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The Irrationality of Illegal Fishing (Part 1): Everybody Cheats Irrationally

May 30, 2014

Why does a fisher, or anyone for that matter, break the law? The dominant explanation in crime research is that people break the law after having conducted an analysis of the costs, benefits, and likelihood of getting caught. This is what is called the rational economic theory of crime.

But what if many people break the law for non-rational reasons?  That is, what if people do not usually conduct an analysis of the expected costs and benefits before breaking the law, but instead cheat according to non-economic factors? This is the subject of the very clever book “The Honest Truth About Dishonesty” by behavioral economist Dan Ariely.

Dan Ariely Book

I’ve recently had the pleasure of revisiting Ariely’s book to consider how his findings might explain illegal fisher decision-making and point to ways to reduce overall non-compliance in fisheries. It is some of these findings, and my speculations, that I’d like to share with you here as a radically different way to look at the problem of illegal fishing. And since the findings are numerous, I’ll be breaking them up into various blog posts over the following month. 

Part 1: Everybody Cheats Irrationally

In this first part of the series, I want to present to you what is perhaps the most important finding from Ariely and his colleague’s work: everybody cheats and economics has little or nothing to do with it. 

How did Ariely and his colleagues come to this conclusion? By using a basic math game, monetary rewards for correct answers, and an experimental condition that ingeniously involves a fake paper shredder. Here’s a description of the method, as performed at MIT:

We posted announcements all over the MIT campus (where I was a professor at the time), offering students a chance to earn up to $ 10 for about ten minutes of their time. * At the appointed time, participants entered a room where they sat in chairs with small desks attached (the typical exam-style setup). Next, each participant received a sheet of paper containing a series of twenty different matrices (structured like the example you see on the next page ) and were told that their task was to find in each of these matrices two numbers that added up to 10 (we call this the matrix task, and we will refer to it throughout much of this book). We also told them that they had five minutes to solve as many of the twenty matrices as possible and that they would get paid 50 cents per correct answer (an amount that varied depending on the experiment). Once the experimenter said, “Begin!” the participants turned the page over and started solving these simple math problems as quickly as they could…

Now imagine you are in another setup, called the shredder condition, in which you have the opportunity to cheat. This condition is similar to the control condition, except that after the five minutes are up the experimenter tells you, “Now that you’ve finished, count the number of correct answers, put your worksheet through the shredder at the back of the room, and then come to the front of the room and tell me how many matrices you solved correctly.” If you were in this condition you would dutifully count your answers, shred your worksheet, report your performance, get paid, and be on your way…

With the results for both of these conditions, we could compare the performance in the control condition, in which cheating was impossible, to the reported performance in the shredder condition, in which cheating was possible. If the scores were the same, we would conclude that no cheating had occurred. But if we saw that, statistically speaking, people performed “better” in the shredder condition, then we could conclude that our participants overreported their performance (cheated) when they had the opportunity to shred the evidence. And the degree of this group’s cheating would be the difference in the number of matrices they claimed to have solved correctly above and beyond the number of matrices participants actually solved correctly in the control condition.

Based on this experiment, Ariely found that many people cheated when given the chance. 

In the control condition, participants solved on average of four out of twenty problems. Participants in the shredder condition claimed to have solved an average of six— two more than in the control condition. And this overall increase did not result from a few individuals who cheated egregiously, but from lots of people who cheated by just a little bit. 

Such an outcome would not at all accord with a rational economic model. Why wouldn’t the participants in the “shredder” condition cheat by more than just two problems? Economics would suggest that the cheaters should cheat by far more. 

Of course, proponents of rational economics in this case might say that it would be “rational” to cheat by just a little. Two counter arguments would be that 1) the monetary reward of 50 cents per question was not enough to risk detection and that 2) participants did not want to risk detection by reporting a higher answer score (say 20 out of 20). 

Thankfully, Ariely and his colleagues tested both explanations and found that neither mattered. In fact, in the case of offering higher monetary rewards, cheating actually went down!  Here’s Ariely description of modifying the reward structure:

We set up another version of the matrix experiment, only this time we varied the amount of money the participants would get for solving each matrix correctly. Some participants were promised 25 cents per question; others were promised 50 cents, $ 1, $ 2, or $ 5. At the highest level, we promised some participants a whopping $ 10 for each correct answer. What do you think happened? Did the amount of cheating increase with the amount of money offered? …It turned out that when we looked at the magnitude of cheating, our participants added two questions to their scores on average, regardless of the amount of money they could make per question. In fact, the amount of cheating was slightly lower when we promised our participants the highest amount of $ 10 for each correct answer…slightly lower when we promised our participants the highest amount of $ 10 for each correct answer. [emphasis added]

As for testing whether standing out as a cheater mattered, Ariely and his colleagues modified their game to tell the experimental condition participants that the average correct score was double the true average. They found that such a modification made no difference in the level of cheating at all.

We tested this idea in our next experiment. This time, we told half of the participants that the average student in this experiment solves about four matrices (which was true). We told the other half that the average student solves about eight matrices. Why did we do this? Because if the level of cheating is based on the desire to avoid standing out, then our participants would cheat in both conditions by a few matrices beyond what they believed was the average performance (meaning that they would claim to solve around six matrices when they thought the average was four and about ten matrices when they thought the average was eight). So how did our participants behave when they expected others to solve more matrices? They were not influenced even to a small degree by this knowledge. They cheated by about two extra answers (they solved four and reported that they had solved six) regardless of whether they thought that others solved on average four or eight matrices. [emphasis added]

This finding that “everybody cheats irrationally” has two important implications for illegal fishing prevention. 

First, it suggests that most illegal fishing is conducted not by specialized, economically rational criminals, but by ordinary fishermen that cheat by just a little. If this is the case, then the low-level illegal fishing of the many might add up to a much more significant problem than the high-level fishing of the few. That is, ten fishermen stealing 1,000 fish each might not matter near as much as thousands of fishermen stealing 100 fish each.

Second, it suggests that  fisheries managers should begin to consider the “irrational” or non-economic factors affecting illegal fishing if they hope to preserve their resources. It is these “irrational” factors that I’ll consider in the following posts.

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