Study Shows How Small Groups Lead to the Emergence of Leaders

A Team, led by NYU Tandon Professor Maurizio Porfiri, finds patterns in how people interact in small groups that leads to the emergence of leaders. (Image: via pixabay / CC0 1.0)
A Team, led by NYU Tandon Professor Maurizio Porfiri, finds patterns in how people interact in small groups that leads to the emergence of leaders. (Image: via pixabay / CC0 1.0)

While the “wisdom of the crowd” shapes the behavior of large groups of people, less is known about small-group dynamics and how individuals interact to make decisions, particularly when it comes to the emergence of leaders, a key area of inquiry in organizational research. The phenomenon is critical to arriving at an understanding of social networks of all kinds.

Now, researchers at the NYU Tandon School of Engineering have cracked the code on how leaders arise from small groups of people over time. The work is detailed in a study Social Information and Spontaneous Emergence of Leaders in Human Groups, published in The Royal Society Interface.

The team comprised Maurizio Porfiri, professor in the of Department of Mechanical and Aerospace Engineering and of biomedical engineering at NYU Tandon; Shinnosuke Nakayama, postdoctoral researcher at NYU Tandon; Elizabeth Krasner, an undergraduate student at NYU Tandon; and Lorenzo Zino, a visiting Ph.D. student at NYU Tandon from Politecnico di Torino, Italy.

An example of network evolution in small groups. The directed links, those with arrows, were generated when one individual changed the answer to that of another. (a) When participants were shown the cumulative performance of others based on the last answers at each round in the main experiment, (b) when participants were shown the cumulative performance of others based on the first answers at each round in the additional experiment. Arrow width identifies the number of such instances, while the node size and colour represent network centrality and performance as a cumulative score, respectively.

An example of network evolution in small groups. The directed links, those with arrows, were generated when one individual changed the answer to that of another. (a) When participants were shown the cumulative performance of others based on the last answers at each round in the main experiment, (b) when participants were shown the cumulative performance of others based on the first answers at each round in the additional experiment. Arrow width identifies the number of such instances, while the node size and color represent network centrality and performance as a cumulative score, respectively. (Image: engineering.nyu.edu)

To conduct the research, the team convened several groups of five volunteers each to participate in a cognitive test arranged in 10 consecutive rounds. The task involved estimating the number of dots displayed for just half a second on a large screen. In each round, participants were asked to choose one from multiple answers using a custom-made clicker, without verbally communicating with one another.

Because the dots were visible for only an instant, group members, lacking the time to count them, had to venture a guess. However, the experiments were structured so that participants could alter their answers based on the answers of others in their group: Once all participants had chosen their initial answers, the screen — viewable by all — displayed the current answers of all members along with their past performance in selecting correct responses.

Participants then had a 10-second window in which to change their responses based on those of the others in the group. The researchers, analyzing how participant responses evolved over the course of the experiment, found that individuals did not choose the simple majority rule, as posited by the wisdom of crowds. Rather, they dynamically decided who to follow in making decisions, based on how well each group member performed over time.

Based on their observations, the researchers inferred a dynamic evolution of the network of interaction, in which participants were nodes and the links were the consequences of social influence. For example, the investigators generated a link from one participant to another if the first had changed his or her answer to that of the second. The speed at which the network grew increased over the course of each of the rounds. Porfiri said:

To discern the influence of social networks within evolving group dynamics, the investigators noted that:

Nakayama, the lead author, explained that the behavior of small groups is strikingly different than that of much larger gatherings of people, saying:

Porfiri noted that while most studies of social connections are based on static networks, defined by fixed established relationships, his team’s research looked at functional networks based on constantly changing connections. Porfiri said:

He also said: that these time-based actions are like networks in the brain, where physically distant neurons forge connections toward a specific function, adding:

Provided by: New York University Tandon School of Engineering [Note: Materials may be edited for content and length.]

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