I.5 Tendencias e indicadores de logro lector que muestran evaluaciones nacionales e internacionales
I.5.1 Resultados e indicadores de lectura en pruebas estandarizadas
Here, the simulation and comparison results are presented.
Simulation method I, we fix
z
max=5
, n =6, l = , 5
=0.3, and=0.9
. Then, we set different input parametersm
,
1, , andg
, and we run simulation method I 1000 times to produce the average values of P, Z and AD , respectively. The average P,z
and AD value, respectively, reflect the success ratio, the number of rounds required for achieving the established consensus level, and the amount of preference adjustment required in the simulation experiment. The average values of P,z
and AD , for Simulation method I under differentinput parameters, are listed in Table 26.
Table 26: Average values of P, Z and AD in Simulation method I
g=1 g=2 g=3 0.15 = =0.35 =0.15 =0.35 =0.15 =0.35 m 1 P Z AD P Z AD P Z AD P Z AD P Z AD P Z AD 6 0.1 1 2.149 0.219 1 2.043 0.213 1 2.973 0.308 1 2.83 0.292 0.987 4.228 0.463 1 3.017 0.357 0.3 1 2.146 0.219 1 2.033 0.212 1 2.971 0.306 1 2.813 0.291 0.993 4.218 0.458 1 3.004 0.355 9 0.1 1 2.003 0.209 1 2 0.208 1 2.43 0.252 1 2.229 0.234 1 2.989 0.313 1 2.865 0.3 0.3 1 2 0.209 1 2 0.209 1 2.408 0.25 1 2.166 0.236 1 2.972 0.313 1 2.861 0.301 12 0.1 1 2 0.208 1 2 0.207 1 2.054 0.225 1 2.006 0.221 1 2.68 0.276 1 2.27 0.25 0.3 1 2 0.207 1 2 0.208 1 2.059 0.225 1 2.003 0.222 1 2.654 0.273 1 2.259 0.249 In Simulation method II, we set
z
max=5
, n =6, l =5, and =0.9. We then run simulation method II 1000 times under different input parametersm
,
2, , andg
to get the average values of P, Z and AD , which are listed in Table 27.Table 27: Average values of P, Z and AD in Simulation method II
g=1 g=2 g=3 0.15 = =0.35 =0.15 =0.35 =0.15 =0.35 m 2 P Z AD P Z AD P Z AD P Z AD P Z AD P Z AD 6 0.2 1 3.062 0.269 1 2.875 0.256 0.176 5 0.458 1 3.454 0.341 0 5 0.561 1 4 0.451 0.4 1 2.377 0.252 1 2.036 0.232 0.72 4.969 0.481 1 3.001 0.329 0 5 0.593 1 4 0.475 9 0.2 1 2.653 0.241 1 2.138 0.218 1 3.948 0.338 1 3 0.285 0.192 5 0.459 1 3.312 0.338 0.4 1 2.223 0.23 1 2.027 0.229 1 3.115 0.311 1 2.738 0.286 0.875 4.99 0.483 1 3 0.335 12 0.2 1 2.073 0.215 1 2.055 0.211 1 3.01 0.275 1 2.981 0.271 1 4.252 0.368 1 3 0.296 0.4 1 2.065 0.229 1 2 0.228 1 2.341 0.258 1 2.002 0.241 1 3.672 0.355 1 2.994 0.312 In Simulation method III, the parameters are fixed as
z
max=5
, n =6, l = , 5 =0.28, and
=0.9
. When setting different input parametersm
,
3, , andg
for Simulation method III, we run simulation method III 1000 times to obtain the average values of P, Z andAD, which are listed in Table 28.
Table 28: Average values of P, Z and AD in Simulation method III
g=1 g=2 g=3 0.15 = =0.35 =0.15 =0.35 =0.15 =0.35 m 2 P Z AD P Z AD P Z AD P Z AD P Z AD P Z AD 6 0.2 1 4.009 0.429 1 3.221 0.343 0 5 0.772 1 4 0.617 0 5 1.04 1 4 0.815 0.4 1 4.008 0.427 1 3.203 0.343 0 5 0.772 1 4 0.618 0 5 1.04 1 4 0.816 9 0.2 1 3.064 0.31 1 3.012 0.302 1 4.946 0.59 1 3.045 0.404 0 5 0.779 1 4 0.629 0.4 1 3.051 0.307 1 3.031 0.301 1 4.898 0.581 1 3.029 0.399 0 5 0.769 1 4 0.621 12 0.2 1 3.057 0.285 1 3.021 0.282 1 4.251 0.436 1 3.011 0.355 1 5 0.644 1 3.27 0.456 0.4 1 3.012 0.283 1 3 0.281 1 4.123 0.434 1 3 0.353 1 5 0.638 1 3.194 0.444
Meanwhile, we set g =2, n =7, l = , 6 zmax =5, =0.92, and regarding the threshold values for identifying non-cooperative behaviors, let (1)
=0.3 and
1=0.1 for simulation method I; (2)
2 =0.2 for simulation method II; (3)
=0.25 and
3=0.5 for simulation method III. When setting different parameters m and
, we run Simulation methods I-III 1000 times, respectively, to produce the average values of P, Z and AD , which are described as Figs. 5-7.Fig.5. Average values of P, Z and AD under simulation method I
Fig.6. Average values of P, Z and AD under simulation method II
Fig.7. Average values of P, Z and AD under simulation method III
Let l =7 ,
n =8
,z
max=5
,
1=0.5,
2=0.35 ,
3=0.4,
=0.25,
=0.28 , 2g = , and
=0.25. We run Simulation methods I and I*, II and II*, and III and III* 1000 times under different input parametersm
and
, respectively, to generate the average values ofP
,Z
and AD , which are visualized in Figs. 8-10.Fig.9. Average values of P, Z and AD under simulation methods II and II*
Fig.10. Average values of P, Z and AD under simulation methods III and III* The results of the above simulation experiments and comparative analysis show that:
(1) The SNA-based consensus reaching framework can manage dishonest, disobedient, and divergent behaviors effectively when setting different parameters. Generally, the consensus success ratios are 1 in most cases, and 2-3 consensus rounds are often required to reach the predefined consensus level. Moreover, the adjustment distances are 0.2-0.4 in most cases.
(2) With the increase of the proportion of the experts who take non-cooperative behaviors, the consensus success ratios have the tendency to decrease, and the consensus rounds increase alongside the preference adjustment distances. This implies that the effectiveness of the proposed consensus reaching framework for coping with non-cooperative behaviors varies, depending on the proportion of the experts who take non-cooperative behaviors in the group.
(3) When the values of , 1 and 2 increase or value increases, the average values 3 of P increase, and the average values of Z decrease. These observations imply that the success ratio of reaching the predefined consensus level will be improved and the speed to reach the predefined consensus level will be accelerated when using a relaxed criterion to infer the non-cooperative behaviors or applying a strong penalty.
(4) There are higher consensus success ratios in the SNA-based consensus reaching with non-cooperative behaviors management framework than in the traditional CRP with social trust network, which indicates that the SNA-based consensus reaching framework can increase the success ratio of achieving the predefined consensus level under the presence of non-cooperative behaviors, by effectively dealing with them.
(5) The average consensus rounds in the SNA-based consensus framework with non-cooperative behaviors management are lower than those in the traditional CRP approaches under social trust networks, which means that the proposed consensus framework can accelerate the speed of convergence to achieve the predefined consensus level.
(6) The adjustment distances in the proposed SNA-based consensus reaching framework with non-cooperative behavior management are lower than those in the traditional CRP with social trust network, which implies that the proposed SNA-based consensus reaching framework can decrease the preference information loss by dealing with the non-cooperative behaviors.