Election outcomes are notoriously difficult to predict. In 2016, for example, most polls suggested that Hillary Clinton would win the presidency, but Donald Trump defeated her. Researchers cite multiple explanations for the unreliability in election forecasts — some voters are difficult to reach, and some may wish to remain hidden. Among those who do respond to surveys, some may change their minds after being polled, while others may be embarrassed or afraid to report their true intentions.
In a new perspective piece for Nature, in a special issue devoted to computational social science, SFI researchers Mirta Galesic, Jonas Dalege, Henrik Olsson, Daniel Stein, Tamara van der Does, and their collaborators* propose a surprising way to get around these shortcomings in survey design — not just in the world of politics, but in other types of research as well. While it’s widely assumed that cognitive bias clouds our assessment of the people around us, their research and that of others suggests that in fact, our estimations of what our friends and family believe are often accurate.
“We realized that if we ask a national sample of people about who their friends are going to vote for, we get more accurate predictions than if we ask them who they’re going to vote for,” says Galesic, who is the corresponding author. “We found that people are actually pretty good at estimating the beliefs of people around them.”
That means researchers can gather highly accurate information about social trends and groups by asking about a person’s social circle rather than interrogating their own individual beliefs. That’s because as highly social creatures, we have become very good at sizing up those around us — what researchers call “social sensing.”
When people are selected to represent a particular group, their perceptions, combined with new computational models of human social dynamics, can be used to identify emerging trends and better predict political and health-related developments in particular, the team writes. This approach, combining elements of psychology and sociology, can even be harnessed to devise interventions that “could steer social systems in different directions” after a major event, such as a natural disaster or a mass shooting, they suggest.
“I really hope human social sensing will be included in the standard social science toolbox, because I think it can be a very useful strategy for predicting and modeling societal trends,” Galesic says.
Read the perspective piece, "Human social sensing is an untapped resource for computational social science," in Nature (June 30, 2021)
Read the editorial, “The powers and perils of using digital data to understand human behaviour,” in Nature (July 1, 2021)
* Mirta Galesic (Santa Fe Institute); Wändi Bruine de Bruin (University of Southern California); Jonas Dalege (Santa Fe Institute); Scott Feld (Purdue University); Frauke Kreuter (LMU Munich, University of Maryland); Henrik Olsson (Santa Fe Institute); Drazen Prelec (Sloan School of Management, MIT); Daniel Stein (New York University, Santa Fe Institute); and Tamara van der Does (Santa Fe Institute) are co-authors on the perspective piece.