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Effects of selective attention on continuous opinions and discrete decisions

Author

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  • Si, Xia-Meng
  • Liu, Yun
  • Xiong, Fei
  • Zhang, Yan-Chao
  • Ding, Fei
  • Cheng, Hui

Abstract

Selective attention describes that individuals have a preference on information according to their involving motivation. Based on achievements of social psychology, we propose an opinion interacting model to improve the modeling of individuals’ interacting behaviors. There are two parameters governing the probability of agents interacting with opponents, i.e. individual relevance and time-openness. It is found that, large individual relevance and large time-openness advance the appearance of large clusters, but large individual relevance and small time-openness favor the lessening of extremism. We also put this new model into application to work out some factor leading to a successful product. Numerical simulations show that selective attention, especially individual relevance, cannot be ignored by launcher firms and information spreaders so as to attain the most successful promotion.

Suggested Citation

  • Si, Xia-Meng & Liu, Yun & Xiong, Fei & Zhang, Yan-Chao & Ding, Fei & Cheng, Hui, 2010. "Effects of selective attention on continuous opinions and discrete decisions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3711-3719.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:18:p:3711-3719
    DOI: 10.1016/j.physa.2010.05.010
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    References listed on IDEAS

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    Cited by:

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    2. Juanjuan Zhao & Weili Wu & Xiaolong Zhang & Yan Qiang & Tao Liu & Lidong Wu, 2014. "A short-term trend prediction model of topic over Sina Weibo dataset," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 613-625, October.
    3. Diao, Su-Meng & Liu, Yun & Zeng, Qing-An & Luo, Gui-Xun & Xiong, Fei, 2014. "A novel opinion dynamics model based on expanded observation ranges and individuals’ social influences in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 220-228.
    4. Wang, Qiyao & Jin, Yuehui & Lin, Zhen & Cheng, Shiduan & Yang, Tan, 2016. "Influence maximization in social networks under an independent cascade-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 20-34.
    5. Si, Xia-Meng & Wang, Wen-Dong & Ma, Yan, 2016. "Role of propagation thresholds in sentiment-based model of opinion evolution with information diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 549-559.

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