BRINGING

SEXY BACK…

TO ECONOMICS

by

Ocean Gebhardt

© 2010 Ocean Gebhardt

All rights reserved. No part of this document or the related files may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording, or otherwise) without the prior written permission of the publisher.

Note to Reader

As you can probably see, a lot of hard work and effort went into writing this book. While we at Dumb Agent have had a lifelong passion for economics, this book is the result of years of research, discussion, reading, formulations, and just plain hard work. Judging from the fact that you are reading it, I assume you find it of some value, and therefore I ask that you do not share or distribute it among your friends for free.

This book, and all content in it, is copyrighted. Since you paid for it, you have access to any and all information contained herein without restriction. Those who haven’t, such as friends, colleagues, classmates, acquaintances, etc., do not. If they find the content worth reading, they should find it worth paying for.

If you would like to bring about various changes to this agreement, you can always contact me at ocean@dumbagent.com and we can work something out.

I also ask that you do not share this book in forums, FTP sites, newsgroups, bulletins, and the like. The only place you should have access to this book is via the Dumb Agent website and the links provided therein.

Ocean Gebhardt

-October, 2010

Table of Contents

Introduction

Economics has become a controversial subject lately, or as controversial as such an unsexy sounding subject can be. It is regarded as a science, and its practitioners – economists - see themselves as no less than scientists. However, unlike other scientists, who tend to deduce, economists are in the science of predicting, which means that in time, their mistakes will inevitably become glaring and obvious. For this reason, they are sometimes reviled. During the 2008 United States presidential campaign, Democrat candidate Hillary Clinton famously remarked: “I don’t put my lot in with economists,” while the Republican candidate, John McCain, said that he would “trust the people and not the so-called economists to give the American people a little relief.” So much for scientists.

This same time period saw the rise of Freakonomics, and other such books, which used economic principles to explain everyday phenomena, or to show how facts that we considered obvious could be anything but. Many of these books were bestsellers. Suddenly even non-economists, such as Malcolm Gladwell, were using the same concepts in their books, mixed in with their usual literature.

So, what gives? Is economics an exact science that can be used to explain (and therefore predict) human behavior? Or is it an academic pursuit for people who like charting and graphing new theories, just to see them overturned the next year, so they can “update” their theories to fit new times? We saw this confusion recently with the latest credit crisis: economists were blamed for not foreseeing the events that led to the downturn, but the downturn led to the popularity of behavioral economics. A bust or a boon?

A

Economics… is constantly evolving. Even evolutionary theory gets revised and updated; are we a direct descendant of Ida’s, or was she closer to the lemur family?

t the risk of sounding like an economist, the answer is: both. Economics, like any other science, is constantly evolving. Even evolutionary theory gets revised and updated; are we a direct descendant of Ida’s, or was she closer to the lemur family? This doesn’t mean we should decide not to “put our lot in with economists,” any more than we should decide to do away with oncologists because we still haven’t found a cure for cancer.

We should, however, be able to sit back and look at economics every now and then from a slightly elevated position, so we can see what we have discovered, what needs work, and how much progress has really been made. The fact is, just as economists can make mistakes, the detractors of economics can also distort what has been said in order to cast shadows on the whole subject. We address this in our chapter regarding the economic crisis: what was foreseeable, and who saw what coming? It seems as though we might be in the process of revising very recent history.

Throughout these turbulent economic times, we at Dumbagent have never faltered in our views that economics can help the public good and that the knowledge and implementation of economics is a valuable thing. We also, quite shockingly, have never wavered in our belief that markets are inherently efficient.

* * *

This book is a collection of examples of economic phenomena we have come across during the past few years. Where we have encountered any shortcomings of economics, we try to show you how and why they can occur, along with tips on how to avoid them. At other times, such as in our article about eBay or about when to pump gas, we look at how applying economic principles can help you out in your daily life, rather than relying on your “gut” or on what is standard practice.

We also question conventional wisdom on many topics, such as GMOs, buying local products, and bubbles in general. Applying economic principles to each of these areas, we compare what has become accepted theory to reality. It is sometimes surprising to discover, through this process, how many economic theories we see actually make good sense, and how many have been distorted by hysteria or public interest groups. Like any good economists, we also make predictions: for example, we consider how the movie industry will develop by looking at what is happening right now in the porn movie industry.

There is also the question of bubbles. Most people reading this will have been through two of them in recent times: the Internet stock market bubble of 2001, and the subprime mortgage bubble of 2008. When will the next one occur? In what industry? Who will it affect? Sadly, we’ve seen that by concentrating on scapegoats, many news outlets end up blaming economists for not foreseeing these events. In so doing, the media shoot themselves in the foot, since once having firmly placed the blame, they must draw the logical conclusion that economists will not be able to foresee the next crisis, either. Just as we deconstruct this argument and show how many, if not most, of the mainstream economists did in fact see these crises looming, we can use the same principles to see exactly what a crisis looks like, and where it will hit. Yes, that means we can see bubbles before they burst.

To summarize: in this book you will find a collection of scenarios and events, along with our economic analysis of each. Please note that we do not follow any one political party or movement. We do not -- despite being believers in free markets -- necessarily embrace Libertarian values (see our chapter on gun control). We do, however, think markets work most efficiently when left alone. We believe governments should exist and should help ensure essential needs such as policing and infrastructure, but we do not think they should take the place of market forces, and we will offer various scenarios as to how this can pan out.

We hope that by reading these articles you might discover and learn new principles, and that you’ll be able to eliminate much of the drivel you may come across in everyday life. We also hope to spark your curiosity, and encourage you to keep an open mind on various “accepted” facts, as well as learn to apply new analytical methods to well-worn events. If you find these pages of interest, come see us at Dumbagent.com for more articles like the ones featured here. You can also contact us at any time at our email address: contact@dumbagent.com.

* * *

Before we can start on this journey, however, we owe you an explanation as to why you are reading a book by people who call themselves “Dumb Agents.” If nothing else, our name will guarantee that we’ll never get jobs in the marketing or advertising industries. The fact is that all of our theories are predicated on the Dumb Agent Theory, which states that a group of people who make individual buying and selling decisions for their own gain will reflect true value more precisely than any one individual can. That’s all well said, but this theory is what inspired us to start our website (and therefore be stuck with that name), so we owe it to our readers to show how the theory works in practice and what attracted us to it in the first place. We give a more thorough analysis of the Dumb Agent Theory in the next chapter, along with several short examples of how it has been seen and used. In case it piques your interest, our next step will be to write a book specifically detailing the theory and its possible future uses. Needless to say, we think it holds far-reaching potential.

For now, however, we present a collection of short articles, each discussing different disciplines and principles of economics, along with different ways they apply -- or can be applied to -- what you see, hear, experience or do in everyday situations. We hope to prompt you to take a further interest in economics and to re-evaluate phenomena that may seem commonly accepted as fact. You might even dust off your old economics textbooks and look at old lessons in a new light. Most of all though, we hope you have fun reading it.

Ocean Gebhardt

-October, 2010

THE DUMB AGENT THEORY

Those who are too smart to engage in politics are punished by being governed by those who are dumber.

• Plato (427 BC - 347 BC)

Chapter 1: The Dumb Agent Theory – A Primer

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The Dumb Agent Theory states that the patterns of many people making individual buying and selling decisions will better reflect true value than any one individual can. As you can guess by our name, we are fervent believers in this theory. Its simplicity is stunning. Just take a look at stock market investing: although many experts may spend vast amounts of time looking over charts and information about companies and stocks in order to discover new information, this will be futile in the long term, since all available information is already incorporated into the stock price. This is not to say the Dumb Agent theory can foresee future events, but if you want to collect all available information about stocks in the most efficient manner possible, the Dumb Agent theory is your best bet.

Put this way, it may seem that the Dumb Agent theory does not differ from the Efficient Market Hypothesis, but there is one fundamental difference: no one person needs to know the true value of a stock for the market to find its true value. When many individuals invest, some will offer too much while others will offer too little, but the equilibrium point will equal the true value of the stock. As soon as new information is available it is incorporated into the stock price, and the non-relevant information is weeded out. This may seem more like mysticism than economics, but it is merely the invisible hand of Adam Smith at work.

Rather than delve into more and more detail in an effort to clarify all this (we have another book for that), we’ll simply give you an interesting example: a study performed by Bernard and Thomas on the extremely un-sexy topic of Post Earnings Announcement drift.¹ This is the direction the stock price will “drift” after the underlying company makes its earnings announcement. (Summary: if the announcement is positive, the stock price rises; if negative, it declines. Not exactly groundbreaking). Lucky for us, their study happened to capture some other data as well: the pre-earnings announcement drift.

Bernard and Thomas decided to look at companies that had “surprised” with their most recent earnings announcement. Here, the term “surprised” means that if one of these stocks had been forecast (by researchers, analysts and “the street”) to lose money, they had a positive earnings announcement, and if they had been posited to post a gain, they in fact posted a loss. Despite this “surprise,” look at how the underlying stocks performed in the days leading up to the announcement:

(Bernard – Thomas, 1989)

As you can see in the graph above, the bigger the surprise (i.e., the larger the difference between expected and announced earnings), the bigger the gain or loss of the stock before the announcement. This gain or loss becomes quite evident already 20 to 30 days before the announcement. Barring illegal insider trading (which is highly unlikely for such a big group of stocks), this is a perfect example of how a large group of traders will determine true value more efficiently than any one individual expert, analyst or formula.

Chapter 2: Why We Like It

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Another example is that of Michael Mauboussin, who is an investment strategist at CSFB, and also teaches a course at Columbia Business School. Every year, he would ask his class to estimate what IBM’s total assets had been for the year 1989. It is quite safe to say that no one in the class would know this answer by heart. However, every year, the mean of the students’ choices came to within 5% of the correct figure. This substantiates the theory that a group of people investing will turn a more accurate value for their investment than any one individual.

Consider also the Iraq war: well before the war began, a “war premium” was added to petroleum prices, with the West Texas Intermediate spot oil prices already being 52% higher at the end of 2002 than 12 months prior (and steadily climbing to 73% higher by February 2003). This illustrates how the start of the war was foreseeable by observing markets.²

The same phenomenon has also been observed in association with the Challenger shuttle disaster. After the disaster occurred, NASA’s expert committee needed several months in order to find the cause, after initially blaming the explosion on the space shuttle’s external tank manufactured by the Martin Marietta Corporation.³ The markets, however, gave their verdict immediately: Morton Thiokol, manufacturer of the faulty O-rings, quickly experienced a 12% loss in share price, while the other shuttle contractors remained relatively free of market punishment⁴. The final confirmation came six months later, when the NASA committee charged with investigating the event found that only Morton Thiokol’s O-rings were to blame, while the space shuttle’s external tank -- and indeed all the inputs by other companies -- bore no responsibility.⁵

THE DUMB AGENT THEORY IN REAL LIFE

It is by universal misunderstanding that all agree. For if, by ill luck, people understood each other, they would never agree.

- Charles Baudelaire (1821 - 1867)

Most people reading this will have taken some sort of economics or finance course, and most will have forgotten what they learned in their earliest classes. Even if your interest in the subject has since rekindled, chances are your first lessons in the subject are merely a blur of supply and demand curves and certain shifts, elasticities and LRAC curves.

The worst way we could choose here to describe the Dumb Agent theory would be to discuss it without illustrating how it appears, and can be used, in everyday life. Through the following examples, therefore, we aim to show you how this economic theory can do just that.

In recent times, prediction markets have been gaining in popularity. While using them can be a fun forum for placing bets and hoping to make money, the information garnered can be invaluable. For an example of this, see how prediction markets performed in the 2008 presidential election in the United States (don’t worry, we will be discussing this shortly).

As a side note, we were onetime fans and big proponents of a website called Predictify, where people tried not only to predict posted events, but also to post their own events whose outcomes could be predicted. So, if no one had asked who would win the Colorado senate election, you could submit it to the site as a question. We liked this site for several reasons, chief among which was that anyone could post any question for free. Predictify was also able to skirt the anti-betting laws: If someone wanted to post a question that paid money, the person posting it would have to supply the money, not the predictor. So then all the participants could predict for free, and be capable of winning cash (as some did).

Unfortunately, Predictify has since gone out of business. We believe this would have been avoided had they made their information more available to scrutiny by the general public. For example, it would be interesting to see more specific breakdowns of the prediction data. Likewise, if someone had offered to pay for certain information (as we did at one point), it would have been helpful to offer some (obviously anonymous) information on the predictions being made.

The more information available to be gathered from this type of site, the greater details of patterns that can be determined: What was the overall accuracy of the predictions? Did the offer of a cash return increase or decrease accuracy of predictions? Were responses more accurate when made towards the beginning or later on (when more time had been devoted to thought and research)? In normal betting markets people pay money to bet, as they do in stock markets. With Predictify’s model, there were only upsides. How do the resulting investment picks of the two models compare?

Since we were never supplied these answers, we have attempted to find out for ourselves, and we present our findings here. So, the next time you see a prediction market, a betting parlor or a stock market, look at the price levels and remind yourself that they represent the value given by a majority of participants. What does this matter? Read on to find out.

Chapter 3: Dumb Agent Theory At Work in the 2004 United States Presidential Election

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The 2004 United States presidential election is generally considered to be very similar to the 2000 election. In 2000, Bush and Gore were on the ticket; Bush looked as if he had won, so Gore conceded, only to retract his concession minutes later. What followed was a dispute that rose all the way to the US Supreme Court. In the end, George Bush was adjudicated the winner, and Gore conceded once again, promising that this was “the last phone call.”

In comparison, the 2004 election seemed rather unremarkable. Many of the same factors came into play, although this time the election was between George Bush and John Kerry. George Bush won, once again with some disputes, but they were not carried to the extremes they had been four years earlier.

But behind the scenes, the 2004 presidential election is extremely interesting: it pitted two very different forms of measurement of success against each other. On the one hand was the old method, used since the 1950’s, of exit polls. On the other hand was a new method, not taken very seriously as of yet, called prediction markets.

This election was of course famous for other reasons: it was the second tightest U.S. election in 88 years, and it was the first election since the September 11^th terrorist attacks. In many respects it was a report card on the war on terror, as well as a confrontation between two different governing methods for this new and unknown world. A record number of viewers were on the edge of their seats awaiting the results. What they were waiting for were results, not in the form of election results, but in exit polls -- the results that had been relied upon for the past half century.

On the night of the election, at 12:21 am, the CNN website showed Kerry as the predicted winner by 81 votes. At 1:41 am, after only 57 new votes were counted, Bush was the predicted winner by 48 votes. No reason was proffered for this, although we can safely assume it meant exit poll error, as well as the amount of weight, or importance, placed on the precision of exit polls.

CNN POLL RESULTS AND SCREENSHOTS

12:21am:

01:41am:

SCREENSHOT INFORMATION:

Prediction markets, on the other hand, showed Bush as having a small lead throughout. As Fortune Magazine stated:

“… on election morning the Iowa Electronic Markets continued to predict a Bush victory, as it had since July. Same story at TradeSports.com, where you could also bet on the outcome in each of the 50 states. If you had examined the tally that morning, you could have gone to bed early that night, for it correctly predicted the winner in every state. That is, it foretold not only that Bush would win, but also by precisely how many electoral votes.”⁶

The Iowa Electronics Market showed, in real time, what the outcome of the presidential election looked to be. It meant that, based on all of the available information, the likelihood of the winner was reflected each day. Therefore, on the actual day of the election, the result would be what was shown by the share price, and this is precisely what happened.

Chapter 4: U.S. Election 2008: How Did Prediction Markets Do?

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The moral of the story for the 2004 presidential election was that prediction markets outperformed exit polls. To know who the winner of the election was, you could have either stayed up late, watching contradictory and ever-changing exit polls on TV, or you could have observed the prediction markets and gone to sleep at a decent hour.

The 2008 presidential elections were a highly anticipated event. The economic powerhouse of the world was electing a new president, there were tremendous race and gender milestones sprouting up, as well as other contributing factors which are beyond the scope of this chapter. One aspect in particular interests us here: this was the second chance for prediction markets to prove themselves. In 2004, exit polls had been shown to be not nearly as accurate as prediction markets. Was this just a fluke, or a sign that a better method of foretelling the winner had arisen? To answer this question, we have to start two days after the 2008 election.

The second day following the 2008 United States presidential election between Obama and McCain, we knew who the next President would be and we knew the results from every state but one. Missouri had yet to officially declare a winner, although they had a total of 1,442,673 votes for Barack Obama and 1,436,814 votes for John McCain (Note: If the final results are within a certain margin of error, states will sometimes defer declaring a winner until a recount has been performed in order to ensure accuracy). Since this was a difference of 5,859 votes in favor of Obama, the state was assumed to be blue.

Comparing these results to predictions, we saw that both the prediction markets and the polls were correct. While this showed the legitimacy of prediction markets, it did not do much to prove their advantage over polls.

For one thing, pollsters were now very careful with their results, after they had famously bungled up the polling results in the 2004 election (see previous chapter). This time, both pollsters and news networks were bent on cautiously ensuring that their results were carefully vetted and that more accurate methods were being used before they posted their results. The outcome was that exit polls, while perhaps being more delayed with their preliminary results, showed much greater accuracy than four years earlier. Prediction markets continued to correctly predict who the winner would be, as they had in the last election.

Despite the fact that both polls and prediction markets announced the same winner, we at Dumb Agent were still able to extrapolate some very interesting conclusions about the mechanics of prediction markets. First of all, we took a look at Intrade.com, whose results matched the polling results perfectly. This is satisfying, yet unenlightening.

S

Surowiecki, stated that a monetary interest does not have to be at stake… From the above example, it seems the opposite is true.

econdly, we looked at Betfair.com, whose results also matched the polls with one exception: Missouri. Missouri displayed a “T,” which meant “Too close to call.” This was reflected in the fact that it was the only state still currently counting votes. Therefore, Betfair could be said to show even more precision than the polls.

Thirdly, we looked at the (now defunct) Predictify version of the election showdown results. This only allowed betting for certain states. However, it also worked in a different way. Bettors did not have to place their own money. All of the money was put up by the people asking the question, bringing many more predictors, but with less personal stake.

Here we could see that North Carolina and Missouri were predicted incorrectly, while Ohio and Virginia were deemed “too close to call.” The latter were obvious mistakes, which are interesting to us because they showed the difference between having something to lose when predicting, and only having something to gain. Bettors on Betfair or Intrade had a monetary stake in their predictions, whether they won or lost. Participants in Predictify, however, stood to lose nothing with incorrect predictions.

In The Wisdom of Crowds, James Surowiecki stated that a monetary interest does not have to be at stake for the Dumb Agent theory to be apparent (and for crowds to show their collective wisdom). From the above example, it seems the opposite is true. Not only will the results be more precise if participants have an interest in obtaining a reward (financial or other), but if they stand to lose something for getting the wrong answer, precision further increases.

In other words, when people bet with their own money, as with Intrade and Betfair, the results will be more accurate than when bettors stand only to gain, as with Predictify. This discovery will help in ensuring that prediction markets can be used as better indicators in future elections -- which, in its turn, means that exit polls will have to be ever more precise to compete. Prediction markets have not yet become mainstream, but a few more election results like these past two should help make it an acceptable calculation method for projecting election results, without having to stay up most of the night watching exit poll results.

Chapter 5: The Influenza Prediction Market

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The Dumb Agent theory has been put into practice many times. From the example of a professor asking a question the answer to which the individuals in his class are very likely not to know (see the Mauboussin example in Chapter 2), to markets for such diverse purposes as tracking weather patterns and predicting who will win presidential elections. Corporations such as Yahoo and Google have also used the theory in order to determine future market trends. We will choose one market in particular as a case study: the Influenza Prediction Market by the University of Iowa.⁷

This is not bird flu, but influenza -- which, although not currently receiving as much negative press as other notorious illnesses, is still a deadly disease. Every year, in the United States alone, over 200,000 people are hospitalized with this flu, which leads to about 36,000 deaths^.8 Regrettably, these people would not have died, nor been hospitalized, had they been given access to a vaccine in advance.

Here is the crux of the problem with influenza: due to a shortage of vaccines, irregular (albeit seasonal) outbreaks, and lack of prior knowledge as to where outbreaks will occur, administering vaccinations to people in need can be a vastly complicated process. The Centers for Disease Control publishes information about where and when outbreaks have occurred, but since this information is made available 2-3 weeks after they have occurred, it does not aid prevention efforts very much. If, however, doctors were armed with knowledge of where outbreaks are most likely to happen one to two weeks in advance, they would be able to mobilize staff and resources to cure potential victims. With this in mind, the University of Iowa’s Iowa Electronics Market (IEM) decided to create the Influenza Prediction Market.

The market was composed of 60 doctors and nurses, as well as others in the medical industry in Iowa, who were each given $100 with which to trade. These were called “flu dollars,” but were converted to real dollars once the experiment was over. The participants then became investors in this Influenza Prediction Market, and were able to use it like a futures market. Their $100 purse would rise or fall depending on the accuracy of their predictions. The “shares”, or contracts, were actually the weekly color-coding system used by the CDC, which is:

RED=Widespread,

BLUE=Regional,

PURPLE=Local,

GREEN=Sporadic,

YELLOW=No Activity.

The goal, of course, was not to make extra money for the medical workers, but to see if this market could be utilized to foresee where potential influenza outbreaks would occur. The results were positive: the traders predicted the level of outbreaks with 80% accuracy and with a lead-time of two to three weeks. With preparation four weeks in advance, they were able to get within one color on either side with 90% accuracy. And all this despite the fact that the predictions were for the entire state of Iowa, while almost all of the traders were located near the university, on the eastern side of the state.⁹

This year the market is being expanded to cover the whole country, with doctors and nurses nationwide allowed to trade. The results will be followed closely, by us as well as others, and, if the trend continues, it promises to be very useful. Although it involves an unproven theory, it will certainly be worth using if it can save lives.

The parallels with bird flu are not lost. With outbreaks already occurring, and people worrying about where and when the next will be, any help in identifying where to cull, vaccinate, cure, or evacuate will be useful. This information is generally very hard to come by, and a market like this may prove invaluable for illnesses of this sort.

Chapter 6: The Jury Theorem

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Marie Jean Antoine Nicolas de Caritat, marquis de Condorcet, was born in 1743, and he seemed destined to be like many other 18^th century aristocrats: having lost his father at an early age, he was sent to a religious school by his mother, where he pursued studies in science, mathematics and politics. He became well known, but not as famous as Cavendish, Newton or Benjamin Franklin (although he worked with the latter); he has probably never been discussed within any high school walls since his death in a jail cell outside Paris (this was during the French revolution – a bad time to be an aristocrat in France).

His most famous achievement is the Condorcet method of choosing certain candidates in an election. It is a useful method for elections involving many candidates, ensuring an overall winner when a constant, No. 1 spot is not obvious.

This, however, is not what interests us about him. Rather, our attention is drawn to a work of his which had until recently languished in obscurity, as is unfortunately often the case with many interesting historical facts and theories which fall by the wayside for years or even centuries, only to sprout up again in later times. What we are interested in is a theory he devised in 1785 called the Jury Theorem:

Dealing with political science theory, the Jury Theorem states that with a majority vote, each person has the probability ‘p’ of making the correct decision. Here, of course, we mean any theoretical decision that has a correct as well as an incorrect outcome, i.e., true or false. If the probability that each person makes the correct decision is over 50%, then the probability that a majority of ‘N‘ people vote the correct decision is over 50%, increasing and approaching 100% as ‘N‘ increases and approaches infinity.

An example would be to guess if a defendant is guilty or not guilty, or if an event did or did not occur, or will or will not occur. It is quite apparent that the Jury Theorem is in agreement with the Dumb Agent Theory in its simplest form. Several caveats are:

1. The Jury Theorem assumes there are two decisions to make, a right and a wrong one (binary choice). This does not allow the alternative of more than 2 options, although a study has been carried out by List and Goodin, two political scientists, showing that it can do so.

2. This theorem, like the Dumb Agent Theory, also assumes that people are making their decisions independently. If ‘p‘ is >50%, but one person has influence over many other people, this can skew the result. We see this very often, especially in politics.

3. What’s more, the theorem assumes that each participant’s competence is greater than 1/2. In other words, if each person is more likely to vote incorrectly, then a majority will vote incorrectly, and the optimal number of voters would be 1. The interesting fact about the Dumb Agent Theory is that this doesn’t seem to be a necessary factor. Even if people’s results are across a wide spectrum, we need only take a mean of their choices to achieve an accurate result.

It is interesting to compare the two slightly different theories. The Jury Theorem represents a step forward in explaining how the Dumb Agent Theory and the Wisdom of Crowds work, although much of it is still a mystery.

Chapter 7: We Are What We Read

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Browse any economics or business best-seller list these days, and you will see a sign of the times. The year 2000 brought us The Millionaire Mind, by Thomas J. Stanley, The Millionaire Next Door, by William Danko and Thomas Stanley (Stanley kept busy) and Jack, by Jack Welch; 2006 brought us Why We Want You to Be Rich, by Donald Trump and Robert Kiyosaki, The Tao of Warren Buffett, by Mary Buffett and David Clark, as well as The Starbucks Experience, by Joseph A. Michelli. The year 2009, however, turned out to be a different story.

Perusing any bestseller list of that year, you will see The Black Swan, by Nassim Nicholas Taleb, A Drunkard’s Walk, by Leonard Mlodinow and Outliers, by Malcolm Gladwell. How are these so different? Well, The Black Swan is about how unforeseen events can control our lives; A Drunkard’s Walk is about how randomness rules our lives, and Outliers discusses how the luck of the draw has more of a say in success than any innate abilities. Add to these the obvious Ultimate Depression Survival Guide, by Mar­tin D. Weiss and Suze Orman’s 2009 Action Plan, and you can see where the trend was.  Also worrying is the inevitable emergence of books such as The Myth of the Rational Market by Justin Fox.

Our guess is that, as long as the recession continues, we will see more of these, and many more like House of Cards, by William D. Cohan (about the fall of Bear Stearns), while companies keep requiring bailouts and declaring bankruptcy. Meanwhile, Thomas J. Stanley hasn’t written a book since 2000.

Just as the Dumb Agent theory states that many people individually buying and selling can reflect the true value of a stock, best-seller lists show that many people buying and selling books reflect the proverbial signs of the times. If you want to gauge people’s sentiments, you can spend large amounts of money on focus groups, or you can observe what the crowds are currently reading. These book titles can reflect how people are feeling these days just as accurately as intricate in-depth studies would. If you want to know when we have really emerged from this downturn, you could do worse than check a couple of bestseller lists.

Chapter 8: Dumb Culture Theory

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Among the various objections to the Dumb Agent Theory, a prominent example is culture. If a group of people follow a certain belief or culture, won’t their decisions be influenced by this culture? And doesn’t this mean that the aggregation of their decisions, even if independently made, will not be the most efficient?

A new paper titled Culture Redistribution, by Erzo Luttmer and Monica Singhal of Harvard University, elaborates on this point -- detailing how people’s decision-making processes have strong cultural components. The paper suggests that people can indeed be swayed by their culture in making certain decisions, and that this even carries on into the second generation of immigrants to a new country. The decisions people make will therefore have more to do with tradition and culture than what is more logical and efficient.

We at Dumb Agent do not see this notion as contradicting the Dumb Agent Theory. In fact, what better place is there than the open market for people to make decisions (whether they be buying and selling, or engaging in any other sort of interaction) and where they can find other people willing to take their odds, buy their wares, use their information, etc.? We do not deny cultural components; rather, we think that with a large enough market, these provide many different forms of opportunity that people can and will use.

To drive this point home, we draw attention to an article in The Economist, called “Warfare, Culture and Human Evolution,” which discusses how different forms of culture came about genetically:

Rather, it was population density that turned out to be the key to cultural sophistication. The more people there were, the more exchange there was between groups and the richer the culture of each group became.

In other words, the more people there are interacting, the more culture there will be within individual groups, which in turn will bring about more opportunities for interaction and trade. The Dumb Agent Theory shows how this cycle is perpetuated. Since free markets, while not perfect, tend to be the most efficient form of interaction, the people who use them most will thrive and increase in number, thereby establishing their own individual cultures and continuing the cycle. Its very own Dumb Culture Theory, if you will.

APPLYING ECONOMIC PRINCIPLES TO REAL LIFE

The secret of life is to appreciate the pleasure of being terribly, terribly deceived.

• Oscar Wilde (1854-1900)

When you learn economic or financial principles in class, they seem to be just that: economic or financial principles in class. You learn their names, what charts are involved, and to which exam questions they apply. Maybe you even recognize them in financial or economic journals. You rarely think they might actually be applicable in your everyday life, sometimes in circumstances that couldn’t have less to do with your economics class if you tried.

For example, you might have learned Dollar Cost Averaging as an investment strategy, but never thought of applying it when you fill up your gas tank. Or you might have learned how to calculate depreciation for tax purposes, but never thought of how the same strategy could be useful when buying clothes or toys for your children.

Sometimes the answers aren’t as obvious; for instance, consider the possible application of the same sorts of strategies to other contemporary issues, such as new technologies and uncertainty. Is it safer to shop in a store than online? How much more money is it worth to do so? What if you pay with your credit card, regardless? Or your debit card? Maybe you learned about Schumpeter and what he called “Creative Destruction,” but is it something we are going through at the moment? If the stock market and real estate bubbles are the textbook cases of tomorrow, can we already figure out their lessons today? In fact, are these bubbles as unpredictable as we tell ourselves?

These are the questions we look at in the following chapters. We hope they will make you want to look at those economic theories you have learned in a new light. Do they really only apply in your classroom and to Ben Bernanke? Or do they apply to anyone who goes shopping, gives presents and lives in the modern economy?

Chapter 9: When Should You Pump Gas (and how much)?

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In late 2007, the price of oil per barrel dropped from almost $100 to about $88 in one week. It thereupon proceeded to decline to the $40’s, only to rise again sharply. The IEA has recently declared that the production of oil will level off in 2020; there is still plenty more to provide uncertainty in the meantime. In fact, accounting for wars, attacks, natural disasters, OPEC’s whims, and just standard complications in getting enough oil from the pumps to the refineries to the gas stations to your car, we can say that the one certainty is uncertainty. Prices will fluctuate, we just do not know when, by how much or in which direction. So this raises the question: Do these circumstances reveal any opportunity for saving money?

As a matter of fact: they might.

For those who have invested in stocks for a while (or have seen Boiler Room one too many times), Dollar Cost Averaging might be a familiar term. For most of us, it is not. Dollar Cost Averaging is an investing strategy, attractive because it can maximize revenue but can be applied very passively over time. The general idea is that you invest a set amount, say $100, every month in a certain stock (or group of stocks, or fund, etc). If the stock is cheap, say $20 per share, you will be buying 5 shares, but if it climbs up to $25 per share, you will only be buying 4 shares. With this method you will be buying more of the stock when it is cheap and less of it when it is more expensive.

When gas is more expensive, you will automatically be buying less of it and if it then becomes cheaper, you will automatically buy more.

How do we apply this to gas (or petrol, if you’re in Britain)? First of all, we should establish a certain amount. For example, when gas was at its cheapest point this year, you could fill the gas tank in your car for about $25. Prices have been fluctuating a great deal in the meantime, so at times it could cost you close to $40 to fill up your tank. On the other hand, you quite likely realized that no matter what the price was when you filled up the tank, a day or two later it would usually be quite different.

So if you choose to constantly spend $25, you know at best you will be able to fill the tank. When gas is more expensive, you will automatically be buying less of it, and if it then becomes cheaper, you will automatically buy more. Applied in the long term, this simple and passive method should allow a driver to maximize the amount of gas he buys at lower prices, while minimizing the amount he buys at higher prices. This can be a handy way of averaging out your cost of gas.

Chapter 10: eBay vs. Hand-Me-Downs

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Most younger siblings feel at a disadvantage throughout their entire lives. If you ask them why, chances are they will reply: “hand-me-downs” (along with many, many other grievances). It is not just the younger siblings that have had to endure this injustice; younger cousins, family friends, and even our own sons and daughters have all had to deal with it in one form or another. So what’s behind this rampant and intolerable behavior? Well, nobody knows for sure, but we are going to explore the possibility that, with all the advances in modern technology and increased market accessibility, we might be able to do away with these atrocities once and for all: specifically, through the use of eBay.

This is the hypothesis: instead of holding onto that game or pair of jeans in hopes that the younger sibling will eventually grow old enough to use it, we will sell it on eBay, invest the profits in a high yield savings account, and take the money out later to buy something new for the next child. We will test two different scenarios: one will involve an old game, and another a pair of jeans. We will test the game because this type of product is assumed to be obsolete by the time the younger child comes along, and therefore (presumably) there will be more need to substitute it. We will also test a pair of jeans because, assuming they remain in fashion and maintain the same price over time, they will have different dynamics than would a game when treated as hand-me-downs.

Siblings that are 3 years apart:

Game Boy Advance vs. Game Boy DS - In 2001, a Nintendo Game Boy Advance (GBA) sold for $89.99. The “hand-me-down option” here would have been to hold on to the unit for three years or until the younger sibling was old enough to play with it, rather than buying the new Nintendo DS (DS) which was released 3 years later in 2004. The alternative option would have been to sell the GBA off after a year of use, and then buy the DS for the younger sibling. It did not take us long to discover that, in 2002 a used GBA would probably have sold for about 50% of its original value, or $45. If the $45 were invested in a 5% savings account over three years (compounded monthly), you would only have $52.27 -- not nearly enough for the DS.

Winner: – Hand me Downs

Jeans vs. Jeans - A pair of jeans in 2001 could be bought for $25 (in current prices, since we’re assuming no change). If, one year later, your kid grows out of them, you could sell the pair on eBay for $7.50. Once again, $7.50 invested over 3 years at 5% interest makes only $8.71. So a hand-me-down pair of Jeans would be worth $7.50, while you would currently have $8.71 to spend on jeans had you sold them on eBay 3 years earlier.

Of course, a pair of hand-me-down jeans could also be worn by the younger sibling, in which case once again you receive $25 of value, regardless. Therefore..

Winner: Hand me Downs

Relatives one generation apart: Eventually, after running more scenarios, it becomes fairly obvious that more time is needed in order for this investment strategy to become worthwhile. That time can best be attained by simply waiting one generation (there are few options other than waiting). This would mean that when your child grows tired of the GBA, you can sell it on eBay for $45 and then invest the earnings until your child has had a child of their own who would want the future equivalent of the device. Let’s assume that this would take place over the course of 25 years. Placing $45 in a savings account for 25 years with a rate of 5% compounded monthly would give you a future value of $156.66, hopefully enough (or nearly) for the latest and greatest handheld console. Likewise, if you were to sell the old pair of jeans and use the invested proceeds for your kid’s kid 25 years later, the $7.50 you earned would be worth $26.11, just enough to buy your grandchild a pair of new jeans.

Winners: eBay and eBay

In conclusion, it seems as if the eBay approach is economically rational when more time is involved. This time should be enough for the savings to appreciate, thanks to compound interest. Also note that regardless, the younger person does not receive the same amount of utility as the older one unless they are separated by roughly one generation.

Of course, a hand-me-down savings account might not be worth keeping track of for 25 years, but I also wouldn’t keep that old pair of jeans around thinking the future generation will appreciate the sentimental value.

In both scenarios, of course, buying new items for both siblings is ideal (from the siblings’ point of view).

Chapter 11: The Cost of Information

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The advent of a new phenomenon always brings about a certain amount of resistance. When products became available online, for example, it took some time for many people to become accustomed to making their purchases over the Internet, and some have yet to do so.

However, for those of us who still opt to make our purchases in conventional brick-and-mortar stores, our unwillingness to buy online may have no connection with our unwillingness to adopt new technologies. The real reason may simply be that we place greater value on our personal information than the money and time we save from shopping online.

Recently, an acquaintance, whom we will call Darryl, decided to spend around $100 more for an item at a traditional store than the online price, simply because the online checkout form was “asking too many personal questions.” Now, we all know that a good portion of the data that we submit is used for market research and data analysis and that we can potentially be spammed by submitting data to the wrong destinations. Not to mention the rare possibility that we may fall victim to identity theft. We therefore decide that we’d be better off buying certain products in a store rather than buying them online, even if it means spending more.

This raises the question: how much of this is rational and how much is pure emotion? If we go to a store, we might pay with a credit card, which has a great deal of information stored with it. We think nothing of using debit cards at gas stations, or using reward points or store cards, and we sometimes provide social security numbers when renting movies. Executing the same transactions online, however, can give us a feeling of less control, as we are made fully aware of the information we’re giving away by the act of filling out those purchase forms. Was Darryl correct in paying more to purchase (by credit card) from a store?

There is no straightforward answer to this, but as time goes by it will be less and less of a problem. Companies such as Paypal and Verisign already offer secure transactions and SSL certificates. Although most consumers do not fully understand how encryption works, they trust these sites. And people will get more and more accustomed to not conducting these transactions over public computers or unsecured networks. Some will set up e-mail addresses for this specific purpose (so as not to risk spam on their main e-mail accounts). Besides, the clerk working at a gas station is just as liable to take or sell or lose your personal information as a hacker is to find and use your information on the Internet.

In conclusion, the best form of protection seems to be your head. Remember that your information is always susceptible in one way or another, so prepare for the worst:

• Never use debit cards if at all possible (money lost from these is hardly ever seen again).

• Keep track of expenses on your credit cards and report any suspicious activity right away.

• Keep your cards’ 1-800 numbers saved in your phone (for that purpose).

And finally, we believe Darryl was wrong in spending more. Although peace of mind is a personal matter, this choice does not seem logically justified, as the information he did not want to send online would still be sent from the store’s online system.

CHAPTER 12: Saving Dolphins and Polar Bears

This subject was first brought up (to our knowledge) by Robert Frank in his excellent book The Economic Naturalist. We choose to use it here merely as a springboard for more thought and discussion. We do not claim, however, to have devised the idea ourselves.

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Certain animals, like dolphins, polar bears, whales and the northern hairy-nosed wombat, are struggling for their existence while other animals, such as cows and chickens, thrive and multiply. Why? Are there general patterns we can extrapolate from this phenomenon? If so, is it possible to use these rules and a little economics to reverse trends of extinction?

Well let’s see…

One main difference between the species that are endangered and those that are not is that we kill more of the latter. Cows, chickens and lambs are slaughtered every day, while any whaling expedition is met with outrage by much of the international community. Given this fact: why are whale populations declining so dramatically, while those bovine counterparts are doing just the opposite? In fact, it is arguable that if it weren’t for humans, cows would have gone extinct a long time ago. However, deciding that we should allow anyone to slaughter all the whales they want is not the solution.

Let’s examine this a bit further.

Why are cows and chickens being slaughtered en masse? The answer is obvious: nutrition. We, as a species, started to domesticate animals in order to derive nutrients from their meat, their milk, their eggs, and so forth, not to mention their ability to do work for us such as tilling the soil. So people started to keep these animals for themselves to provide a source of wealth and sustenance. Then, at some moment in time, some enterprising individuals had the foresight to realize that they could keep more than they needed and sell the excess produce for a profit. Over time, they developed the view that it was in their best interest to keep these animals’ populations sustainable. In other words, farmer John won’t slaughter all of his cows tomorrow because after feasting on beef for a couple weeks, he will not have anything left to eat, nor anything to use for making money. This guarantees that cows will continue to roam our pastures for the foreseeable future.

Whales are also used for food, oil, perfume, and other products. Why then, aren’t the fishermen of the world trying to keep them alive and keep their populations thriving as well? The reason is simple: ownership. If a whaler in New Zealand decides not to slaughter a whale, he knows that a Japanese whaler will, and be better off for it. It is in no one’s interest to let these creatures live, since they can be snatched up at any point by someone else.

Our proposal, therefore, is to allow ownership of these creatures. If certain whales, as well as their offspring, are owned by someone, that person will want to keep the population alive. Of course, we will have to allow these people to use and/or sell them for their own purposes, but we will be virtually guaranteed that whales will not die out, since that would be in nobody’s interest.

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