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«BASIC HUMAN DECISION MAKING: An Analysis of Route Choice Decisions by Long-Haul Truckers John Holland Knorring Advisor: Professor Alain L. Kornhauser ...»

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2.6.1 Value of work done by Todd Burner The most important conclusion that Todd came up with in his thesis relates to his work with the perceived speed curves that he generated from the Logit model. His conclusion on the risk aversion of the drivers in relation to avoiding congestion is a very valuable conclusion. According to Todd, “Perhaps the most interesting characteristic of the perceived speed curves is how insensitive the perceived speed is to the percentage of trucks that use the bypass (or looking at the inverse, how sensitive the percentage of trucks on the bypass is to a very small changes in perceived speed on the downtown route)…. This implies that truck drivers are very sensitive to small changes in perceived speeds, which by extension indicates a very high value for time.” These findings are quite interesting because they suggest that there is some sort cost function that can be associated with the time differences. Todd suggests that due to the level of risk aversion shown by the drivers, the cost should be fairly significant.

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In addition to Burner’s work on the perceived speed curves of downtown routes, he also laid a solid foundation for continued study in his definitions and assumptions.

When one considers stop definition, quite possibly the most important definition for this type of work given the data set, Burner’s definition, forward progress of less than two miles over a thirty-minute period, allows for an accurate benchmark to base stops.

2.6.2 Drawbacks to work performed by Todd Burner ‘99 The work that Todd Burner ’99 performed on his senior thesis is fantastic. His analysis of route choice via perceived congestion on alternate routes was very important to the field of route choice. The most important drawback to his work though is in his data set. His data set consisted of a group of around 20,000 trucks over a 7-day period.

While this may seem to be a very sizeable data set, when considering the vast size of the United States interstate highway network, in many places it was quite difficult to get enough data points to be able to do a thorough analysis. With the considerably larger data set used in this study, it is now possible to fully carry out an analysis of this nature.

2.7 Prospect Theory Perhaps some of the most important work being done on the cutting edge of economics is related to Princeton professor Daniel Kahneman. His ground breaking empirical research that resulted in what is now known as prospect theory is some of the most powerful current work on economics and decision-making. Professor Kahneman, together with Amos Tversky, developed a theory for explaining why individuals’

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decision making under uncertainty deviates from what is predicted by standard economic theory.27 Standard economic theory suggests that individuals are “rational” decision makers. This means that they will make decisions that are totally rational. For example, when faced the option of receiving $100 with certainty, or with $5000 with certainty and all other things being equal, the rational decision maker will choose the $5000. This area of rational decision-making begins to get a little less clear though when the concept of variance or a gamble are introduced into the proposition. Lets say now we have a new situation. You can now receive $5000 with certainty, or after flipping one coin, if it is heads you receive $10,000 and if it is tails you receive nothing. What would you pick?

Kahneman and Tversky attempted to explain why people act like they do with Prospect Theory.

2.7.1 Profile of Kahneman and Tversky Both Daniel Kahneman and Amos Tversky are of Israeli dissent. They both were in the Israeli army where Tversky received a commendation for bravery while Kahneman developed a psychological profiling system for screening army recruits. Today however, both of these individuals work is more highly studied by Wall Street elites than by military scholars.28 This is a totally logical conclusion as the work done by these two individuals is primarily focused on decision making under uncertainty and Wall Street is one of the most uncertain places in the world. In addition, uncertainty influences almost every major decision. The outcome of every capital allocation is unknown, but Wall 27 Bernstein, Peter L., “Against the Gods: The Remarkable Story of Risk”, John Wiley & Sons, New York, 1996, pg. 71.

28 Ibid.

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Street professionals need to be able to make decisions without being paralyzed by the analysis of possible outcomes.

2.7.2 Forecasting the Future In doing studies on pilots, Kahneman noticed that an individual’s performance on successive runs in general regressed towards the mean.29 In other words, if a pilot had a particularly good landing attempt, it was more likely that the next landing attempt would not be as good as the previous and visa versa. This idea raised some interesting questions for Kahneman. Would it be possible to forecast the future merely by studying a data set on the whole, then focusing on recent events and suggesting the subsequent events would occur closer to the mean? Essentially, what Kahneman was examining is a principle of auto-correlation in random events. An easy example to describe the idea of autocorrelation is in relation to basketball players. Often times while watching a game, fans will notice that a player has become “hot” or that they have a “hot hand” where their present probability of sinking a shot is greater than their historical probability of making a shot. Athletes often refer to this concept as “being in the zone.” The problem with this idea is that it is not true. Studies have shown that the probability that a player makes the next shot given that he has made successive shots is not statistically different from their regular historical probability of making a shot. Additionally, in the world of investing, if one looks at annualized daily volatilities of stock returns and compares them to annualized weekly volatilities of stock returns, one would see auto-correlation in practice if the annualized weekly volatility numbers were greater than the annualized daily 29 Ibid.





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numbers. However, truth be told, the annualized numbers for both sets of calculations are identical. One would only see higher volatilities for weekly numbers if the data were auto-correlated, meaning that days when the stock market went up were followed by other up days and visa versa. These two examples relate to Kahneman and Tversky because they suspected that people err in forecasting because they falsely believe in autocorrelations and do not believe strongly enough in regression to the mean.30 In order to test their hypothesis, they designed a number of experiments to test how people make decisions when faced with uncertainty.

The results of their experiments were quite remarkable. The conclusions of Prospect Theory went against popular belief in the rationality assumption for decision makers. Kahneman and Tversky suggested that this was because of two basic shortcomings in humans. The first shortcoming is that emotion destroys self-control, which is essential to rational decision-making. For example, in the heat of the moment, it is totally realistic that individuals would change from being risk averse to risk seekers.

The only problem with this proposition is that it violates the first principle of preference evaluation: completeness. As it relates to behavior of drivers, the incidence of “road rage” is significantly higher in cases involving congestion because of the increased emotional strain.31 Secondly, people suffer from what psychologists call “cognitive difficulties”, in that they are unable to fully understand what they are dealing with.32 In other words, people do not fully understand what they are dealing with because they distort their perception of reality around them. People are overly, and irrationally, afraid of low probability high drama events than they should be and they are under concerned

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with routine events. For example, if asked about shark attacks, people conjure up ideas about Jaws and become overly worried when entering the water when the real probability that they get attacked is quite remote. However, if asked about the propensity to die from a fall down the stairs, people are less concerned because walking up and down the stairs is a routine occurrence. For this example, walking up and down the stairs is the base case where individuals make “normal” decisions. They are very familiar with the task of walking up and down the stairs. In reality though, the actual probability of dying from a fall down the stairs according to the National Safety Council is around 1:200,000 which is 25 times more likely then the 1:5,000,000 chance of dying from a shark attack.33 Because people don’t fully understand what they are dealing with as it relates to being attacked by a shark, they allow their emotions to destroy self-control; their behavior as it relates to decision-making is affected.

2.7.3 Asymmetry of Risk Some of the more important work that Kahneman and Tversky did in the behavioral sciences was through examining individual’s profiles for risk and how they change given format changes in the questions. They found that people “treat costs and uncompensated losses differently, even though their impact on wealth is identical.”34 They showed this by performing a number of experiments.

One experiment that they performed was a basic coin flip gamble. Individuals had the option of either taking an amount with certainty, in this case $3000, or choose to gamble on the outcome of a “coin flip” with the probability of “heads” being 80% and 33 http://dsc.discovery.com/convergence/sharkweek2002/quiz/quiz.html 34 Ibid. Bernstein pg. 272.

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resulting in a gain of $4000. Under the circumstance of a “tail” toss, they gambler would get $0. Kahneman and Tversky found that individuals overwhelmingly preferred the certain outcome even though the payoff of the risky bet was $3200.35 This result was not too unusual as the behavior could be sourced to risk aversion on the part of the participants. However, the results were much more interesting when Kahneman and Tversky changed the premise around from receiving $3000 with certainty to losing that same amount of money. The same holds for the gamble. In this case with gains now losses, they found that now the vast majority where now taking the gamble. People who just shortly before were risk averse were now risk seekers! This result is quite puzzling as it goes against the central tenets of rational behavior.

In another case study, Kahneman and Tversky asked a group of respondents about their risk aversion as it related to saving lives and death. The story they told the respondents was that there is a rare disease breaking out in a community that is expected to kill 600 people. They respondents have two options available to them to save lives. In the first questioning session, people were asked to choose between the first plan, which would save 200 lives with certainty, and the second plan that would save all 600 with a probability of one third and nobody would be saved with probability two thirds. Most of the respondents opted for the risk-averse choice of the first plan. In the second questioning session, Kahneman and Tversky switched the question around so that the questioning involved the words “die”. Option three now is that 400 of the 600 people will die while option four is that there will be a one third probability that nobody dies and a two-thirds probability that 600 people will die. It is important to note that now the options are posed in the frame of reference of death as opposed to survivors. This small 35 Ibid.

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change in phrasing resulted in an incredible change in risk behavior. 78% of the subjects were now risk seekers compared to 72% previously being risk averse.36 The question is now, what to do with this information? People switching from risk averse to risk seeking is inconsistent with many of the previously stated assumptions of rational behavior. Primarily, respondents originally preferred A (certainty) to B (the gamble), but then later preferred B (the gamble) to A (certainty) though the changes in wealth were equivalent. This violates the completeness property, assuming of course that certainty is not “suitably close” to gambling. Kahneman and Tversky came to the conclusion that people are not necessarily risk averse, as they would have chosen the gamble based on the alternative phrasing. Rather, they suggest that people, on the whole, understand variance and uncertainty, but what it really comes down to is that people strongly dislike losing and that decision makers appetite for risk is not in reference to how much wealth they currently have, but rather in reference to how the decision will affect their level of wealth. People remember their losses and carry them forward in their minds much more than their wins.37 This idea of loss aversion is concurrent also in the area of sport psychology. Dan Gable, a wrestler at Iowa State University and eventual Olympian was renowned for saying that avoiding the embarrassment of losses and failure drove him to train and prepare much harder than training for the satisfaction of winning matches. Interestingly, Gable was so averse to losses that he won a gold medal in the 1972 Munich games without surrendering a single point.38 36 Ibid. pg. 273 37 Ibid. pg. 273-274 38 Taken from www.dangable.com

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