#8/25: Megamistakes

Megamistakes was published in 1989 – so, it’s especially interesting to look at a book about forecasting which is over 20 years old. Most of this article will be quotes of the book. Enjoy!

The most prominent reason why technological forecasts have failed is that the people who made them have been seduced by technological wonder.

The forecasters who construct them are blinded by their emotions and lose perspective of commonsense economic considerations.

A key premise of forecasting is that a passionate focus on technology for its own sake spells disaster. No doubt, many of the same errors are being repeated today.

How much I love these three sentences. Year and year again we see some people praising some new technology to be the “next big thing”, “bigger than the internet” or whatever and a few years later nobody remembers about that technology.

As one expert noted: “We can build all kinds of mass-transit vehicles, but no one has yet found what’s going to make people want to get out of their cars and ride them”

A common technological trap. I thought for the most part of my life that technology can solve practically every problem. But if you look deeper, the problem is often psychological or cultural. Today, we see that lots of young people don’t necessarily need a car – forty years ago this was unthinkable.

Computer forecasts are exceptional in this regard. IT is one of very few industries where optimism was warranted.

This is really exceptional. Even the overoptimistic forecasts became true.

The method did not matter. Asking the right questions did. Nuclear ships had no effect on the industry. Lower-cost international competitors did.

This was a common theme in the last decades. Especially, in the US people thought about technology but neglected international developments.
Look at the flight industry. Flights didn’t really got cheaper because Boeing developed an innovate air plane. It got cheaper because of deregulation and lower barriers to entry.

It is good advice to be skeptical of any forecast that calls for a new age.

The lesson to be learned here is that although we read so much today about how things will change rapidly, the home of the future will probably look pretty much like the home of today

If you exclude computers then not so much has changed in the last 10 – 20 years. We pretty much life in the same houses, shower in the same shower, eat practically the same food (also people buy much more organic today). Cars haven’t really changed – the most change came through computers. Things like notebooks, smart phones, car navigation systems, etc.

In 1987 Tyzoon Tyebjee conducted some experiments on biases in new product forecasting. […] He found that the very act of participating in the new product planning process led to overly optimistic forecasts.

This is an interesting result for especially for technology startups, or more so for investors.

He [Nigel Calder] is particularly impressed with the performance of Barbara Wooten, a social scientist who made prediction in the 1964 study. She made “forecasts that seemed damp and depressing at the time.” In hindsight, however, they turned out to be remarkably accurate. Why? According to Calder, because she presumed “that the pattern of social life would not be remarkably different”

Later there’s more about demographic forecasting and it works pretty well.

There is absolutely no evidence that complicated mathematical models provide more accurate forecasts than much simpler models that incorporate intuitively pleasing rules of thumb. In growth market forecasting it seems less important whether the model is fancy or not than whether the model incorporates the right assumptions.

In the excellent book Forecasting (a newer version is freely available) they basically came to the same result. Often simple methods like exponential smoothing work better than highly complicated models.

In a 1985 article in Management Science, Everette Gardner and Ed McKenzie proposed a simple mathematical model that incorporates a “damped” trend. […] They and others, including myself, have tested their model in many different types of applications. Almost universally, it has been found to be more accurate.

And dampend trends work even better. You can read more about them int he same book as above for free: 7/4 Damped trend methods.

A central thesis of diffusion of innovation is that some initial group of customers purchase new products. They are called the innovators. […] After a while, a second group of customers, opinion leaders, enters the market. Rapid market growth ensues as many other consumers imitate the purchases of these respected members of society. […] Then growth slows, as nearly everyone is using the product.

The problem with using research arising out of the diffusion of innovations, the product life cycle, and market growth curves is that they ignore the fact that market growth is not guaranteed, or even likely.

Like the quote says the big problem is that the diffusion of innovation models only explains successful products, so it’s not of much use pre-success.

The Zeitgeist concept is used to explain the fact that inventions and discoveries tend to be made simultaneously by researchers working independently.

Consequently, the Zeitgeist means that inventions and discoveries are due less to the power of individual genius than to the spirit of the times.

The Zeitgeist also casts doubts on the merits of consensus forecasts. It implies that a consensus forecast is not difficult to obtain, but that the consensus may be more indicative of present beliefs than of actual future outcomes.

We’ve seen a lot of inventions happen simultaneously. Schnaars also notes that not only inventions are covered under the Zeitgeist concept but also forecasts. It’s interesting that each decade had its own Zeigeist and the forecasts mirrored it dramatically. Even Schnaars himself contributes to this by emphasizing the industrial strength of Japan and other non-US countries which was a big topic in the mid-late 80’s.
Here are some excerpts from Megamistakes:

Cars would also move into the jet age. All three American automakers spent heavily on turbine cars using jet technology. The transfer of technology from airplanes to autos failed to generate a growth market. It was an impractical idea.

All in a setting where children wanted to grow up to be astronauts rather than computer wizards. People were fascinated by space travel.

Imagine yourself in the late 1970s forecasting that energy prices would decline throughout the 1980s. For one, it never would have happened. Such a forecast would have been preposterous at the time. All the indicators of the day pointed to ever higher oil prices. Some even talked of the end of the petroleum age. Even if you had made the forecast, who would have believed it? It was inconsistent with the beliefs of the day.

The last two sentences are insightful. It’s hard to argue against the general belief. In science, there’s the observation that beliefs don’t die, but the people having them. It’s probably the same for normal people. In 10 or 20 years we will look back and saw some stupid belief we have today.

What to do?
Start with a simple price-performance analysis:

* What additional benefit does this product offer over existing entries?
* Will consumers have to, and be willing to, pay extra for it?
* Does the product offer a benefit over existing products that justifies a higher price?

This will filter out easily half of the promising technologies. In this book, Schnaars applies these three questions to a lot of different technologies, like moving side walks and video conferencing.

Predicting social trends is one of the most difficult forecasts to make. Social trends involve people, who, unlike physical quantities, do not behave according to physical laws.

In the permissive 1960s, for example, who would have predicated that a conservative President would be overwhelmingly elected in 1980? Radical college students in the 1960s saw a revolution in this country as a real possibility. In the 1980s, those same persons flocked to business schools and coveted highly paid careers in investment banking.

Demographic forecasts that sought to predict birthrates and other events that had not yet occurred often proved mistaken.

There are two different things here. Forecast of social trends and demographic forecasts. Social trends are extremely hard and probably more random than most of the things. Demographic forecasts, however, can be rather accurate if they people are actually born, yet.

For example, in 1960 Business Week analyzed census data and concluded: “During the next twenty years the number of Americans over 75 will increase to 9 million.” This forecast of the elderly segment of the population proved close to perfect. The 1980 Census counted 8.94 million persons over the age of seventy-five.

However, a problem is if you assume demographic and cultural forecasts behave identically.

Forecasts are particularly vulnerable when they assume that a growth market will result when a large group of consumers enters the primary age for heavy demand of a product category. Such forecasts assume that the younger group will follow the pattern set by its parents.

Like Elster said, either the young will do the same as the parents or something different. Don’t assume that younger people will do the same as their parents.

Are forecasts possible?

Yet a Business Week editorial foresaw the oil crisis about two years before the actual event. It stated that “the stage is being set for an energy crisis in the U.S. by the end of this decade. […] In a time of international crisis, oil supplies could be cut off.”

This is one example. There were a people who foresaw the 2008 financial crises and there were a ton of people who questioned the sustainability of Groupon.

Surprisingly, firms holding a commanding share of their market are often among the last to foresee potential threats to their bread-and-butter products. As a result, market leaders often miss the opportunities that they themselves should have created.

Remarkably, time and time again, in industry after industry, market opportunities have been more apparent to outsiders than to those with a dominant position in the industry.

We see this time and time, again. Schnaars describes one of the slide rule manufacturers. A giant – then came the micro chip and they though that this doesn’t really influence their market. A decade later they shrunk into a small business.
A similar thing happened to IBM where they declined the possibility of creating (enterprise) software, etc. etc.

Guidelines:

  • Avoid technological wonders
  • Ask fundamental questions about markets:
    • Who are the customers?
    • How large is the market?
    • Will the new technology offer them a real benefit over existing and subsequent substitutes?
    • Is the technology cost-effective relative to those substitutes?
    • Is the derived benefit worth the price you will have to charge?
    • Are cost efficiencies probable?
    • Are social trends moving toward or away from this market?
    • Does the innovation require users to do things differently?
    • Does the innovation go against customs, culture, or established business practices?
  • Be suspicious of trend projections
  • Avoid extrapolating the issues of the day
  • Challenge Assumptions

There are some alternatives to “traditional” forecasting:

  • Scenario Analysis – I would recommend Solving Though Problems as a nice book on scenario analysis
  • Follow rather than lead – be innovator
  • Perpetual innovation: always innovate -> start small, try, then scale
  • Assume that the future will be similar to the present

Such an awesome book. I really loved reading Megamistakes. It’s full of insights. Schnaars shows different forecasts from different time periods. It’s astonishing how strong the Zeigeist influenced their forecasts. Furthermore, a lot of time he quotes other forecasters and their experiences. And you can get a used copy for under $5. It’s a fantastic book and should be read by nearly everyone! Recommendation!

Sunk costs and education

DSC_4320 by archer10 (Dennis)

Lately, I’ve talked to quite a few people who graduated or a near graduating and observed the following:
About 70% of these people invested a ton of effort and time into getting great grades, doing internships at famous companies and traveling to other countries; mostly with the aim of keeping their career options open

There is nothing wrong with that behavior. It’s allows you to keep options open. However, now the sunk costs fallacy comes into play. What’s the idea behind that fallacy?

Sunk costs are costs which can’t be recovered. An example (from Wikipedia) is non-refundable movie tickets. You could either watch the movie, even if its horrible or just throw the tickets away and do something else. Rational behavior would be to do something else if this something else (for example, eating ice cream) has a higher utility than watching the horrible movie, even if you paid $50 for the movie ticket. The sunk costs shouldn’t influence your decision.
The fallacy is that people do include sunk costs instead of ignoring them. How does that apply to education/careers?

Let’s go back to our successful students. They finally graduated and have now the chance to choose the future career. What I’ve experienced is that they don’t want to throw away their past efforts and decided that they should rather choose a career in management consulting/investment banking/research instead of what they actually want to do; something that would make them happier. And all this just because they don’t want to have the feeling that they wasted some of their time.

Firstly, you don’t have wasted your time. See it this way: All your experience that you earned in this time helped you to come to the conclusions you have now.

Secondly, it’s quite normal to act like this, however not the best way. The underlying phenomena is loss-aversion. People try to avoid loss more than they try to get profit. Approximately $2 loss is equal to $1 earned.

Thirdly, this is what I call the second division effect/strategy. It comes from football leagues. There is the premiere league or first division where all the top players of a country play in. And there is the second division, where good but not great players play in.
The idea is this: You could either be a mediocre player in the premiere league or a top player in the second division. I personally be rather the top player in the second division. Interestingly, there is research which shows similar results for most of the population.
They were asked to choose one job. On the first job they would earn $50k but everybody else would earn $60k. On the other job they would earn $40k but everybody else would earn $30k. The majority choose the second job, the second division job.

All in all, the best thing is simply try to avoid to fall into this trap but when you gain more insight into yourself, you’ll probably be able to climb out of this pitfall.

#4/25: Experimental Auctions

I decided to write in bullet-point type of stile because these summaries/reviews slowly become wall of texts which for one take long to write and make it, in my opinion, harder to read.

  • Values are revealed by actions — if you pay $2 for some chocolate than you value the chocolate more than your $2
  • How can we measure the value of non-market goods?
  • => Revealed preference methods, i.e. (particular) auctions.
  • Erases the problem of hedonistic pricing which requires market prices a priori.
  • Also better than stated preference methods, i.e. surveys and trails — the big problem is that these methods are hypothetical and allow strategic behavior (manipulation)
  • Experimental auctions are a better choice because of:
  • a) They use real money and real goods.
  • b) They can be carried out in real environments, e.g. supermarkets

Value theory

  • Willingness to Pay (WTP): if person do not own the good, how much is the person willing to pay to get the extra good?
  • Willingness to Accept (WTA): if person owns the good, how much does person need to accept the loss of the good?
  • However difference between WTP and WTA depends on the risk behavior. Problematic: We have to estimate Arrow-Pratt but measuring risk-aversion is hard and destroys the sense of experimental auctions.
  • Furthermore, we have to include time. Proposed formula: WTP today = Expected Value – commitment costs, which includes uncertainty, lack of information, patience, reverse transaction, freedom, etc.

Preliminaries

  • What’s the objective? Do we want to test, explore or generalize?
  • Simple experiment design: Attribute vs. Attribute, however we need 2^\text{\#Attributes} different test cases.
  • Better: Fractional factorial design. Assumes there are no interaction effects which makes the main effects separately identifiable.
  • e.g. three attributes (H vs. L, S vs. L and B vs. R). Normal design:

    Factorial design, just needs:
  • Randomize treatment but control for time effects (time of the day, weekday, etc.)
  • Furthermore, control within-subject change. E.g. with ABA designs, i.e. first treatment A, then B, then A.
  • For experimental auctions however a problem, i.e. demand reduction effect. Solution: Only one treatment will be binding and will be picked randomly.
  • Furthermore, external effects, e.g. superior information about market prices. Solution: Inform people as good as possible; you can also provide substitute products for sale at a fixed price.

Field vs. laboratory

  • pro lab: better control, less confounding variables
  • pro field: more realistic, self-selecting population -> less sample-selection bias, more knowledge in established environment & experience, reduced costs
  • Problem: Hawthorne effect, i.e. if people think that they are being watched they change their behavior

Conducting experimental auctions

  • Qualitative study prior designing to learn about decision-making processes and general info
  • Focus groups and pretests to met objective
  • Teach them about your mechanism and optimal behavior
  • Real-money practice rounds with another good helps to learn how auctions work
  • You can do the auction anonymously — but that can be relaxed

Endowment vs. full bidding approach

  • Endowment bidding: Give participants the inferior good or they have it beforehand. Let them bid on how much they want to pay or receive to give up endowed good. This measures the real value between the endowed and auctioned good.
  • Full bidding: Here you don’t get the exact value because of transaction costs, however you have an estimate for the absolute value.
  • Generally things to consider: Is consumption important? Is there reference dependence/loss aversion (esp. endowment bidding)? Does the endowment signal something about prices?

Mechanism

  • English auction: works very well in practice and is highly accurate; furthermore people mostly understand how it works
  • Second price auction: everybody gives one bid, the highest wins and pays second highest price — Works quite well, especially for high value bidders
  • Random nth price: like 2nd price but n is drawn randomly, i.e. the people with the n highest bids get the good for the nth highest price — Similar to 2nd price but works better for low value bidders.
  • BDM mechanism: people bid and random number is drawn, if number smaller than bid, then person gets good for drawn number — good for field studies but creates no real market environment

Some of the case studies
The books presents a lot of different case studies which are quite detailed. I won’t go into them because this would be beyond the scope of this short summary.

Auction design problems

  • novelty of the experiment experience: learning effects; price increases because people learn about the optimal strategy
  • preference learning: high bids to learn about unfamiliar products
  • off-the-margin bidders are problematic => solution: random nth price auction

Ideas

  • preference reversal: arbitrage causes value adjustments not preference adjustments
  • hypothetical auctions still not revealing => idea: calibration function.
  • => can work in some environments, e.g. trading cards
  • => doesn’t work so well in others, esp. new products

Influences on choice and valuation

  • context matters
  • goods matters
  • information matters
  • exchange institutions matter
  • market experience matters
  • price information sometimes matters
  • substitutes and complements matter

idea: consequential vs. inconsequential mechanisms

  • consequential means that the probability of binding lies between 0 and 1
  • sadly not revealing, real auction still better but not as worse as hypothetical auction

Empirical results

  • internal validity of experimental auctions is high
  • reliability is high, but influences on choice and valuation still matter
  • some anomalies don’t exist in the long run – however, there’s also things like prospect theory and loss-aversion.
  • preference reversal: market creates rationality through natural selection or budget constrains
  • even with anchoring the valuation is still valid in relative terms

Future

  • great tool for the future
  • lots of opportunities for research

I love this book. It has enough theory and a ton of case studies — the authors don’t shy away from real problems and resent possible solutions. I seems like a rather new area but I can see a lot of potential in using experimental auctions. Especially e-commerce sites like amazon could profit highly by providing a service for producers to get WTP estimates. All in all, a superb book, more of such books, please!

#3/25: Explaining Social Behavior

The first chapter is generally about explanations in science and more particularly in the social sciences. Elster emphasizes that just-so stories are often not enough. Imagine a story where someone didn’t go to college because he didn’t knew that he could. Does this explain why? No, we should ask why he didn’t knew that he could go to college. The author presents different theories about social science and comes back to them later.
Often mechanisms are used to explain behavior, he argues that outcomes are more important than the internal mechanism. E.g. if people behave with bounded rationality then this doesn’t mean that they calculate utilities for each choice but that they behave in a way that is similar to the prediction made by the mechanism of bounded rationality. There are several other mechanisms, like cognitive dissonance, loss-aversion, reference dependence, etc. The field of behavioral economics covers them mostly today.
The last chapter of the first part talks about interpretation and explanation. Elster argues that the basically the same. E.g. in interpreting a text, one explains its intent or the behavior.

The second part talks about the mind. The authors distinguishes between two types of motivations. There is wanting something, i.e. with the aim to put effort into fulfilling the want and wishing something, i.e. without the aim to put effort into the wish. This is quite interesting and he uses this definitions to argue why people don’t achieve what they planned to achieve because they only wished it not wanted it. Furthermore, there are different kind of requirements of motivation, i.e. general interest, reasoning, e.g. I want a good job therefore I should learn things and passion.

The second part talks about self-interest and altruism. He discusses the view, that I take, that all action is based on self-interest, e.g. altruism is based on an internal want to feel good about oneself. Secondly, there is the great mechanism of reciprocity, i.e. if somebody helped you, you will probably help him in the future.

Myopia and foresight go into the process of motivation for longer time horizons. Planning is in important process which is often underestimated. The characteristic of discounted value or utility is important there. If somebody values the present much stronger than the future, they will be reluctant to give up present utility for future utility. Here comes the weakness of will into play. Elster argues that weakness of will can be explained either by wishful thinking or temporary change in motivation or change over time.

The second last chapter in this section talks about beliefs which are a important topic in behavioral economics. A interesting observation is that experts do often worse than simple statistical models in decision making. I wrote about Atul Gawande who introduced check lists into surgery with phenomenal results. Or I read about using decision charts for classifying deceases which also worked better than a group of experts.
Secondly, there is the big field of biases. Some biases are regression to the mean, i.e. over time a deviation to the mean will move toward the mean. One study which ignored this was about air pilots. The researchers tested if praise and punishment helps to learn better. They praised the pilots if they done well and punished them if they didn’t. Often pilots did worse after they were praised – the researchers thought that it was because of the praises but it was just the regression to the mean, i.e. naturally the couldn’t do well every time.
An other biases is the availability bias, i.e. people see things that just happened as more important. You can see this for example that after a flood more people will buy a insurance and it will slowly decay over time. There are tons of different biases. The RSOAP created a neat file which includes lots of different cognitive biases.
Other belief mechanisms are magical thinking, i.e. you act as if you can influence the outcome but you can’t and rationalization, i.e. you make up a story to explain your behavior ex-post.

The last chapter in this part covers emotions. Generally, emotions influence actions but they decay quite fast. I.e. emotional behavior, e.g. attacking someone because of rage can be controlled if you just take the time to calm down.
An interesting aspect is rationalization of emotions. Elster presents the following example: Somebody envies his neighbors’ car, but he learned that envy isn’t good. Therefore he rationalizes that his neighbor got his wealth/car by immoral means because if he wouldn’t he would realize that he could have got the car if he learned more or worked harder but so he hasn’t to.
An other interesting, and quite famous fallacy in economics, is the sunk-cost fallacy. That is, somebody continues a unprofitable activity instead of accepting failure and doing something profitable. You probably heard it lots of time, e.g. “we invested $20m into this project, we can’t just let it die” or people who are continuing their career although they rather would do something else. The interesting trait of this fallacy is that it becomes worse in time.

The third part talks about actions. Elster defines action as intentional behavior or goal-oriented behavior, i.e. reflexes aren’t action. Action is framed by external and internal filter, e.g. legal or economic restriction or internal filters like beliefs. Furthermore, action depends on desire and opportunities.

Action is also often depended on situations. E.g. people can be talkative at work but be rather silent at home. Elster takes this to explain why kids are often so different at school and at home. Furthermore, he takes the stance that character is often more local than global, i.e. response is situational.
The next chapters talk about rational choice and rationality. Generally, rationality is subjective, i.e. each one’s utility is composed of different parts but we are constrained by costs, i.e. search cost or more general transaction costs and opportunity costs.
He talks about some paradoxes, e.g. voting or the lawn-mowing paradox. That is, that a person would let his lawn mowed by someone else for $x, but also wouldn’t mow an other’s lawn for more than $x. This can generally attributed to loss aversion.

The last chapter talks methods that help dealing with this irrationality. One is adding penalty a priori, e.g. if you eat more than two bars of chocolate you aren’t allowed to watch TV. Empirically, this doesn’t work so well. An other is adding premiums a priori which is just the other side of the coin.
One interesting method is eliminating choice, i.e. just buying one chocolate bar.

The second to last part talks about links between behavior and evidence from natural sciences.
Elster talks about experiments where animals got rewarded a treat for different behavior. In one case the animal got one treat after X times pushing a trigger and the alternative was a machine where you got randomly treats. Interesting enough, the mice favored the latter and it was harder to unlearn. He argues that people and animals try to find patterns and people often think that they see patterns and try to activate a trigger although it was purely random.
An interesting application was natural selection as a mechanism for providing rationality. One example would be competing firms in a market. In the long run only the rational firms will survive, i.e. the mechanism indirectly filters the outcome.

The last part talks about interactions. Elster takes a whole chapter about unintended consequences which is easily one of my favorites topics. I wrote a bit about the topic so I will just talk about the hog effect which, at least, I observe quite often. The hog effect is that future change isn’t anticipated in forecasts. One example is the increase of cigarette taxes in 1993 in Germany. The gov forecast that the tax increase will increase overall tax dramatically. Instead the consumers smoked less, switched to other products which weren’t affected by the tax and some companies created cigarette-like products which didn’t fall under this tax. In the end, the tax increase dramatically lowered the overall tax income.

An other interesting topic is that of trust. Generally, trust is incredible important in human interaction. He talked about the interesting case of diamond merchants which is a quite small community and they are relying heavily on trust. E.g. for them a verbal agreement is as good or better than a written one.
If trust is broken, then in general and of course in the diamond merchant community, ostracism will follow.

Elster points out that social norms are often not rational and can be harmful. One example is the need for mediocrity in some communities which destroys success of people. Or the mistreatment of homosexuals in the past and in some communities today.

The last chapter talks about collective belief formation which is quite interesting. One rather well-known study analyzed the drinking behavior of college students and found that most students drink more than they would like because they think others want to drink more. That leads so a too high level of drinking, also each one individually would drink far less. This is called pluralistic ignorance and basically says that nobody believes in X but everybody thinks that all other people belief in X.
Therefore, you can often see snowballing of non-conformism. That is, if enough people are non-conform than other non-conformists aren’t afraid anymore and present themselves also as non-conformists.

All in all, I really enjoyed this book. It’s a bit lengthy in parts but utterly interesting. I especially liked that Elster uses
proverbs and excerpts of novels to explain behavior which makes it more lively. I think it’s a must-read if you want to learn more about social behavior and is great if you (want to) work as a economists, social scientists or similar.