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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Opinion Dynamics Model with Bounded Confidence and the Sleeper Effect.

Jing Wei1,2, Yuguang Jia1, Hengmin Zhu1

  • 1School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

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This study introduces a new opinion evolution model incorporating psychological effects like the sleeper effect and discounted opinions. Simulations show the sleeper effect significantly impacts opinion convergence across diverse network environments.

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Area of Science:

  • Social dynamics
  • Computational social science
  • Psychological modeling

Background:

  • Opinion evolution is complex, influenced by individual and social factors, including psychological effects.
  • Classical models like bounded confidence do not fully capture these nuances.
  • The sleeper effect, where messages gain influence over time, is a key psychological factor.
  • Discounted opinions, representing reduced impact of certain information, also play a role.

Purpose of the Study:

  • To propose a novel opinion evolution model integrating the sleeper effect and discounted opinions.
  • To simulate opinion dynamics under varying network conditions and model parameters.
  • To analyze the impact of the sleeper effect and discounting on opinion aggregation and convergence.

Main Methods:

  • Developed a new opinion evolution model building upon the bounded confidence framework.
  • Incorporated 'discounted opinions' and the 'sleeper effect' into the model's update rules.
  • Conducted simulation experiments on three distinct initial network structures.
  • Systematically varied parameters such as thresholds and discounting ratios.

Main Results:

  • The sleeper effect demonstrably influences the speed and pattern of opinion convergence.
  • Different initial network structures lead to varied responses to the sleeper effect.
  • The ratio of discounted opinions affects the overall aggregation and stability of collective opinions.
  • Model simulations provide insights into the interplay between psychological factors and social influence.

Conclusions:

  • The proposed model offers a more comprehensive understanding of opinion evolution by including psychological elements.
  • The sleeper effect is a critical factor that can accelerate or decelerate opinion convergence depending on the environment.
  • Future research can explore more complex psychological states and network topologies.