The implication for marketing executives is that they should de-emphasize designing, making, and selling would-be hits and focus instead on creating portfolios of products that can be marketed using real-time measurement of and rapid response to consumer feedback.

The aurhors recommend five measures for more effective marketing campaigns which take social network effects into account:

1. Increase the number of bets, and decrease their size
2. Focus on detection, measurement, and feedback
3. Follow through with flexible marketing budgets
4. Exploit naturally emerging social influence
5. Build flexibility into supply chains and contracts

Their results are based on academic work published earlier this year by Watts as well as Matthew Salganik and Peter Dodds: Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market

Read more: Marketing in an Unpredictable World, by Duncan J. Watts and Steve Hasker

UPDATE: I’ve heard from some readers that the Harvard Business Review link didn’t work for them. Here is an alternate link to the paper, hosted by Columbia: Marketing in an Unpredictable World

UPDATE 2: For discussion of a very similar “portfolio” approach to dealing with complex or uncertain environments, this time in the context of business strategy, see my earlier post Strategy in an Unknowable Universe

3. Does your study measure how well what people say they look for matches with what they actually look for?

Most of what I do is work on corruption in poor countries. If I want to know how much someone is paying in bribes, I’m not going to ask them, “How much did you pay in bribes last year?” I’m going to say, “The guy down the street from you, who looks pretty much like you, how much did he pay?” Similarly, in the speed-dating study we ask people, “What do you care about?” We also ask them, “The average man, what do you think he cares about?” But then we actually see how they behave in the game. And, not at all surprisingly, what they say the average man cares about lines up much more closely with what they actually reveal through their actions than what they claimed they cared about beforehand. In particular, everyone — both men and women — says they care less about physical attractiveness than the average.

4. Do you think speed-dating is more efficient than traditional search methods?

In some sense, it’s efficient: there are all these slice studies on how 10 seconds’ worth of observation is as predictive of your experience with a professor as a semester’s worth, and they’ve reduced it to 2 seconds and that’s just as good; and they’ve reduced it to just a photo and that’s pretty good, too. So you learn a lot in four minutes, perhaps as much in four minutes as you do in a much longer superficial interaction like, say, a date. So, this does meaningfully provide you with 20 rapid-fire dates, to the extent that we form as much of an impression in 4 minutes, or 10 seconds, as we do in 4 hours. The thing that’s left out of this neat decomposition of people into attributes, though, is actually learning to love someone. And that’s what I think is kind of missing. Focusing on people as a bundle of attributes almost makes people think about this decision in the wrong frame of mind.

5. Do you think people become unwilling to commit because of all the choices dating services enable?

Yes. And the way that you can make these choices — just the very fact that it’s set up in this way — distorts the way people choose. There was an article in the New York Times on a backlash against Internet dating, and I wonder to what degree that’s at least partly as a result of these sorts of realizations.

6. The results of your speed-dating studies, particularly with regard to intelligence and physical appearance, seem to reinforce gender stereotypes. Why do you think this is?

Well, they are stereotypes for a reason. However, it’s not as simple as, “I avoid all women who are ambitious or intelligent.” It’s about, “Intelligence and ambition is OK until it supersedes my own.” It’s also worth mentioning that these are average effects — there are surely men who do not have this property. I like to think I’m one of them: my significant other is definitely a lot smarter than I am. When her grandmother heard about me, she said, “I told your mother this, and now I’m going to tell you: never let a man think you’re smarter than he is. Men don’t like that.” Everyone laughed and thought this was so anachronistic, but it shows up in our data. Grandma’s views on dating aren’t so dated after all!

Read more: Dating Data: Economic Theory and the Search for a Mate

Our answers to each of these questions can radically change the outcome of a decision making process, for better or for worse. In his latest Mauboussin on Strategy article, Michael Mauboussin surveys the many behavioral factors that go into forming comparisons, and offers some advice for making comparisons which are appropriate to the situation.

Read more: Mauboussin on Strategy: How Do You Compare?

Previously:

• More Than You Know
• Mauboussin on Discounted Cash Flow Models
• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin

In the book’s conclusion you mention some of the things the experts still don’t understand about investing. Can you talk about the directions for future research?

If you look at the world of finance, there are many, many open questions. For example, we don’t really understand how capital markets get to efficiency. There are some theories that are widely used in the world of finance, including mean-variance and no-arbitrage assumptions. I suspect these traditional ideas will eventually be superseded by this idea of complex adaptive systems, or the wisdom of crowds.

I think that the recent developments in neuroscience and decision making are absolutely fantastic. Another area that is really intriguing are the statistical regularities, like the power laws, that have come out of the study of physical systems, like earthquakes. In biological science, we know things like body mass and metabolic rate also follow a power law, a scaling property, and we have ways to explain those phenomena reasonably well. We see many of those same power laws in social sciences, yet we really have no causal mechanisms. So we don’t know why city sizes follow a power law or why the sizes of corporations follow a power law.

The last idea I’d mention is the flight simulator for the mind. One of the challenging things about investing is it’s very difficult to get timely and clear-cut feedback. If you’re a handicapper at the racetrack or you’re a weather forecaster, you get feedback pretty immediately on the decisions that you make, and that helps you calibrate and improve your decision-making process. When you purchase or sell a stock, you really don’t know in a timely fashion whether that decision was a good or a bad one. So an interesting question is whether we could create some sort of artificial environment that allows people to get better feedback on their decisions.

Read more: Guppies, ants and golf swings: Mental models for investors

Previously:

• Mauboussin on Discounted Cash Flow Models
• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin

SpreadPlayer relies on sound to revise the concept of financial visualization. The installation translates concepts used in finance into auditive ideas such as frequency and volume. It transmits the state of the stock market through sound, liberating the user’s eyes and stimulating alternative senses such as the ear, the musical sense or the sense of rhythm.

The installation includes an MP3 player, a proprietary software package, a real time connection to the capital markets and the packaging of the product. The player displays variations in prices of specific stocks, as well as fluctuation in indices in real time. It offers the option of modifying the sound output with the “melody” tool (which changes the average price of a stock), cancelling the “noise” of a stock (eliminating its financial peaks) or calculating an average of the up and down movements. Visually, it evokes the aesthetics of other players (iPod, Creative Zen, etc.) as well as their software (iTunes, Windows Media Player), emphasizing the idea of a mass market product.

SpreadPlayer offers a new concept, the notion of “auditive representation”. It reintroduces, at an individual and portable level, the usual reliance of brokers of multi-tasking while working from trading rooms. Traders, for example, use their sight to watch the piece of the market in which they are buying or selling, but remain connected to the broader market by overhearing the conversations of their fellow traders. By reintroducing sound in the individual experience of the market, SpreadPlayer redefines the traditional concept of financial visualization.

Derivart’s resident economic sociologist is Daniel Beunza, who just joined the faculty of the Columbia Business School as an Assistant Professor. For more information on Derivart, including some of their other projects, check out this post at We Make Money Not Art: Art, Finance and Technology.

Perhaps the best way to understand this new model of the brain is to think of a giant warehouse. Your brain is the greatest inventory system on earth. It constantly takes in information, breaks it down, and puts in on its warehouse shelves—that’s analysis. Your brain then compares the new information with other items on other shelves. When it finds a match, it pulls those items off the shelves and puts them together in a flash of intuition. The combination of analysis and intuition becomes “creative insight,” which is “the ability to take existing pieces of information and combine them in novel ways that lead to greater understanding and suggest new behaviors and
responses.”

Duggan has used this insight to build up the strategic intuition framework as a way to quickly make decisions which nonetheless are built on a strong analytical foundation.

Read more:

• Strategic intuition: The key to innovation
• Coup d’Oeil: Strategic Intuition in Army Planning

Just took lots of huge losses to offset some gains, made my stomach turn.

Why is it that realizing what had previously been only a paper loss is so painful for investors? Shouldn’t it be the declining market price that is most painful, not simply the sale that realizes our loss? This bias hurts the performance of the portfolios of many investors. We are too slow to sell losers, letting money languish in a bad investment instead of selling it off and putting the money to more productive use. Financial advisors employ some psychological tricks to try and soften the pain of these realized losses. One is the well-known strategy of selling off losers in December for tax purposes. A sale that would be framed as an investment loss in November can be re-framed as simply a tax deducation if sold in December. Another technique is to “swap assets” by selling the losing position and immediately using the proceeds to buy into a new position.

Normal investors like swaps because swaps blur mental accounts and distract their attention from the fact that they are realizing losses. Consider the mental accounting benefits of swaps recommended by Gross in his manual for brokers: “The two separate transactions (moving out of the loss and moving into a new position) are made to flow together by the magic words ‘transfer your assets.’ The prospect thought he was making a single decision, switching one investment into another. He was not being asked to think in terms of selling XYZ…”

What can investors do to avoid the trap of holding losing positions too long? Statman proposes one such strategy — “loss harvesting”:

…a rule that mandates loss harvesting at the end of every quarter makes it easier to realize losses because it makes loss realization automatic. (Note also how the term “harvesting” creates a positive frame. Harvesting connotes sweet fruit, not bitter losses.)

Read more: Martha Stewart’s Lessons in Behavioral Finance, by Meir Statman

To provide a stark illustration, Oyer compares two sets of Stanford Business School classes: the classes of 1986 and 1987 (who graduated prior to the market crash of 1987), and the classes of 1988 and 1989 (who graduated post-crash). In the twenty years since graduation, the pre-crash classes have earned an estimated $85-250 million more than the post-crash classes, which works out to several hundred thousand dollars per student. Even a decade after entering the workforce, the class of 1988 was still making substantially less than students who graduated just a year ahead of them from Stanford.

Another recent study found similar effects for undergraduates as well. Philip Oreopoulos, Till Von Wachter, and Andrew Heisz found that it can take a full decade for the wages of those who graduated during a recession to catchup to those of their peers who graduated during better economic conditions.

The bottom line? cross your fingers, prospective MBAs! Market conditions at graduation have a significant impact on lifetime earnings, but there’s little anyone can do to effect or time the macroeconomic environment. But if you’re serious about managing your future, Oyer does have one recommendation: risk averse students should short a broad stock market index when they begin their studies, as a hedge against a market downturn hurting future wages. If the market heads south, you may be left with less lucrative job offers, but atleast you’ll have profited from your market hedge.

Read more:

• Hello, Young Workers: One Way to Reach the Top Is to Start There [nytimes.com registration required]
• The Making of an Investment Banker: Macroeconomic Shocks, Career Choice, and Lifetime Income, by Paul Oyer
• The Short- and Long-Term Career Effects of Graduating in a Recession: Hysteresis and Heterogeneity in the Market for College Graduates, by Philip Oreopoulos, Till Von Wachter, and Andrew Heisz

Researchers sometimes argue that statisticians have little to contribute when few realizations of the process being estimated are observed. We show that this argument is incorrect even in the extreme situation of estimating the probabilities of events so rare that they have never occurred. We show how statistical forecasting models allow us to use empirical data to improve inferences about the probabilities of these events. Our application is estimating the probability that your vote will be decisive in a U.S. Presidential election, a problem that has been studied by political scientists for more than two decades…

Read more: Estimating the Probability of Events That Have Never Occurred: When Is Your Vote Decisive?

Sheena Iyengar, a professor at Columbia Business School, researches the situations in which more choice can make us less well off, or where we prefer to have our choices artificially restricted. Her research has yielded counterintuitive results about the relationship between choice and satisfaction. Here she describes a few of her studies:

To explore consumer responses to extensive options, we conducted a field investigation in an upscale grocery store, Draeger’s, in Menlo Park, Calif. On two consecutive Saturdays, a tasting booth for Wilkin & Sons exotic jams was arranged. As consumers passed the tasting booth, they encountered a display with either six or 24 different jams. We observed and calculated the number of people who stopped at the tasting booth as well as the number of people who chose to purchase the jam in question.

The results are striking. They demonstrate that although extensive choice is initially more enticing than limited choice, limited choice is ultimately more motivating. In fact, 60 percent of the passersby approached the table in the extensive-choice condition, as compared to only 40 percent in the limited-choice condition. However, 30 percent of the consumers who encountered the limited selection actually purchased a jam, whereas only 3 percent of those exposed to the extensive selection made a purchase.

In subsequent studies we found that people are actually less satisfied with the choices they make if selected from a larger set of options. For instance, the same Godiva chocolate chosen from a set of 30 chocolates is considered to be less delicious than if it is chosen from a set of six. Moreover, we found that the negative consequences of too much choice extend beyond consumer contexts to work contexts. An examination of individuals completing a task chosen from a larger range of options as compared to a smaller set of options revealed that people performed better at their chosen activities if they have chosen the activity from a smaller range of options.

These results have important implications for the business world, that we are just beginning to understand. Marketers in particular need to be careful to structure consumer choice to prevent regret from eroding the satisfaction people get from their purchasing decisions. Ms. Iyengar has done choice research in many other contexts as well, which I’ll discuss in later posts.

Read more: Hermes Magazine – Choice and its Discontents

Previously:

• Too Many Choices: Who Suffers and Why?
• The Paradox of Choice

Nassim Nicholas Taleb
Dean’s Professor, Sciences of Uncertainty, UMASS at Amherst

Mild vs. Wild Randomness
The talk is on the nontrivial difference between Mild (Gaussian) and Wild randomness (non-Gaussian), its consequences for knowledge, prediction, and social fairness, and how it renders much of the statistical tools ineffectual. Related papers can be found on Taleb’s website: http://www.fooledbyrandomness.com/. Of particular relevance are /epistemologyfattails.pdf, /knolwedge.pdf, and /amherstclass/blackswanexcerpts.pdf (the last of which requires a username and password which will be included in the email announcement).

Anyone want to share access to that last file, which I assume from the filename is excerpts from Taleb’s next book, The Black Swan? Comments are open!

Previously: Columbia Collective Dynamics Group

From their overview:

The Collective Dynamics Group is a dedicated research effort, led by Professor Duncan Watts, the unifying theme of which is the application of modern mathematical and computational techniques to problems relevant to the social sciences. Examples of current projects include the structure and evolution of social networks, the dynamics of disease epidemics and cultural fads, the role of social information in financial markets, and the use of the Internet as a tool for social science research. The group, which meets weekly, consists of graduate students and post-doctoral researchers from mathematics, sociology, and economics.

Several of the group’s research projects should have important ramifications for those in the business world.

Interpersonal Influence, Contagion, and Collective Decision Making:

People constantly influence each other in all facets of life. Social contagion is the spreading of ideas, rumors, and behavior through a population via interpersonal influences. Collective decisions are generated by a social contagion process which is (often greatly) augmented by the machinery of mass media. Consequently, understanding interpersonal influence is crucial to understanding the behavior of both individuals and groups. Our projects on influence are divided between online experiments and conceptual mathematical models. We have developed a generalized model of contagion that reconciles and extends previously disparate models of contagion from the social and biological sciences. We are interested in standard biological contagion alone since the collective behavior of people is almost always important in how diseases spread. For example, motivated by observations of the SARS outbreak in 2002, we are exploring the effect on a contagion’s spread due to people moving between subpopulations with some frequency. We are also currently developing an online experiment which will explore interpersonal influence in `cultural markets’ (markets for cultural products, such a books, music, celebrity, etc) and how individual behaviors aggregate to produce collective outcomes.

Social Search, Collective Problem Solving, and Organizational Robustness:

The ability to solve problems collectively is central to the long term stability of any group of people, from a small business marketing a new product to nations confronting global economic crises. Real world collective problem solving is inherently a decentralized, distributed activity. When faced with a novel, ambiguous problem defined at the group level, individuals must determine how to coordinate their actions with others by exchanging ideas, knowledge, and questions. A key aspect of this coordination is search. How do invididuals find others who can at least partially answer or rephrase poorly specified problems? We approach this issue of what we call social search by building conceptual models and online experiments. For example, we have constructed a simple, sociologically plausible model of social networks that shows them to be searchable under general conditions. This is the so-called Small World hypothesis, the notion that two random individuals can find a way to connect to each other through a small number of intermediary contacts. For the past few years, we have been running a global small world experiment, where people send email to friends and acquaintances trying to find a sequence of contacts leading to `target’ individuals. In related work, we model modern organizations as reinforced hierarchical networks of individuals searching for information bearers among their peers. Being effective at collective problem solving leads to a tradeoff between specialized efficiency and flexible robustness.

I haven’t had the opportunity yet to read through any of their findings in detail yet, but I’m looking forwad to it.

He distills his analysis into four key points:

• Edge is key: you should be investing money in areas of the market where you have an analytical edge over the market at large
• Greater opportunity suggests a larger bet: the greater the edge you have over the market, the more you should invest
• Mean/variance is not the best way to think about maximizing long-term wealth if you are reinvesting your investment proceeds: the standard type of mean/variance portfolio optimization proposed by Markowitz is relevant for one-time financial decisions, but in situations where profits are reinvested, optimizing your geometric mean of returns is better
• Applying the Kelly Criterion is hard psychologically: adhering to the Kelly Criterion means accepting volatile returns — as investors we are risk averse and frequent losses are stressful psychologically, even if they provide the path to maximizing returns

Read the paper here: Mauboussin on Strategy: Size Matters