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Financing Fishery Transitions to Sustainability

September 2, 2014
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EDF has put together this excellent video on the California Fisheries Fund, an innovative financial institution that has helped California fishermen finance their transition to sustainable fishing practices. It’s a great model, and it needs to be replicated elsewhere. 

Update: Michael posted a great question as to what the loans are used for. Here’s a little more info.

As of June 2006, the fund has made 23 loans – ranging from $50,000 to $350,000 – that total more than $2.6 million. The permitted uses for the loans are:

  • Gear purchase or modification
  • Vessel purchases or improvements
  • Fishing permit or quota purchase
  • Capital equipment upgrades for dockside infrastructure, processing capacity and transportation
  • Working capital for business growth

If you’d like a brief case study, here’s an excerpt from a CFF briefing document:

Steve Fitz, captain of the F/V Mr. Morgan, will continue his family tradition, operating the only commercial fishing operation in the United States that uses Scottish Seine gear, a selective and eco-friendly way to catch groundfish. Steve’s loan from the CFF helped him buy the Mr. Morgan from his uncle and start up Mr. Morgan Fisheries, a fishing business based in Half Moon
Bay, CA, specializing in sustainably harvested groundfish and Dungeness crab.

 

Mr. Morgan Fisheries is known for its sand dabs, Petrale sole and chilipepper rockfish—all species sustainably managed under a catch share program. Like all other participants in this catch share, Steve receives an individual fishing quota for several groundfish species that may be harvested throughout the year, with requirements for full accountability of every pound of fish harvested, and a human observer on every fishing trip.

For more information on the program, check out the California Fisheries Fund website

Fighting Illegal Fishers with Satellite Intelligence

September 1, 2014

This is an exciting time for marine law enforcement. A number of NGOs and research groups are now working on ways to monitor fishing vessels via satellite data. Where previously this was just a pipe dream for environmentalists, we’re now starting to see real movement.

World Wildlife Fund was arguably one of the first movers with its construction and analysis of open-source AIS data, and I blogged on this two years ago. Meanwhile, it seems that the Pew Charitable Trusts’ Campaign to End Illegal Fishing has moved into the clear position of leadership since then. There is a very excellent article on this over at Grist. Here are the highlights:

1. Pew has teamed up with Oxford group, Satellite Application Catapult, to create a big database of identifying information on fishing vessels with satellite trackers (mostly large fishing vessels).

By combining satellite-gathered signals from ship transponders with other data, whether crowdsourced or supplied by fishing enforcement agencies, Catapult can piece together a cheat sheet that identifies any vessel by name history, ID number, and the details of its fishing license.

Once the relevant authorities have access to that information, they will be able to spot illegal or unreported fishing in even the most remote areas, then zoom in to make the arrest. That big Ukranian ship hanging out in the marine protected area? Probably not a tourist.

2. This comes on the heals of a smaller, successful collaboration between Pew and a Virginia based group called Sky Truth.

Last year, the group partnered with a small but spunky nonprofit located in West Virginia, SkyTruth, to use open-source satellite data to detect and document illegal fishing around Easter Island, a smidge of an island in a tuna-rich corner of the South Pacific, about 2,000 miles off the Chilean coast…

Without reliable IDs, the SkyTruth team instead tried to narrow in on the likely suspects by using low-res radar imagery to detect the presence of a ship, and sometimes even its speed and direction, then cross-referencing that with the transponder signal (or lack thereof). The boats that did not identify themselves and could not otherwise be accounted for, SkyTruth surmised, were probably up to no good.

After a year of watching Easter Island, SkyTruth had enough data to estimate the total amount of fishing that was going on, and had singled out more than 40 unidentified vessels that had been unknown to Chilean maritime authorities.

3. We still lack the necessary incentives for most fishing vessels to submit to satellite monitoring technologies.

If retail chains demand traceability from their suppliers, and can promise them a premium price at the counter, then it’s in fishermen’s best interest to opt into a monitoring system. Instead of putting the onus on enforcement agencies, this is a way to shift the burden of proof to the fishermen who want to do business legitimately. Then, if the good guys keep their transponders on, it will be even easier to spot shady behavior from afar. (You get a better ocean! You get a better ocean! Everybody gets a better ocean!)

Climate Change Impacts on the Seafood Industry

September 1, 2014

The IPCC has put out a report detailing how the seafood industry will be severely impacted by climate change. The latest MEAM newsletter has this useful summary.

Among its forecasts:

-  A loss of US $17-41 billion in global fisheries landings by 2050 with global warming of 2°C;
-  An increase in fishery yields of 30-70% in high latitudes and a decrease in fishery yields of 40-60% in the tropics and Antarctica with global warming of 2°C; and
-  Reduced access to marine protein for 400 million people.

The briefing “Climate Change Implications for Seafood and Aquaculture” is available here.

Underwater Photos

September 1, 2014

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From March through July I took time off to pursue a longtime interest to learn how to freedive in the waters of the Red Sea off the coast of Dahab, Egypt. If you don’t know, freediving is a magical sport that allows you to reach what first must surely be thought of as impossible depths of the ocean and the mind. (Health and Spirituality Magazine has a terrific overview of the physiological and mental aspects here.)

In my own case, I learned how to far better manage fear and to better trust myself. The body flexibility required also led me to break up a lot of scar tissue in my back from an autoimmune disorder I’ve managed for the past 8 years. Frankly, I’ve never felt healthier or more relaxed, and that’s saying something when you’ve just started a PhD program. 

I also ultimately ended up posting some impressive depths for a complete noob. My greatest depth was 47 meters/154 feet and I reached that with bi-fins. But the dive I’m most proud of was a “no fins” dive (exactly what it sounds like) of just under two minutes down to 42 meters/144 feet during a competition in a rather chilly lake in southern Germany. 

Will I keep up the freediving? Well, certainly for vacations, but it now looks like I’ll be entering “early retirement” from the competitions. My new home of Michigan isn’t exactly the best place to train. I’ll always have the memories though…oh…and of course I’ll also have the very cool photos taken by my friend and underwater photographer, Nanna Kreutzmann. It was not an easy photo session, but I am really happy with the results.

 

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The Link between Human Health and Marine Ecosystem Health

September 1, 2014

 

The latest edition of the Marine Ecosystem and Management (MEAM) from the University of Washington has a great interview with Alasdair Harris, executive director of Blue Ventures, on the link between human health and marine ecosystem health. 

In particular, Blue Ventures has found that by tackling public health needs, they improve the human security of coastal Madagascar, and in turn reduce the human stressors to the marine environment. Here’s the skinny:

The unmet health needs of the country’s semi-nomadic Vezo fishing communities are particularly acute, with clinics located up to 50 km away from some villages. In coastal western Madagascar, the fertility rate is nearly 7 births per woman. Fewer than 10% of Vezo women have access to contraceptives, despite up to 90% wanting to be able to plan their pregnancies. With the population doubling every 10-15 years, Vezo communities are finding it increasingly difficult to provide for their growing families. And overfishing and destructive fishing practices pose significant threats to the marine ecosystems upon which their livelihoods depend.

Our community health program, called Safidy (http://goto.blueventures.org/health), meaning “the freedom to choose” in Malagasy, has been operational since 2007. Safidy was established in direct response to the unmet family planning needs of Vezo communities. It upholds reproductive rights by offering couples the information and contraceptive options they need to freely choose the number and spacing of their births. Results from this program have been published in both conservation (http://bit.ly/Safidy1) and public health journals (http://bit.ly/Safidy2).

 

As for impacts, Blue Ventures has seen general fertility declines of up to 40% in the communities it has served. Now that’s impressive.

We now provide community-based health services to around 20,000 people and are scaling up across two additional LMMA zones. Within the 40 communities that we serve in and around the Velondriake LMMA in southwest Madagascar, the Safidy program has led to an increase in the proportion of women using contraception from under 10% to 55% in just six years. This has averted over 800 unintended pregnancies, leading to a decrease in the region’s general fertility rate by 40%. We know that addressing unmet reproductive health needs within this kind of context can also reduce maternal and child mortality by up to 30%.

Read the full interview here.

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.

The Irrationality of Illegal Fishing (Part 4): The Hidden Costs of Poor Enforcement

June 23, 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 4: The Hidden Costs of Poor Enforcement

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In the last post in this series, we explored two factors that Dan Ariely’s theory of irrational cheating suggests can be managed for reduced cheating. These factors are 1) the discontentedness and 2) the mental tiredness of potential cheaters. Importantly, it is likely that these two factors play a role in fisheries management – quite often low-quality management can lead to dissatisfaction among fishermen, and the status of the fishery’s resources and competition can lead to considerable mental tiredness among fishers as they struggle to make a living. Thus, with Ariely’s theory of irrational cheating, we find the first areas where we might act to reduce irrational illegal fishing.

Today I’ll consider four additional factors that can be managed to reduce irrational cheating, and all of these factors relate to how authorities police the system. They 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. I’m sure you’ll see quite quickly the implications each of these manageable factors have for fisheries management, and I’ll take some time to provide some speculation for each. 

Collectively, the factors discussed here show that there are presently hidden and irrational costs to poor enforcement in fisheries management. That is, poor enforcement does not just simply allow illegal fishers to “get away with it”, but also also permits illegal fishers (or the lack thereof) to send important signals to other fishers about how they should behave.

The first enforcement factor to consider is that of how authorities do, or do not, target the worst offenders when cheating is present in a system. In the course of Ariely’s work, he conducted many clever studies, but perhaps none so stand out as the time he and a colleague employed an actor to act either as a villain or moral agent. The study revealed that cheating is contagious, and particularly so when potential cheaters witness someone rampantly breaking the rules. 

The study again involved Ariely’s math test with a fake shredder, which allowed him and his colleagues to see how much people really cheated (see past posts for more details). Here is the set up to the study, which Ariely affectionally refers to as the “Madoff condition” in honor of Bernie Madoff:

What would happen if our participants could observe someone else— a Madoff in the making— cheating egregiously? Would it alter their level of cheating? 

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?

So what happened when an actor did just what Ariely describes above? In the Madoff condition, the experiment participants claimed to have solved an average of fifteen out of twenty problems, an additional eight problems beyond the control condition (which had no opportunity for cheating), and an additional three matrices beyond the shredder condition in which there was no actor. In short, the observance of an egregious cheater raised cheating by roughly double, all while the economic gains remained the same.

While the insight here may not offer new recommendations for fisheries enforcement – that we should target the worst offenders, who likely act in a rational manner – it does suggest that the consequences of inaction are much greater than just letting a “bad apple” continue his or her cheating ways. This research tells us that illegal fishing that appears to be rational, isolated, and large-scale can have significant knock-on effects by promoting irrationally-motivated illegal operations among other fishers. 

The second enforcement factor to consider in the cheating game is that of which “worst violators” are targeted by the authorities. In particular, Ariely’s research reveals that it is very important that enforcement officers target fishers that fall both within the main fishing community, or those which we might call members of the “in-group”, and those fisheries that fall outside of it, which me might call members of the “out-group”. 

Why? Well, apparently when we observe egregious cheating by insiders, or members of the “in-group, we observe it as a signal to cheat quite a bit more. This is what we saw described above in the “Madoff” condition. This is a concerning effect, yet we also see that the opposite is true when we observe cheating by members of the “out-group”. In fact, when we observe cheating by outsiders, we apparently see it as an opportunity to affirm our superiority over others, and act quite a bit better than we would have otherwise.

Let’s return to Ariely’s “Madoff” condition, which is described above, to see how we can arrive at this more sophisticated insight into cheating behavior. 

Ariely and his colleagues followed up their Madoff condition with a modification that had the deviant participant (again, secretly an actor) wear clothing indicating he was a student at a rival school. The result was that the other participants did not look upon a significant cheat as a signal to behave the same, but instead saw “Madoff-level behavior” as a signal to abstain from significant wrong-doing. 

Here’s the description:

The structure of our next experiment was the same as in the Madoff condition: our actor stood up a few seconds into the experiment and announced that he had solved everything and so forth. But this time there was one fashion-related difference: the actor wore a University of Pittsburgh sweatshirt. 

Let me explain. Pittsburgh has two world-class universities, the University of Pittsburgh (UPitt) and Carnegie Mellon University (CMU). Like many institutions of higher learning that are in close proximity, these two have a long-standing rivalry…

We conducted all of these experiments at Carnegie Mellon University , and all our participants were Carnegie Mellon students. In the basic Madoff condition, David had worn just a plain T-shirt and jeans and had therefore been assumed to be a Carnegie Mellon student, just like all the other participants. But in our new condition, which we named the “outsider-Madoff condition,” David wore a blue-and-gold UPitt sweatshirt. This signaled to the other students that he was an outsider— a UPitt student— and not part of their social group; in fact, he belonged to a rival group…

[I]f the increase in cheating in the Madoff condition was due to an emerging social norm that revealed to our participants that cheating was acceptable in their social group, this influence would operate only when our actor was part of their in-group (a Carnegie Mellon student) and not when he was a member of another, rival group (a UPitt student). The crucial element in this design, therefore, was the social link connecting David to the other participants: when he was dressed in a UPitt sweatshirt, would the CMU students continue to play copycat , or would they resist his influence? [Emphasis added]

The results of the study did indeed confirm a significant role for the perception of in-group and out-group behavior. The participants that observed the actor cheat when identified as a UPItt student claimed to have solved only nine problems. While this was still more than the control condition (by about two matrices), they cheated far less than if they had thought the cheater was a Carnegie Mellon student!  In all, the introduction of an “outsider” element to the cheating reduced the cheating from about 15 problems to 9 problems!

And even more incredibly, the level of cheating under the outsider condition was even less than under the standard shredder condition, where participants typically cheated by about 12 problems. 

This means that when the violator is perceived to be part of the out-group, irrational cheating can reduce significantly from baseline amounts, while when it is perceived as part of the in-group, irrational cheating can take off. 

The implications for fisheries enforcement are then clear. Fisheries enforcement activities should be sure to target significant cheaters among both in-group and out-group fishers. This recommendation makes me think of the illegal fishing situation in West Africa, where foreign trawlers are responsible for significant amounts of illegal fishing, and are increasingly the targets of enforcement efforts. Yet rarely do we hear about enforcement actions taken against illegal fishers among the domestic fishing fleets in West Africa. It suggests that enforcement agencies may be missing an important opportunity to reduce irrational illegal fishing by not targeting illegal local fishers as well. 

The third enforcement factor that might be better managed to reduce irrational cheating is that of how positive deviants are highlighted among potential cheaters. “Positive deviance” is occurs when a potential cheater acts in an uncharacteristically moral way. (For an example, recall the story of George Washington and the apple tree, when he declared, “I cannot tell a lie.”) Ariely’s research suggests that the observation of positive deviance it is yet another factor affecting cheating. 

Ariely and his colleagues came upon the role of such moral reminders when they modified the Madoff condition in yet another way. In particular, they had their actor dressed as an “in-group” student of Carnegie Mellon, but rather than cheat, the actor pointed out that he could cheat egregiously and then appeared to take the math test as any other. Thus, the actor demonstrated his capacity to cheat, and appeared to restrain himself. 

Here again is a description:

[W]e set up another experiment, with a different type of social-moral information. In the new setup, we wanted to see whether erasing any concern about being caught but without giving an enacted example of cheating would also cause participants to cheat more. We got David [the actor] to work for us again, but this time he interjected a question as the experimenter was wrapping up the instructions. “Excuse me,” he said to the experimenter in a loud voice, “Given these instructions, can’t I just say I solved everything and walk away with all the cash? Is this okay?” After pausing for a few seconds, the experimenter answered, “You can do whatever you want.” For obvious reasons, we called this the question condition. Upon hearing this exchange, participants quickly understood that in this experiment they could cheat and get away with it. If you were a participant, would this understanding encourage you to cheat more? Would you conduct a quick cost-benefit analysis and figure that you could walk away with some unearned dough ? After all, you heard the experimenter say, “Do whatever you want,” didn’t you? 

When this question was posed, and the actor did not proceed to get up immediately and take all the money possible, a very interesting thing happened. The participants still cheated, but by far less than in the standard “Madoff” condition…and even less than the standard “shredder” condition. In this “question” condition, the participants claimed to have solved an average of ten problems— about three more problems than in the control condition (which means they did cheat) but by about two fewer problems than in the shredder condition and by five fewer than in the Madoff condition.

Did you get that? They cheated by less when it was pointed out that they could cheat and get away with it! Now, Ariely saw that only the question-making was of important in this condition, but its just as clear that the condition involved a clever participant (secretly an actor) that chose not to act as he had suggested he might. To me, it leaves a very clear suggestion, that the highlighting of positive deviance, or good behavior, could result in a significant reduction in baseline irrational cheating. 

As for fisheries management, this means there could be a significant benefit if enforcement activities did not just highlight the bad behavior of illegal fishers, but also the good behavior of highly-compliant fishermen. 

The fourth and final enforcement factor that we will consider in this post is how authorities work to end minor, perhaps even innocuous cheating problems. This is relevant as the work of Ariely and his colleagues shows that such minor cheating can lead to cheating elsewhere, and in much more serious ways. 

In other words, cheating can snowball. This was revealed by another study that involved Ariely’s math test and fake shredder, only this time, participants were asked to wear either fake or real designer sunglasses (in fact, all were real).

Here’s the set up:

AT THE START of the experiment, we assigned each woman to one of three conditions: authentic, fake, or no information. In the authentic condition, we told participants that they would be donning real Chloé designer sunglasses. In the fake condition, we told them that they would be wearing counterfeit sunglasses that looked identical to those made by Chloé (in actuality all the products we used were the real McCoy). Finally, in the no-information condition, we didn’t say anything about the authenticity of the sunglasses. 

Once the women donned their sunglasses, we directed them to the hallway, where we asked them to look at different posters and out the windows so that they could later evaluate the quality and experience of looking through their sunglasses. Soon after, we called them into another room for another task. What was the task? You guessed it: while the women were still wearing their sunglasses we gave them our old friend, the matrix task. 

Now imagine yourself as a participant in this study. You show up to the lab, and you’re randomly assigned to the fake condition . The experimenter informs you that your glasses are counterfeit and instructs you to test them out to see what you think. You’re handed a rather real-looking case (the logo is spot-on!), and you pull out the sunglasses, examine them, and slip them on. Once you’ve put on the specs, you start walking around the hallway, examining different posters and looking out the windows. But while you are doing so, what is going through your head? Do you compare the sunglasses to the pair in your car or the ones you broke the other day? Do you think, “Yeah, these are very convincing. No one would be able to tell they’re fake.” Maybe you think that the weight doesn’t feel right or that the plastic seems cheap. And if you do think about the fakeness of what you are wearing, would it cause you to cheat more on the matrix test? Less? The same amount? 

The result of the study was that many more people cheated when they were wearing the “fake” sunglasses than when they were wearing the “real” sunglasses. While 30 percent of the participants in the authentic condition reported solving more problems than they actually had, a whopping 74 percent of those in the fake condition reported solving more problems than they actually had. (Meanwhile, in the control condition, in which the authenticity of the glasses was not revealed, only 42 percent of the women cheated, which was not – in fact – statistically different from the “authentic” condition.)

Ariely and his colleagues found this snowballing effect rather intriguing, so continued to play with their “real” and “fake” sunglasses in other ways. In a rather different study, Ariely & Co. had their participants play a few hundred rounds of a game where they counted dots for money, and – per usual – there was ample room provided for people to cheat.  

What happened when this was combined with “real” and “fake” sunglasses? Interestingly, those with “fake” sunglasses cheated much much more. And while all participants cheated more over time, it was particularly bad for those people who somehow got it in their head that they were the sort of people that do such things (i.e. by wearing fake sunglasses).

I think we all might agree that these results are rather surprising. Can just believing you’re wearing fake designer sunglasses lead you to be someone more likely to steal money in a game? Apparently, the answer is yes. 

The study’s conclusion makes me rather fearful for fisheries where fishers are regularly permitted to cheat in minor ways. I’ve seen such situations arise when enforcement officers are sympathetic to fishers’ needs to fish “every now and then” during closed seasons, and when regulations are so tricky that fishermen might be permitted to fish for significant time periods without the proper documentation. 

This all goes to suggest that it can be rather important that fisheries managers work to ensure that most of their fishers are not just “compliant where it matters” but instead endeavor to have most fishers in 100% compliance with existing rules and regulations. I would suspect that some cases might require that there simply be more enforcement actions taken to engender compliance, but I could also see how this might require some reform of the rules and regulations to reclassify regular fisher behavior as acceptable.  

In sum, Ariely’s theory of irrational cheating offers us a variety of factors related to enforcement activities that might be better managed to reduce irrational illegal fishing. These factors are 1) the targeting of worst offenders, whose actions can have significant knock-on effects; 2) the targeting of both in-group and out-group offenders, which provide positive and negative signaling to other fishers; 3) the highlighting of positive deviance, which can permit fishers to reflect on their behavior and act in a more moral manner; and 4) the stopping of minor cheating problems, which can snowball into serious problems over time.

1. Act in a timely manner to catch the worst offenders. Delaying the apprehension of worst offenders can irrationally signal to other fishers that it is ok to cheat.

2. Ensure that both in-group and illegal fishers are targeted. Observing illegal fishing by members of a fisher’s “in-group” can be a meaningful signal that it is ok to cheat as well, while observing illegal cheating by outsiders – ideally through their apprehension – can allow fishers to reflect upon their behavior and act more morally.

3. Highlight the good behavior of compliant fishers. Fishers’ awareness of other fishers that chose not to act in a non-compliant manner offers an opportunity for they themselves to reflection and improve upon their own behavior.

4. Aim for 100% compliance, even when minor cheating appears to be innocuous. Cheating begets cheating, and an insignificant cheat can lead to more serious problems elsewhere. This might mean increased enforcement, but could also include regulatory reform.

Of course, a bigger question among enforcement agencies is “Which fisheries face significant illegal fishing?”  This is because enforcement agencies typically face steep budget constraints and must be quite careful about how they deploy their resources.  Again, Ariely’s research offers some suggestions as to how we might measure irrational illegal fishing, which as we’ve previously discussed, could well be more damaging to a fishery than rational, economically motivated illegal fishing.  I’ll be tackling this in the next post.

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