Causal processing device and causal processing method

Abstract

To provide each stakeholder with information suitable for building consensus among multiple stakeholders.SOLUTION: In a causal node diagram that is a data structure that connects a node of a causal item to a node of a consequential item with links, a causal processing device 100 has a node selection part 12 that accepts the selection of a given node that constitutes the causal node diagram, and a node causal part 13 that extracts, as causal nodes, the node that causes the given node and the node that results from the given node among the nodes that belong to a stakeholder other than the given stakeholder to which the given node belongs, by tracing the links to the given node, and a drawing part 14 that visualizes a given node and a causal node in the causal node diagram in association with detailed data associated with the node items.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a causal processing device and a causal processing method. [Background technology]

[0002] System dynamics has been proposed as a way to understand complex social structures by modeling them. One method of system dynamics uses a causal loop diagram (CLD), which models things in terms of cause and effect relationships. CLDs are effective in supporting consensus building because they are a data structure that makes it easy to understand the ripple effects by tracing the cause-and-effect relationships and plotting a path. However, CLDs often have a complex form with many nodes, which are conceptual elements, making them difficult to interpret.

[0003] Patent Document 1 describes a technology for extracting important nodes by analyzing loops and patterns from a CLD. The method in Patent Document 1 automatically simplifies the CLD so that important nodes are emphasized, thereby processing large and complex CLDs to make them easier for humans to understand. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-146734 Summary of the Invention [Problem to be solved by the invention]

[0005] In urban development workshops (meetings), it is often difficult to reach a consensus among multiple stakeholders, such as residents and government agencies. For example, a government agency may try to introduce measures to increase tourism in order to revitalize the area. However, the increase in tourists can cause disadvantages to residents living near tourist destinations, such as noise pollution. In this way, measures can sometimes involve trade-offs. In order to reach a consensus among multiple stakeholders, the CLD is shared within the workshop and discussions are carried out based on that CLD. For example, the workshop facilitator prepares a CLD node for each Key Performance Indicator (KPI), which is a quantitative indicator for achieving goals.

[0006] In order to reach consensus among stakeholders at an early stage, it is desirable to provide fair information that broadly describes the impact on each participating stakeholder, rather than providing (biased) information that is advantageous to only certain stakeholders in the prepared public documents such as the CLD. Biased information will create distrust among stakeholders and prolong consensus-building. However, conventional technologies such as Patent Document 1 do not simplify CLD from the viewpoint of making it easier to understand the relationship between oneself and others. Therefore, a method is required to mechanically extract information suitable for forming a consensus.

[0007] Therefore, the main object of the present invention is to provide each stakeholder with information suitable for forming a consensus among multiple stakeholders. [Means for solving the problem]

[0008] In order to solve the above problem, Disclosure The causal processing device has the following features. Book DisclosureIn a causal node diagram, which is a data structure in which one or more nodes are grouped by a frame indicating each stakeholder and in which nodes of cause items are connected by links to nodes of result items, the Runo a node selection unit that accepts a node selection; The aforementioned Selected by the node selector By following the links to the nodes, Selected The node belongs to Rusu The above-mentioned nodes belonging to a stakeholder other than the stakeholder Selected A node that causes a node, Selected a node causality unit that extracts nodes that are the result of the nodes as causal nodes; of the causal node diagram Selected a visualization unit that visualizes the nodes and the causal nodes in association with detailed data related to the items of the nodes, The node causal unit What stakeholder does the selected node belong to? Another stakeholder belongs to Among the nodes, The aforementioned The result node and the node that are closest to the selected node are selected by following the arrow. The aforementioned The method is characterized by highlighting the cause node that is closest to the selected node by tracing the arrow origin. Other means will be described later. [Effects of the Invention]

[0009] According to the present invention, it is possible to provide each stakeholder with information suitable for forming a consensus among a plurality of stakeholders. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a configuration diagram of a causal processing device according to an embodiment of the present invention. [Figure 2] FIG. 2 is a hardware configuration diagram of a causal processing device according to the present embodiment. [Figure 3] FIG. 10 is a diagram showing a display screen of a CLD displayed by a drawing unit according to the present embodiment. [Figure 4]FIG. 4 is a diagram showing a display screen of a CLD simplified from the CLD of FIG. 3 according to this embodiment. [Figure 5] FIG. 10 is a diagram of a display screen for explaining the process of tracing a causal node from a selected node according to the present embodiment. [Figure 6] FIG. 6 is a diagram of a display screen in the case where a causal node is traced from a selected node different from that in FIG. 5 according to this embodiment. [Figure 7] FIG. 2 is a diagram showing a display screen of a map display according to the embodiment. [Figure 8] FIG. 8 is a diagram showing a display screen of a map display when KPI-related data different from that shown in FIG. 7 according to this embodiment is selected. [Figure 9] FIG. 10 is a diagram showing a display screen of a KGI graph and a KPI graph according to the present embodiment. [Figure 10] FIG. 10 is a diagram showing a display screen in which three types of displays according to this embodiment are associated with each other by arrows. [Figure 11] FIG. 4 is a diagram illustrating a configuration of a user management table according to the present embodiment. [Figure 12] FIG. 10 is a diagram showing the configuration of a group management table (optional) according to the present embodiment. [Figure 13] FIG. 4 is a diagram illustrating a configuration of a node management table according to the present embodiment. [Figure 14] FIG. 2 is a diagram illustrating a configuration of a link management table according to the present embodiment. [Figure 15] FIG. 10 is a diagram illustrating the configuration of a connection structure management table according to the present embodiment. [Figure 16] FIG. 10 is a diagram illustrating the configuration of a node display state management table according to the present embodiment. [Figure 17] FIG. 10 is a diagram illustrating the configuration of a KPI graph management table according to the present embodiment. [Figure 18] FIG. 10 is a diagram illustrating the configuration of a space information management table according to the present embodiment. [Figure 19] FIG. 10 is a diagram showing the configuration of a generated link information management table according to the present embodiment. [Figure 20] 10 is a flowchart showing a main process of the causal processing device according to the present embodiment. [Figure 21]10 is a flowchart showing details of the CLD simplification process according to the present embodiment. [Figure 22] 10 is a flowchart showing details of the display / non-display process of an intermediate node of a CLD according to the present embodiment. [Figure 23] 10 is a flowchart showing details of a KPI graph drawing process for a selected node according to the present embodiment. [Figure 24] 10 is a flowchart showing details of a map data list registration process for a selected node according to the embodiment; [Figure 25] 10 is a flowchart showing details of a process for tracing a causal node from a selected node according to the present embodiment. [Figure 26] 10 is a flowchart showing details of a KPI graph drawing process for causal nodes according to the present embodiment. [Figure 27] 10 is a flowchart showing details of a process for registering a causal node in a map data list according to the embodiment; [Figure 28] 10 is a flowchart showing details of a KGI graph drawing process according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.

[0012] FIG. 1 is a configuration diagram of a causal processing device 100. The causal processing device 100 includes a processing unit 10 and a storage unit 20. The processing unit 10 includes a simplification unit 11, a node selection unit 12, a node causality unit 13, and a drawing unit 14. The simplification unit 11 hides some elements of the causal node diagram. The simplification unit 11 selects the nodes to be displayed so as to leave only the nodes located on the boundary of the frame representing the stakeholder. When a link through which a hidden node passes is selected, the simplification unit 11 switches to display the hidden node related to the selected link.

[0013] The node selection unit 12 accepts the selection of a predetermined node (selected node) that constitutes a causal node diagram (CLD), which is a data structure in which one or more nodes are grouped by frames indicating stakeholders and in which nodes of causal items are connected by links to nodes of resultant items. The node causality unit 13 follows links to a predetermined node and extracts, as causal nodes, nodes that are the cause of the predetermined node and nodes that are the result of the predetermined node among nodes that belong to stakeholders other than the predetermined stakeholder to which the predetermined node belongs. The closer the causal node is from the selected node (the number of links to be followed in CLD), the more desirable it is.

[0014] The drawing unit 14 visualizes predetermined nodes and causal nodes in the causal node diagram in association with detailed data related to the node items, as exemplified below. A graph having a first axis indicating time or position and a second axis indicating the item value on the first axis (hereinafter referred to as a KPI graph). Map data including displays showing item values. These detailed data are visualizations of historical data of past times that have been input in advance to the causal processing device 100, and simulation data of future times that have been calculated in advance by the causal processing device 100, and the like.

[0015] The memory unit 20 stores a user management table 21 (details in Figure 11), a group management table 22 (details in Figure 12), a node management table 23 (details in Figure 13), a link management table 24 (details in Figure 14), a connection structure management table 25 (details in Figure 15), a node display state management table 26 (details in Figure 16), a KPI graph management table 27 (details in Figure 17), a spatial information management table 28 (details in Figure 18), and a generated link information management table 29 (details in Figure 19).

[0016] FIG. 2 is a hardware configuration diagram of the causal processing device 100. The causal processing device 100 is configured as a computer 900 having a CPU 901 , a RAM 902 , a ROM 903 , an HDD 904 , a communication I / F 905 , an input / output I / F 906 , and a media I / F 907 . The communication I / F 905 is connected to an external communication device 915. The input / output I / F 906 is connected to an input / output device 916. The media I / F 907 reads and writes data from a recording medium 917. Furthermore, the CPU 901 controls each processing unit by executing a program (also called an application or an app for short) loaded into the RAM 902. This program can also be distributed via a communication line or recorded on a recording medium 917 such as a CD-ROM and distributed.

[0017] An overview of the causal processing device 100 will be described below with reference to the screen views of FIGS. 3 is a diagram showing a display screen of the CLD displayed by the drawing unit 14. Here, a town development workshop involving multiple stakeholders is shown as an example.

[0018] Before the workshop begins, the facilitator inputs geospatial information and KPI data related to the town into the causal processing device 100. The facilitator also inputs into the causal processing device 100 a CLD that incorporates the impact of measures and relationships between stakeholders into a model. During the workshop, the causal processing device 100 presents the current state of the town and the impact of the measures implemented by the facilitator to the stakeholders as geospatial information and KPI graphs. This allows each stakeholder to perceive the town's issues, vision, and measures as their own, making it easier to reach a consensus among multiple stakeholders.

[0019] Furthermore, in workshops, stakeholders discuss measures to resolve issues. The causal processing device 100 displays changes resulting from the implementation of measures using geospatial information and KPI graphs, and presents the impact of the measures on each stakeholder using CLD. This allows stakeholders to confirm their own relationships with others and find a good compromise, such as whether a measure may not immediately benefit them but will ultimately benefit them, or whether a measure may benefit them but cause problems for others, leading to support in building consensus on the measures.

[0020] CLD201 in Figure 3 is a directed graph in which nodes such as "walkability" and "outing rate" are connected by link arrows. A node in CLD201 is prepared for each KPI indicator, for example. The arrows in CLD201 indicate causal relationships, with the base of the arrow being the node of the cause and the tip of the arrow being the node of the result. Furthermore, the line types of the arrows in CLD201 are such that a solid line indicates a positive correlation and a wavy line indicates a negative correlation. For example, a solid arrow connects "walkability" to "outing rate," showing a causal relationship in which increasing "walkability" also increases "outing rate." On the other hand, a wavy arrow connects "traffic congestion" to "transportation (comfort level)," showing a causal relationship in which an increase in "traffic congestion" decreases "transportation (comfort level)."

[0021] Additionally, one or more nodes in the CLD are grouped into frames that represent stakeholders. For example, "walkability," "outing rate," and "transportation" are grouped as KPI indicators that affect the stakeholder, the residents. On the other hand, "City X" and "Tourists" are stakeholders separate from the residents, and a consensus must be reached among multiple stakeholders in the workshop.

[0022] FIG. 4 is a diagram showing a display screen of a CLD simplified from the CLD of FIG. The simplification unit 11 creates the CLD202 of Fig. 4 by hiding some of the nodes and links from the CLD201 of Fig. 3. Here, the simplification unit 11 targets the routes "number of events → traffic congestion → transportation" and "number of events → road congestion → transportation" in the CLD201 for simplification. The simplification unit 11 then leaves only "number of events → transportation" as the generated link. Details of the process by which the simplification unit 11 selects the nodes and links to leave will be described later with reference to Fig. 16; for example, this process leaves nodes that are points of contact with others and important nodes that have been set in advance. Furthermore, when the simplified link "Number of Events →Transportation" is clicked, the simplification unit 11 displays the nodes and links that were hidden during simplification on a separate screen 203.

[0023] FIG. 5 is a diagram of a display screen for explaining the process of tracing a causal node from a selected node. For example, the facilitator receives a proposal to increase the "number of events" and selects the "number of events" node from the CLD in Fig. 5. The node selection unit 12 sets the "number of events" node selected by the user as the "selected node" (the thick-lined circle node in the figure).

[0024] The node causal unit 13 defines as a "causal node" a node that has a causal relationship with the selected node output by the node selection unit 12. For example, for the selected node "number of events," there is no cause node that serves as the source of the arrow in the CLD of Fig. 5, and there are three nodes that serve as the destination of the arrow: "transportation," "length of stay," and "brand power." The node causality unit 13 adopts the following three nodes, which are the nodes at the destination of the arrow from the selected node and are points of contact with others, as result nodes (thick circle nodes in the figure) among the causal nodes. Note that the other stakeholders are other stakeholders than the stakeholder "XX City" to which the selected node "Number of Events" belongs. "Transportation" belonging to "Residents." Note that the causal part of node 13 can be traced as "Number of events → Transportation → Outing rate," but "Transportation," located on the boundary between the "X City" frame and the "Residents" frame, is closer, so it is set as the result node. - "Length of stay" belonging to "tourists." "Number of tourists" belonging to "XX Company". Note that the node causal part 13 traces "Number of events → Brand power", but "Brand power" belongs to "XX City" and does not belong to others. In this case, the node causal part 13 traces the result nodes in order of closest distance, such as "Number of events → Brand power → Number of tourists", and finds the result nodes that belong to others.

[0025] FIG. 6 is a diagram of a display screen in which a causal node is traced from a selected node different from that in FIG. The node selection unit 12 sets the node of "number of days of stay" selected by the user as the "selected node" (the thick-lined circle node in the drawing). The node causal unit 13 extracts the following causal nodes for the selected nodes output by the node selecting unit 12. For the selected node of "Length of Stay", the cause node "Number of Events" which is the arrow source and belongs to "XX City" For the selection node of "length of stay", the arrow heads to the result node "tourism income" that belongs to "XX company". The arrow points to the selection node for "length of stay" and to the result node "income and expenditure" that belongs to "XX city." In this case, the order is traced from length of stay → tourism income → income and expenditure. The arrow points to the selection node for "length of stay" and to the result node "walkability" which belongs to "residents." In this case, the order is length of stay → tourism revenue → income and expenditure → streetscape improvement → walkability. In this way, the node causal unit 13 traces the KPI for each stakeholder (X city, X company, resident) until it reaches the corresponding node owned by each stakeholder.

[0026] FIG. 7 is a diagram showing a display screen for displaying a map. The drawing unit 14 displays the KPI-related data related to each node, for the selected nodes output by the node selection unit 12 and the causal nodes output by the node causal unit 13, superimposed on the map display as shown in FIG. The map display in Figure 7 displays auxiliary information for the map, including a display field for a list of spatial information such as tourist spots, railway routes, and bus routes, a selection field for KPI-related data such as tourist flow and resident flow, and a playback bar that displays the playback time in formats such as "hh:mm YYYY / MM / DD". The drawing unit 14 also displays, on the map, black dot tourist icons indicating the item selected in the KPI-related data selection field (here, tourist flow) and lines indicating their movement trajectories. In this example, many tourists are staying on the tourist island in the upper left of the map. Furthermore, when the triangular play button is clicked on the play bar, as time passes, the drawing unit 14 animates the tourist icons at each time, drawing trajectories as they move across the map.

[0027] FIG. 8 is a diagram showing the display screen of the map display when KPI-related data different from that shown in FIG. 7 is selected. The drawing unit 14 also displays, on the map, black circle resident icons indicating the item selected in the selection field for KPI-related data (here, resident flow) and lines indicating their movement trajectories. In this example, many residents are moving to the business district in the lower right corner of the map. In this way, even when displaying map data showing the same topography, by changing the item of focus, the user can check various KPI-related data.

[0028] FIG. 9 shows a display screen of the KGI graph and the KPI graph. The drawing unit 14 displays the following KPI graphs related to the selected nodes output by the node selection unit 12 and the causal nodes output by the node causal unit 13 side by side as shown in FIG. Selection node = [Tourist] Graph related to length of stay. Note that [Tourist] indicates the node of the stakeholder "Tourist". · Cause node = [XX City] Graph related to the number of events. ·Result node = [XX Company] Graph related to tourism revenue. Furthermore, the drawing unit 14 may also display a KGI graph related to the KPI graph side by side, as shown in Fig. 9. A Key Goal Indicator (KGI) is a numerical index of a final goal calculated based on the KGI indicator. For example, if the KGI is sales in the tourism industry, the KPI may be the number of tourists, the average tourist price, etc.

[0029] FIG. 10 is a diagram of a display screen in which three types of displays are associated with each other using arrows. The drawing unit 14 displays the selected node = [tourist] number of days of stay, its cause node = [XX city] number of events, and the result node = [XX company] tourism revenue, each associated with the KPI graph in Fig. 9 using arrows. This allows the user to intuitively understand which node corresponds to which KPI graph as detailed information about the node of interest. Furthermore, the drawing unit 14 displays the selected node = [tourist] number of days of stay, its cause node = [XX city] number of events, and the result node = [XX company] tourism revenue, each associated with the map display (selection column for KPI-related data) in Fig. 7 using arrows. This allows the user to intuitively understand which node corresponds to which map display as detailed information about the node of interest.

[0030] In addition, in the map display at the top of Figure 10 (KPI-related data selection column), data (arrow head) related to each KPI node (arrow base) of the CLD is associated as follows: The CLD "Number of Events" is associated with the spatial information "Location of Events." The CLD "length of stay" is associated with the spatial information "tourist flow." The CLD "tourism revenue" is associated with the spatial information "tourist traffic." Here, the names of the displayed spatial information, such as "event venue" and "tourist flow" (displays indicating the item values ​​of the KPI nodes), are associated with each KPI node of the CLD in the node management table 23 of Figure 13 or the spatial information management table 28 of Figure 18 (not shown).

[0031] An overview of the causal processing device 100 has been explained above with reference to the screen views of Figures 3 to 10. Below, details of each table used by the causal processing device 100 will be explained with reference to Figures 11 to 19. Note that the data content of each table may be changed as appropriate during discussions with stakeholders via the editing function of the CLD provided by the causal processing device 100.

[0032] 11 is a diagram showing the configuration of the user management table 21. The user management table 21 associates a user ID indicating a stakeholder, a display name thereof, and an arbitrary group ID to which the user belongs. 12 is a diagram showing the configuration of the (optional) group management table 22. The group management table 22 associates group IDs with group names. 13 is a diagram showing the configuration of the node management table 23. The node management table 23 associates the node IDs that make up the CLD, the display names of the nodes, the owner IDs that are the stakeholders to which the nodes belong, an importance flag that indicates whether the node should be retained even after simplification, and a KGI flag (optional) that indicates whether the node can be associated with a KGI graph.

[0033] 14 is a diagram showing the configuration of the link management table 24. The link management table 24 associates the link IDs that make up the CLD with the link type (1 for a positive correlation, -1 for a negative correlation), and a delay flag (indicating whether propagation of the causal relationship occurs with a delay). 15 is a diagram showing the configuration of the connection structure management table 25. The connection structure management table 25 associates a cause node ID, a result node ID, and a link ID connecting the two nodes as a link connection structure. Note that the connection structure management table 25 may manage the cause node and result node as nodes that have a connection relationship, without distinguishing between them. 16 is a diagram showing the configuration of the node display state management table 26. The node display state management table 26 associates node IDs with initial display flags that indicate nodes to be kept even when initially simplified, and display flags that indicate nodes to be kept at present. The initial display flag is set to "1" to indicate that the nodes will be kept for nodes whose importance flag in the node management table 23 is "1" and for nodes that are points of contact with others.

[0034] Fig. 17 is a diagram showing the configuration of the KPI graph management table 27. The KPI graph management table 27 associates the KPI graph ID of a KPI graph displayed in Fig. 9 and the like, a graph name (arbitrary), and an associated node ID associated with that KPI graph. Note that if the graph name (arbitrary) is not managed in the KPI graph management table 27, the graph name may be managed separately in a database or the like in which the numerical data of the graph is registered. Fig. 18 is a diagram showing the configuration of the spatial information management table 28. The spatial information management table 28 associates the data ID of the map display shown in Fig. 7 and the like, the data name (arbitrary), and the related node ID associated with the map display. Note that if the data name (arbitrary) is not managed by the spatial information management table 28, the data name may be managed separately in a DB or the like in which raw data of spatial information is registered. Fig. 19 is a diagram showing the configuration of the generated link information management table 29. The generated link information management table 29 associates the generated link ID of the generated link explained in Fig. 4 with the link type and the configuration node ID. The configuration node IDs are arranged in the order of cause → effect.

[0035] 20 is a flowchart showing the main processing of the causal processing device 100. Details of each processing will be described later in the subroutines from FIG. In S110, the simplification unit 11 simplifies the CLD prepared in advance by hiding some of the nodes and links as described in FIG. 4, so that the hidden nodes are included in the displayed nodes. In S120, the simplification unit 11 draws a CLD consisting only of the display nodes (intermediate nodes) of the CLD simplified in S110. Then, the simplification unit 11 displays intermediate nodes for areas that the user wants to see in detail, and hides intermediate nodes for areas that the user does not want to see.

[0036] In S130, the node selection unit 12 selects the node clicked on the CLD displayed in S120 as the selected node and acquires the node ID. For example, in Fig. 5, the "Number of Events" node is the selected node. In S210, the node selection unit 12 draws a KPI graph (KPIs that each measure considers important) related to the selected node as shown in Fig. 10. This allows the user to understand the relationship between KPIs among multiple stakeholders. In S220, the node selection unit 12 registers the selected node in a list of KPI-related data in the map data, as shown in Fig. 10. When this KPI-related data is selected, the drawing unit 14 shows the impact of the measures on the map data using colors and animations.

[0037] In S230, the node causal unit 13 traces the causal nodes from the selected node as shown in FIGS. In S310, the node causal unit 13 draws a KPI graph (KPIs of other parties affected by the measure) related to the causal node traced in S230, in the same manner as in S210. In S320, the node causal unit 13 registers the causal node traced in S230 in a list in the KPI-related data of the map data, similarly to S220. In S330, the drawing unit 14 draws a KGI graph related to the drawn KPI graph. The process of S330 is not essential.

[0038] FIG. 21 is a flowchart showing the details of the CLD simplification process (S110). In S111, the simplification unit 11 refers to the node management table 23 and the connection structure management table 25, and acquires the node IDs that are located on the boundary of the owner frame of the CLD and are connected to nodes of other owners. In S112, the simplification unit 11 acquires the node IDs corresponding to the importance flag=1 in the node management table 23 that has been input in advance. In S113, the simplification unit 11 sets the initial display flag of the node display state management table 26 to "1" for the node ID acquired in S111 and S112, respectively, thereby designating the node as one to be retained even after simplification.

[0039] In S114, the simplification unit 11 selects the nodes to be left in S113, and causes the drawing unit 14 to draw the nodes. The drawing unit 14 obtains the owner ID for the node ID to be left from the node management table 23, and places the node in the corresponding owner area on the CLD screen.

[0040] In S115, the simplification unit 11 causes the drawing unit 14 to draw links connecting the nodes to be left. To this end, the drawing unit 14 refers to the connection structure management table 25 and acquires the link IDs between the placed nodes. Then, the drawing unit 14 refers to the link management table 24 and determines the type of link for the link ID between the placed nodes. In this determination process, for example, the link type is determined by multiplying the link types (1 or -1) of the set of links connected in series. For example, suppose that nodes are connected in the order A → B → C, and the link type of A → B = 1 (positive correlation), and the link type of B → C = -1 (negative correlation). When this link set is simplified to "A → C", the link type of A → C is determined to be 1 x (-1) = -1 (negative correlation).

[0041] In S116, the simplification unit 11 registers the information of the link drawn in S115 in the generated link information management table 29.

[0042] FIG. 22 is a flowchart showing the details of the display / non-display process (S130) of the intermediate node of the CLD. The simplification unit 11 executes S121 to S124 to display the intermediate nodes. In S121, the simplification unit 11 acquires the link ID of the link clicked on the CLD. In S122, the simplification unit 11 refers to the generated link information management table 29 and acquires the constituent node ID of the clicked link. In S123, the simplification unit 11 refers to the node management table 23, the link management table 24, and the connection structure management table 25, and draws the configuration node IDs obtained in S122 and the links connecting them, including intermediate nodes, on the CLD. In S124, the simplification unit 11 refers to the node display state management table 26 and sets the display flag of the configuration node ID acquired in S122 to "1."

[0043] The simplification unit 11 executes S125 to S127 to hide the intermediate nodes. In S125, a hide button or the like is clicked on the CLD in the simplification unit 11. The hide button is, for example, the "x" button in the reference numeral 203 in FIG. In S126, the simplification unit 11 acquires and hides intermediate node IDs and links in the hidden range displayed in S121 to S124. For example, in CLD202 in Fig. 4, the "traffic congestion" node and the "road congestion" node are hidden from CLD201 as intermediate nodes. In S127, the simplification unit 11 draws a link connecting the endpoints of the hidden range. For example, in FIG. 4, the "event count" node and the "transportation" node are endpoints. Therefore, the drawing unit 14 draws the link connecting these endpoints as a "generated link," similar to S115.

[0044] FIG. 23 is a flowchart showing the details of the KPI graph drawing process (S210) for the selected node. In S211, the node selection unit 12 refers to the KPI graph management table 27, extracts from the KPI graph management table 27 an entry with a related node ID that matches the selected node ID, and extracts the KPI graph ID. In steps S212 to S214, the node selection unit 12 executes KPI graph drawing in step S213 for each extracted KPI graph ID. Note that the numerical data and graph names of the graphs drawn in the KPI graph drawing in step S213 are stored in advance in a DB as, for example, a CSV file and are managed in association with the graph IDs. In S213, the node selection unit 12 draws the graph corresponding to the extracted KPI graph ID on the KPI screen together with the user name (display name in the user management table 21).

[0045] FIG. 24 is a flowchart showing the details of the map data list registration process (S220) of the selected node. In S221, the node selection unit 12 refers to the spatial information management table 28, extracts from the spatial information management table 28 an entry with a related node ID that matches the selected node ID, and extracts the data ID thereof. In steps S222 to S224, the node selection unit 12 executes the data name registration process of step S223 for each extracted data ID. In S223, the node selection unit 12 registers the data name corresponding to the extracted data ID in the KPI-related data area of ​​the Map screen.

[0046] 25 is a flowchart showing the details of the process (S230) of tracing the causal nodes from the selected node. This flowchart corresponds to the process described with reference to FIGS. In S231, the node causal unit 13 acquires the owner ID of the selected node ID from the node management table 23. In S232, the node causal unit 13 acquires IDs other than the owner ID from the user management table 21 based on the user ID.

[0047] In S233 to S236, the node causal unit 13 executes a loop to acquire nodes that will be the cause node and the effect node of the selected node for each of the other owners acquired in S232. In S234, the node causal unit 13 refers to the node management table 23, the connection structure management table 25, and the node display state management table 26, and acquires the causal node of other owners that is closest to the selected node and has a display flag of "1." The closest location is, for example, the location with the fewest number of links to follow. In S235, the node causal unit 13 refers to the node management table 23, the connection structure management table 25, and the node display state management table 26, and obtains the result node of the other owners that is closest to the selected node and has a display flag of "1."

[0048] In S237, the node causal unit 13 may highlight the acquired cause node and effect node as causal nodes on the CLD screen by changing the color of the node or the thickness of the border of the node.

[0049] FIG. 26 is a flowchart showing the details of the KPI graph drawing process (S310) for causal nodes. In S311, the node causal unit 13 refers to the KPI graph management table 27, extracts from the KPI graph management table 27 an entry with a related node ID that matches the causal node ID, and extracts the KPI graph ID. In steps S312 to S314, the node causal unit 13 executes the KPI graph drawing process in step S313 for each extracted KPI graph ID. In S313, the node causal unit 13 draws the KPI graph corresponding to the extracted KPI graph ID on the KPI screen such as that shown in FIG.

[0050] FIG. 27 is a flowchart showing the details of the process of registering a causal node in the map data list (S320). In S321, the node causal unit 13 extracts an entry of an associated node ID that matches the causal node ID from the spatial information management table 28, and extracts the data ID thereof. In steps S322 to S224, the node causal unit 13 refers to the spatial information management table 28 and performs the data name registration process of S323 for each extracted data ID. In S323, the node causal unit 13 registers the data name corresponding to the extracted data ID in the KPI-related data area on the Map screen.

[0051] FIG. 28 is a flowchart showing the details of the KGI graph drawing process (S330). In steps S331 to S336, the drawing unit 14 sequentially acquires node IDs whose KGI flags are "1" from the node management table 23, and executes the processes of steps S332 to S335. In S332, the drawing unit 14 obtains from the KPI graph management table 27 the entry of the related node ID that matches the obtained node ID, and obtains the KPI graph ID of that entry. In S333, the drawing unit 14 extracts from the node management table 23 an entry with a node ID that matches the acquired node ID, and acquires its owner ID. In S334, the drawing unit 14 extracts from the user management table 21 an entry with a user ID that matches the acquired owner ID, and acquires the display name thereof. In S335, the drawing unit 14 draws the graph corresponding to the acquired graph ID and the acquired display name in the KGI area of ​​the KPI screen shown in FIG.

[0052] The causal processing device 100 of this embodiment described above provides each stakeholder with information suitable for forming a consensus among multiple stakeholders, as shown in the following (1) to (3). (1) As shown in Figure 4, the simplification unit 11 simplifies the CLD so as to leave only nodes that are points of contact with others, i.e., nodes on the boundaries between stakeholders. This allows other stakeholders' nodes to remain displayed, preventing certain stakeholders from feeling alienated from the CLD. In addition, the user can operate the simplified CLD, causing the drawing unit 14 to switch between expanded and hidden display of nodes. (2) As shown in Figures 5 and 6, the node causal unit 13 traces causal nodes that are close to the selected node so as to extract nodes that are points of contact with others, that is, nodes that are on the boundary between stakeholders. This allows other stakeholders' nodes to remain displayed, preventing certain stakeholders from feeling alienated from the CLD. (3) As shown in Figure 10, the drawing unit 14 displays the results of simulating the effectiveness of measures as detailed data for the node of interest, such as KPI graphs and map information, in a form that corresponds to the CLD node. This not only allows the presence or absence of causal relationships to be confirmed, but also the ripple effects of measures and the relationships between stakeholders in detail (allowing everyone to confirm their impact), thereby supporting consensus building among multiple stakeholders. In other words, by not only leaving out important nodes but also presenting qualitative and quantitative data related to those nodes, the situation can be better understood and more specific measures can be considered.

[0053] This allows each stakeholder participating in a workshop to understand not only their own interests but also the relationships and trade-offs between interests of others, for example, based on a CLD that models the relationships between residents, towns, and businesses.

[0054] Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the scope of the claims. For example, the above-described embodiments have described the present invention in detail, and it is not necessary to include all of the described configurations. Furthermore, it is possible to add configurations of other embodiments to the configurations. In addition, it is possible to add, delete, or replace part of the configurations.

[0055] Furthermore, it is possible to add, remove, or replace part of the configuration of each embodiment with other configurations. Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partly or entirely realized in hardware, for example, by designing them as integrated circuits. Furthermore, each of the above-mentioned configurations and functions may be realized by software, with a processor interpreting and executing a program that realizes each function.

[0056] Information such as programs, tables, and files that realize each function can be stored in a storage device such as a memory, a hard disk, or an SSD (Solid State Drive), or in a storage medium such as an IC (Integrated Circuit) card, an SD card, or a DVD (Digital Versatile Disc).The cloud can also be used. In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected. Furthermore, the communication means connecting the devices is not limited to wireless LAN, but may be changed to wired LAN or other communication means. [Explanation of symbols]

[0057] 10 Processing section 11 Simplification Section 12 Node selection section 13 Node Causal Part 14 Drawing section (visualization section) 20 Memory section 21 User Management Table 22 Group Management Table 23 Node Management Table 24 Link Management Table 25 Connection Structure Management Table 26 Node display status management table 27 KPI graph management table 28 Spatial Information Management Table 29 Generated Link Information Management Table 100 Causal Processing Device

Claims

1. a node selection unit that accepts selection of nodes that constitute a causal node diagram, the causal node diagram being a data structure in which one or more nodes are grouped by frames that represent each stakeholder, and in which nodes representing cause items are connected by links to nodes representing result items; a node causality unit that follows links to the node selected by the node selection unit to extract, as causal nodes, a node that is a cause of the selected node and a node that is a result of the selected node, from among nodes that belong to a stakeholder other than the stakeholder to which the selected node belongs; a visualization unit that visualizes the selected node and the causal node in the causal node diagram in association with detailed data related to the node items, The node causality unit highlights, among nodes belonging to a stakeholder other than the stakeholder to which the selected node belongs, result nodes that are close in distance when tracing arrow destinations from the selected node and cause nodes that are close in distance when tracing arrow origins from the selected node. Causal processor.

2. The visualization unit visualizes a graph having a first axis indicating time or position and a second axis indicating an item value on the first axis as detailed data related to the item of the node. The causal processing device according to claim 1 .

3. The visualization unit visualizes map data including a display showing an item value as detailed data related to the item of the node. The causal processing device according to claim 1 .

4. the causal processing device further includes a simplification unit that hides some elements of the causal node diagram; The simplification unit selects the nodes to be displayed so as to leave only the nodes located on the boundary of the frame representing the stakeholder. The causal processing device according to claim 1 .

5. When a link through which a hidden node passes is selected, the simplification unit switches to display the hidden node related to the selected link. The causal processing device according to claim 4 .

6. The causal processing device includes a node selection unit, a node causal unit, and a visualization unit, the node selection unit receives a selection of nodes constituting the causal node diagram, which is a data structure in which one or more nodes are grouped by frames representing respective stakeholders and in which nodes representing cause items are connected by links to nodes representing result items; the node causality unit follows links to the node selected by the node selection unit to extract, as causal nodes, nodes that are causes of the selected node and nodes that are results of the selected node from among nodes that belong to a stakeholder other than the stakeholder to which the selected node belongs; the visualization unit visualizes the selected node and the causal node in the causal node diagram in association with detailed data related to node items; The node causality unit highlights, among nodes belonging to a stakeholder other than the stakeholder to which the selected node belongs, result nodes that are close in distance when tracing arrow destinations from the selected node and cause nodes that are close in distance when tracing arrow origins from the selected node. Causal processing methods.

Images