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The Wisdom of Crowds
The thesis of the book, as the author states at the beginning:
under the right circumstances, groups are remarkably intelligent, and are often smarter than the smartest people in them. Groups do not need to be dominated by exceptionally intelligent people in order to be smart.
The book begins with a series of examples of “crowd wisdom”, ranging from the TV show “Who Wants to be a Millionaire?” and its “ask the audience”, to the stock market indicating the company most likely to be at fault hours after the Challenger disaster. These cases all demonstrate the four conditions that comprise wise crowds - independence, diversity of opinion, decentralization, and a way to aggregate the results. Similar results are to be found in sports betting, and in Google’s results, determined by examining the number of links pointing to any given page. One way to take advantage of this wisdom of crowds is through the use of ”prediction markets”, such as the Iowa Electronic Markets, where people buy and sell probabilities as if they were stocks. In the right circumstances, prediction markets are an excellent way of turning the knowledge of many people into reasonably accurate predictions.
The importance of diversity is covered in the second chapter. A crowd can’t be wise if everyone always picks the same answer as everyone else. Examples include product markets, where there is usually an initial wide range of different attempts in a new market, which is quickly winnowed down to the successful designs; and honey bees, which send out scouts in all directions, but only return to those areas where flowers have been found. Diversity is important to “wise crowds”, because it expands the range of possible solutions proposed. In large groups, diversity comes naturally, but in smaller groups, it’s necessary to support and actively encourage it, to avoid the dangers of “groupthink”. When people give in to their conformist tendencies, and are afraid to stick their necks out, the quality of decisions suffers.
Independence of action and thought is important for the wisdom of crowds. If everyone thinks alike, then they’re less likely to arrive at a good answer to a given problem, because they’re less likely to fall into “groupthink”. “The more influence we exert on each other, the more likely it is that we will believe the same things and make the same mistakes”.
American Football coaching is cited an example of the “herd mentality”, based on the work of David Romer examining the “best 4th down strategy” (pdf). It turns out that statistically, most teams would be better off trying to make the touchdown or 1st down, rather than going for the field goal, in many cases. However, since the accepted wisdom is to kick, going against the grain of the relatively small pool of decision makers (professional football coaches) would not be an easy choice to make consistently, especially for the risk averse.
Herding behavior often occurs because people seek safety in numbers, but it can lead to problematic results when independence is required. “Information cascades” are what occurs when an initial decision is made by a few people, and then more or less accepted uncritically by more and more people. This isn’t necessarily a recipe for disaster, as we can’t all evaluate everything in our lives, but must trust others to come to good conclusions. However, at times, it can be disastrous when the original information and decisions were wrong, but continue to be accepted by an ever-wider circle. Luckily, for most people, the more important a decision is, the more likely they are to examine the facts themselves, rather than simply fall in line. Information cascades actually work reasonably well much of the time, but the basic problem is that they are a sequential, rather than parallel process. If you’re trying to harness the wisdom of crowds, you must attempt to have all decisions made at the same time, rather than one at a time.
This chapter covers decentralization - where it works, where it doesn’t and what can go wrong. Decentralized, aggregate behavior is a key aspect of things like free market economies, flocks of birds, and is something that has been touted as a virtuous way of running a company as of late, with small, self-organizing teams. Decentralization allows people, or more generally, components of a system, to act freely and independently of one another, and still interact to produce coordinated results.
Linux is cited as an example of a decentralized system with a central aggregator - Linus Torvalds. As most people know by this point, Linux is worked on collaboratively by many programmers throughout the world, but often, different people come up with competing solutions to the same problem. This is good at finding and testing diverse approaches to see, in practice rather than in theory, which one actually works the best. Ultimately, however, the ‘best’ solutions are not selected by popular vote, but by Linus, who is responsible for taking the results of the decentralized development process, and aggregating them into something useful by selecting the ‘best’ bits and pieces.
Also discussed is the decentralization of the intelligence community, and the negatives involved in the difficulty of sharing information, cited as one factor in the failure of the intelligence community to predict and prevent the 9/11 attacks. The problem, however, was not decentralization, but decentralization with no way to aggregate the results into something useful.
One such way of aggregating information was a proposed futures market based on potential events in the Middle East, and elsewhere, which was, however, not allowed to get off the ground due to squeamishness about the idea of buying and selling bets about, say, a leader’s chance of being assassinated in any particular year. This market could have been a useful tool, perhaps not in predicting precise events, but in collecting information about the general state of things in places where information is at times difficult to gather, and unfettered freedom of expression suppressed.
This chapter covers what are known as “coordination problems”, which are defined as problems that don’t necessarily have an objectively “correct” answer, but which are framed in terms of coordinating actions with everyone else’s actions. For instance, driving on a freeway requires that you coordinate your speed and actions with those of other drivers, and possibly even the time of day when you drive in order to avoid getting stuck in traffic. Groups are not guaranteed to come up with optimal solutions, but often do.
One solution to coordination problems is central planning - having one omniscient authority that makes some calculations and tells everyone how to act as a consequence. This is, however, often not possible, feasible, or desirable.
Coordination problems are often quite difficult to solve, with one example being a bar, where, if it’s more than 60% full, no one enjoys themselves, but do if it’s under that capacity. Several computer models have been built with agents that follow simple strategies and do manage to coordinate well enough to keep the bar at around 60%.
In some cases, cultural references help us solve coordination problems, both by giving us reference points (ask two people to meet at a given time without communicating the time to one another, and they’ll likely pick 12 noon), or norms, such as “drive on the right”. Conventions also lower the amount of thinking you have to do about certain situations - it’s easier just to follow the rules or guidelines rather than make a conscious decision after weighing all the possibilities. This often frees us to think about more important things.
Corporations are supposed to operate in order to maximize profits, and should be immune to things like social conventions - yet it turns out that they’re not nearly as rational as might be imagined. One example cited is movie theaters, which charge the same price for the latest hit, as for flops that are on their way out. Charge too much for hits, and you risk losing out on concessions, where movie theaters actually make a lot of their money, but by that logic, lowering the price for less popular movies would get more people into the theater.
Markets can also be effective coordination mechanisms. Experiments conducted with students, who know only the maximum price they will pay, or minimum they will sell for, show prices rapidly converging on an optimal price, even though that price is higher than buyers would like, and lower than what sellers would prefer. Real markets often lack lots of information, and indeed the students found the experiment “chaotic and confusing” - and yet, the market worked. Markets aren’t perfect, of course, but they are often the best, if not perfect, way of coordinating disparate buyers and sellers.
Cooperation problems are superficially similar to coordination problems, but with a key difference: coordination problems can be solved with all players acting in their own interests, whereas cooperation problems require players to “look at the bigger picture”, as part of an organization or society.
Behavioral studies have demonstrated that people will forego a reward in a simple game in order punish someone perceived to be playing unfairly, even when doing so does not benefit them at all. In other words, people, being social animals have a sense of ‘fairness’, even if this isn’t rational in economic terms. This extends to a sense that rewards should be correlated to efforts and accomplishments, and this sense is part of the reason why large organizations can exist in the first place.
Trust is often secondary to long term relationships in terms of promoting ‘fair’ behavior: if you know you’ll see someone again and again, you’re less likely to attempt to cheat them.
Capitalism works in part because it’s possible to trust those beyond an established circle of friends and family, and only works where there are institutions that promote this trust. When you are reasonably certain that you can buy a product and that it will work as advertised, you don’t need to inspect in detail each and every thing that you purchase. This makes the flow of goods and services, and increases the general welfare of a society.
This chapter discusses the idea of ‘coordination problems’, using traffic as an example, beginning with a discussion of London’s “congestion pricing”. Because traffic was so bad, a market-based solution was found that pushed people to evaluate their access of downtown London via a car: during the day, it costs a certain amount of money to drive into central London. This accomplishes two things: rather than dictating to drivers what they can and cannot do, it leaves everyone free to do as they so choose, but puts a direct cost on accessing the downtown area during certain hours. People who really do need to go there at that time will pay the money, but find the roads less crowded. Other people, without such strong necessities, will take the time to walk, cycle or use public transportation. London is hardly alone in using such a system; Singapore has used congestion charges since the 1970ies, although clearly implementing that kind of unpopular policy is easy in an authoritarian country.
The discussion continues, touching on the subject of traffic flow, and the ideal conditions that produce, a smooth, steady flow, rather than traffic jams, or erratic start and stop conditions. Surprisingly, having just the right amount of cars on the freeway is important: two many creates obvious problems, but two few causes problems as well; with two few cars, people tend to speed up and slow down more erratically than with a steady stream of traffic.
The book’s web site: http://www.randomhouse.com/features/wisdomofcrowds/
Wikipedia page on the book: http://en.wikipedia.org/wiki/The_Wisdom_of_Crowds
For a more in-depth, comprehensive summary of The Wisdom of Crowds, check out GetAbstract.com