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The Irrationality of Illegal Fishing (Part 5): Measuring the Problem

June 26, 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.

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 sharing them in a series format. 

Part 5: Measuring the Problem

Sting operations for lobster are a common occurrence in U.S. fisheries, generally because it is a resource that is easily stolen. But perhaps there is another way to identify a problem in a fishery than the ease with which a marine resource is taken?

Sting operations for lobster are a common occurrence in U.S. fisheries, generally because it is a resource that is easily stolen. But perhaps there is another way to identify a problem in a fishery than through risk profiling?

In the last post in this series, we explored four factors related to how rules are enforced that Dan Ariely’s theory of irrational cheating suggests can be managed to reduce cheating. These factors are 1) the targeting of worst offenders, 2) the targeting of in-group and out-group offenders, 3) the highlighting of positive deviance, and 4) the stopping of minor cheating problems before they grow. As we saw, these factors lead us to consider a number of ways to change how we enforce the rules and regulations of fisheries management. 

Today we’ll take a step back from enforcement considerations and ponder the question, “How do we know when illegal fishing is a problem?”  As is well known, fisheries managers face serious budget constraints as they conduct their business of keeping their stocks healthy, ecosystems sustained, and fleets profitable. Since enforcement actions can be very costly, fisheries managers need some way of determining when illegal fishing is a problem in one fishery or another in order to best allocate their funds. Once again, Dan Ariely’s work offers some new insight into the art and practice of fisheries, particularly a new way that fisheries managers might determine when and where there is a problem. 

Specifically, Dan Ariely’s work suggests that surveys of potential (if not actual) cheaters can provide very important information on the scale of cheating, particularly when that cheating is irrational, or non-economically motivated. This suggestion then leads us to consider how it might be useful to employ fisher surveys to identify compliance problems.

There are two experiments from Dan Ariely’s body of work that allow us to speculate as to the utility of fisher compliance surveys. 

The first was touched on in the last blog, but is interesting enough to bare repeating here (though in shorter form). The study involved Ariely’s math test, a fake paper shredder, a monetary reward, and – in a very clever move – the use of an actor.  The math test gave participants something to cheat at, the fake paper shredder gave Ariely a way to measure cheating, the money gave motive, and the actor gave a social cue as a whether or not it was “ok” to cheat.   

Here again is a quick (and entertaining) description of the experiment, which Ariely dubbed the “Madoff” condition:

Imagine that you are a participant in our so-called Madoff condition. You’re seated at a desk, and the experimenter gives you and your fellow participants the instructions. “You may begin!” she announces. You dive into the problem set, trying to solve as many matrices as possible to maximize your earnings. About sixty seconds pass, and you’re still on the first question. The clock is ticking. 

“I’ve finished!” a tall, skinny, blond-haired guy says as he stands up and looks at the experimenter. “What should I do now?” 

“Impossible,” you think. “I haven’t even solved the first matrix!” You and everyone else stare at him in disbelief. Obviously, he’s cheated. Nobody could have completed all twenty matrices in less than sixty seconds. 

“Go shred your worksheet,” the instructor tells him. The guy walks to the back of the room, shreds his worksheet, and then says, “I solved everything, so my envelope for the extra money is empty. What should I do with it?” 

“If you don’t have money to return,” the experimenter replies, unfazed, “put the empty envelope in the box, and you are free to go.” The student thanks her, waves good-bye to everyone, and leaves the room smiling, having pocketed the entire amount. Having observed this episode, how do you react? Do you become outraged that the guy cheated and got away with it? Do you change your own moral behavior? Do you cheat less? More? It may make you feel slightly better to know that the fellow who cheated so outrageously was an acting student named David, whom we hired to play this role. We wanted to see if observing David’s outrageous behavior would cause the real participants to follow his example, catching the “immorality virus,” so to speak, and start cheating more themselves. 

Here’s what we found. In the Madoff condition, our participants claimed to have solved an average of fifteen out of twenty matrices, an additional eight matrices beyond the control condition, and an additional three matrices beyond the shredder condition. In short, those in the Madoff condition paid themselves for roughly double the number of answers they actually got right. [Emphasis added]

So again, this study shows us that an individual’s perception of what others do matters a lot as they make a decision to cheat or not. But since none of the benefits or potential costs changed, the important factor was irrational, or non-economic. What mattered was the cue provided by the behavior of others. And herein lies what might matter so much for fisher compliance surveys.

Though there are a few instances of where surveys have been used to measure compliance among fishers, they are generally not considered to be part of the fisheries management toolkit. This is likely due to the question of accuracy. Most surveyors appear to know enough to ask fishers not about their own behavior, but rather that of others (it’s easier to tell the truth about others wrong-doing, of course). But even still, there’s no way fishers could be very accurate, so why measure what they believe to be happening? 

Ariely’s research provides a compelling response: perception matters. That is, if fishers, or participants in any system for that matter, perceive others to be breaking the rules, they get a social cue to do the same. Perceptions of wrong-doing lead to wrong-doing, so measuring that perception could well be just as important as knowing how much non-compliance is actually occurring. 

So score “1” for the idea of fisher compliance surveys. 

At the same time that surveys might reveal participants perception of cheating in a system, Ariely’s work also suggests that surveys on the behavior of others, can in fact inform on the survey respondents own behavior. Ostensibly, this provides insight into the process of rationalization. If we cheat, we like to believe that others cheat as well, as a way to justify our own behavior. 

For this, let’s return another study presented in the last blog post. This other study is that which involved the use of “real” and “fake” designer Chloé sunglasses. Ariely had his study participants wear these sunglasses and engage in a variety of experiments, including his fake paper shredder experiment and another experiment that required the counting of dots for money. The twist in all of this was that all of the glasses were the real deal. No fakes were used.

The results of the study were quite fascinating. It was found that just by wearing sunglasses that were believed to be counterfeit, the study participants were able to much more easily engage in cheating. And equally as interesting, a questionnaire filled out by the study participants revealed that the wearers of “fake” sunglasses more often believe that others are capable of immoral behavior.

It’s this questionnaire that’s quite pertinent for this blog. Here’s the description from Ariely of his sunglasses-influenced questionnaire:

[W]e asked another group of participants to put on what we told them were either real or counterfeit Chloé sunglasses. Again, they dutifully walked the hall examining different posters and views from the windows. However, when we called them back to the lab, we did not ask them to perform our matrix or dots task. Instead, we asked them to fill out a rather long survey with their sunglasses on. In this survey, we asked a bunch of irrelevant questions (filler questions) that are meant to obscure the real goal of the study. Among the filler questions , we included three sets of questions designed to measure how our respondents interpreted and evaluated the morality of others. 

The questions in set A asked participants to estimate the likelihood that people they know might engage in various ethically questionable behaviors. The questions in set B asked them to estimate the likelihood that when people say particular phrases, they are lying. Finally, set C presented participants with two scenarios depicting someone who has the opportunity to behave dishonestly, and they were asked to estimate the likelihood that the person in the scenario would take the opportunity to cheat. 

The questions themselves are quite interesting, so I’ll offer them here.

Set A: How likely are people you know to engage in the following behaviors? 

  • Stand in the express line with too many groceries.
  • Try to board a plane before their group number is called.
  • Inflate their business expense report.
  • Tell their supervisor that progress has been made on a project when none has been made.
  • Take home office supplies from work.
  • Lie to an insurance company about the value of goods that were damaged.
  • Buy a garment, wear it, and return it.
  • Lie to their partner about the number of sex partners they have had.

Set B: When the following lines are uttered, how likely is it that they are a lie? 

  • Sorry I’m late, traffic was terrible.
  • My GPA is 4.0.
  • It was good meeting you. Let’s have lunch sometime.
  • Sure, I’ll start working on that tonight.
  • Yes, John was with me last night.
  • I thought I already sent that e-mail out. I am sure I did.

Set C: How likely are these individuals to take the action described? 

  • Steve is the operations manager of a firm that produces pesticides and fertilizers for lawns and gardens. A certain toxic chemical is going to be banned in a year, and for this reason is extremely cheap now. If Steve buys this chemical and produces and distributes his product fast enough, he will be able to make a very nice profit. Please estimate the likelihood that Steve will sell this chemical while it is still legal.
  • Dale is the operations manager of a firm that produces health foods. One of their organic fruit beverages has 109 calories per serving. Dale knows that people are sensitive to crossing the critical threshold of 100 calories. He could decrease the serving size by 10 percent. The label will then say that each serving has 98 calories, and the fine print will say that each bottle contains 2.2 servings. Please estimate the likelihood that Dale will cut the serving size to avoid crossing the 100-calorie- per-serving threshold.

Amazingly, the conclusion from this questionnaire was that counterfeit products cause us to view others as less than honest. Specifically, when reflecting on the behavior of people they know (set A), participants in the counterfeit condition judged their acquaintances to be more likely to behave dishonestly than did participants in the authentic condition. For the list of common excuses (set B), participants believed that they would be more likely told as lies. And for the story descriptions (set C), the participants believed the characters in each as being more likely to choose the shadier option.

This nicely shows that when we do “bad” things, even in minor ways, we can imagine others as more likely to do other “bad” things. This is a fantastic insight as we consider using fisher compliance surveys. It could well be that when fishers report a high degree of wrong-doing in their fishery, as committed by others, they are in fact informing on themselves. 

Pulling this together, Ariely’s work suggests that fisher compliance surveys could be used as a regular “arrow in the quiver” of fisheries managers. These surveys, when constructed to ask fishers about the behavior of others, could be used to reveal how much fishers are either perceiving wrong-doing in the fishery, as committed by others, or in fact projecting their own wrong-doing on to others. Given how poor our ability currently is to measure illegal fishing at the fishery level, it seems like this would be a very effective solution to the measurement problem.

At this point, we’ve covered most of what I believe Ariely’s body of work can teach us, or at least suggest to us, about how we can better manage fisheries to reduce illegal fishing. But, of course, some of the ideas we’ve presented are not entirely new to fisheries work. In the next and final post in this series, I’ll summarize the my conclusions from these blog posts and provide some information on how the research and speculation aligns with the work of other researchers.

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