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The role of inertia in modeling decisions from experience with instance-based learning.

Dutt V, Gonzalez C - Front Psychol (2012)

Bottom Line: One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE).This paper demonstrates the predictive benefits of incorporating one particular implementation of inertia in an existing IBL model.The generalization process demonstrates both the advantages and disadvantages of including inertia in an IBL model.

View Article: PubMed Central - PubMed

Affiliation: School of Computing and Electrical Engineering and School of Humanities and Social Sciences, Indian Institute of Technology Mandi, India.

ABSTRACT
One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE). A number of computational models based upon the Instance-Based Learning Theory, a theory of DFE, have included different inertia implementations and have shown to simultaneously account for both risk-taking and alternations between alternatives. The role that inertia plays in these models, however, is unclear as the same model without inertia is also able to account for observed risk-taking quite well. This paper demonstrates the predictive benefits of incorporating one particular implementation of inertia in an existing IBL model. We use two large datasets, estimation and competition, from the Technion Prediction Tournament involving a repeated binary-choice task to show that incorporating an inertia mechanism in an IBL model enables it to account for the observed average risk-taking and alternations. Including inertia, however, does not help the model to account for the trends in risk-taking and alternations over trials compared to the IBL model without the inertia mechanism. We generalize the two IBL models, with and without inertia, to the competition set by using the parameters determined in the estimation set. The generalization process demonstrates both the advantages and disadvantages of including inertia in an IBL model.

No MeSH data available.


Related in: MedlinePlus

The MSD for the R-rate, the MSD for the A-rate, and the MSD for the combined R-rate and A-rate for different values of pInertia parameter in IBL-Inertia model (the corresponding MSDs for the IBL model are also plotted as dotted lines for comparison). The IBL-Inertia model used the calibrated parameters for d and s parameters (i.e., d = 6.41 and s = 1.40).
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Figure 3: The MSD for the R-rate, the MSD for the A-rate, and the MSD for the combined R-rate and A-rate for different values of pInertia parameter in IBL-Inertia model (the corresponding MSDs for the IBL model are also plotted as dotted lines for comparison). The IBL-Inertia model used the calibrated parameters for d and s parameters (i.e., d = 6.41 and s = 1.40).

Mentions: Figure 3 shows the MSDs for the IBL-Inertia model calibrated on the combined R-rate and A-rate as a function of pInertia values in the estimation set. It also shows the three corresponding MSDs from the original IBL model (shown as dotted lines in Figure 3) for comparison purposes (these MSDs are also reported in Table 1). The MSDs for the R-rate, the A-rate, and the sum of the MSDs for the R-rate and A-rate in the IBL-Inertia model are below the corresponding MSDs in the IBL model for all values of pInertia greater than 0.05 and less than 0.90. Thus, including inertia in the IBL model and calibrating all model parameters improves the model’s ability to account for the average R-rate and A-rate compared with the IBL model without inertia. Also, the advantages of including pInertia parameter seem to be present over a large range of this parameter’s variation.


The role of inertia in modeling decisions from experience with instance-based learning.

Dutt V, Gonzalez C - Front Psychol (2012)

The MSD for the R-rate, the MSD for the A-rate, and the MSD for the combined R-rate and A-rate for different values of pInertia parameter in IBL-Inertia model (the corresponding MSDs for the IBL model are also plotted as dotted lines for comparison). The IBL-Inertia model used the calibrated parameters for d and s parameters (i.e., d = 6.41 and s = 1.40).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368322&req=5

Figure 3: The MSD for the R-rate, the MSD for the A-rate, and the MSD for the combined R-rate and A-rate for different values of pInertia parameter in IBL-Inertia model (the corresponding MSDs for the IBL model are also plotted as dotted lines for comparison). The IBL-Inertia model used the calibrated parameters for d and s parameters (i.e., d = 6.41 and s = 1.40).
Mentions: Figure 3 shows the MSDs for the IBL-Inertia model calibrated on the combined R-rate and A-rate as a function of pInertia values in the estimation set. It also shows the three corresponding MSDs from the original IBL model (shown as dotted lines in Figure 3) for comparison purposes (these MSDs are also reported in Table 1). The MSDs for the R-rate, the A-rate, and the sum of the MSDs for the R-rate and A-rate in the IBL-Inertia model are below the corresponding MSDs in the IBL model for all values of pInertia greater than 0.05 and less than 0.90. Thus, including inertia in the IBL model and calibrating all model parameters improves the model’s ability to account for the average R-rate and A-rate compared with the IBL model without inertia. Also, the advantages of including pInertia parameter seem to be present over a large range of this parameter’s variation.

Bottom Line: One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE).This paper demonstrates the predictive benefits of incorporating one particular implementation of inertia in an existing IBL model.The generalization process demonstrates both the advantages and disadvantages of including inertia in an IBL model.

View Article: PubMed Central - PubMed

Affiliation: School of Computing and Electrical Engineering and School of Humanities and Social Sciences, Indian Institute of Technology Mandi, India.

ABSTRACT
One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE). A number of computational models based upon the Instance-Based Learning Theory, a theory of DFE, have included different inertia implementations and have shown to simultaneously account for both risk-taking and alternations between alternatives. The role that inertia plays in these models, however, is unclear as the same model without inertia is also able to account for observed risk-taking quite well. This paper demonstrates the predictive benefits of incorporating one particular implementation of inertia in an existing IBL model. We use two large datasets, estimation and competition, from the Technion Prediction Tournament involving a repeated binary-choice task to show that incorporating an inertia mechanism in an IBL model enables it to account for the observed average risk-taking and alternations. Including inertia, however, does not help the model to account for the trends in risk-taking and alternations over trials compared to the IBL model without the inertia mechanism. We generalize the two IBL models, with and without inertia, to the competition set by using the parameters determined in the estimation set. The generalization process demonstrates both the advantages and disadvantages of including inertia in an IBL model.

No MeSH data available.


Related in: MedlinePlus