This book explores some of the complexities of what motivates humans. It attacks a stereotype that says only financial rewards matter, and exaggerates the extent to which people adopt that fallacy. His style is similar to Malcolm Gladwell’s, but with more substance than Gladwell.

The book’s advice is likely to cause some improvement in how businesses are run and in how people choose careers. But I wonder how many bosses will ignore it because their desire to exert control over people outweighs their desire to create successful companies.

I’m not satisfied with the way he and others classify motivations as intrinsic and extrinsic. While feelings of flow may be almost entirely internally generated, other motivations that he classifies as intrinsic seem to involve an important component of feeling that others are rewarding you with higher status/reputation.

Shirking may have been a been an important problem a century ago for which financial rewards were appropriate solutions, but the nature of work has changed so that it’s much less common for workers to want to put less effort into a job. The author implies that this means standard financial rewards have become fairly unimportant factors in determining productivity. I think he underestimates the importance they play in determining how goals are prioritized.

He believes the changes in work that reduced the importance of financial incentives was the replacement of rule-following routine work with work that requires creativity. I suggest that another factor was that in 1900, work often required muscle-power that consumed almost as much energy as a worker could afford to feed himself.

He states his claims vaguely enough that they could be interpreted as implying that broad categories of financial incentives (including stock options and equity) work poorly. I checked one of the references that sounded like it might address that (”When performance-related pay backfires”), and found it only dealt with payments for completing specific tasks.

His complaints about excessive focus on quarterly earnings probably have some value, but it’s important to remember that it’s easy to err in the other direction as well (the dot-com bubble seemed to coincide with an unusual amount of effort at focusing on earnings 5 to 10 years away).

I’m disappointed that he advises not to encourage workers to compete against each other without offering evidence about its effects.

One interesting story is the bonus system at Kimley-Horn and Associates, where any employee can award another employee $50 for doing something exceptional. I’d be interested in more tests of this – is there something special about Kimley-Horn that prevents abuse, or would it work in most companies?

He had a mathematical formula which he thinks rigorously defines intelligence. But he didn’t specify what he meant by the set of possible environments, saying that would be a 50 page paper (he said a good deal of the work on the test had been done last week, so presumably he’s still working on the project). He also included a term that applies Occam’s razor which I didn’t completely understand, but it seems likely that that should have a fairly non-controversial effect.

The environments sound like they imitate individual questions on an IQ test, but with a much wider range of difficulties. We need a more complete description of the set of environments he uses in order to evaluate whether they’re heavily biased toward what Monte-Carlo AIXI does well or whether they closely resemble the environments an AI will find in the real world. He described two reasons for having some confidence in his set of environments: different subsets provided roughly similar results, and a human taking a small subset of the test found some environments easy, some very challenging, and some too hard to understand.

It sounds like with a few more months worth of effort, he could generate a series of results that show a trend in the AIQ of the best AI program in any given year, and also the AIQ of some smart humans (although he implied it would take a long time for a human to complete a test). That would give us some idea of whether AI workers have been making steady progress, and if so when the trend is likely to cross human AIQ levels. An educated guess about when AI will have a major impact on the world should help a bit in preparing for it.

A more disturbing possibility is that this test will be used as a fitness function for genetic programming. Given sufficient computing power, that looks likely to generate superhuman intelligence that is almost certainly unfriendly to humans. I’m confident that sufficient computing power is not available yet, but my confidence will decline over time.

Brian Wang has a few more notes on this talk

There are only a few basic stories. If you think in stories, that means you are telling yourself the same thing over and over

but his point that stories allow storytellers to manipulate our minds deserves more emphasis. For me, one of the hardest parts of learning how to beat the stock market was to admit that I did poorly when I was influenced by stories, and did well mainly when I relied on numbers that are available and standardized for most companies, and on mechanical rules which varied little between companies (I sometimes use different rules for different industries, but beyond that I try to avoid adapting my approach to different circumstances).

For example, The stories I heard about Enron’s innovative management style gave me a gut feeling that it was a promising investment. But its numbers showed an uninteresting company, and persuaded me to postpone any investment.

But I’ve only told you a story here (it’s so much easier to do than provide rigorous evidence). If you really want good reasons, try testing for yourself story versus non-story approaches to something like the stock market.

(HT Patri).

This book does an impressive job of replacing ad hoc rules of statistics with rigorous logic, but it is difficult enough to fully understand that most people will only use small parts of it.

He emphasizes that probability theory consists of logical reasoning about the imperfect information we have, and repeatedly rants against the belief that probabilities or randomness represent features of nature that exist independent of our knowledge. Even something seemingly simple such as a toss of an ordinary coin cannot have some objectively fixed frequency unless concepts such as “toss” are specified in unreasonable detail. What we think of as randomness is best thought of as a procedure for generating results of which we are ignorant.

He derives his methods from a few simple axioms which appear close to common sense, and don’t look much like they are specifically designed to produce statistical rules.

He is careful to advocate Bayesian methods for an idealized robot, and avoids addressing questions of whether fallible humans should sometimes do something else. In particular, his axiom that the robot should never ignore information is a goal that will probably reduce the quality of human reasoning in some cases where there’s too much information for humans to handle well.

I’m convinced that when his methods can be properly applied and produce different results than frequentist methods do, we should reject the frequentist results. But it’s not obvious how easy it is to apply his methods properly, nor is it obvious whether he has accurately represented the beliefs of frequentists (who I suspect often don’t think clearly enough about the issues he raises to be clearly pinned down).

He does a good job of clarifying the concept of “induction”, showing that we shouldn’t try to make it refer to some simple and clearly specified rule, but rather we should think of it as a large set of rules for logical reasoning, much like the concept of “science”.

This book makes a good argument that a major change from strongly hierarchical societies to fairly egalitarian societies happened to the human race sometime after it diverged from Chimpanzees and Bonobos. Not due to any changes in attitudes toward status, but because language enabled low-status individuals to cooperate more effectively to restrain high-status individuals, and because of he equalizing effects of weapons. Hunter-gatherer societies seem rather consistently egalitarian, and the partial reversion to hierarchy in modern times may be due to the ability to accumulate wealth or the larger size of our societies.

He provides a plausible hypothesis that this change enabled group selection to become more powerful than in a typical species, but that doesn’t imply that group selection became as important as within-group selection, and he doesn’t have a good way of figuring out how important the effect was.

He demonstrates that humans became more altruistic, using a narrow biological definition of altruism, but it’s important to note that this only means agreeing to follow altruistic rules. He isn’t able to say much about how well people follow those rules when nobody notices what they’re doing.

Much of the middle of the book recounting anthropological evidence can be skipped without much loss – the most important parts are chapters 8 and 9.

This book analyzes will, mainly problems connected with willpower, as a form of intertemporal bargaining between a current self that highly values immediate temptation and future selves who prefer that current choices be more far-sighted. He contrasts simple models of rational agents who exponentially discount future utility with his more sophisticated and complex model of people whose natural discount curve is hyperbolic. Hyperbolic discounting causes time-inconsistent preferences, resulting in problems such as addiction. Intertemporal bargains can generate rules which bundle rewards to produce behavior more closely approximating the more consistent exponential discount model.

He also discusses problems associated with habituation to rewards, and strategies that can be used to preserve an appetite for common rewards. For example, gambling might sometimes be rational if losing money that way restores an appetite for acquiring wealth.

Some interesting ideas mentioned are that timidity can be an addiction, and that pain involves some immediate short-lived reward (to draw attention) in addition to the more obvious negative effects.

For someone who already knows a fair amount about psychology, only small parts of the book will be surprising, but most parts will help you think a bit clearer about a broad range of problems.

John Salvatier has drawn my attention to a paper describing A Practical Liquidity-Sensitive Automated Market Maker [pdf] which fixes some of the drawbacks of the Automated Market Maker that Robin Hanson proposed.

Most importantly, it provides a good chance that the market maker makes money in roughly the manner that a profit-oriented human market maker would.

It starts out by providing a small amount of liquidity, and increases the amount of liquidity it provides as it profits from providing liquidity. This allows markets to initially make large moves in response to a small amount of trading volume, and then as a trading range develops that reflects agreement among traders, it takes increasingly large amounts of money to move the price.

A disadvantage of following this approach is that it provides little reward to being one of the first traders. If traders need to do a fair amount of research to evaluate the contract being traded, it may be that nobody is willing to inform himself without an expectation that trading volume will become significant. Robin Hanson’s version of the market maker is designed to subsidize this research. If we can predict that several traders will actively trade the contract without a clear-cut subsidy, then the liquidity-sensitive version of the market maker is likely to be appropriate. If we can predict that a subsidy is needed to generate trading activity, then the best approach is likely to be some combination of the two versions. The difficulty of predicting how much subsidy is needed to generate trading volume leaves much uncertainty.

[Updated 2010-07-01:
I've reread the paper more carefully in response to John's question, and I see I was confused by the reference to "a variable b(q) that increases with market volume". It seems that it is almost unrelated to what I think of as market volume, and is probably better described as related to the market maker's holdings.

That means that the subsidy is less concentrated on later trading than I originally thought. If the first trader moves the price most of the way to the final price, he gets most of the subsidy. If the first trader is hesitant and wants to see that other traders don't quickly find information that causes them to bet much against the first trader, then the first trader probably gets a good deal less subsidy under the new algorithm. The latter comes closer to describing how I approach trading on an Intrade contract where I'm the first to place orders.

I also wonder about the paper's goal of preserving path independence. It seems to provide some mathematical elegance, but I suspect the market maker can do better if it is allowed to make a profit if the market cycles back to a prior state.
]

This short book has a few interesting ideas.

The most surprising ones involve favorable claims about China’s collectivist period (but without any claim that that period was better overall).

China under Mao apparently had a fairly decentralized economic system, with reasonable performance-based incentives for local officials, which meant that switching to functioning capitalism required less change than in Russia.

Chinese health apparently improved under Mao (in spite of famine), possibly more than it has since, at least by important measures such as life expectancy. This is reportedly due to more organized and widespread measures against ordinary communicable diseases under collectivism.

That wouldn’t mean prohibiting orders that act a lot like market orders. People could still be allowed to place an order to sell at a limit of a penny. But having an explicit limit price would discourage people from entering orders that under rare conditions end up being executed at a price 99 percent lower than expected.

It wouldn’t even require that people take the time to type in a limit price. Systems could be designed to have a pseudo-market order that behaves a lot like existing market orders, but which has a default limit price that is, say, 5 percent worse than the last reported price.

However, it’s not obvious to me that those of us who didn’t sell at ridiculously low prices should want any changes in the system. Moderate amounts of money were transferred mainly from people who mistakenly thought they were sophisticated traders to people who actually were. People who are aware that they are amateurs rarely react fast enough to declines to have done anything before prices recovered. The decline looked like it was primarily the result of stop-loss strategies, and it’s hard to implement those without at least superficially imitating an expert investor.