People tend to misrepresent their interest in and willingness to pay for public projects in which they have a direct financial stake. Caltech scientists are using fMRI to analyze the behavior of people’s brains during economic decision making. By adjusting answers to valuation questions, the researchers were able to see on fMRI the solution to the economically interesting “public goods free-rider problem.”
A Caltech press release explains:
As part of this experiment, volunteers were divided up into groups. “The entire group had to decide whether or not to spend their money purchasing a good from us,” Rangel explains. “The good would cost a fixed amount of money to the group, but everybody would have a different benefit from it.”
The subjects were asked to reveal how much they valued the good. The twist? Their brains were being imaged via fMRI as they made their decision. If there was a match between their decision and the value detected by the fMRI, they paid a lower tax than if there was a mismatch. It was, therefore, in all subjects’ best interest to reveal how they truly valued a good; by doing so, they would on average pay a lower tax than if they lied.
“The rules of the experiment are such that if you tell the truth,” notes Krajbich, who is the first author on the Science paper, “your expected tax will never exceed your benefit from the good.”
In fact, the more cooperative subjects are when undergoing this entirely voluntary scanning procedure, “the more accurate the signal is,” Krajbich says. “And that means the less likely they are to pay an inappropriate tax.”
This changes the whole free-rider scenario, notes Rangel. “Now, given what we can do with the fMRI,” he says, “everybody’s best strategy in assigning value to a public good is to tell the truth, regardless of what you think everyone else in the group is doing.”
And tell the truth they did—98 percent of the time, once the rules of the game had been established and participants realized what would happen if they lied. In this experiment, there is no free ride, and thus no free-rider problem.
“If I know something about your values, I can give you an incentive to be truthful by penalizing you when I think you are lying,” says Rangel.
While the readings do give the researchers insight into the value subjects might assign to a particular public good, thus allowing them to know when those subjects are being dishonest about the amount they’d be willing to pay toward that good, Krajbich emphasizes that this is not actually a lie-detector test.
“It’s not about detecting lies,” he says. “It’s about detecting values—and then comparing them to what the subjects say their values are.”
From a scientific point of view, says Rangel, these experiments break new ground. “This is a powerful proof of concept of this technology; it shows that this is feasible and that it could have significant social gains.”
And this is only the beginning. “The application of neural technologies to these sorts of problems can generate a quantum leap improvement in the solutions we can bring to them,” he says.
Indeed, Rangel says, it is possible to imagine a future in which, instead of a vote on a proposition to fund a new highway, this technology is used to scan a random sample of the people who would benefit from the highway to see whether it’s really worth the investment. "It would be an interesting alternative way to decide where to spend the government’s money," he notes.
Here’s a podcast of Antonio Rangel talking about the research his group is conducting:
Full story: Caltech Scientists Develop Novel Use of Neurotechnology to Solve Classic Social Problem…
Abstract in Science: Using Neural Measures of Economic Value to Solve the Public Goods Free-Rider Problem
Image credit: Wellcome Images
