designed to quickly identify complaints targeted at them. (They can refuse to answer some questions.)

They then select a matching complaint, like “You actually are racist”, read the top voted essay, and take the ending comprehension test. Having passed the test, they are now publicly listed among targets who have heard and understood the complaint.

We might want to allow those who’ve heard complaints about them to respond in some way, though perhaps that risks too much acrimony. Less problematically, we might allow compliments as well as complaints to be created and heard via this same structure.

But, bottom line: it seems quite feasible to let complainers know that the targets of their complaints have heard and understood them. Which is what complainers often say is the main thing they want: to be heard.

From a conversation with Agnes Callard.

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Shoulda-Listened Futures

Over the decades I have written many times on how prediction markets might help the intellectual world. But usually my pitch has been to those who want to get a better actionable info out of intellectuals, or to help the world to make better intellectual progress in the long run. Problem is, such customers seem pretty scarce. So in this post I want to outline an idea that is a bit closer to a business proposal, in that I can better identify concrete customers who might pay for it.

For every successful intellectual there are (at least) hundreds of failures. People who started out along a path, but then were not sufficiently rewarded or encouraged, and so then either quit or persisted in relative obscurity. And a great many of these (maybe even a majority) think that the world done them wrong, that their intellectual contributions were underrated. And no doubt many of them are right. Such malcontents are my intended customers.

These “world shoulda listened to me” customers might pay to have some of their works evaluated by posterity. For example, for every $1 saved now that gains a 3% real rate of return, $19 in real assets are available in a century to pay historians for evaluations. At a 6% rate of return (or 3% for 2 centuries), that’s $339. Furthermore, if future historians needed only to randomly evaluate 1% of the works assigned them, then if malcontents paid $10 per work to be maybe evaluated, historians could spend $20K (or $339K) per work they evaluate. Considering all the added knowledge and tools to which future historians may have access, that seems enough to do a substantial evaluation, especially if they evaluate several related works at the same time.

Given a substantial chance (1% will do) that a work might be evaluated by historians in a century or two, we could then create (conditional) prediction markets now estimating those future evaluations. So a customer might pay their $20 now, and get an immediate prediction market estimate of that future evaluation for their work. That $20 might pay $10 for the (chance of a) future evaluation and another $10 to establish and subsidize a prediction market over the coming centuries until resolution.

Finally, if customers thought market estimate regarding their works looked too low, then they could of course try to bet to raise those estimates. Skeptics would no doubt lie waiting to bet against them, and on average this tendency of authors to bet to support their works would probably subsidize these markets, and so lower the fees that the system needs to charge.

Of course even with big budgets for evaluations, if we want future historians to make reliable enough formal estimates that we can bet on in advance, then we will need to give them a well-defined-enough task to accomplish. And we need to define this task in a way that discourages future historians from expressing their gratitude to all these people who funded their work by giving them all an A+.

I suggest we have future historians estimate each work’s ideal attention: how much attention each particular work should have been given during some time period. So we should pick some measure of attention, a measure that we can calculate for works when they are submitted, and track over time. This measure should weigh if the dissertation was approved, the paper was published and where, how many cites did it get, etc. If we add up all the initial attention for submitted works, then we can assign historians the task of (counterfactually) reallocating this total attention across all the submitted works. So to give more attention to some, they’d have to take away attention from others.

Okay, so now they can’t give every work an A+. (And we ensure that bet assets have bounded values.) But our job isn’t done. We also need to give them a principle to follow when allocating attention among all these prior works. What objective would they be trying to accomplish via this reallocation of attention?

I suggest that the objective just be intellectual progress, toward the world having access to more accurate and useful beliefs. A set of works should have gotten more attention if in that case the world would have been more likely to have more quickly come to appreciate valuable truths. And this task is probably easier if we ask future historians to use their future values in this task, instead of asking them to try to judge according to our values today.

These evaluation tasks probably get easier if historians randomly pick related sets of works to evaluate together, instead of independently picking each work to evaluate. And this system can probably offer scaled fees, wherein the chance that your work gets evaluated rises linearly with the price you paid for that chance. There are probably a lot more details to work out, but I expect I’ve already said enough for most people to decide roughly how much they like this idea.

Once there were many works in this system, and many prediction markets estimating their shoulda-been attention, then we could look to see if market speculators see any overall biases in today’s intellectual worlds. That is, topics, methods, disciplines, genders, etc. to which speculators estimate that the world today is giving too little attention. That could be pretty dramatic and damning evidence of bias, by someone, evidence to which we’d all be wise to attend.

One obvious test of this approach would be to assign historians today the task of reallocating attention among papers published a century or two ago. Perhaps assign multiple independent groups, and see how correlated are their evaluations, and how that correlation varies across topic areas. Perhaps repeating in a decade or two, to see how much evaluations drift over time.

Showing these correlations to potential customers might convince them that there’s a good enough chance that such a system will later correctly vindicate their neglected contributions. And these tests may show good scopes to use, for related works and time periods to evaluate together, and how narrow or broad should be the expertise of the evaluators.

This whole shoulda-listened-futures approach could or course also be applied to many other kinds of works, not just intellectual works. You’d just have to establish your standards for how future historians are to allocate shoulda attention, and trust them to actually follow those standards. Doing tests on works from centuries ago here could also help to show if this is a viable approach for these kinds of works.

Added 7am 28Apr: On average more assets will be available to pay for future evaluations if the fees paid are invested in risky assets. So instead of promising a particular percentage chance of evaluation, it may make more sense to specify how fees will be invested, set the (real) amount to be spent on each evaluation, and then promise that the chance of evaluation for each work will be set by the investment return relative to the initial fee paid. Yes that induces more evaluations in state of the world where investments do better, but customers are already accepting a big chance that their work will never be directly evaluated.

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Try-Two Contest Board

Imagine that a restaurant wants to ask its associates (cooks, servers, etc.) what are the best two menu items to put on its menu as specials on a particular night. They have a large set of possible menu items to consider, the measure of success is menu item sales revenue, and they want a mechanism that is both fun and easy. (Which rules out conditional prediction markets, at least for now.)

Here’s an idea. Start with a contest board like this, on a wall near associates:

Continue reading "Try-Two Contest Board" »

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