Monthly Archives: December 2012

More signaling

Centurion: Where is Brian of Nazareth?
Brian: You sanctimonious bastards!
Centurion: I have an order for his release!
Brian: You stupid bastards!
Mr. Cheeky: Uh, I’m Brian of Nazareth.
Brian: What?
Mr. Cheeky: Yeah, I – I – I’m Brian of Nazareth.
Centurion: Take him down!
Brian: I’m Brian of Nazareth!
Victim #1: Eh, I’m Brian!
Mr. Big Nose: I’m Brian!
Victim #2: Look, I’m Brian!
Brian: I’m Brian!
Victims: I’m Brian!
Gregory: I’m Brian, and so’s my wife!

– Monty Python’s Life of Brian

It’s easy for everyone to claim to be Brian. What Brian (and those who wish to identify him) need is a costly signal: an action that’s only worth doing if you are Brian, given that anyone who does the act will be released. In Brian’s life-or-death situation it is pretty hard to arrange such a thing. But in many other situations, costly signals can be found. An unprotected posture can be a costly signal of confidence in your own fighting ability, if this handicap is small for a competent fighter but dangerous for a bad fighter. College can act as a costly signal of diligence, if lazy, disorganized people who don’t care for the future would find attending college too big a cost for the improved job prospects.

A situation requires costly signaling when one party wishes to treat two types of people differently, but both types of people want to be treated in the better way. An analogous way to think of this as a game is that Nature decides between A or -A, then the sender looks at Nature’s choice, and gives a signal to the receiver, B or -B. Then the receiver takes an action, C or -C. The sender always wants the receiver to do C, but the receiver wants to do C if A and -C if -A. To stop the sender from lying, you can modify the costs to the sender of B and -B.

Suppose instead that the sender and the receiver perfectly agreed: either both wanted C always, or both wanted C if A and -C if -A. Then the players can communicate perfectly well even if all of the signals are costless – the sender has every reason to tell the receiver the truth.

If players can have these two kinds of preferences, and you have two players, these are the two kinds of signaling equilibria you can have (if the receiver always wants C, then he doesn’t listen to signals anyway).

Most of the communication in society involves far more than two players. But you might suppose it can be basically decomposed into two player games. That is, if two players who talk to each other both want C iff A, you might suppose they can communicate costlessly, regardless of who the first got the message from and where the message goes to. If the first one always wants C, you might expect costly signaling. If the second does, you might expect the message to be unable to pass that part in the chain. This modularity is important, because we mostly want to model little bits of big communication networks using simple models.

Surprisingly, this is not how signaling pairs fit together. To see this, consider the simplest more complicated case: a string of three players, playing Chinese Whispers. Nature chooses, the sender sees and tells an intermediary, who tells a receiver, who acts. Suppose the sender and the intermediary both always want C, while the receiver wants to act appropriately to Nature’s choice. By the above modular thesis, there will be a signaling equilibrium where the first two players talk honestly for free, and the second and third use costly signals between them.

Suppose everyone is following this strategy: the sender tells the intermediary whatever she sees, and the intermediary also tells the receiver honestly, because when he would like to lie the signal to do so is too expensive. Suppose you are the sender, and looking at Nature you see -A. You know that the other players follow the above strategy. So if you tell the intermediary -A, he will transmit this to the receiver, though he would rather not modulo signal prices. And that’s too bad for you, because you want C.

Suppose instead you lie and say A. Then the intermediary will pay the cost to send this message to the receiver, since he assumes you too are following the above set of strategies. Then the receiver will do what you want: C. So of course you lie to the intermediary, and send the message you want with all the signaling costs of doing so accruing to the intermediary. Your values were aligned with his before taking into account signaling costs, but now they are so out of line you can’t talk to each other at all. Given that you behave this way, he will quickly stop listening to you. There is no signaling equilibrium here.

In fact to get the sender to communicate honestly with the intermediary, you need the signals between the sender and the intermediary to be costly too. Just as costly as the ones between the intermediary and the receiver, assuming the other payoffs involved are the same for each of them. So if you add an honest signaling game before a costly signaling game, you get something that looks like two costly signaling games.

For example, take a simple model where scientists observe results, and tell journalists, who tell the public. The scientist and the journalist might want the public to be excited regardless of the results, whereas the public might want to keep their excitement for exciting results. In order for journalists who have exciting news to communicate it to the public, they need to find a way of sending signals that can’t be cheaply imitated by the unlucky journalists. However now that the journalists are effectively honest, scientists have reason to misrepresent results to them. So before information can pass through the whole chain, the scientists need to use costly signals too.

If you have an arbitrarily long chain of people talking to each other in this way, with any combination of these two payoff functions among the intermediaries, everyone who starts off always wanting C must face costly signals, of the same size as if they were in an isolated two player signaling game. Everyone who wants C iff A can communicate for free. It doesn’t matter whether communicating pairs are cooperative or not, before signaling costs. So for instance a whole string of people who apparently agree with one another can end up using costly signals to communicate because the very last one talks to someone who will act according to the state of the world.

So such things are not modular in the way you might first expect, though they are easily predicted by other simple rules. I’m not sure what happens in more complicated networks than strings. The aforementioned results might influence how networks form, since in practice it should be effectively cheaper overall to direct information through smaller numbers of people with the wrong type of payoffs. Anyway, this is something I’ve been working on lately. More here.

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Death Is Very Sad

Tolstoy’s The Death of Ivan Ilyich is a simple but heart-breaking story of a dying man. In this passage, Ivan finds it very hard to translate his far outside view about his death to a near inside view:

Ivan Ilych saw that he was dying, and he was in continual despair.

In the depth of his heart he knew he was dying, but not only was he not accustomed to the thought, he simply did not and could not grasp it.

The syllogism he had learnt from Kiesewetter’s Logic: “Caius is a man, men are mortal, therefore Caius is mortal,” had always seemed to him correct as applied to Caius, but certainly not as applied to himself. That Caius — man in the abstract — was mortal, was perfectly correct, but he was not Caius, not an abstract man, but a creature quite, quite separate from all others. He had been little Vanya, with a mamma and a papa, with Mitya and Volodya, with the toys, a coachman and a nurse, afterwards with Katenka and will all the joys, griefs, and delights of childhood, boyhood, and youth. What did Caius know of the smell of that striped leather ball Vanya had been so fond of? Had Caius kissed his mother’s hand like that, and did the silk of her dress rustle so for Caius? Had he rioted like that at school when the pastry was bad? Had Caius been in love like that? Could Caius preside at a session as he did? “Caius really was mortal, and it was right for him to die; but for me, little Vanya, Ivan Ilych, with all my thoughts and emotions, it’s altogether a different matter. It cannot be that I ought to die. That would be too terrible.”

Such was his feeling.

“If I had to die like Caius I would have known it was so. An inner voice would have told me so, but there was nothing of the sort in me and I and all my friends felt that our case was quite different from that of Caius. and now here it is!” he said to himself. “It can’t be. It’s impossible! But here it is. How is this? How is one to understand it?”

He could not understand it, and tried to drive this false, incorrect, morbid thought away and to replace it by other proper and healthy thoughts. But that thought, and not the thought only but the reality itself, seemed to come and confront him. (more)

We could each gain great insight into ourselves if only we could consistently take the features we believe apply to many folks around us, and honestly ask ourselves if they apply to us as well. Folks around us are often boring, failures, irritating, misguided, vain, and, yes, dying. Are we?

In Tolstoy’s story the people around Ivan overwhelming cared about how Ivan’s death would affect them. They were eager to appear like the proper sort of caring person, but in fact didn’t care much. To comfort themselves, they preferred to blame Ivan for his problems, and refused to directly acknowledge that he was in fact dying.

Reading reviews of the story, I find that some (e.g.) also prefer to blame Ivan for his sad death. Tolstoy presents Ivan as a flawed person living a flawed life, and reviewers seem to think that Tolstoy was saying this is why his death was sad. Which seems to me to miss the point: no matter how your life went your death will be sad, especially since most around you will be focused more on how your death affects them than on how it affects you.

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Beware Gods Out There

Bryan Caplan notes that we’d actually treat X-men quite differently from the stories:

In the X-men comics, t.v. series, and movies, normal humans instinctively treat super-powered mutants with fear and disgust. The popular mutant policy options are: (a) register them as deadly weapons, (b) preemptively imprison them, or (c) kill them one and all.

Is this how real-world humans would actually react to the emergence of super-humans? I seriously doubt it. As long as the mutants accepted conventional norms of their societies, we’d treat them like celebrities or sports stars. Each country would take nationalistic pride in “their” mutants, just as each country now takes pride in their freakishly talented countrymen in the Olympics. …

If 5% of mutants tried to seize power, existing authorities would almost certainly recruit the remaining 95% to defend themselves – and hasten to add that “The best defense is a good offense.” If the U.S. and U.S.S.R. could competitively embrace former Nazi scientists after World War II, it’s hard to believe that the world’s leading governments would ever decide, “The only good mutant is a dead mutant.” (more)

As it happens, I just re-watched the first three episodes of the original Star Trek TV series, all of which were about super-powerful human-like beings, seen as monsters to be killed or isolated. In the third episode, a brush with something just outside the galaxy kick-starts rapid ESP-power growth in a few crew members, who then get big heads about it, and so must be killed:

KIRK: You must help me. Before it goes too far.
DEHNER: What he’s doing is right for him and me.
KIRK: And for humanity? You’re still human.
KIRK: At least partly, you are, or you wouldn’t be here talking to me.
DEHNER: Earth is really unimportant. Before long, we’ll be where it would have taken mankind millions of years of learning to reach.
KIRK: What will Mitchell learn in getting there? Will he know what to do with his power? Will he acquire the wisdom?
DEHNER: Please go back while you still can.
KIRK: Did you hear him joke about compassion? Above all else, a god needs compassion. Mitchell! Elizabeth.
DEHNER: What do you know about gods?
KIRK: Then let’s talk about humans, about our frailties. As powerful as he gets, he’ll have all that inside him.
DEHNER: Go back.
KIRK: You were a psychiatrist once. You know the ugly, savage things we all keep buried, that none of us dare expose. But he’ll dare. Who’s to stop him? He doesn’t need to care. Be a psychiatrist for one minute longer. What do you see happening to him? What’s your prognosis, Doctor? (more)

After they kill him they apparently never go back to this place again, even though it had done the same thing to a previous ship. In the real world, of course, groups would eagerly be sending ships to the area in the hope of creating their own gods, or becoming gods themselves.

Do we understand why fiction and reality are so different here? I think so – resisting an illicit dominator is our most common hero story, and early TV writers seeking a mass audience for stories set “out there” naturally focused on the very human scenario of humans becoming extreme out there. So of course they tell stories of how out there makes people into powerful illicit dominators, who heros resist.

Beware: powerful illicit dominators resisted by heroes remains an all too tempting story for us to tell about our future as well.

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Reasons To Reject

A common story hero in our society is the great innovator, opposed by villains who unthinkingly reject the hero’s proposed innovation, merely because it requires a change from the past. To avoid looking like such villains, most of us give lip service to innovation, and try not to reject proposals just because they require change.

On the other hand, our world is extremely complex, with lots of opaque moving parts. So most of us actually have little idea why most of those parts are they way they are. Thus we usually don’t know much about the effects of adopting any given proposal to change the status quo, other than that it will probably make things worse. Because of this, we need a substantial reason to endorse any such proposal; our default is rejection.

So we are stuck between a rock and a hard place – we want both to reject most proposals, and to avoid seeming to reject them just because they require change, even though we don’t specifically know why they would be bad ideas. Our usual solution: rationalization.

That is, we are in the habit of collecting reasons why things might be bad ideas. There might be inequality or manipulation, the rich might take control, it might lead to war, the environment might get polluted, mistakes might be made, regulators might be corrupted, etc. With a library of reasons to reject in hand, we can do simple pattern matching to find reasons to reject most anything. We can thus continue to pretend to be big fans of innovation, saying that unfortunately in this case there are serious problems.

I see (at least) two signs that suggest this is happening. The first sign is that my students are usually quick to name reasons why any given proposal is a bad idea, but it takes them lots of training to be able to elaborate in any detail why exactly a reason they name would make a proposal bad. For example, if they can identify anything about the proposal that would involve some people knowing secrets that others do not, they are quick to reject a proposal because of “asymmetric information.” But few are ever able to offer a remotely coherent explanation of the harm of any particular secret.

The other sign I see is when people consider the status quo as a proposal, but do not know that it actually is the status quo, they seem just as quick to find reasons why it cannot work, or is a bad idea. This is dramatically different from their eagerness to defend the status quo, when they know it is the status quo. When people don’t know that something actually works now, they assume that it can’t work.

This habit of pattern matching to find easy reasons to reject implies that would-be innovators shouldn’t try that hard to respond to objections. If you compose a solid argument to a particular objection, most people will then just move to one of their many other objections. If you offer solid arguments against 90% of the objections they could raise, they’ll just assume the other 10% holds the reason your proposal is a bad idea. Even having solid responses to all of their objections won’t get you that far, since most folks can’t be bothered to listen to them all, or even notice that you’ve covered them all.

Of course as a would be innovator, you should still listen to objections. But not so much to persuade skeptics, as to test your idea. You should honestly engage objections so that you can refine, or perhaps reject, your proposal. The main reason to listen to those with whom you disagree is: you might be wrong.

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Why don’t futurists try harder to stay alive?

A significant share of the broader ‘singularitarian’ community believes that they have a chance to live for hundreds of years, if they can survive until the arrival of an AI singularity, whole brain emulation, or just the point at which medical technology is advancing fast enough to keep extending our health-span by at least a year each year (meaning we hit ‘escape velocity‘ and can live indefinitely). Some are sufficiently hopeful about this to have invested in cryonics plans, hoping to be revived in the future, including Robin Hanson. Many others plan to do this, or think they should. (For what it’s worth, I am not yet convinced cryonics is worth the money – for reasons I am writing up – but I do think it warrants serious consideration.)

But there are much more mundane ways of increasing the chance of making it to this glorious future: exercise regularly, eat a nutritious diet low in refined carbohydrates, don’t smoke or hang around those who do, drink in moderation, avoid some illegal drugs, develop strong social supports to lower suicide and other mental health threats, have a secure high-status job, don’t live in an urban area, don’t ride a motorbike, get married (probably), and so on. While the futurist community isn’t full of seriously unhealthy or reckless people, nor does it seem much better in these regards than non-futurists with the same education and social class. A minority enjoy nutritional number crunching, but I haven’t observed diets being much better overall. None of the other behaviours are noticeably better.

I am fairly confident that the lowest hanging fruit would be raising fitness levels, which may even be lower among us than the general population. In addition to the immediate benefits regular and strenuous exercise has on confidence, happiness and productivity, it makes you live quite a bit longer. One study suggests that just 15 minutes of moderate exercise per day adds three years to your life expectancy (HT XKCD).

Now, maybe you are skeptical that those few years will allow you to live long enough to reach the end of involuntary death. Probably they won’t, but the whole life extension approach is to bank on a low chance of a giant payoff (living for hundreds or thousands of years). Furthermore, as the Singularity Institute has compellingly argued, we should not think we can confidently predict when AGI will be invented, if at all. The same is true to a lesser extent of progress towards whole brain emulation, or ending ageing. Furthermore, cryonics preservation procedures, and the selection of organisations that offer cryonics are gradually improving. Extending your life by five to ten years by doing all the ordinary things right could really make the difference; at least anyone considering gambling on cryonics should surely also find regular jogging worth their time.

I have even heard smart people claim that there is no need to worry about staying healthy because new technology will cure any diseases you get by the time you get them. But uncertainty about how soon such technologies will appear, combined with the high potential reward of living a little longer, would suggest exactly the opposite.

If I had to provide a cynical explanation for this apparently conflicting behaviour, I would suggest people are signing up for cryonics, or engaging in nutritional geekery, to signal their rationality and membership of a particular social clique. Going to the gym, even if it is a better bet for extending your life, doesn’t currently have the same effect. If you fear you’re stuck in that or some similar trap, consider using Stickk or Beeminder to make sure you do the rational thing.

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Future Filter Fatalism

One of the more colorful vignettes in philosophy is Gibbard and Harper’s “Death in Damascus” case:

Consider the story of the man who met Death in Damascus. Death looked surprised, but then recovered his ghastly composure and said, ‘I am coming for you tomorrow’. The terrified man that night bought a camel and rode to Aleppo. The next day, Death knocked on the door of the room where he was hiding, and said I have come for you’.

‘But I thought you would be looking for me in Damascus’, said the man.

‘Not at all’, said Death ‘that is why I was surprised to see you yesterday. I knew that today I was to find you in Aleppo’.

That is, Death’s foresight takes into account any reactions to Death’s activities.

Now suppose you think that a large portion of the Great Filter lies ahead, so that almost all civilizations like ours fail to colonize the stars. This implies that civilizations almost never adopt strategies that effectively avert doom and allow colonization. Thus the mere fact that we adopt any purported Filter-avoiding strategy S is strong evidence that S won’t work, just as the fact that you adopt any particular plan to escape Death indicates that it will fail.

To expect S to work we would have to be very confident that we were highly unusual in adopting S (or any strategy as good as S), in addition to thinking S very good on the merits. This burden might be met if it was only through some bizarre fluke that S became possible, and a strategy might improve our chances even though we would remain almost certain to fail, but common features, such as awareness of the Great Filter, would not suffice to avoid future filters.

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Silly Mayans

In my morning paper, today’s possible apocalypse was mentioned in five comics, but no where else. I’ve heard many mention the issue of the last few weeks, but mostly mocking it; none seem remotely concerned. Why so many mentions of something so few believe? To mock it of course – to enjoy feeling superior to fools who take such things seriously.

So are we ridiculing only those who fear apocalypse based on ancient predictions, or all who fear apocalypse? Alas, as I’ve discussed before, it seems we ridicule all of them:

On average, survivalists tend to display undesirable characteristics. They tend to have extreme and unrealistic opinions, that disaster soon has an unrealistically high probability. They also show disloyalty and a low opinion of their wider society, by suggesting it is due for a big disaster soon. They show disloyalty to larger social units, by focusing directly on saving their own friends and family, rather than focusing on saving those larger social units. And they tend to be cynics, with all that implies. (more)

Over the years I’ve met many folks who say they are concerned about existential risk, but I have yet to see any of them do anything concrete and physical about it. They talk, write, meet, and maybe write academic papers, but seem quite averse to putting one brick on top of another, or packing away an extra bag of rice. Why?

Grand disaster is unlikely, happens on a large scope, and probably far away in time, all of which brings on a very far view, wherein abstract talk seems more apt than concrete action. Also, since far views are more moral and idealistic, people seem especially offended about folks preparing selfishly for disaster, and especially keen to avoid that appearance, even at the expense of not preparing.

This seems related to the wide-spread rejection of cryonics in a world that vastly overspends on end of life medicine; more folks pay a similar amount to launch their ashes into space than try to extend life via cryonics. The idea of trying to avoid the disaster of death by returning in a distant future also invokes a far view, wherein we more condemn selfish acts and leaving-the-group betrayal, are extra confident in theories saying it won’t work, and feel only weak motivations to improve things.

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Today Is Filter Day

By tracking daily news fluctuations, we can have fun, join in common conversations, and signal our abilities to track events and to quickly compose clever commentary. But for the purpose of forming accurate expectations about the world, we attend too much to such news, and neglect key constant features of our world and knowledge.

So today, let us remember one key somber and neglected fact: the universe looks very dead. Yes, there might be pockets of life hiding in small corners, but for billions of years billions of galaxies full of vast resources have been left almost entirely untouched and unused. While we seem only centuries away making a great visible use of our solar system, and a million years from doing the same to our galaxy, any life out there seems unable, uninterested, or afraid to do the same. What dark fact do they know that we do not?

Yes, it is possible that the extremely difficultly was life’s origin, or some early step, so that, other than here on Earth, all life in the universe is stuck before this early extremely hard step. But even if you find this the most likely outcome, surely given our ignorance you must also place a non-trivial probability on other possibilities. You must see a great filter as lying between initial planets and visibly expanding civilizations, and wonder how far along that filter we are. In particular, you must estimate a substantial chance of “disaster”, i.e., something destroying our ability or inclination to make a visible use of the vast resources we see. (And this disaster can’t be an unfriendly super-AI, because that should be visible.)

Assume that since none of the ~1020 planets we see has yet given rise to a visible expanding civilization, each planet has a less than one in 1020 chance of doing so. If so, what fraction of this 1020+ filter do you estimate still lies ahead of us? If that fraction were only 1/365, then we face at least a 12% chance of disaster. Which should be enough to scare you.

To make sure we take the time to periodically remember this key somber fact, I propose that today, the day before winter solstice, the darkest day of the year, be Filter Day. I pick the day before to mock the wishful optimistic estimate that only 1/365 of the total filter remains ahead of us. Perhaps if you estimate that 1/12 of the filter still lies ahead, a filter we have less than a 2% chance of surviving, you should commemorate Filter Day one month before winter solstice. But then we’d all commemorate on different days, and so may not remember to commemorate at all.

So, to keep it simple, today is Filter Day. Take a minute to look up at the dark night sky, see the vast ancient and unbroken deadlands, and be very afraid.

What other activities makes sense on Filter Day? Visit an ancient ruin? A volcano? A nuclear test site? The CDC? A telescope?

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Not Science, Not Speculation

I often hear this critique of my em econ talks: “This isn’t hard science, so it is mere speculation, where anyone’s guess is just as good.”

I remember this point of view – it is the flattering story I was taught as a hard science student, that there are only two kinds of knowledge: simple informal intuition, and hard rigorous science:

Informal intuition can help you walk across a street, or manage a grocery list, but it is nearly hopeless on more abstract topics, far from immediate experience and feedback. Intuition there gives religion, mysticism, or worse. Hard science, in contrast, uses a solid scientific method, without which civilization would be impossible. On most subjects, there is little point in arguing if you can’t use hard science – the rest is just pointless speculation. Without science, we should just each user our own intuition.

The most common hard science method is deduction from well-established law, as in physics or chemistry. There are very well-established physical laws, passing millions of empirical tests without failure. Then there are well-known approximations, with solid derivations of their scope. Students of physical science spend years doing problem sets, wherein they practice drawing deductive conclusions from such laws or approximations.

Another standard hard science method is statistical inference. There are well-established likelihood models, well-established rules of thumb about which likelihood models work with which sorts of data, and mathematically proven ways to both draw inferences from data using likelihood models, and to check which models best match any given data. Students of statistics spend years doing problems sets wherein they practice drawing inferences from data.

Since hard science students can see that they are much better at doing problem sets than the lessor mortals around them, and since they know there is no other reliable route to truth, they see that only they know anything worth knowing.

Now, experienced practitioners of most particular science and engineering disciplines actually use a great many methods not reducible to either of these methods. And many of these folks are well aware of this fact. But they are still taught to see the methods they are taught as the only reliable route to truth, and to see social sciences and humanities, which use other methods, as hopeless delusional, wolves of intuition in sheep’s clothing of apparent expertise.

I implicitly believed this flattering story as a hard science student. But over time I learned that it is quite wrong. Humans and their civilizations have collected a great many methods that improve on simple unaided intuition, and today in many disciplines and fields of expertise the experienced and studied have far stronger capacities than the inexperienced and unstudied. And these useful such methods are not remotely we’ll summarized as formal statistical inference or deduction from well-established laws.

In economics, the discipline I know best, we often use deduction and statistical inference, and many of our models look at first glance like approximations derived from well-established fundamental results. But our well-established results have many empirical anomalies, and are often close to tautologies. We often have only weak reasons to expect many common model assumptions. Nevertheless, we know lots, much embodied in knowing when which models are how useful.

Our civilization gains much from our grand division of labor, where we specialize in learning different skills. But a cost is that it can take a lot of work to evaluate those who specialize in other fields. It just won’t do to presume that only those who use your methods know anything. Much better is to learn to become expert in another field in the same way others do; but this is usually way too expensive.

Of course, I don’t mean to claim that all specialists are actually valuable to the rest of us. There probably are many fraudulent fields, best abolished and forgotten, or at least greatly reformed. But there just isn’t a fast easy way to figure out which are those fields. You can’t usually identify a criminal just by their shifty eyes; you usually have look at concrete evidence of crime. Similarly, you can’t convict a field of fraud based on your feeling that their methods seem shifty. You’ll have to look at the details.

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Grounded Visionaries

I recently got to spend ten minutes explaining prediction markets to a (nice, smart) software billionaire. He had already been exposed to the basic idea, but from me he came to understand the larger potential for markets on decision consequences. He said they could be useful inside for-profit firms, like hedge funds. I suggested that software firms could also benefit from better estimates on user satisfaction, rates of bugs, and making deadlines. He quickly countered that software visionaries, in charge of implementing an unusual vision, shouldn’t be held to the conventional wisdom of a crowd.

The conversation moved before I could reply that prediction markets aren’t about crowds or conventional wisdom, and that even unconventional concepts can gain from grounded estimates on their implementation details. Alas this seems another example of the usual excuse making; even those who see big gains from prediction markets elsewhere tend to find excuses for why such gains are not to be found in their organization.

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