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Method for judging willingness-to-pay

A technology of willingness to pay and influencing factors, applied in the field of judging willingness to pay and judging willingness to pay based on a Bayesian network model, can solve the problems of inability to repeat predictions, reduce prediction accuracy, and ignore interrelationships, so as to improve authenticity and scientific Sexuality, avoid subjectivity and poor inhibition, improve the effect of precision

Inactive Publication Date: 2017-12-19
CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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AI Technical Summary

Problems solved by technology

Existing regression models mostly use the assumption of local independence, only considering the significant relationship between variables and willingness to pay, ignoring the relationship between personal social attributes and understanding of survey content
Moreover, the model established by the regression model is only a single-layer, linear model structure, which can only be predicted in one direction in one direction and cannot be repeated.
The value of each influencing factor in the regression model must be clearly quantified and determined, and the requirements for information or data are high. If the value of each influencing factor cannot be clearly known, the accuracy of prediction will be greatly reduced

Method used

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  • Method for judging willingness-to-pay

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] Example 1 Obtaining influencing factors related to willingness to pay

[0048] (1) Collect the social characteristics and willingness to pay information of different groups of people through questionnaires. The questionnaire on the service value of the Beijing South-to-North Water Diversion Water Resources Reserve Action consists of three parts, which are the social characteristics of the respondents, the respondents' understanding of the survey background, and the respondents' willingness to pay. The first part is to collect relevant social background information of the respondents, including age, gender, education, income, occupation, housing type and other six questions; Survey to understand the situation; the third part is to obtain the respondent's willingness to pay, that is, the personal WTP value.

[0049] A total of 906 sample data were received, 776 were valid data, and the effective rate was 85.7%. The population of this survey covers different genders, age...

Embodiment 2

[0064] Example 2 Constructing a Bayesian Network Model of Willingness to Pay

[0065] (1) Classify the three influencing factors directly related to the willingness to pay as the first level of the Bayesian network structure; classify the factors that affect the first level as the second level, and establish a Bayesian network structure diagram, such as figure 1 shown. It can be seen from the figure that one variable can affect two sub-nodes at the same time. For example, the change of the age (AG) node can not only affect the monthly family income (FI) of the node, but also affect the public's approval of the node for the project to spend manpower, material and financial resources to reserve water resources. degree (EC), participation in public welfare activities (CW). Based on the Bayesian network, it can comprehensively display the influence of different factors on the willingness to pay and the relationship between them.

[0066] (2) The conditional probabilities of most...

Embodiment 3

[0072] Example 3 Sensitivity Analysis Affecting Willingness to Pay

[0073] Sensitivity analysis using Bayesian network can find out the key factors affecting willingness to pay. By changing the probability table of the selected node variable, the change of the probability table of the target node variable can be seen. The importance of the selected node variable SN to the target node variable TN is expressed by the importance index I:

[0074]

[0075] Among them, P(TN) is the prior probability of the target node variable TN, and P(TN|SN) is the conditional probability of the target node TN under the change of the selected node variable SN. The importance index I reflects the degree of influence of the selected node variables on the target node, and the important factors affecting the willingness to pay can be obtained by comparing the size of I.

[0076] This paper selects the variable state with the largest importance index I among the variables of each node, and the t...

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Abstract

The invention discloses a method for judging willingness to pay, comprising: acquiring influencing factors related to willingness to pay, constructing a Bayesian network model of willingness to pay according to the influencing factors, and judging willingness to pay according to the Bayesian network model . The Bayesian network model of the present invention includes a three-layer structure. The method of the invention takes into account the interrelationships between various influencing factors, constructs a Bayesian network model with a multi-layer structure, and links each influencing factor, so that the prediction result has high accuracy and is more in line with the actual situation. In addition, it can determine the key factors and influence methods that affect the willingness to pay, and provide a basis for the subsequent formulation of work plans and decision-making.

Description

technical field [0001] The invention relates to a method for judging willingness to pay, in particular to a method for judging willingness to pay based on a Bayesian network model, and belongs to the field of willingness to pay judgment. Background technique [0002] At present, there are many methods for research on willingness to pay. In previous related research, multiple linear regression models, Probit, Tobit and linear regression (OLS) models are usually used to conduct regression analysis on willingness to pay. Existing regression models mostly adopt the assumption of local independence, only considering the significant relationship between variables and willingness to pay, ignoring the relationship between personal social attributes and understanding of survey content. Moreover, the model established by the regression model is only a single-layer, linear model structure, which can only be used for one-time prediction in one direction and cannot be repeated. The valu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q30/02
CPCG06Q10/04G06Q30/0201G06Q30/0202
Inventor 彭卓越殷峻暹张丽丽梁云雷冠军
Owner CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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