Our framework speaks to all these applications.” Or to limit pollution, or climate change, or the overharvesting of a common resource. “The objective may be to bring a business project to fruition,” Georgiadis says, “or it could be to raise funds for a noble cause. Yet, the model can offer insights into combatting the free-rider problem in other contexts as well-particularly in situations that unfold over time, giving each party multiple opportunities to respond to the ever-changing actions of others. “Moreover, if one assumes that the entrepreneurs become more experienced as the startup grows, they are essentially foregoing ever larger pay-thus effectively making ever larger ‘payments’-until the goal is reached,” says Georgiadis. Each day’s work, then, represents lost wages paid toward the business’s future launch, at which point prespecified shares of the business will be paid out, but only if the business is successful. Entrepreneurs devote time to their startup earning zero or below market wages. Interestingly, the researchers argue that their model captures the incentives structure at many startups. “My temptation to free ride, to skip an hour of work, when we’re close to completion is big, because I’m working really hard,” says Georgiadis. Why would the incentives to loaf increase as the project nears completion? With the end in sight, everyone is working harder-which makes the prospect of an afternoon off even more tantalizing. Therefore, we need bigger payments to kill those free riding incentives,” says Georgiadis. “The basic idea is that the closer you are to completion, the bigger the stakes are and the bigger the incentives of the different parties to free ride. This, says Georgiadis, is because the temptation to freeload increases as you approach the finish line. Cvitanić, a professor of mathematical finance at Caltech, sums it up this way: “The size of the penalty for each individual should be such that everyone feels everyone else’s pain.”Īnd everyone’s contribution increases as the project nears completion. But importantly, it is calculated to wipe out any personal incentive to work at a rate that is inefficient for the group. The exact amount that teammates contribute might differ from one individual to the next, based on factors like the size of their role or their productivity. Moreover, “by giving each agent a large lump-sum payment when the goal is reached, her bottom line is essentially magnified, and thus she has incentives to choose actions that are best not only for her, but also for the group as a whole,” Georgiadis says. The accumulating fees act as a tax or penalty for freeloading, forcing individual teammates to internalize the costs of the group’s inefficiencies. So long as the group works together efficiently, members will each get back what they contributed, plus interest. Then, and only then, are lump sum payments returned to the contributors, in prespecified amounts. They formulated a theoretical situation in which everyone working on a long-term project contributes incremental fees to a third party until the entire project is complete. But it also offers a way to combat thorny political issues involving the free-rider problem, such as how countries share the responsibility of curbing greenhouse emissions or preventing the overharvesting of resources. The researchers’ solution can be used in business collaborations to ensure that teams work together efficiently. In a new mathematical model he developed with colleague Jakša Cvitanić, Georgiadis proposes a clever way of getting around the free-rider problem, particularly for somewhat involved collaborations that unfold over time. In other words, as Georgiadis puts it, “because I only care about my benefits and costs-that is, I do not internalize the benefits of my labor to the other team members-I am going to do less work.” Individually, we want to enjoy the fruits of our team’s labor, but we aren’t as keen to incur the costs. This is because what is best for the group tends to differ from what is best for each of us. “Any time you have two or more people working together, each acting in his or her own best interest, they behave less efficiently than if they were working alone,” says George Georgiadis, an assistant professor of strategy at the Kellogg School. But collaborations also have their downsides, including one that economists have long tried to eradicate: the free-rider problem.
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