Social indicator quantification method and device

Abstract

To evaluate a plan in a further appropriate manner and to support consensus building regarding the plan including the determination of the plan.SOLUTION: A social impact indicator quantification apparatus 103 quantitatively calculates social impact indicators for a plan. The social impact indicator quantification apparatus 103 includes: a logic model construction unit 1032 for constructing a quantifiable logic model 1042 that indicates a causal relation regarding the plan in order to quantify the social impact indicators, and that includes a plurality of layers represented by an edge connecting nodes; a simulation unit 1033 for calculating a quantitative value of each of the plurality of social impact indicators based on the quantifiable logic model 1042; and an evaluation unit 1034 for using the quantitative values to perform relative evaluation of the plan.SELECTED DRAWING: Figure 14

Description

[Technical Field]

[0001] The present invention relates to a technique for quantitatively evaluating social impact indicators (social indicators). [Background technology]

[0002] In recent years, the Sustainable Development Goals (SDGs) have been formulated to address global social issues, and various organizations, including companies and local governments, are placing importance on efforts to achieve these goals in their business operations, management, and activities. The SDGs call for achieving sustainable development in a balanced and integrated manner across the three dimensions of society, the environment, and the economy. For this reason, the activities of organizations, including companies, must consider the impact and effect on these three dimensions. To do this, it is necessary to predict the impact of business operations and other activities along the value axes of society, the environment, and the economy, and to carry out activities to improve value.

[0003] To achieve these activities, environmental and economic indicators—such as carbon dioxide emissions, which belong to environmental value, and corporate profits, which belong to economic value—can be objectively quantified through carbon dioxide measurements and corporate activity reports, and these are already being implemented. Meanwhile, social value includes objective indicators such as inequality and fairness, but also subjective aspects such as the continuity of local culture through regional development, the quality of life of local residents, and their sense of happiness. Furthermore, subjective indicators must take into account changes over time. For example, even if a facility's construction reduces subjective attachment to a landscape, it is likely to ease as residents become accustomed to it. Against this backdrop, social impact indicators are increasingly being used to evaluate the social impact of policies and investments as social value in local government policy decisions and financial institutions' investment and lending decisions. This requires comparing the differences in social impact indicators between multiple policy candidates and predicting their changes over time. Logic models are often used to evaluate social impact indicators.

[0004] As a technology related to the above background, Patent Document 1 describes a method using a logic model as a technology for visualizing and evaluating social impact indicators. The communication support system described in Patent Document 1 acquires basic communication information through discussion support using templates to support logical discussion development in communication using the Internet, etc., and supports the construction of a logic model from the basic information. Furthermore, Patent Document 2 describes technology related to a network model related to a logic model used to evaluate social impact indicators. The advertising campaign planning support device described in Patent Document 2 represents a consumer psychology model using a Bayesian network model in which multiple contact point nodes each having a conditional probability table, multiple nodes each having a conditional probability table, etc. are related by links indicating causal relationships. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-56377 [Patent Document 2] Japanese Patent Application Laid-Open No. 2009-252126 Summary of the Invention [Problem to be solved by the invention]

[0006] Systems such as those in Patent Document 1 are effective means for creating logic models. However, the evaluation of social impact indicators in the system in Patent Document 1 is qualitative, making it difficult to make the necessary comparisons between the many candidate policies.

[0007] Furthermore, Patent Document 2 shows a consumer psychology model, which is expressed as a Bayesian network model in which multiple contact point nodes with conditional probability tables, multiple nodes with conditional probability tables, etc. are related by links indicating causal relationships. However, in Patent Document 2, the information added to the edges of the graph model, which quantitatively evaluates social impact indicators and enables comparisons between multiple policy candidates and changes in subjective indicators over time, is conditional probability, making it difficult to evaluate the impact of regional values ​​and changes in values ​​over time. Therefore, the objective of the present invention is to more appropriately evaluate policies, investments, etc. (simply referred to as policies) and support consensus building, including the formulation and decision-making of policies. [Means for solving the problem]

[0008] To solve the above problem, we quantitatively estimate the effect on social impact indicators when policy parameters are changed.

[0009] More specifically, a social indicator quantification device having a processor that executes a program and a storage device that stores the program is used to quantify the social indicators in a policy. Multiple A social indicator quantification method for quantitatively calculating social impact indicators, the method including: a logic model construction process for constructing a logic model showing causal relationships related to policies, the logic model having multiple hierarchies expressed by edges connecting nodes, in order to quantify the social impact indicators; a simulation process for calculating quantitative values ​​of each of the multiple social impact indicators based on the logic model; and an evaluation process for performing a relative evaluation of the policies using the quantitative values. The plurality of layers are an input layer, an activity layer, an output layer, an outcome layer, and an impact layer, and the logic model construction process includes a node selection process for selecting the nodes by selecting social impact indicators in the plurality of layers from the plurality of social impact indicators. This is a method for quantifying social indicators.

[0010] The present invention also includes a social index quantification device that executes the social index quantification method, a program that causes the device to function as a computer, and a storage medium that stores the program. [Effects of the Invention]

[0011] According to the present invention, it is possible to more appropriately evaluate measures and support consensus building regarding measures, including decision-making on measures. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a diagram illustrating an example of a system configuration of a social impact index quantification system according to a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the hardware configuration of each device that configures the social impact index quantification system according to the first embodiment. [Figure 3] 1 is a flowchart illustrating a social impact index quantification process according to the first embodiment. [Figure 4] FIG. 10 is a diagram illustrating nodes selected in the node selection process S303 according to the first embodiment. [Figure 5] FIG. 10 is a diagram illustrating a logic model in which edges are connected according to the first embodiment. [Figure 6] FIG. 10 is a diagram illustrating a logic model in which weights are added to each edge according to the first embodiment. [Figure 7] FIG. 10 is a diagram illustrating a state in which a part of a logic model in which weights are added to each edge according to the first embodiment is extracted. [Figure 8] FIG. 4 is a diagram illustrating an example of parameter settings of a driving node according to the first embodiment. [Figure 9] FIG. 10 is a diagram for explaining the quantification process according to the first embodiment. [Figure 10] FIG. 10 is a diagram illustrating parameters of a driving node according to the first embodiment. [Figure 11] FIG. 10 is a graph showing the number of occurrences of action requests according to the first embodiment. [Figure 12] FIG. 10 is a graph showing the number of occurrences of action requests according to the first embodiment. [Figure 13] FIG. 10 is a diagram showing a simulation result in the simulation process S306 according to the first embodiment. [Figure 14] 1 is a functional block diagram of a social impact index quantification device according to a first embodiment. [Figure 15] FIG. 10 is a diagram showing participant data according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] An embodiment of the present invention will be described below. This embodiment supports decision-making regarding policies, including consensus building among various stakeholders. To this end, a quantitative value of a social impact index (social index) for each of multiple policies is calculated based on an effective graph showing a logic model that indicates the causal relationships between multiple policies. This allows for a relative evaluation of multiple policies. Examples showing specific examples of this embodiment will be described below. [Example]

[0014] In Example 1, the formulation of a policy regarding energy consumption in a certain local government will be described as an example. For this reason, in this embodiment, the "power saving acceptance" when natural energy is introduced in a region is assumed as the social impact index. In this example, a social impact index quantification system 100, which is a computer system, is used to support the formulation of a policy to improve environmental awareness in a target region. Specifically, the social impact index quantification system 100 is used to predict how much the introduction of natural energy power generation facilities and the implementation of requests to residents to save electricity will change the acceptance of power saving among local residents, and to provide, for example, the policy with the highest improvement prediction.

[0015] 1 is a diagram illustrating an example of the system configuration of a social impact index quantification system 100 (social index quantification system) according to Example 1. The social impact index quantification system 100 includes a first information terminal 101, a second information terminal 102, a third information terminal 106, a database 104, and a social impact index quantification device 103 (social index quantification device). These are connected to each other so as to be able to communicate with each other via a network 105 such as the Internet, a LAN (Local Area Network), or a WAN (Wide Area Network).

[0016] The first information terminal 101 is a computer used by participants 1 to n (n is an integer equal to or greater than 1; hereinafter collectively referred to as participants), such as residents. These acquire information on the structure of the logic model envisioned by the participants and the indicators they consider important, and transmit this to the social impact indicator quantification device 103. For example, this can be realized by a communication-enabled information processing device such as a personal computer or a smart device.

[0017] The second information terminal 102 is an information terminal used by a decision maker such as a local government, and is a computer for displaying the logic model and simulation results from the social impact index quantification device 103. The second information terminal 102 can also be realized by a communication-enabled information processing device such as a personal computer or a smart device.

[0018] The third information terminal 106 is an information terminal used by a supporter (for example, a facilitator) who supports the formulation of measures. The third information terminal 106 is a computer for displaying the logic model and simulation results from the social impact index quantification device 103. The third information terminal 106 can also be realized by a communication-enabled information processing device such as a personal computer or a smart device.

[0019] The social impact index quantification device 103 receives the structures of the logic models of the participants and the indicators that they consider important from the first information terminal 101. Then, the social impact index quantification device 103 executes information processing related to the process of constructing a quantifiable logic model from the acquired logic model, the execution of a simulation of the quantifiable logic model, and the quantification of the social impact index.

[0020] Here, the data summarizing the results received from the first information terminal 101 will be described. FIG. 15 is a diagram showing participant data 1041 according to this embodiment. As shown in FIG. 15, the participant data 1041 indicates the number of important inputs, which indicates the number of inputs by participants for each indicator that they have entered as important. For this purpose, the logic model construction unit 1032 counts the number of inputs and records them. Alternatively, the input data by each participant may be used as the participant data 1041. Furthermore, the participant data 1041 includes information regarding measures including indicators for each demographic. This information regarding measures may be managed as separate information.

[0021] Furthermore, the social impact index quantification device 103 transmits the quantifiable logic model and the execution results of the information processing (for example, the quantitative value of the social impact index) to each device such as the second information terminal 102. The database 104 stores data necessary for the social impact index quantification device 103. Note that the database 104 may be realized as the storage device 202 of the social impact index quantification device 103.

[0022] Next, we will explain the hardware configuration of each device that makes up the social impact index quantification system 100. Figure 2 shows an example of the hardware configuration of each device that makes up the social impact index quantification system 100 according to this embodiment. Here, each device, namely, a first information terminal 101, a second information terminal 102, a third information terminal 106, and a social impact index quantification device 103 (hereinafter referred to as a computer 200), has a processor 201, a storage device 202, an input device 203, an output device 204, and a communication interface (communication IF) 205.

[0023] These are connected to each other via a bus 206. The processor 201 controls the computer 200 and executes calculations according to programs. The storage device 202 functions as a memory that serves as a work area for the processor 201. The storage device 202 is also a non-transitory or temporary recording medium that stores various programs and data. Examples of the storage device 202 include a ROM (Read Only Memory), a RAM (Random Access Memory), an HDD (Hard Disk Drive), and a flash memory.

[0024] Here, the processor 201 is executed in accordance with a logic model construction program, a simulation program, and an evaluation program stored in the storage device 202. These programs may be configured as separate programs, or may be configured as a program including at least two of them.

[0025] The input device 203 accepts input of data and the like from a user. Examples of the input device 203 include a keyboard, a mouse, a touch panel, a numeric keypad, a scanner, and a microphone. The output device 204 outputs data such as the results of calculations by the processor 201. Examples of the output device 204 include a display, a printer, and a speaker. Note that since the social impact index quantification apparatus 103 can be realized by a server, the input device 203 and the output device 204 can be omitted. Furthermore, the input device 203 and the output device 204 may be configured as an integrated device, such as a touch panel. The communication IF 205 connects to the network 105 and transmits and receives data to and from other devices.

[0026] Here, a description will be given of functional blocks for realizing the functions of the above-mentioned social impact index quantification device 103. Fig. 14 is a functional block diagram of the social impact index quantification device 103 according to this embodiment. As shown in Fig. 14, the social impact index quantification device 103 is connected to a network 105, and has a communication unit 1031, a logic model construction unit 1032, a simulation unit 1033, and an evaluation unit 1034. In the example shown in Fig. 14, the database 104 is provided in the social impact index quantification device 103.

[0027] First, a communication unit 1031 corresponds to the communication IF 205 in FIG. 2, and connects to the network 105 to transmit and receive data to and from other devices.

[0028] First, the logic model construction unit 1032 constructs the above-mentioned logic model. Then, the simulation unit 1033 executes a simulation of the constructed logic model and quantifies the social impact index indicated by the simulation. Then, the evaluation unit 1034 evaluates the policy using the quantified social impact index (quantitative value).

[0029] 2. That is, the logic model construction unit 1032 executes calculations in the processor 201 in accordance with a logic model construction program. The simulation unit 1033 executes calculations in the processor 201 in accordance with a simulation program. Furthermore, the evaluation unit 1034 executes calculations in the processor 201 in accordance with an evaluation program. Details of these processes will be described later.

[0030] 14 stores participant data 1041 and a quantifiable logic model 1042, which will be described later. The quantifiable logic model 1042 will also be simply referred to as a logic model.

[0031] Next, the social impact indicators and logic model used in this example will be explained in detail. While there is generally no clear definition of social impact indicators, in this example they are indicators that show the social impact created by activities and policies. Social impact indicators target issues such as equality, livelihoods, health, nutrition, poverty, safety, and justice. Specific examples of local social impact indicators also include subjective indicators such as local residents' attitudes toward work and awareness of the SDGs. Social impact indicators are also increasingly being used in the financial field, such as ESG investment and social impact bonds, to consider the impact on society and the environment in investment decisions.

[0032] For this reason, it is desirable to evaluate social impact indicators and use the results when formulating policies. There are several methods for evaluating social impact indicators, one of which is the logic model. This logic model is expressed as a network model consisting of nodes and edges, where the nodes represent various indicators and the edges represent the causal relationships between the indicators.

[0033] A logic model is a conceptual tool for clarifying the logic of a social program. Specifically, a logic model can clarify how invested resources will logically affect the targeted social impact indicators. A logic model is composed of five main layers (hierarchies). The logical flow is from the first layer to the fifth layer.

[0034] A description of each layer is provided below.

[0035] The first layer, the input layer, includes both resources and constraints for the policy. Resources are used when the policy is implemented, and include not only human, material, and financial resources, but also cultural resources. It is important to utilize these resources and generate effective social impact indicators within the constraints.

[0036] The second layer: the activity layer is the policy or the actions and activities for implementing the policy, including processes, work and actions.

[0037] The third layer, the Output Layer, is the direct result of the Activities Layer by the implementer. For the beneficiaries, this result includes business activities such as products and services provided, and ongoing support for the project.

[0038] The fourth layer, the outcome layer, is the ripple effect of the output layer above, and refers to the indicators necessary to improve the social impact indicators. "Results" often include subjective indicators such as increased motivation, satisfaction, and recognition as targets of ripple effects. "Results" can sometimes be divided into short-term and long-term effects.

[0039] The fifth layer, the impact layer (social impact layer), is the final goal shared by the implementer or all stakeholders, and is an indicator of improvement toward solving social problems. While broadly speaking, issues addressed in the SDGs, such as reducing disparities, may be set as more local goals depending on the organization. Also, output or outcome indicators may be used as substitutes for social impact indicators. In this case, intermediate indicators are considered as targets. This logic model can be expressed as a directed graph showing the causal relationships. Logic models and directed graphs will be discussed later using Figures 4 to 6.

[0040] Conventional logic models focus on logically clarifying ripple effects. The outcome layer, in particular, includes qualitative and subjective indicators of ripple effects. These indicators are necessary given the purpose of social impact indicators and are considered effective in assessing the validity of the logic. However, the qualitative and subjective indicators mentioned above present challenges that make comparisons between measures difficult. For example, when considering measures to improve (maximize) social impact indicators, comparisons between measures can be an important consideration. However, due to these challenges, it becomes unclear when comparing the degree of improvement in social impact indicators between measures.

[0041] Therefore, in this embodiment, the difference in social impact index between many policy candidates is quantitatively predicted (quantified) and support is provided for the consideration of policies that will maximize the social impact index. In this case, a logic model (directed graph) for the policy candidates is created, and its contents are explained below.

[0042] FIG. 3 is a flowchart of a social impact index quantification process (social index quantification method) according to the first embodiment. This social impact index quantification process is broadly divided into two processing blocks: a logic model construction process S301 for enabling quantification, and a simulation process S302. First, the quantification-oriented logic model construction process S301 includes a node selection process S303, an edge weight prediction process S304, and a driving node parameter setting process S305. The node selection process S303 selects nodes required for the logic model using data obtained through workshops or the like with IT support. Furthermore, the edge weight prediction process S304 predicts and adds weights to edges based on the selection for quantification. This completes the creation of a logic model. Then, the driving node parameter setting process S305 sets parameters for nodes (referred to as driving nodes) that enable the logic model created in S304 to be simulated, thereby creating a quantifiable logic model. The logic model construction process S301 is performed using the first information terminal 101 and data acquired from participants (inputs indicating that the indicators are important).

[0043] Furthermore, in the simulation process S302, the logic model constructed in S301 is quantified and evaluated for policy formulation. To this end, the simulation process S302 includes a simulation process S306 and an analysis process S307. The simulation process S306 executes a time evolution process starting from a driving node added to the logic model, thereby executing a quantification simulation of the social impact index.

[0044] Furthermore, analysis processing S307 analyzes the results of simulation processing S306. Details of each processing will be explained in order below. In the following explanation, a specific example will be taken of a request for power saving when natural energy is introduced in a simple region. The social impact index in this case will be "power saving acceptance" and the measure will be "power saving request." In the following, the social impact index will also be simply referred to as "index." In order to quantify the social impact index, the introduction of driving nodes and the addition of weights to edges will be added to the logic model. The processing of this embodiment will be explained with a focus on these, following the flowchart in Figure 3. The processing entity below will be configured as shown in the functional block diagram in Figure 14.

[0045] First, the node Selection In process S303, the logic model construction unit 1032 selects indicators for each of the five layers in the logic model. Here, in the logic model of this embodiment, the five layers are considered to be grouped into two layers that behave differently from the perspective of quantification. The grouping method is to group the input layer, activity layer, and output layer into a first layer (input / activity / output). Similarly, the outcome layer and impact layer are grouped into a second layer (outcome / social impact). In this process, each of the above layers, that is, the nodes Selection is executed.

[0046] Here, Fig. 4 is a diagram showing the nodes selected (picked up) in the node selection process S303. The layers shown in Fig. 4 are a first layer 401 and a second layer 402. Fig. 4 also shows indexes in the first layer 401 and the second layer 402. The indexes shown in the first layer 401 are as follows: Power generation facility size (solar) 403 Power generation facility size (nano hydro) 404 Power saving request level 405 Number of requests for power saving (weekdays, tight) 406 Number of requests for power saving (weekdays, non-pressure) 407 Number of requests for power saving (holidays, tight) 408 Number of requests for power saving (holidays, not tight) 409 Of course, other indicators can be added or deleted. Indicators shown as examples for the second layer 402 are environmental awareness 410, dissatisfaction with requests to save electricity (negative node) 411, and power saving acceptance 412. Indicators other than these can also be added or deleted for the second layer 402. Here, dissatisfaction with requests to save electricity 411 is noted as a negative node.

[0047] Here, when the index value becomes negative, it indicates an improvement, and the positive and negative direction of improvement is opposite to that of other indexes, such as environmental consciousness 410. For example, when these indexes are quantified, the index "environmental consciousness" 410 becomes positive as the value increases, as the consciousness of consideration increases. On the other hand, the index "dissatisfaction with requests to save electricity" 411 becomes negative as the value increases, as the dissatisfaction increases. This makes post-processing simulations complicated, so the polarity of negative nodes is reversed, indicating that dissatisfaction decreases as the value increases.

[0048] (For example, this can be considered equivalent to converting the indicator "satisfaction with power saving requests" into a positive node.) In addition, for indicators where it is difficult to determine the direction of improvement, it is necessary to decide in advance whether to label them as such.

[0049] Here, the first layer selects indicators (nodes) of activities related to power generation facilities and power saving requests. These power saving requests are assumed to request a certain amount of power reduction when there is a shortage of natural energy power. In addition, since the occurrence of power saving requests depends on the amount of natural energy generated, the scale of power generation facilities and the degree of power saving requests are used as indicators.

[0050] Power saving requests are also divided into several categories depending on the situation. In this example, they are divided by a combination of day of the week and whether the power is urgent or not. First, the day of the week is divided into Monday to Friday (weekdays) or Saturday and Sunday (holidays). Then, the outdoor temperature is divided into four indicators, depending on whether it indicates a critical situation (above 35°C or below 5°C) or a non-critical situation (other than the above conditions).

[0051] Furthermore, the selection of these indicators, i.e., nodes, can be realized by creating a database of related keywords in advance and selecting the necessary indicators from the database 104, or by selecting them in a workshop or the like. The logic model construction unit 1032 then selects each indicator in accordance with the operations on each terminal device and predetermined rules. These indicators become nodes in the logic model. The workshop includes the acquisition of data from various terminals using the social impact indicator quantification system 100.

[0052] In the edge weight prediction process S304, the logic model construction unit 1032 connects the nodes selected in S303 with edges and predicts the weight of each edge. Here, the connections at the edges are originally intended to logically express causal relationships, but in the case of quantification, a calculation formula may be inserted between the nodes.

[0053] An example of this is shown in Figure 5. Figure 5 is a diagram showing a logic model in which edges are connected according to this embodiment. In the example of Figure 5, process 503 for calculating power supply and demand is inserted in the input / activity / output layer. The reason for this insertion is that fluctuations in the amount of power generated by power generation facilities can affect the frequency of power saving requests. To calculate this effect in detail, the power supply and demand calculation can be realized by simulation. Alternatively, for a simple calculation, a method can be used in which the frequency of power saving requests is determined in advance (for example, two power saving requests occur in an average month) without taking power generation facilities into consideration.

[0054] Here, there are four edges 514 input to index 510, four edges 515 input to index 511, and two edges 516 input to index 512. In Fig. 5, edge 515 is an edge input to a negative node (index 511), and is therefore depicted with a dashed line to clearly indicate that it is different from a positive node.

[0055] Furthermore, a logic model in which the logic model construction unit 1032 has assigned (predicted) weights (numbers from -1 to 1) to each edge (514, 515, 516) in FIG. 5 is shown in FIG. 6. Furthermore, a diagram in which four edges 614 of index 610 are extracted from the logic model in FIG. 6 is shown in FIG. 7. For index 710, the weight assigned to the edge input from index 706 is 0.0 (figure number 717). Furthermore, the weight assigned to the edge input from index 707 is +0.66 (figure number 718), and the weight assigned to the edge input from index 708 is 0.0 (figure number 719). Furthermore, the weight assigned to the edge input from index 709 is +0.34 (figure number 720).

[0056] In this embodiment, the logic model constructor 1032 adds (predicts) weights in accordance with the following rules. 1. The absolute value of the sum of the weights of the edges input to one index node is "1". 2. Separate nodes into which edges with negative weights are input from nodes into which edges with positive weights are input. Note that it is desirable that the logic model construction unit 1032 outputs the logic models shown in Figures 4 to 6 to each terminal device using the communication unit 1031. As a result, each terminal device can display the logic models shown in Figures 4 to 6 on the output device 204.

[0057] Furthermore, the logic model construction unit 1032 determines and adds weight values ​​to edges based on the values ​​(preference order) of the policy decision maker or interested parties. This example will be explained using FIG.

[0058] In Figure 7, indicator 710 "Environmental Consciousness" is connected to four nodes, starting from the top: indicator 706 "Power Saving Request - Weekday - Pressure," indicator 707 "Power Saving Request - Weekday - Not Pressured," indicator 708 "Power Saving Request - Weekend - Pressured," and indicator 709 "Power Saving Request - Weekend - Not Pressured." This shows the logic behind how the four power saving requests change environmental consciousness. The weights indicate the degree of influence. While the degree of influence can be derived objectively (statistical correlation values), here we will assume that it is given in subjective preference order. A weight of "0.0" indicates no influence or is not considered, and an absolute large weight is assigned to the order of greatest influence. Negative weights are assigned in the case of a negative influence.

[0059] For example, in the example of FIG. 7, the logic model construction unit 1032 determines that the influence of the indicator 707 "power saving request, weekday, not under pressure" (weight +0.66, diagram number 718) is greatest. The logic model construction unit 1032 also determines that the next most important indicator is the indicator 706 "power saving request, weekend, not under pressure" (weight +0.34, diagram number 720). The logic model construction unit 1032 then determines that the rest are irrelevant (assuming that a power saving request during times of pressure will not be perceived positively). If these are expressed in order of preference, the degree of the power saving request to "environmental awareness" is "power saving request, weekday, not under pressure" > "power saving request, weekend, not under pressure."

[0060] As mentioned above, the sum of the weights is set to "1," so the logic model construction unit 1032 can estimate (predict) the weights from the order of preference using linear allocation or the like. In the example of FIG. 7, it is 0.0 + 0.66 + 0.0 + 0.34 = 1. The logic model construction unit 1032 can also add weights to other nodes in the same way. In other words, if an edge input to a negative node has a negative weight, the sum is set to "-1." Also, as per rule 2 above, negative nodes and positive nodes are considered separately, and if an edge with a positive weight is input to a certain indicator node, an edge with a negative weight is not input.

[0061] Next, the parameter setting S305 of the driving node will be described. Here, the driving node is a node required for a quantifiable logic model, and is a definition not found in conventional logic models. The logic model construction unit 1032 selects a node from the input / activity / output layer as the driving node to be considered as a candidate measure. In this embodiment, it is desirable to select the indicators "power generation facility" 403, 404 and the indicator "power saving request level" 405 shown in FIG. 4 as candidate measures when they are changed. For this reason, the logic model construction unit 1032 specifies the indicators "power generation facility size (solar)" 403, "power generation facility size (nano-hydro)" 404 and "power saving request level" 405 as driving nodes. Alternatively, when calculations are not performed within the input / activity / output layer, the logic model construction unit 1032 sets the following indicators as driving nodes. Indicator "Power saving request, weekday, tight" 406 Indicator "Power saving request, weekday, non-pressure" 407 Indicator "Power saving request, weekend, tight" 408 Indicator "Power saving request, weekend, non-pressure" 409 Furthermore, the logic model construction unit 1032 can also exclude the indicator “power generation facility scale (photovoltaic)” 403 , the indicator “power generation facility scale (nano-hydro)” 404 , and the indicator “power saving request level” 405 .

[0062] Here, it is necessary to set a time series parameter for the driving node in advance. The time series parameter is, for example, a value for each month from January to December. For the indicator "power generation facility size (solar power)" 403, the time series parameter can be the system capacity (kW) of solar power generation. Generally, the system capacity of a power generation facility does not change over time, so the same number (10 kW, for example) is given as the time series parameter from January to December.

[0063] On the other hand, when the indicator "power saving request, weekday, tight" 406 is set as the driving node, the number of power saving requests in January, the number of power saving requests in February, etc. are set as time series parameters. Fig. 8 shows an example of parameter setting for the driving node according to this embodiment. In this example, the parameter setting is assumed to be in monthly units for one year.

[0064] Also, the upper diagram 801 in FIG. 8 is an example of a solar power generation system capacity, and the lower diagram 802 in FIG. 8 is an example of the number of times a power saving request has occurred. Furthermore, since the output of the driving node may have different units, it needs to be normalized to a numerical value such as 0 to 1 as necessary. Here, any function can be used for normalization, but basically it is sufficient as long as it reflects the degree of influence on the logic in the subsequent stage. For example, one method is to normalize using the maximum value among driving nodes with the same unit. That is, in parameter setting S305 of the driving node, the logic model construction unit 1032 sets (specifies) the time series parameters that it determines as described above.

[0065] Through the logic model construction process S301 consisting of the above steps S303 to S305, the logic model construction unit 1032 constructs a quantifiable logic model 1042. The logic model construction unit 1032 stores the constructed quantifiable logic model 1042 in the database 104.

[0066] Next, the simulation process S302 will be described. First, in simulation processing S306, the simulation unit 1033 executes a time-evolution simulation for the quantifiable logic model. This simulation is executed in a minimum time step equivalent to one hour, which can be changed to a unit of one month, etc., as necessary. Furthermore, for each hourly step, the simulation unit 1033 transfers values ​​to the connected nodes according to the parameters set in the driving nodes. Then, the simulation unit 1033 executes predetermined processing in the connected nodes according to the transferred values. As a result, quantification processing for the nodes is executed.

[0067] Here, the quantification process by the simulation unit 1033 will be described using FIG. 9, taking the node "environmental awareness" shown in FIG. 7 as an example. This node shown in FIG. 9 has four inputs, so n=4 in the calculation formula 920 shown as a function in FIG. 9. The simulation unit 1033 calculates the index value (S t , S t indicates the index value at a certain time step t.) is determined. Here, the initial value S0 is set to 0.

[0068] However, if the initial value is known in advance from a questionnaire or the like, a value other than 0 can be specified as the initial value. The simulation unit 1033 executes the processing of this calculation formula for the nodes of the outcome / social impact layer at a certain time step, and calculates the index value of each node. Furthermore, the simulation unit 1033 advances the time step in a time-evolving manner and calculates the index value of each node (S t+1 ) is updated. Then, the simulation unit 1033 repeatedly processes the time step a predetermined number of times, thereby obtaining the index value of each node after the predetermined number of times. In this example, the time step is set to one month, and the period is one year, so the predetermined number is 12 times (12 months).

[0069] Furthermore, the simulation unit 1033 can compare the difference in index values ​​between different measures by simulating the same logic model while changing the parameters of the driving nodes. In this way, the simulation unit 1033 can also search for a measure that maximizes the index value. Since the above index values ​​include the social impact index (power saving acceptance in this example), which is the target node for quantification, this simulation makes it possible to quantify the social impact index. Furthermore, the social impact index quantified in this way can be analyzed by the evaluation unit 1034 between different measures. This will be explained later in S307.

[0070] Here, a quantification example will be described, showing specific values ​​for this example. In this example, the data required to set the parameters of the driving node are the power generation potential of natural energy and the amount of power consumed by residents. It is also assumed that a power saving request will be made at a certain rate when natural energy is insufficient compared to demand, and that if there is still a shortage after the power saving request, power will be purchased from the electricity market, etc. In this example, the system capacity of the photovoltaic power generation facility, the effective head of the small hydroelectric power generation facility, and the degree of power saving request are set as parameters of the driving node.

[0071] The simulation unit 1033 estimates the amount of power generated from the scale of each power generation facility, and also performs supply and demand calculations in advance from the amount of power consumed by residents, to calculate time-series parameters for the occurrence of power saving requests. This calculation is performed in advance in this embodiment, but it is desirable to perform it during the simulation process S302.

[0072] Here, the parameters of the driving node are shown in Figure 10. As shown in Figure 10, there are 36 possible combinations of measures. For the sake of explanation, a measure number is assigned to each measure in Figure 10. Measure number 1 is (0kW, 0m, 30%), and measure number 2 is (10kW, 0m, 30%). In other words, the numbers are assigned in the order of solar power, small hydropower, and power saving request level. Also, measure number 36 is (20kW, 2m, 0%).

[0073] Graphs showing the pre-calculated number of times that action requests occurred for each action number are shown in Figures 11 and 12. There are four indicators for action requests, as shown in Figure 4. These indicators are the number of power-saving requests (weekday, tight) 406, the number of power-saving requests (weekday, not tight) 407, the number of power-saving requests (holiday, tight) 408, and the number of power-saving requests (holiday, not tight) 409. Figure 11 shows the number of power-saving requests (weekday, tight) 406 and the number of power-saving requests (weekday, not tight) 407 for each of the 36 actions per year. Figure 12 also shows the number of power-saving requests (holiday, tight) 408 and the number of power-saving requests (holiday, not tight) 409.

[0074] It is desirable that the simulation unit 1033 outputs the graphs shown in Figures 11 and 12 to each terminal device using the communication unit 1031. As a result, each terminal device can display the graphs shown in Figures 11 and 12 on the output device 204. In addition, in this processing, the simulation unit 1033 uses the weights shown in Figure 6 as the weights of each edge of the quantifiable logic model.

[0075] Next, the simulation results of the simulation process S306 are shown in Fig. 13. Fig. 13 shows the values ​​for each measure of the nodes of the quantifiable logic model, "environmental awareness," "dissatisfaction with power saving requests (negative node)," and the social impact index, "power saving acceptance." Since "dissatisfaction with power saving requests" is a negative node, it is shown as a positive value in Fig. 13, but unlike other indexes, a positive value indicates a worsening trend. Therefore, it is considered to be essentially a negative value.

[0076] Furthermore, looking at the node "Power Saving Acceptance" for policy numbers 1 to 9, we can see that this index is decreasing. On the other hand, the scale of solar and small hydroelectric power generation facilities is increasing for policy numbers 1 to 9. In other words, this logic model shows that as the scale of power generation increases, the power saving acceptance decreases. Intuitively, one would think that the introduction of renewable energy would increase the power saving acceptance, but the opposite effect was found.

[0077] In the analysis process S307, the evaluation unit 1034 analyzes the simulation results based on the above. As a result, the evaluation unit 1034 identifies that the cause is the "environmental awareness" and "dissatisfaction with the power saving request" nodes in the previous stage. This logic model is based on the subjective assumption that "environmental awareness" increases when a power saving request is made. However, conversely, this suggests that if there is an abundance of power generation facilities, there will be no power shortage and environmental awareness will weaken.

[0078] On the other hand, even a slight lack of power can cause a certain level of dissatisfaction. Therefore, even if the power generation scale is increased and the number of power saving requests is reduced as a result of the analysis process S307, the social impact index will actually be lowered unless it is reduced to zero. Therefore, in this embodiment, when the power saving request level is not 0, the measures that maximize the social impact index are measure numbers 19 and 20, and their respective index values ​​are -19.19. Through the analysis process S307, the evaluation unit 1034 searches for such maximum values. Here, these maximum values ​​are relative to each index. Therefore, the evaluation unit 1034's search for the maximum value means performing a relative evaluation to build consensus on each measure. Furthermore, the evaluation unit 1034 can identify the measure that maximizes the social impact index as a measure that improves the social impact index.

[0079] Then, the evaluation unit 1034 uses the communication unit 1031 to output the measures that will improve the social impact index to the second information terminal 102. As a result, the second information terminal 102 can display the measures on the output device 204. Therefore, it is possible to make suggestions about improvement measures to the decision maker. The evaluation unit 1034 may also output the measures that will improve the social impact index to the first information terminal 101 or the third information terminal 106. As a result, the first information terminal 101 or the third information terminal 106 can display the measures on the output device 204. Therefore, it is also possible to present the above-mentioned suggestions to participants and supporters. This concludes the explanation of the first embodiment. [Example]

[0080] In Example 2, a policy is an example of evaluating businesses and companies when making ESG investments. Note that in this example, processing is executed with the same configuration as in Example 1. In Example 2, the driving node may be an index related to the business or company that is the investment target. For example, this may be the number of employees in a region, or the percentage of the investment that is returned to the region (regional distribution rate).

[0081] The outcome / social impact layer uses indicators related to the SDGs or indicators that the investing company considers important. For example, subjective indicators such as CO2 emissions (or reductions) or employee job satisfaction can also be used. The connection relationships and weights of the logic model's edges can be constructed according to the investing company's standards. The resulting quantifiable logic model is then used to run simulations by varying the parameters of the driving nodes that affect the investment's impact, and the social impact indicators are quantified. This allows the system to propose to investment decision makers the investment business or corporate structure (such as the investment distribution rate) that will maximize the social impact indicator.

[0082] This concludes the description of each embodiment of the present invention. According to each embodiment, it is possible to quantify the social impact index numerically. Furthermore, by taking into account the change in subjective index over time and comparing the numerical values ​​of the social impact index for each of a large number of candidate policies, it is possible to achieve a relative evaluation between policies. This makes it possible to support consensus building regarding policies. [Explanation of symbols]

[0083] 100...social impact index quantification system, 101...first information terminal, 102...second information terminal, 103...social impact index quantification device, 104...database, 105...network, 106...third information terminal, 1031...communication unit, 1032...logic model construction unit, 1033...simulation unit, 1034...evaluation unit, 1041...participant data, 1042...quantifiable logic model, 200...computer, 201...processor, 202...storage device, 203...input device, 204...output device, 205...communication IF

Claims

1. A social indicator quantification method for quantitatively calculating a plurality of social impact indicators of a policy using a social indicator quantification device having a processor that executes a program and a storage device that stores the program, comprising: The processor: a logic model construction process for constructing a logic model showing causal relationships related to policies in order to quantify the social impact indicators, the logic model having multiple layers expressed by edges connecting nodes; a simulation process for calculating quantitative values ​​of each of the plurality of social impact indicators based on the logic model; Execute an evaluation process to perform a relative evaluation of the measures using the quantitative values; the plurality of layers are an input layer, an activity layer, an output layer, an outcome layer, and an impact layer; The logic model construction process includes a node selection process for selecting the nodes by selecting social impact indicators at the plurality of hierarchical levels from the plurality of social impact indicators.

2. The social indicator quantification method according to claim 1, In the logic model construction process, the logic model having the edge weights is created; A social indicator quantification method for calculating the quantitative value by the simulation process using the edge weights.

3. The social indicator quantification method according to claim 2, the social indicator quantification device is connected to a terminal device used by a participant in the policy; A social indicator quantification method for predicting the weight of the edge in accordance with the input from the participant transmitted from the terminal device in the logic model construction process.

4. The social indicator quantification method according to claim 1, A social indicator quantification method, wherein the logic model is a directed graph in which the edges indicate the causal relationship between two social impact indicators among a plurality of social impact indicators.

5. A social indicator quantification device that quantitatively calculates multiple social impact indicators of a policy, a logic model construction unit that constructs a logic model showing causal relationships related to policies in order to quantify the social impact indicators, the logic model having multiple layers expressed by edges connecting nodes; a simulation unit that calculates quantitative values ​​of each of the plurality of social impact indicators based on the logic model; an evaluation unit that performs a relative evaluation of the measures using the quantitative values; the plurality of layers are an input layer, an activity layer, an output layer, an outcome layer, and an impact layer; The logic model construction unit selects the nodes by selecting social impact indicators at the plurality of hierarchical levels from the plurality of social impact indicators.

6. 6. The social index quantification device according to claim 5, the logic model construction unit creates the logic model having the edge weights, The simulation unit is a social indicator quantification device that calculates the quantitative value using the weight of the edge.

7. 7. The social index quantification device according to claim 6, Further, a communication unit connected to a terminal device used by a participant in the policy is provided, The logic model construction unit is a social indicator quantification device that predicts the weight of the edge in accordance with input from the participant transmitted from the terminal device during processing.

8. 6. The social index quantification device according to claim 5, A social indicator quantification device, wherein the logic model is a directed graph in which the edges indicate the causal relationship between two social impact indicators among a plurality of social impact indicators.

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