Development of a Genetic Algorithm for Estimation for a DeGroot Opinion Diffusion Model

2021 ◽  
Author(s):  
Kara Layne Johnson ◽  
Nicole Bohme Carnegie
Keyword(s):  
Genetic Algorithm ◽  
Diffusion Model

scholarly journals Evolving an Information Diffusion Model Using a Genetic Algorithm for Monthly River Discharge Time Series Interpolation and Forecasting

2014 ◽  
Vol 15 (6) ◽  
pp. 2236-2249 ◽  
Author(s):  
Chengzu Bai ◽  
Mei Hong ◽  
Dong Wang ◽  
Ren Zhang ◽  
Longxia Qian
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Diffusion Model ◽  
Information Diffusion ◽  
Fuzzy Inference ◽  
Yellow River ◽  
Genetic Operators ◽  
Discharge Time ◽  
Local Optima ◽  
Information Diffusion Model

Abstract The identification of the rainfall–runoff relationship is a significant precondition for surface–atmosphere process research and operational flood forecasting, especially in inadequately monitored basins. Based on an information diffusion model (IDM) improved by a genetic algorithm, a new algorithm (GIDM) is established for interpolating and forecasting monthly discharge time series; the input variables are the rainfall and runoff values observed during the previous time period. The genetic operators are carefully designed to avoid premature convergence and “local optima” problems while searching for the optimal window width (a parameter of the IDM). In combination with fuzzy inference, the effectiveness of the GIDM is validated using long-term observations. Conventional IDMs are also included for comparison. On the Yellow River or Yangtze River, twelve gauging stations are discussed, and the results show that the new method can simulate the observations more accurately than traditional IDMs, using only 50% or 33.33% of the total data for training. The low density of observations and the difficulties in information extraction are key problems for hydrometeorological research. Therefore, the GIDM may be a valuable tool for improving water management and providing the acceptable input data for hydrological models when available measurements are insufficient.

scholarly journals Performance of a Genetic Algorithm for Estimating DeGroot Opinion Diffusion Model Parameters for Health Behavior Interventions

2021 ◽  
Vol 18 (24) ◽  
pp. 13394
Author(s):  
Kara Layne Johnson ◽  
Jennifer L. Walsh ◽  
Yuri A. Amirkhanian ◽  
Nicole Bohme Carnegie
Keyword(s):  
Genetic Algorithm ◽  
Social Network ◽  
Diffusion Model ◽  
Simulation Study ◽  
Model Misspecification ◽  
Ordinal Scale ◽  
Model Parameters ◽  
Well Performance ◽  
Practical Applications ◽  
Social Network Intervention

Leveraging social influence is an increasingly common strategy to change population behavior or acceptance of public health policies and interventions; however, assessing the effectiveness of these social network interventions and projecting their performance at scale requires modeling of the opinion diffusion process. We previously developed a genetic algorithm to fit the DeGroot opinion diffusion model in settings with small social networks and limited follow-up of opinion change. Here, we present an assessment of the algorithm performance under the less-than-ideal conditions likely to arise in practical applications. We perform a simulation study to assess the performance of the algorithm in the presence of ordinal (rather than continuous) opinion measurements, network sampling, and model misspecification. We found that the method handles alternate models well, performance depends on the precision of the ordinal scale, and sampling the full network is not necessary to use this method. We also apply insights from the simulation study to investigate notable features of opinion diffusion models for a social network intervention to increase uptake of pre-exposure prophylaxis (PrEP) among Black men who have sex with men (BMSM).

scholarly journals Improving the Accuracy of the Diffusion Model in Highly Absorbing Media

2007 ◽  
Vol 2007 ◽  
pp. 1-6 ◽  
Author(s):  
Alexander X. Cong ◽  
Haiou Shen ◽  
Wenxiang Cong ◽  
Ge Wang
Keyword(s):  
Genetic Algorithm ◽  
Diffusion Model ◽  
Numerical Experiments ◽  
Boltzmann Transport Equation ◽  
Optical Parameters ◽  
Boltzmann Transport ◽  
Scattering Media ◽  
Simulation Data ◽  
Photon Propagation ◽  
Absorbing Media

The diffusion approximation of the Boltzmann transport equation is most commonly used for describing the photon propagation in turbid media. It produces satisfactory results in weakly absorbing and highly scattering media, but the accuracy lessens with the decreasing albedo. In this paper, we presented a method to improve the accuracy of the diffusion model in strongly absorbing media by adjusting the optical parameters. Genetic algorithm-based optimization tool is used to find the optimal optical parameters. The diffusion model behaves more closely to the physical model with the actual optical parameters substituted by the optimized optical parameters. The effectiveness of the proposed technique was demonstrated by the numerical experiments using the Monte Carlo simulation data as measurements.

scholarly journals Spatial Interpolation of Annual Runoff in Ungauged Basins Based on the Improved Information Diffusion Model Using a Genetic Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Mei Hong ◽  
Ren Zhang ◽  
Dong Wang ◽  
Longxia Qian ◽  
Zhenghua Hu
Keyword(s):  
Genetic Algorithm ◽  
Diffusion Model ◽  
Information Diffusion ◽  
Yellow River ◽  
Annual Runoff ◽  
Observation Data ◽  
Ungauged Basins ◽  
Ungauged Catchments ◽  
Geostatistical Methods ◽  
Information Diffusion Model

Prediction in Ungauged Basins (PUB) is an important task for water resources planning and management and remains a fundamental challenge for the hydrological community. In recent years, geostatistical methods have proven valuable for estimating hydrological variables in ungauged catchments. However, four major problems restrict the development of geostatistical methods. We established a new information diffusion model based on genetic algorithm (GIDM) for spatial interpolating of runoff in the ungauged basins. Genetic algorithms (GA) are used to generate high-quality solutions to optimization and search problems. So, using GA, the parameter of optimal window width can be obtained. To test our new method, seven experiments for the annual runoff interpolation based on GIDM at 17 stations on the mainstream and tributaries of the Yellow River are carried out and compared with the inverse distance weighting (IDW) method, Cokriging (COK) method, and conventional IDMs using the same sparse observed data. The seven experiments all show that the GIDM method can solve four problems of the previous geostatistical methods to some extent and obtains best accuracy among four different models. The key problems of the PUB research are the lack of observation data and the difficulties in information extraction. So the GIDM is a new and useful tool to solve the Prediction in Ungauged Basins (PUB) problem and to improve the water management.

A new lithium diffusion model in layered oxides based on asymmetric but reversible transition metal migration

2020 ◽  
Vol 13 (4) ◽  
pp. 1269-1278 ◽  
Author(s):  
Kyojin Ku ◽  
Byunghoon Kim ◽  
Sung-Kyun Jung ◽  
Yue Gong ◽  
Donggun Eum ◽  
...  
Keyword(s):  
Transition Metal ◽  
Diffusion Model ◽  
Layered Oxides ◽  
Layered Oxide ◽  
Reversible Transition ◽  
Lithium Diffusion ◽  
Metal Migration

We propose a new lithium diffusion model involving coupled lithium and transition metal migration, peculiarly occurring in a lithium-rich layered oxide.

Does the Name-Race Implicit Association Test Measure Racial Prejudice?

2011 ◽  
Vol 58 (4) ◽  
pp. 271-277 ◽  
Author(s):  
Don van Ravenzwaaij ◽  
Han L. J. van der Maas ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Diffusion Model ◽  
Implicit Association Test ◽  
Alternative Explanation ◽  
Group Membership ◽  
Racial Prejudice ◽  
Decision Time ◽  
Association Test ◽  
Implicit Association ◽  
Model Decomposition ◽  
Test Measure

Research using the Implicit Association Test (IAT) has shown that names labeled as Caucasian elicit more positive associations than names labeled as non-Caucasian. One interpretation of this result is that the IAT measures latent racial prejudice. An alternative explanation is that the result is due to differences in in-group/out-group membership. In this study, we conducted three different IATs: one with same-race Dutch names versus racially charged Moroccan names; one with same-race Dutch names versus racially neutral Finnish names; and one with Moroccan names versus Finnish names. Results showed equivalent effects for the Dutch-Moroccan and Dutch-Finnish IATs, but no effect for the Finnish-Moroccan IAT. This suggests that the name-race IAT-effect is not due to racial prejudice. A diffusion model decomposition indicated that the IAT-effects were caused by changes in speed of information accumulation, response conservativeness, and non-decision time.

Impact of Context Information on Metaphor Elaboration

2018 ◽  
Vol 65 (6) ◽  
pp. 370-384 ◽  
Author(s):  
Veronika Lerche ◽  
Ursula Christmann ◽  
Andreas Voss
Keyword(s):  
Diffusion Model ◽  
Processing Speed ◽  
A Priori ◽  
Model Fit ◽  
Verbal Task ◽  
Context Information ◽  
Speed Of Processing ◽  
Good Account ◽  
Dependent Variables ◽  
Decision Settings

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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