For decades, a wide variety of research communities have each been studying how individuals learn from one another, and how that shapes the broader network. But these communities rarely share their findings across academic boundaries. 

“Each field is more or less independent, but the conclusions they’ve found are somewhat similar,” says SFI Professor Mirta Galesic, who is organizing a workshop that is meeting April 16-19. The meeting convenes a range of researchers from psychologists and evolutionary anthropologists to statistical physicists and computer scientists to share (socially) what they have discovered independently about social learning.

Computer scientists might glean tools from animal and human researchers that could help them design better machine learning algorithms. Statistical physicists might offer insights about why certain rules work, while psychology could advance the insights about what people do toward what is good for us to do.

Computer scientists have already learned from biology how social insects like ants and bees work together to solve problems. For instance, ant colony optimizations algorithms, developed from an understanding of how ants use pheromone trails to find food, have been used to model protein folding and to optimize traffic routing. 

“We’ve learned how ants and bees explore a space for possible solutions and then communicate to each other what they’ve discovered,” says Galesic. “Maybe this workshop will allow us to add how humans do it.” 

Psychologists, like Galesic, have a lot of qualitative insights into how people learn. But they don’t have good quantitative models to transfer that knowledge into social networks to understand how beliefs spread. Insights from computer science could also help psychologists study what conditions make it more effective to learn on your own, and when it’s better to learn from others. 

The more people from different fields talk to one another, the more they are discovering they have come across similar findings independently. “One discipline may have findings that resemble what another discipline has already been doing for 20 years,” says Galesic. That can give the false impression that people have been doing interdisciplinary work. “It’s really just that they are rediscovering the same outcomes.”

By providing a space for true interdisciplinary conversations, Galesic hopes the group can begin to understand how broad these common findings really are.

Read more about the workshop, Integrating different perspectives on social learning