In this issue:I'm More Than OK
I'm More Than OK
Entitled employees are a challenge to manage
Does this sound like somebody you work with? Inflated self-perception and unrealistic expectations; tends to blame others for mistakes; feels deserving of high levels of praise and reward, regardless of actual performance. If you answered yes, then Paul Harvey feels your pain, but he also hopes his research into "psychological entitlement" will help you, especially if you're that person's boss. "From the workplace perspective, how do you manage this sort of person? That's the tricky part," he says. "If you're a manager, it's very difficult to change how someone views themselves."
Harvey, an assistant professor of management in the Whittemore School of Business and Economics, has published more than a dozen papers on personality and workplace interactions. But the issue of psychological entitlement, a term for people with unreasonably high expectations of how life should treat them, has become a specialty. "Usually life kind of beats you down, then you dial it back and learn from your mistakes. People are noticing anecdotally that with entitled people—employees, students—that never happens. They go through setbacks and failures and still come out thinking highly of themselves, never blaming themselves," he says. Harvey is studying the cognitive process by which such self-opinion is maintained despite evidence to the contrary. One key is a "self-serving bias," in which good things are seen as their doing and bad things are other people's fault.
One part of his work compares the levels of entitlement among different age groups, as reflected in psychology surveys in which people rate their agreement with various statements. He found that Generation Y, the group born between 1980 and 2000, displayed much higher levels of entitlement than other generations. Among the factors that may have contributed to this tendency, Harvey hypothesizes, are smaller families, a booming economy, feel-good advertising and trends in parenting and teaching that emphasize self-esteem. Harvey's findings and conclusions have drawn plenty of attention. A New York Post story about it was headlined "The worst generation? Gen Y workers are the pits," and Harvey was interviewed by Fox News. Harvey admits he's worried that even before the work is published in a peer-reviewed journal it may get reduced to a sound bite in a political debate, but he says such comparisons are an important part of understanding the phenomenon.
How to cope with people who have excessive feelings of entitlement? So far Harvey has only found what doesn't work: To his surprise, extra feedback from bosses can actually make things worse. "[Entitled employees] are comfortable with higher levels of feedback, but only as long as it's positive," he notes. In a recent paper, he speculates that "high levels of charisma" are a key to managing entitled folks.
Harvey is 33, barely older than the Gen Y group itself, and admits to having noticed the entitlement phenomenon among students at UNH and other schools. He adds that it's not a solely American problem, either; it has been noted in ma ny countries. "Everywhere, when t his is mentioned, heads start nodding and eyes start rolling."
In Colombia, creating plans for sustainable fishing
Two years ago, natural resources graduate student Lina Maria Saavedra-Diaz began the initial phase of a two-part study of traditional, small-scale "artisanal" fishing practices in nine coastal villages in her native Colombia.
Over several months she netted both more information than her doctoral thesis could accommodate and a marriage proposal from a 70-year-old man with three wives.
Happily, the marriage offer was an indication that what she feared—the indigenous villagers would keep her at arm's length—didn't occur. But she also had to convince them that she wouldn't gather data and then disappear when the work was done.
"That was the hardest part," Saavedra-Diaz says, "convincing them that the research was for them and that I would come back after analyzing my findings."
She gathered the traditional knowledge that fishermen have passed down through generations. "They know who is fishing, when to fish, what problems they are facing. They have knowledge we don't."
Because their livelihoods are threatened by overexploitation, pollution, deforestation and climate change, the research is no academic exercise; it is critically needed if the fishing villages are to survive.
The goal is to help local villages and government agencies craft community-based fisheries management plans tailored to the specific needs of the coastal villages. The outcome would not be a topdown bureaucracy but would instead involve both parties as equal management partners. It would be a modern plan applied to what in most cases is a very primitive practice.
"Their equipment is very rudimentary," says Saavedra-Diaz. "For night fishing, they'll have a long pole in the middle of the boat with a Coleman lamp hooked up to a gas tank—it's like a bomb. They use their hands, feet and teeth to play out and reel in the fishing lines, and old car parts to weigh down fishing gear."
Saavedra-Diaz, whose thesis advisor is natural resources professor Andy Rosenberg, returns to Colombia next June to share the results of her analysis and begin the process of creating the fisheries management plans.
She notes that by living among the fishing communities communities she gained much more than scientific data; she acquired a greater appreciation for the simpler things in life, which is all that the villagers have. "If I ever have a house, it will be a small house, and if I have 'things,' they'll be simple things. Much more important is laughing and having a good meal with your family. That's how they live."
Artificial intelligence that reaches a decision—fast
There aren't many research t op i c s i n c o mp ut e r science that would fit on Oprah's show, but assistant professor of computer science Wheeler Ruml just might have one: self-help for robots. Ruml and his group of graduate and undergraduate students in artificial intelligence are trying to help robots be more decisive—to help them make up their minds, so to speak.
Planning is the way researchers in artificial intelligence think about the question "What's the robot going to do next?" More specifically, "We're trying to formalize planning," says Ruml. Plenty of AI research is tackling this problem, but Ruml's group is keying in on a specific element: time. "Finding the best solution isn't any good if it takes a million years, so we want to find a good-enough solution within set bounds," he says.
His work could be considered a variant of the famous traveling-salesman problem, where the task is to make a trip to multiple cities as short as possible. Mathematicians have chewed over this problem for more than a century, but a generalized solution remains out of reach.
Ruml cites a real-world example: His collaboration with Boeing to design artificial intelligence programs to help schedule aerial refueling of military craft. "That's a big optimization problem," he says, ticking off the variables that must be juggled in real time: number and types of craft in the air and on the ground, their range and speed, fuel availability, manpower limits, weather, the overall needs of the fleet. If the optimal solution requires everybody sitting around for hours waiting for an answer, it's useless.
"The conventional approach is to take optimal methods and modify them in straightforward ways, in a time-sensitive approach"—that is, see if you can get to the best answer more quickly. "It's possible to do better than that by having methods that are specially designed to be sub-optimal—and designed to be time-sensitive," says Ruml.
Recent papers by Ruml and colleagues, with titles like "Where to Put the Randomness?" and "The Joy of Forgetting," tackle pieces of the puzzle, including how to establish bounds so your result won't to be too "suboptimal" and how to best make use of modern computers that have multiple chips working on the same problem at once.
"Usually, first you consider one thing and then you consider another—you're always looking at one thing. The question is how to parallelize it," says Ruml. "Minimizing the need for communication is very important. We're finding it's effective to get different cores to carve off big chunks of the problem to work on, and only if the program gets tired of working on its chunk does it have to communicate: 'I'm tired of this now and want another piece.'"
All this demonstrates that artificial intelligence, a field which seems to have suffered from a failure to meet inflated expectations, is alive and well. "It's true that we don't have walking, talking robots that do your dishes ... but AI has been successful in very tightly constrained environments," he says. "Every time you run a credit card, there's an AI system checking for fraud. On the Boeing 777 aircraft the engines were designed by an AI system. When Netflix or Amazon recommends a product to you, that's an example of an AI system." ~