Tag Archives: Project

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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Open Policy Evaluation

Hypocrisy is a tribute vice pays to virtue. La Rochefoucauld, Maximes

In some areas of life, you need connections to do anything. Invitations to parties, jobs, housing, purchases, business deals, etc. are all gained via private personal connections. In other areas of life, in contrast, invitations are made open to everyone. Posted for all to see are openings for jobs, housing, products to buy, business investment, calls for proposals for contracts and grants, etc. The connection-only world is often suspected of nepotism and corruption, and “reforms” often take the form of requiring openings to be posted so that anyone can apply.

In academia, we post openings for jobs, school attendance, conference attendance, journal publications, and grant applications for all to see. Even though most people know that you’ll actually need personal connections to have much of a chance for many of these things. People seems to want to appear willing to consider an application from anyone. They allow some invitation-only conferences, talk series, etc., but usually insist that such things are incidental, not central to their profession.

This preference for at least an appearance of openness suggests a general strategy of reform: find things that are now only gained via personal connections, and create an alternate open process whereby anyone can officially apply. In this post, I apply this idea to: policy proposals.

Imagine that you have a proposal for a better policy, to be used by governments, businesses, or other organizations. How can you get people to listen to your proposal, and perhaps endorse it or apply it? You might try to use personal connections to get an audience with someone at a government agency, political interest group, think tank, foundation, or business. But that’s stuck in the private connection world. You might wait for an agency or foundation to put out an open call for proposals, seeking a solution to exactly the problem your proposal solves. But for any one proposal idea, you might wait a very long time.

You might submit an article to an open conference or journal, or submit a book to a publisher. But if they accept your submission, that mostly won’t be an endorsement of whether your proposal is good policy by some metric. Publishers are mostly looking at other criteria, such as whether you have an impressive study using difficult methods, or whether you have a book thesis and writing style that will attract many readers.

So I propose that we consider creating an open process for submitting policy proposals to be evaluated, in the hope of gaining some level of endorsement and perhaps further action. This process won’t judge your submission on wit, popularity, impressiveness, or analytical rigor. Their key question is: is this promising as a policy proposal to actually adopt, for the purpose of making a better world? If they endorse your proposal, then other actors can use that as a quality signal regarding what policy proposals to consider.

Of course how you judge a policy proposal depends on your values. So there might be different open policy evaluators (OPE) based on different sets of values. Each OPE needs to have some consistent standards by which they evaluate proposals. For example, economists might ask whether a proposal improves economic efficiency, libertarians might ask if it increases liberty, and progressives might ask whether it reduces inequality.

Should the evaluation of a proposal consider whether there’s a snowball chance in hell of a proposal being actually adopted, or even officially considered? That is, whether it is in the “Overton window”? Should they consider whether you have so far gained sufficient celebrity endorsements to make people pay attention to your proposal? Well, those are choices of evaluation criteria. I’m personally more interested in evaluating proposals regardless of who has supported them, and regardless of their near-term political feasibility. Like how academics say we do today with journal article submissions. But that’s just me.

An OPE seems valid and useful as long as its actual choices of which policies it endorses match its declared evaluation criteria. Then it can serve as a useful filter, between people with innovative policy ideas and policy customers seeking useful ideas to consider and perhaps implement. If you can find OPEs who share your evaluation criteria, you can consider the policies they endorse. And of course if we ever end up having many of them, you could focus first on the most prestigious ones.

Ideally an OPE would have funding from some source to pay for its evaluations. But I could also imagine applicants having to pay a fee to have their proposals considered.

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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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Toward Micro-Likes

Long ago when electricity and phones were new, they were largely unregulated, and privately funded. But then as the tech (and especially the interfaces) stopped changing so fast, and showed big scale and network economies, regulation stepped in. Today social media still seems new. But as it hasn’t been changing as much lately, and it also shows large scale and network economies, many are talking now about heavier regulation. In this post, let me suggest that a lot more change is possible; we aren’t near the sort of stability that electricity and phones reached when they became heavily regulated.

Back in the early days of the web and internet people predicted many big radical changes. Yet few then mentioned social media, the application now most strongly associated with this new frontier. What did we miss? The usual story, which I find plausible, is that we missed just how much people love to get many frequent signals of their social connections: likes, retweets, etc. Social media gives us more frequent “attaboy” and “we see & like you” signals. People care more than we realized about the frequency, relative to the size, of such signals.

But if that’s the key lesson, social media should be able to move a lot further in this direction. For example, today Facebook has two billion monthly users and produces four million likes per minute, for an average of about three likes per day per monthly user. Twitter has 300 million monthly users, who send 500 million tweets per day, for less than two tweets per day per monthly user. (I can’t find stats on Twitter likes or retweets.) Which I’d say is actually a pretty low rate of positive feedback.

Imagine you had a wall-sized screen, full of social media items, and that while you browsed this wall the direction of your gaze was tracked continuously to see which items your gaze was on or near. From that info, one could give the authors or subjects of those items far more granular info on who is paying how much attention to them. Not only on how often how much your stuff is watched, but also on the mood and mental state of those watchers. If some of those items were continuous video feeds from other people, then those others could be producing many more social media items to which others could attend.

Also, so far we’ve usually just naively counted likes, retweets, etc., as if everyone counted the same. But we could instead use non-uniform weights based on popularity or other measures. And given how much people like to participate in synchronized rituals, we could also create and publicize statistics on what groups of people are how synchronized in their social media actions. And offer new tools to help them synchronize more finely.

My point here isn’t to predict or recommend specific changes for future social media. I’m instead just trying to make the point that a lot of room for improvement remains. Such gains might be delayed or prevented by heavy regulation.

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The Model to Beat: Status Rank

There’s been much discussion of income inequality over the last few years. However, I just randomly came across what should be a seminal related result, published in 2010 but mostly ignored. Let me do my bit to fix that.

People often presume that policy can mostly ignore income inequality if key individual outcomes like health or happiness depend mainly on individual income. Yes, there may be some room for promoting insurance against income risk, but not much room. However, people often presume that policy should pay a lot more attention to inequality if individual outcomes depend more directly on the income of others, such as via envy or discouragement.

However, there’s a simple and plausible income interdependence scenario where inequality matters little for policy: when outcomes depend on rank. If individual outcomes are a function of each person’s percentile income rank, and if social welfare just adds up those individual outcomes, then income policy becomes irrelevant, because this social welfare sum is guaranteed to always add up to the same constant. Income-related policy may influence outcomes via other channels, but not via this channel. This applies whether the relevant rank is global, comparing each person to the entire world, or local, comparing each person only to a local community.

That 2010 paper, by Christopher Boyce, Gordon Brown, and Simon Moore, makes a strong case that in fact the outcome of life satisfaction depends on the incomes of others only via income rank. (Two followup papers find the same result for outcomes of psychological distress and nine measures of health.) They looked at 87,000 Brits, and found that while income rank strongly predicted outcomes, neither individual (log) income nor an average (log) income of their reference group predicted outcomes, after controlling for rank (and also for age, gender, education, marital status, children, housing ownership, labor-force status, and disabilities). These seem to me remarkably strong and robust results. (Confirmed here.)

The irrelevance of individual income and reference group income remained true whether the group within which a person was ranked was the entire sample, one of 19 geographic regions, one of 12 age groups, or one of six gender-schooling groups. This suggests that the actual relevant comparison group is relatively narrow. If people cared mainly about their global rank in the whole sample, then analyses of rank within groups should have missed an effect of the rank of the group, which should have appeared as an effect of reference group income. But such effects weren’t seen.

It these statistical models were the correct model of the world, then income policy could only include influence social welfare via the control variables of age, gender, education, marital status, children, housing ownership, labor-force status, and disabilities. You couldn’t improve social welfare directly by redistributing income, though redistribution or taxation might help by changing control variables.

But even that conclusion seems premature. The key idea here is that people care about their social status rank, and income should only be one of many factors contributing to social status. So we should really be looking at models where all of a person’s observable features can contribute to their status. For each feature, such as personality or marital status, we should ask if our data is best described as that factor contributing directly to social status, which is then ranked to produce individual outcomes, or whether that factor also influences individual outcomes via some other channel, that doesn’t pass through social status. It is only effects via those other channels that might change overall social welfare.

This seems a straightforward statistical exercise, at least for someone with access to relevant data. Who’s up for it?

Added 3 June 2020: More data supporting rank hypothesis.

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All Pay Liability

We could raise government revenue much more efficiently than we now do, with less damage to the economy for any given amount of revenue raised. For example, we could tax fixed characteristics like height instead of income, we could tax traffic congestion a lot more, and we could do better at taxing pollution, including carbon. Recently I posted on a more efficient system of property taxes, that allows more revenue to be raised at a lower cost. Today, I’ll post on a more efficient system of accident liability, which similarly raises more revenue at a lower cost.

Some don’t want me to talk about these things. They hope to “starve the beast” by drying up government revenue sources. That seems to me a lost cause, the sort of logic that pushed radicals toward generic destruction, hoping that eventually the masses would get fed up and revolt. I instead expect a better world overall if governments adopt more efficient policies, including more efficient tax policies.

Regarding accident liability, we want a system that will encourage good levels of care and activity by all who can influence accident rates. For example, regarding car accidents we want drivers to pick good car models, speeds, sleep, and maintenance frequencies. We also want them to take into account the possibility of hurting others via accidents when they choose how often they drive. In addition, we want a system that induces fewer actual court cases, which are expensive, and that asks courts to make fewer judgements, in which they might err.

The simplest system is no liability. Courts just don’t get involved. This has the lowest possible rate of court cases, namely zero. It creates good incentives for accident victims to set their care and activity levels well, but gives rather poor incentives for others to set such things well.

The next simplest system is strict liability. This induces good care and activity by potential injurers, but not from potential victims. It also induces a high rate of court cases; nearly every accident results in a lawsuit. While the parties might settle out of court, if a case goes to trial the court must determine responsibility, i.e., who caused the accident, and how much damages the victim suffered as a result.

Relative to strict liability, systems of negligence cut the rate of court cases, but at the cost of asking courts to make more judgements. As with strict liability, courts must judge who is responsible and victim damage levels. But in addition, courts must also ask themselves if that injurer took enough care to prevent the accident. For each of visible parameter, the courts must judge both the actual level of care taken, and the optimal level of care.If the injurer took enough care overall, that injurer does not owe damages. And if that no damages situation is the usual case, there are fewer court cases, as there are fewer lawsuits.

In practice, however, courts can only look at a small number of injurer choice parameters visible enough to them, such as driving speed. Far more parameters, including all injurer activity level parameters, remain invisible, and so are not considered. Negligence doesn’t create good incentives to set all those less visible parameters.

There are standard variations on these systems, such as allowing contributory negligence on the part of the victim. But all of these systems fail to induce optimal levels of care and activity in someone. We have long known, however, of a simple system that gets pretty much all of these things right, and in addition only asks courts to judge who is responsible for an accident and victim damage levels. (I didn’t invent this system; it is mentioned in many law & econ texts.) In this simple system, courts do not need to consider anyone’s actual or ideal levels of care or activity.

This simple system is to make all responsible parties pay the damage levels of all other parties hurt by the accident. The trick is that they pay all of these amounts to the government, instead of to each other. As each party now internalizes all of the damage suffered by all of the parties, they should choose all their private care and activity levels well. And the government gets more revenue to boot.

The big problem with this all-pay liability system is that none of these responsible parties, including the victims, want to report this accident to the government. They’d all rather pretend it didn’t happen. So the government needs some other way to find out about accidents. In dense areas where they government already has access to mass surveillance systems, they can just use those systems. In other areas, governments might offer bounties to third parties who report accidents, and put strong penalties on those who fail to report their own accidents. Or the system might revert to other liability rules in contexts where governments might otherwise detect accidents too infrequently.

With all-pay liability, we expect a lawsuit for every accident. But in that suit the courts only need to judge who is responsible and victim damage levels. No other judgements need be made. So if we could find simple streamlined ways to make these judgements, this system might not be that expensive to administer. And then we’d have both better accident prevention and more available government revenue.

(Yes, people might want to buy insurance against the risk of making these payments. Yes, if multiple parties could coordinate to prevent accidents together, this system might induce them to spend too much on prevention. Hopefully we could identify such efforts and treat them differently.)

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Yay Stability Rents

Six years ago I posted on the idea of using combinatorial auctions as a substitute for zoning. Since then, news on how badly zoning has been messing up our economy has only gotten worse. I included the zoning combo auction idea in my book The Age of Em, I’ve continued to think about the idea, and last week I talked about it to several LA-based experts in combinatorial auctions.

I’ve been pondering one key design problem, and the solution I’ve been playing with is similar to a solution that also seems to help with patents. I asked Alex Tabarrok, whose office is next door, if he knew of any general discussion of such things, and he pointed me to a long (110 page) 2016 paper called “Property is another name for monopoly” by Eric Posner and Glen Weyl. (See also this technical paper.) And that turned out to be a relatively general argument for using the specific mechanism that I was considering using in zoning combo auctions, get this, as a new standard kind of property right for most everything! Looking for web discussion, I find a few critical responses, and one excellent 2014 Interfuildity post on the basic idea. In this post I’ll go over the basic idea and some of its issues, including two that Posner and Weyl didn’t consider. Continue reading "Yay Stability Rents" »

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A Call To Adventure

I turn 58 soon, and I’m starting to realize that I may not live long enough to finish many of my great life projects. So I want to try to tempt younger folks to continue them. Hence this call to adventure.

One way to create meaning for your life is join a grand project. Or start a new one. A project that is both obviously important, and that might also bring you personal glory, if you were to made a noticeable contribution to it.

Yes, most don’t seek meaning this way. But many of our favorite fictional characters do. If you are one of the few who find grand adventures irresistibly romantic, then this post is for you. I call you to adventure.

Two great adventures actually, in this post. Both seem important, and in the ballpark of doable, at least for the right sort of person.

ADVENTURE ONE: The first adventure is to remake collective decision-making via decision markets (a.k.a. futarchy). Much of the pain and loss in the world results from bad decisions by key organizations, such as firms, clubs, cities, and nations. Some of these bad decisions result because actors with the wrong mix of values hold too much power. But most result from our not aggregating info well; people who could have or did know better were not enticed enough to share what they know. Or others didn’t believe them.

We actually know of a family of simple robust mechanisms that typically do much better at aggregating info. And we have a rough idea of how organizations could use such mechanisms. We even had a large academic literature testing and elaborating these mechanisms, resulting in a big pile of designs, theorems, software, computer simulations, lab tests, and field tests. We don’t need more of these, at least for now.

What we need is concrete evolution within real organizations. Like most good abstract ideas, what this innovation most needs are efforts to work out variations that can fit well in particular existing organization contexts. That is, design and try out variations that can avoid the several practical obstacles that we know about, and help identify more such obstacles to work on.

This adventure less needs intellectuals, and more sharp folks willing to get their hands dirty dealing with the complexities of real organizations, and with enough pull to get real organizations near them to try new and disruptive methods.

Since these mechanisms have great potential in a wide range of organizations, we first need to create versions that are seen to work reliably over a substantial time in concrete contexts where substantial value is at stake. With such a concrete track record, we can then push to get related versions tried in related contexts. Eventually such diffusion could result in better collective decision making worldwide, for many kinds of organizations and decisions.

And you might have been one of the few brave far-sighted heroes who made it happen.

ADVENTURE TWO: The second adventure is to figure out real typical human motives in typical familiar situations. You might think we humans would have figured this out long ago. But as Kevin Simler and I argue in our new book The Elephant in the Brain: Hidden Motives in Everyday Life, we seem to be quite mistaken about our basic motives in many familiar situations.

Kevin and I don’t claim that our usual stated motives aren’t part of the answer, only that they are much less than we like to think. We also don’t claim to have locked down the correct answer in all these situations. We instead offer plausible enough alternatives to suggest that the many puzzles with our usual stories are due to more than random noise. There really are systematic hidden motives behind our behaviors, motives substantially different from the ones we claim.

A good strategy for uncovering real typical human motives is to triangulate the many puzzles in our stated motives across a wide range of areas of human behavior. In each area specialists tend to think that the usual stated motive deserves to be given a strong prior, and they rarely think we’ve acquired enough “extraordinary evidence” to support the “extraordinary claims” that our usual stated motives are wrong. And if you only ever look at evidence in a narrow area, it can be hard to escape this trap.

The solution is expect substantial correlations between our motives in different areas. Look for hidden motive explanations of behaviors that can simultaneously account for puzzles in a wide range of areas, using only a few key assumptions. By insisting on a high ratio of apparently different puzzles explained to new supporting assumptions made, you can keep yourself disciplined enough not to be fooled by randomness.

This strategy is most effective when executed over a lifetime. The more different areas that you understand well enough to see the key puzzles and usual claims, the better you can triangulate their puzzles to find common explanations. And the more areas that you have learned so far, the easier it becomes to learn new areas; areas and methods used to study them tend to have many things in common.

This adventure needs more intellectual heroes. While these heroes may focus for a time on studying particular areas, over the long run their priority is to learn and triangulate many areas. They seek simple coherent accounts that explain diverse areas of human behavior. To figure out what the hell most humans are actually up to most of the time. Which we do not actually know now. And which would enable better policy; today policy reform efforts are often wasted due to mistaken assumptions about actual motives.

Wouldn’t someone who took a lifetime to help work that out be a hero of the highest order?

Come, adventures await. For the few, the brave, the determined, the insightful. Might that be you?

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