TY - GEN
T1 - Impact of social influence on adoption behavior
T2 - 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
AU - Sarkar, Soumajyoti
AU - Aleali, Ashkan
AU - Shakarian, Paulo
AU - Armenta, Mika
AU - Sanchez, Danielle
AU - Lakkaraju, Kiran
N1 - Funding Information:
We end this study with a retrospective analysis to understand the dynamics of influence. In an attempt to quantify the effect of the influence on subjects in a more constrained setting, we measure at every timestep, the ratio of individuals who adopted the peer decision at the respective timestep to the number of individuals (in their respective group to which it belongs) who switched to their influence decision at least once within their lifecycle (Ttreat). Note that this is different from previous measures in 2 ways: first we retrospectively filter out users who never adopted their peer decision (in the real world these are users who would not be susceptible to influence or are immune as such). Second, we analyze this ratio at the end of their exploration phase, in Round 18, when everybody have supposedly settled down. We define a symbol N (u) as the number of time steps for which a user u adopts Cu in Ttreat (this is measured retrospectively aggregating all timesteps beforehand). Formally it is defined as: Success ratio(t) = |{u | du(t)=Cu}|. The denominator denotes the number of individuals who have adopted the peer decision at least once from Rounds 13 to 18. The comparison shown in Figure 9 among the four groups (the No message group does not have any influence decision) demonstrates that while the EC group adopts the influence decision more quickly than other groups, the stimulus in signals quantity at Round 16 in DC group affected the participants. This is confirmed when the effects of DC strongly outstrip those observed from EC in the last round where both groups receive 6 signals. VI. CONCLUSION We present a controlled experiment that demonstrates how individuals can deviate from the optimal choices as a result of social influence. While an early cascade influences decision-makers to deviate most from their choice when aggregated over the game, the speed with which individuals switch to their peers’ decision further increases when an impulsive stimuli is present. Such conclusions can have diverse impacts on real world use-cases where we can devise strategies to influence people towards making a better choice even when there is little motivation. Acknowledgments. Some of the authors are supported through the ARO grant W911NF-15-1-0282. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. We would like to thank Glory Emmanuel-Avina and Victoria Newton for their insights and assistance with preliminary experiment design.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/27
Y1 - 2019/8/27
N2 - It is widely believed that the adoption behavior of a decision-maker in a social network is related to the number of signals it receives from its peers in the social network. It is unclear if these same principles hold when the “pattern” by which they receive these signals vary and when potential decisions have different utilities. To investigate that, we manipulate social signal exposure in an online controlled experiment with human participants. Specifically, we change the number of signals and the pattern through which participants receive them over time. We analyze its effect through a controlled game where each participant makes a decision to select one option when presented with six choices with differing utilities, with one choice having the most utility. We avoided network effects by holding the neighborhood network of the users constant. Over multiple rounds of the game, we observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the six choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar choices, (2) when the quantity of social signals vary over time, the probability that a participant selects the decision similar to the one reflected by the social signals and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals and (3) an early subjugation to higher quantity of peer social signals turned out to be a more effective strategy of social influence when aggregated over the rounds.
AB - It is widely believed that the adoption behavior of a decision-maker in a social network is related to the number of signals it receives from its peers in the social network. It is unclear if these same principles hold when the “pattern” by which they receive these signals vary and when potential decisions have different utilities. To investigate that, we manipulate social signal exposure in an online controlled experiment with human participants. Specifically, we change the number of signals and the pattern through which participants receive them over time. We analyze its effect through a controlled game where each participant makes a decision to select one option when presented with six choices with differing utilities, with one choice having the most utility. We avoided network effects by holding the neighborhood network of the users constant. Over multiple rounds of the game, we observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the six choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar choices, (2) when the quantity of social signals vary over time, the probability that a participant selects the decision similar to the one reflected by the social signals and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals and (3) an early subjugation to higher quantity of peer social signals turned out to be a more effective strategy of social influence when aggregated over the rounds.
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U2 - 10.1145/3341161.3342882
DO - 10.1145/3341161.3342882
M3 - Conference contribution
AN - SCOPUS:85078829938
T3 - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
SP - 226
EP - 233
BT - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
A2 - Spezzano, Francesca
A2 - Chen, Wei
A2 - Xiao, Xiaokui
PB - Association for Computing Machinery, Inc
Y2 - 27 August 2019 through 30 August 2019
ER -