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Social influence and the collective dynamics of opinion formation.

Moussaïd M, Kämmer JE, Analytis PP, Neth H - PLoS ONE (2013)

Bottom Line: A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions.In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions.These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.

View Article: PubMed Central - PubMed

Affiliation: Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany.

ABSTRACT
Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. Based on the observation of 59 experimental subjects exposed to peer-opinion for 15 different items, we draw an influence map that describes the strength of peer influence during interactions. A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions. In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions. Additional simulations reveal the existence of a tipping point at which one attractor will dominate over the other, driving collective opinion in a given direction. These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.

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Three representative examples of the collective dynamics observed in the computer simulations.For each example, the initial opinion map is shown on the left-hand side (experimental data), and the final opinion map after N = 300 rounds of simulations on the right-hand side. The opinion maps represent the proportion of individuals with a given opinion (x-axis) and a given confidence level (y-axis). As in Fig. 1, the normalized opinion is the actual opinion divided by the true value. The correct answer is represented by the red dashed lines (corresponding to a value of 1). Outliers with normalized opinion greater than 2 are not shown. The arrow maps represent the average movements over both opinion and confidence dimensions during simulations. Examples 1, 2, and 3 correspond to the questions “What is the length of the river Oder in kilometers? ”, “How many inhabitants has the East Frisian island Wangerooge?”, and “How many gold medals were awarded during the Olympics in China in 2008?”, respectively. The final convergence point may be determined by a dense cluster of low confidence individuals, as illustrated by Example 2 (majority effect), or by a few very confident individuals as in Example 3 (expert effect).
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pone-0078433-g005: Three representative examples of the collective dynamics observed in the computer simulations.For each example, the initial opinion map is shown on the left-hand side (experimental data), and the final opinion map after N = 300 rounds of simulations on the right-hand side. The opinion maps represent the proportion of individuals with a given opinion (x-axis) and a given confidence level (y-axis). As in Fig. 1, the normalized opinion is the actual opinion divided by the true value. The correct answer is represented by the red dashed lines (corresponding to a value of 1). Outliers with normalized opinion greater than 2 are not shown. The arrow maps represent the average movements over both opinion and confidence dimensions during simulations. Examples 1, 2, and 3 correspond to the questions “What is the length of the river Oder in kilometers? ”, “How many inhabitants has the East Frisian island Wangerooge?”, and “How many gold medals were awarded during the Olympics in China in 2008?”, respectively. The final convergence point may be determined by a dense cluster of low confidence individuals, as illustrated by Example 2 (majority effect), or by a few very confident individuals as in Example 3 (expert effect).

Mentions: Fig. 5 shows the dynamics observed for three representative examples of simulations. Although a certain level of opinion fragmentation still remains, a majority of individuals converge toward a similar opinion. As shown by the arrow maps in Fig.5, the first rounds of the simulation exhibit important movements of opinions among low-confidence individuals (as indicated by the large horizontal arrows for confidence lower than 3), without increase of confidence (as shown in Fig. S2). After a certain number of rounds, however, a tipping point occurs at which a critical proportion of people meet up in the same region of the opinion space. This creates a subsequent increase of confidence in this zone, which in turn becomes even more attractive to others. This results in a positive reinforcement loop, leading to a stationary state in which the majority of people end up sharing a similar opinion. This amplification process is also marked by a sharp transition of the system’s global confidence level (Fig. S2), which is a typical signature of phase transitions in complex systems [2].


Social influence and the collective dynamics of opinion formation.

Moussaïd M, Kämmer JE, Analytis PP, Neth H - PLoS ONE (2013)

Three representative examples of the collective dynamics observed in the computer simulations.For each example, the initial opinion map is shown on the left-hand side (experimental data), and the final opinion map after N = 300 rounds of simulations on the right-hand side. The opinion maps represent the proportion of individuals with a given opinion (x-axis) and a given confidence level (y-axis). As in Fig. 1, the normalized opinion is the actual opinion divided by the true value. The correct answer is represented by the red dashed lines (corresponding to a value of 1). Outliers with normalized opinion greater than 2 are not shown. The arrow maps represent the average movements over both opinion and confidence dimensions during simulations. Examples 1, 2, and 3 correspond to the questions “What is the length of the river Oder in kilometers? ”, “How many inhabitants has the East Frisian island Wangerooge?”, and “How many gold medals were awarded during the Olympics in China in 2008?”, respectively. The final convergence point may be determined by a dense cluster of low confidence individuals, as illustrated by Example 2 (majority effect), or by a few very confident individuals as in Example 3 (expert effect).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3818331&req=5

pone-0078433-g005: Three representative examples of the collective dynamics observed in the computer simulations.For each example, the initial opinion map is shown on the left-hand side (experimental data), and the final opinion map after N = 300 rounds of simulations on the right-hand side. The opinion maps represent the proportion of individuals with a given opinion (x-axis) and a given confidence level (y-axis). As in Fig. 1, the normalized opinion is the actual opinion divided by the true value. The correct answer is represented by the red dashed lines (corresponding to a value of 1). Outliers with normalized opinion greater than 2 are not shown. The arrow maps represent the average movements over both opinion and confidence dimensions during simulations. Examples 1, 2, and 3 correspond to the questions “What is the length of the river Oder in kilometers? ”, “How many inhabitants has the East Frisian island Wangerooge?”, and “How many gold medals were awarded during the Olympics in China in 2008?”, respectively. The final convergence point may be determined by a dense cluster of low confidence individuals, as illustrated by Example 2 (majority effect), or by a few very confident individuals as in Example 3 (expert effect).
Mentions: Fig. 5 shows the dynamics observed for three representative examples of simulations. Although a certain level of opinion fragmentation still remains, a majority of individuals converge toward a similar opinion. As shown by the arrow maps in Fig.5, the first rounds of the simulation exhibit important movements of opinions among low-confidence individuals (as indicated by the large horizontal arrows for confidence lower than 3), without increase of confidence (as shown in Fig. S2). After a certain number of rounds, however, a tipping point occurs at which a critical proportion of people meet up in the same region of the opinion space. This creates a subsequent increase of confidence in this zone, which in turn becomes even more attractive to others. This results in a positive reinforcement loop, leading to a stationary state in which the majority of people end up sharing a similar opinion. This amplification process is also marked by a sharp transition of the system’s global confidence level (Fig. S2), which is a typical signature of phase transitions in complex systems [2].

Bottom Line: A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions.In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions.These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.

View Article: PubMed Central - PubMed

Affiliation: Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany.

ABSTRACT
Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. Based on the observation of 59 experimental subjects exposed to peer-opinion for 15 different items, we draw an influence map that describes the strength of peer influence during interactions. A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions. In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions. Additional simulations reveal the existence of a tipping point at which one attractor will dominate over the other, driving collective opinion in a given direction. These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.

Show MeSH
Related in: MedlinePlus