Information processing apparatus and information processing method

Abstract

To provide an information processing apparatus and an information processing method for predicting behavior of a consumer.SOLUTION: An information processing apparatus (10) includes: a node generation unit (12) which generates a plurality of nodes indicating a plurality of consumers; a first network generation unit (13) which generates a first network in which the nodes, which are divided into a plurality of groups, included in each group are connected to each other; a second network generation unit (14) which generates a second network in which the nodes included in different groups are connected to each other; and a state transition determination unit (15) which determines whether the state of a consumer indicated by each node has transitioned from a first state to a second state, based on a value indicating information amount of a consumer indicated by a node connected from each node in the first network, and a value indicating information amount of a consumer indicated by a node connected from each node in the second network.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device and an information processing method. [Background technology]

[0002] A method for predicting the demand for a new product has been disclosed (see Patent Document 1). This device calculates the predicted consumer demand when the new product is released by analyzing the results of a questionnaire in which non-confidential specification information of the new product is disclosed. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-004395 Summary of the Invention [Problem to be solved by the invention]

[0004] For example, in the method described in Patent Document 1, a survey is conducted on a scale of 1,000 consumers. In general, to analyze the results of a survey, it is necessary to obtain a sufficient number of responses. However, obtaining a sufficient number of responses poses a problem in that the financial and time burdens on the survey administrator must be taken into consideration. For this reason, there is a demand for a device that can predict consumer behavior regardless of the results of the survey responses.

[0005] The present disclosure is intended to solve the above-mentioned problems, and has an object to provide an information processing device and an information processing method that are capable of predicting consumer behavior. [Means for solving the problem]

[0006] The information processing device of the present disclosure is characterized in that it includes a node generation unit that generates a plurality of nodes representing a plurality of consumers; a first network generation unit that divides the plurality of nodes into a plurality of sets and generates a first network in which the nodes included in each set are connected to each other; a second network generation unit that generates a second network in which the nodes included in different sets are connected to each other; and a state transition determination unit that determines whether the state of the consumer represented by each node has transitioned from a first state to a second state based on a value indicating the amount of information possessed by the consumer represented by the node to which each node is connected in the first network and a value indicating the amount of information possessed by the consumer represented by the node to which each node is connected in the second network. [Effects of the Invention]

[0007] According to the present disclosure, in a network formed by multiple nodes representing multiple consumers, by determining whether the state of each consumer has transitioned based on a value indicating the amount of information possessed by the consumer represented by the connected node, it is possible to simulate the transition of the consumer's psychological state, thereby making it possible to predict consumer behavior. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a schematic configuration of an information processing system according to a first embodiment. [Figure 2] FIG. 2 is a diagram showing an example of a hardware configuration of an information processing device according to the first embodiment. [Figure 3] FIG. 2 is a diagram showing an example of a hardware configuration of an information processing device according to the first embodiment. [Figure 4] 4 is a flowchart showing processing performed by the information processing device according to the first embodiment. [Figure 5] FIG. 3 is a diagram showing an example of setting information acquired by the information processing device according to the first embodiment. [Figure 6]Figure 6A is a schematic diagram showing a family network for a one-person family generated by an information processing device in embodiment 1, Figure 6B is a schematic diagram showing a family network for a two-person family generated by an information processing device in embodiment 1, Figure 6C is a schematic diagram showing a family network for a three-person family generated by an information processing device in embodiment 1, Figure 6D is a schematic diagram showing a family network for a four-person family generated by an information processing device in embodiment 1, Figure 6E is a schematic diagram showing a family network for a five-person family generated by an information processing device in embodiment 1, and Figure 6F is a schematic diagram showing a family network for a six-person family generated by an information processing device in embodiment 1. [Figure 7] Figure 7A is a schematic diagram showing an extra-family network formed by a regular graph, Figure 7B is a schematic diagram showing an extra-family network formed by a small-world graph, and Figure 7C is a schematic diagram showing an extra-family network formed by a random graph. [Figure 8] 6 is a graph showing an example of a simulation result obtained by the information processing device according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. Embodiment 1 FIG. 1 is a block diagram showing a schematic configuration of an information processing system 100 according to the first embodiment. The information processing system 100 is a system in which each consumer acquires information via an information network formed by multiple consumers, and the system performs a simulation to predict consumer behavior by simulating the consumer's behavior based on the acquired information. In the first embodiment, the consumer who is the subject of the simulation is also referred to as an agent. As shown in FIG. 1, the information processing system 100 according to the first embodiment includes an information processing device 10, an input device 21, and an output device 22, which are connected to each other wirelessly or via a wired connection so as to be able to communicate with each other.

[0010] The input device 21 transmits signals to the information processing device 10, thereby inputting various pieces of information to be used when the information processing device 10 performs processing. For example, the input device 21 is configured by a keyboard, a mouse, a touch panel, a microphone, or other devices that can input information. When an operator performs an input operation on the input device 21, the input device 21 inputs a signal corresponding to the input operation to the information processing device 10.

[0011] The output device 22 outputs the results of processing performed by the information processing device 10. For example, the output device 22 is configured with a display device such as a liquid crystal display. The output device 22 converts the signal from the information processing device 10 into a form that can be recognized by an operator, such as an image.

[0012] The information processing device 10 includes a setting information acquisition unit 11, a node generation unit 12, a first network generation unit 13, a second network generation unit 14, and a state transition determination unit 15. The setting information acquisition unit 11 acquires setting information indicating various setting values ​​when the information processing device 10 performs processing. The setting information acquisition unit 11 acquires the setting information based on a signal from an input device 21, for example. The setting information acquisition unit 11 may be configured to acquire the setting information from a storage unit (not shown) that is included in the information processing device 10 and stores information, or may be configured to acquire the setting information from a computer (not shown) that is connected to the information processing device 10 so as to be able to communicate with the information processing device 10.

[0013] The node generating unit 12 generates a plurality of nodes representing a plurality of agents based on the setting information acquired by the setting information acquiring unit 11.

[0014] The first network generation unit 13 divides the multiple nodes generated by the node generation unit 12 into multiple sets that simulate a family, and generates a family network as a first network that represents an information network within the family in which the nodes included in each set are connected to each other.

[0015] The second network generation unit 14 generates an extra-family network as a second network representing an information network outside the family in which nodes included in different sets are connected. The extra-family network simulates an information network between agents other than family members, such as an information network formed by a school, workplace, or other community. In the intra-family network and extra-family network, the connections between nodes indicate a relationship in which information is transmitted between the agents represented by these nodes through conversation, communication, or other means.

[0016] The state transition determination unit 15 determines the transition of each agent's state among the following psychological states of each agent indicated by each node: a non-cognitive state, which is a state in which the accumulated amount of target information has not reached a preset threshold; a cognitive state, which is a state to which the agent transitions from the non-cognitive state when the accumulated amount of target information reaches a preset threshold; a motivated state, which is a state to which the agent transitions from the cognitive state based on the amount of information possessed by the agent indicated by the connected node in the information network; and a determined state, which is a state to which the agent transitions from the motivated state. The non-cognitive state simulates a state in which the agent is not aware of the target information, the cognitive state a state in which the agent is aware of the target information but has no intention of acting based on the information, the motivated state a state in which the agent has an intention to act based on the target information, and the determined state a state in which the agent has decided to act.

[0017] The state transition determination unit 15 determines whether the state of each agent has transitioned from a non-cognitive state as an initial state to a cognitive state as a first state, based on a value indicating the amount of information that the agent represented by each node acquires from information sources other than the agent represented by the node connected to the information network. Information sources other than the agent represented by the node connected to the information network include, for example, television, the Internet, magazines, newspapers, and other media. For example, the state transition determination unit 15 determines that the agent has transitioned from a non-cognitive state to a cognitive state when the value indicating the amount of information possessed by each agent, which is calculated based on the value indicating the amount of information that the agent represented by each node acquires from information sources other than the agent represented by the node connected to the information network, exceeds a preset threshold.

[0018] Furthermore, the state transition determination unit 15 determines whether the state of each agent has transitioned from the cognitive state to the motivated state as a second state, based on a value indicating the amount of information possessed by the agent indicated by the connected node in the family network and a value indicating the amount of information possessed by the agent indicated by the connected node in the non-family network. For example, the state transition determination unit 15 determines that the state of each agent has transitioned from the cognitive state to the motivated state when a value indicating the strength of the agent's intention to take action, calculated based on the value indicating the amount of information possessed by the agent indicated by the connected node in the family network and the value indicating the amount of information possessed by the agent indicated by the connected node in the non-family network, exceeds a preset threshold.

[0019] Furthermore, the state transition determination unit 15 determines, based on a preset probability, whether a specific agent among the motivated agents has transitioned from the motivated state to the third determined state. For example, when a condition based on a preset probability is satisfied, the state transition determination unit 15 determines that a representative agent representing each family network among the motivated agents has transitioned from the motivated state to the third determined state. The state transition determination unit 15 also performs multiple trials to determine whether each agent has transitioned from a non-cognitive state to a cognitive state, whether the cognitive state has transitioned to a motivated state, and whether the motivated state has transitioned to a determined state, and increases the value indicating the amount of information possessed by each agent with each trial, thereby simulating state transitions of each agent based on information propagated among multiple agents over time. Details of the determinations made by the state transition determination unit 15 will be described later.

[0020] Next, the hardware configuration of the information processing device 10 will be described with reference to FIGS. 2 and 3. FIG. 2 is a block diagram showing an example of the hardware configuration of the information processing device 10 according to the first embodiment, and FIG. 3 is a block diagram showing an example of the hardware configuration of the information processing device 10 according to the first embodiment, which is different from that shown in FIG. 2. For example, as shown in FIG. 2, the information processing device 10 includes a processor 10a, a memory 10b, and an I / O port 10c, and is configured so that the processor 10a reads and executes a program stored in the memory 10b. The memory 10b may be, for example, a non-volatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, or EEPROM. The memory 10b may also be a magnetic disk, a flexible disk, an optical disk, a compact disk, a minidisk, a DVD, or the like. The memory 10b may also be an HDD or an SSD.

[0021] 3, the information processing device 10 includes a processing circuit 10d and an I / O port 10c, which are dedicated hardware. The processing circuit 10d is configured, for example, by a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, a system LSI (Large-Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. Each function of the information processing device 10 is realized by the processor 10a or the processing circuit 10d, which is dedicated hardware, executing a program that is software, firmware, or a combination of software and firmware. The information processing device 10 may also include hardware and circuit elements other than those described above. The information processing device 10 may be configured as a single device, or its functions may be distributed across multiple devices, with each function being realized through the cooperation of these multiple devices.

[0022] Next, with reference to FIGS. 4 to 8, a process performed by the information processing device 10 according to the first embodiment will be described. FIG. 4 is a flowchart showing the process performed by the information processing device 10 according to the first embodiment. The process performed by the information processing device 10 shown in FIG. 4 is a process of performing a simulation to predict behavior based on changes in the psychological state of each agent. As shown in FIG. 4, when the information processing device 10 starts the process, first, the setting information acquisition unit 11 acquires setting information for performing each process from the input device 21 (step ST01). In this process, the information processing device 10 acquires, for example, setting information for generating a plurality of nodes by the node generation unit 12, setting information for generating an intra-family network by the first network generation unit 13, setting information for generating an outside-family network by the second network generation unit 14, and setting information for determining the state transition of each agent by the state transition determination unit 15.

[0023] Specifically, in the processing of step ST01, the information processing device 10 acquires setting information indicating information such as the total number of nodes generated by the node generation unit 12, information indicating the attributes of the agents indicated by each node, the number of connected nodes in the family network, the number of family networks, the number of connected nodes in the network outside the family, the increase in the amount of information held by each agent for each attempt, the number of processing attempts, various weighting coefficients, initial values ​​of various variables, etc. The attributes of each agent include, for example, whether or not the agent is an adult, whether or not the agent is the representative agent in the family network, etc.

[0024] Fig. 5 is a diagram showing an example of setting information acquired by the information processing device 10. As shown in Fig. 5, the information processing device 10 acquires, as the setting information, for example, weighting coefficients indicating the influence of each medium when adults and minors acquire information from each medium. For example, Fig. 5 shows that the amount of information acquired by adults from the Internet is one-fourth the amount of information acquired by adults from television, and that the amount of information acquired by minors from the Internet is four times the amount of information acquired by minors from television.

[0025] After performing the processing of step ST01, the information processing device 10 generates a plurality of nodes indicating a plurality of agents (step ST02) based on the setting information acquired by the setting information acquisition unit 11. In this processing, the information processing device 10 generates a plurality of nodes linked to the attribute information of each agent based on the setting information acquired in the processing of step ST01.

[0026] After performing the processing of step ST02, the information processing device 10 divides the plurality of nodes generated in the processing of step ST02 into a plurality of sets, and generates a family network in which the nodes included in each set are connected to each other (step ST03). In this processing, the information processing device 10 generates the family network so that, for example, each node constituting the network is a complete graph connected to all other nodes. In this way, the information processing device 10 simulates a state in which information is exchanged between all other agents within the family.

[0027] Figure 6 is a diagram showing an example of a family network generated by information processing device 10, where Figure 6A is a schematic diagram showing a family network for a one-person family generated by an information processing device in embodiment 1, Figure 6B is a schematic diagram showing a family network for a two-person family generated by an information processing device in embodiment 1, Figure 6C is a schematic diagram showing a family network for a three-person family generated by an information processing device in embodiment 1, Figure 6D is a schematic diagram showing a family network for a four-person family generated by an information processing device in embodiment 1, Figure 6E is a schematic diagram showing a family network for a one-person family generated by an information processing device in embodiment 1, and Figure 6F is a schematic diagram showing a family network for a one-person family generated by an information processing device in embodiment 1.

[0028] In the processing of step ST03, the information processing device 10 generates multiple family networks by, for example, assigning to each node a number that can identify which family network the node belongs to, a number that can identify whether the node is an adult or a minor, and a number that can identify whether the node is a representative agent in the family network. Also, in this processing, the information processing device 10 generates multiple family networks so that the ratio of the number of nodes constituting each family network is a preset ratio. Specifically, the information processing device 10 generates multiple family networks so that, in light of real-world statistics, the most common family networks generated are those with two nodes.

[0029] After performing the processing of step ST03, the information processing device 10 generates an outside-family network to which the nodes generated in the processing of step ST02 are connected (step ST04). In this processing, the information processing device 10 generates the outside-family network so that nodes constituting different intra-family networks are connected to each other. For example, the information processing device 10 generates the outside-family network so that multiple nodes constituting the same outside-family network are not connected to each other. Furthermore, the information processing device 10 generates the outside-family network so that the number of nodes to which each node is connected is a value that is preset based on the setting information.

[0030] FIG. 7 is a schematic diagram showing an example of an outside-family network, where FIG. 7A is a schematic diagram showing an outside-family network formed by a regular graph, FIG. 7B is a schematic diagram showing an outside-family network formed by a small-world graph, and FIG. 7AC is a schematic diagram showing an outside-family network formed by a random graph. A regular graph is a graph in which the randomness is 0 and the number of nodes to which each node is connected is constant. The information processing device 10 generates the outside-family network so that it becomes, for example, a regular graph.

[0031] After processing step ST04, the information processing device 10 determines the state transition of each agent (step ST05). In this process, the information processing device 10 determines the state transition of each agent, for example, according to the following formulas (1) to (5). First, in the process of step ST05, the information processing device 10 determines whether each agent satisfies the following formula (1). The information processing device 10 determines that an agent in a non-cognitive state that satisfies formula (1) has transitioned from a non-cognitive state to a cognition state, and determines that an agent in a non-cognitive state that does not satisfy formula (1) will maintain the non-cognitive state. TAd×((D+W)×ε+C×ζ)+IAd×((D+W)×η+C×θ)+m1×ψ>Si ···(1)

[0032] In addition, in the formula (1), TAd is a value indicating the effectiveness of television advertising in the market, and is a value that increases each time the processing of step ST05 is performed. TAd is a value that increases by ι, which is a specific value between 0 and 1, for example, each time the processing of step ST05 is performed. D is a coefficient indicating the attributes of an agent, and the representative agent of the family network is set to D=1, and other agents are set to D=0. W is a coefficient indicating the attributes of an agent, and adult agents other than the representative agent in the family network are set to W=1, and other agents are set to W=0. ε is a coefficient indicating the degree of influence (susceptibility) of the adult agent to television advertisements, and is set to a specific value between 0 and 1, for example. C is a coefficient indicating the agent's attributes, and C=1 is set for minor agents and C=0 for other agents. ζ is a coefficient indicating the degree of influence of the television advertisement on the minor agent, and is set to a specific value between 0 and 1, for example. In this formula (1), TAd×((D+W)×ε+C×ζ) represents the amount of information accumulated by each agent through television advertisements.

[0033] Furthermore, in formula (1), IAd is a value indicating the effectiveness of Internet advertising in the market, and is a value that increases each time the processing of step ST05 is performed. IAd is a value that increases by κ, which is a specific value between 0 and 1, for example, each time the processing of step ST05 is performed. η is a coefficient indicating the degree of influence of the internet advertisement of the adult agent, and is set to a specific value between 0 and 1, for example. θ is a coefficient indicating the degree of influence of the Internet advertisement on the minor agent, and is set to a specific value between 0 and 1, for example. In this formula (1), IAd×((D+W)×η+C×θ) represents the amount of information accumulated by each agent through Internet advertisements.

[0034] Furthermore, in formula (1), m1 is a value indicating the sum of the amount of information accumulated by television advertisements and the amount of information accumulated by internet advertisements for agents in a non-cognitive state who are connected to each agent in the non-family network. ψ is a weighting coefficient, and is set to a value of 0.5, for example. Si is a threshold value for determining whether the amount of information accumulated by each agent is sufficient for the agent to recognize the information, and is set at random to a value between 2 and 3 for each agent, for example.

[0035] Furthermore, in the processing of step ST05, the information processing device 10 determines whether or not an agent in each cognitive state satisfies the following formula (4). The information processing device 10 determines that an agent in a cognitive state that satisfies formula (4) has transitioned from the cognitive state to the motivational state, and determines that an agent in a cognitive state that does not satisfy formula (4) has maintained the cognitive state. Ui=(τ×h2+υ×h3+φ×h4) / di ···(2) Gi=(μ×m2+ρ×m3+δ×m4) / ki ···(3) α×Ri+β×Ui+γ×Gi>Ti...(4)

[0036] In addition, in the formula (2), τ, ν, and φ are weighting coefficients, and are set to specific values ​​between 0 and 1, for example. h2 is a value indicating the sum of the amount of information accumulated by television advertisements and the amount of information accumulated by internet advertisements for agents in a cognitive state connected to each agent in the family network. h3 is a value indicating the total amount of information accumulated by television advertisements and the amount of information accumulated by internet advertisements for motivated agents connected to each agent in the family network. h4 is a value indicating the total amount of information accumulated by television advertisements and the amount of information accumulated by internet advertisements for agents in the determined state connected to each agent in the family network. di is the number of agents connected to each agent in the family network. In other words, di is the total number of agents that make up each agent's family network. In such a formula (2), Ui represents the effect of information from the agents connected in the family network, that is, from the family, on the behavior of each agent.

[0037] In formula (3), μ, ρ, and δ are weighting coefficients, and are set to specific values ​​between 0 and 1, for example. m2 is a value indicating the sum of the amount of information accumulated by television advertisements and the amount of information accumulated by internet advertisements for agents in a cognitive state connected to each agent in a network outside the family. m3 is a value indicating the total amount of information accumulated by television advertising and the amount of information accumulated by internet advertising for agents in a cognitive state connected to each agent in a network outside the family. m4 is a value indicating the total amount of information accumulated by television advertisements and the amount of information accumulated by internet advertisements for agents in a determined state connected to each agent in the non-family network. ki is the number of agents connected to each agent in the extra-family network. In such a formula (3), Gi represents the effect that information from agents connected in the extra-family network has on the behavior of each agent.

[0038] In formula (4), α, β, and γ are weighting coefficients, and are set to specific values ​​between 0 and 1, for example. Ri is the initial utility value for each agent, and is a value that is randomly set for each agent so that it follows a normal distribution with a mean mu1 and a standard deviation δ1 that are preset based on the setting information. In such a formula (4), α×Ri+β×Ui+γ×Gi indicates the strength of each agent's willingness to take action (utility value). Ti is a threshold (utility threshold) for determining whether each agent is in a state to take action, and is set at random to a value between 1 and 2 for each agent, for example.

[0039] Furthermore, in the processing of step ST05, the information processing device 10 judges whether or not the agents in each motivation state satisfy the following formula (5). The information processing device 10 judges that the representative agent whose motivation state satisfies formula (5) has transitioned from the motivation state to the decision state, and judges that the representative agent whose motivation state does not satisfy formula (5) and agents other than the representative agent will maintain their motivation states. σ≦P (5)

[0040] In formula (5), σ is a random value used to determine the transition from the motivated state to the determined state, and is randomly set to a value between 0 and 1 for each representative agent in the motivated state for each trial. P is a value indicating the probability that the representative agent in the willing state will transition to the determined state, and is set as a specific value between 0 and 1, for example. In this way, the information processing device 10 simulates the decision-making process within a family by determining whether the state of the representative agent transitions to a decision state.

[0041] After performing the process of step ST05, the information processing device 10 determines whether or not the number of attempts has reached a preset number (step ST06). In this process, the information processing device 10 determines whether or not the number of attempts has reached the number required to terminate the simulation of the agent's state transition.

[0042] In the process of step ST06, if the number of attempts has not reached the preset number (NO in step ST06), the information processing device 10 performs the process of step ST05 again. In the process of step ST06, if the number of attempts has reached the preset number (YES in step ST06), the information processing device 10 ends the process and outputs the simulation result (step ST07). For example, the information processing device 10 outputs, as the simulation result, information indicating the number of attempts of the process and the state of each agent to the output device 22. By performing such a process, the information processing device 10 can, for example, simulate how information obtained from an agent outside the family is brought back to the family, the brought back information is propagated within the family, and the family decides on consumption behavior.

[0043] FIG. 8 is a graph showing an example of the results of a simulation performed by the information processing device 10. The simulation performed by the information processing device 10 shown in FIG. 8 is the result of performing 50 trials, with the vertical axis indicating the number of family members (number of intra-family networks) whose representative agents are in the determined state, and the horizontal axis indicating the number of trials. In FIG. 8, the number of trials at which the number of family members stops increasing with the number of trials indicates the number of trials at which the representative agents of all families have transitioned to the determined state. Furthermore, the number of trials corresponds to the passage of time in the real world. As shown in FIG. 8, the fewer the number of family members, the fewer opportunities there are for information acquisition between family members, and it takes longer for the representative agents of all families to transition to the determined state.

[0044] As described above, the information processing device 10 according to the first embodiment includes a node generation unit 12 that generates a plurality of nodes representing a plurality of agents, a first network generation unit 13 that divides the plurality of nodes into a plurality of sets and generates an intra-family network in which the nodes included in each set are connected to each other, a second network generation unit 14 that generates an extra-family network in which the nodes included in different sets are connected to each other, and a state transition determination unit 15 that determines whether the state of the consumer represented by each node has transitioned from a cognitive state to a motivational state based on a value indicating the amount of information possessed by the agent represented by the node to which each node is connected in the intra-family network and a value indicating the amount of information possessed by the agent represented by the node to which each node is connected in the extra-family network.

[0045] With this configuration, the information processing device 10 can simulate the transition of the psychological state of an agent in a network formed by a plurality of nodes representing a plurality of agents by determining whether the state of each agent has transitioned based on a value indicating the amount of information possessed by the agent indicated by the connected node, thereby making it possible to predict and analyze the behavior of the agent. For example, by simulating the transition of the psychological state of an agent, the information processing device 10 can predict the process from when the agent is placed in an environment where it can acquire information to when it reaches a motivated state where it intends to act based on the acquired information, and the time it takes to reach that motivated state. Examples of behavior based on the acquired information include purchasing behavior of energy-related goods, home appliances, automobiles, housing, financial products, and other household goods, as well as disposal and selling behavior of these household goods.

[0046] The information processing device 10 is also configured to determine, by the state transition determination unit 15, whether the state of the agent represented by each node has transitioned from a non-cognitive state to a cognitive state based on a value indicating the amount of information the agent represented by each node acquires from information sources other than the agents represented by the nodes connected to each node in the intra-family network and extra-family network of each node, and to determine, based on a preset probability, whether the state of a specific agent among consumers in a motivated state has transitioned from a motivated state to a determined state. Thus configured, the information processing device 10 can simulate the process of transitioning from a non-cognitive state in which no information is recognized to a determined state in which consumption behavior has been decided.

[0047] In the first embodiment, the information processing device 10 is configured to output the processing results by the output device 22, but this is not limited thereto. For example, the information processing device may be configured to output the processing results to a memory unit (not shown) provided in the information processing device and store them in the memory unit, or may be configured to output the processing results to another computer connected to the information processing device 10 so that the information processing device can communicate with the information processing device 10.

[0048] In addition, in the present disclosure, any component of the embodiments may be modified or any component of the embodiments may be omitted. [Explanation of symbols]

[0049] 10: Information processing device 11: Setting information acquisition section 12: Node generation section 13: First network generation unit 14: Second network generation unit 15: State transition determination unit 21: Input device 22: Output device 100: Information Processing Systems

Claims

1. a node generation unit that generates a plurality of nodes representing a plurality of consumers; a first network generation unit that divides the plurality of nodes into a plurality of sets and generates a first network in which the nodes included in each set are connected to each other; a second network generation unit that generates a second network in which nodes included in different sets are connected; and a state transition determination unit that determines whether the state of the consumer indicated by each node has transitioned from a first state to a second state based on a value indicating the amount of information possessed by the consumer indicated by the node to which each node is connected in the first network and a value indicating the amount of information possessed by the consumer indicated by the node to which each node is connected in the second network.

1. An information processing device comprising:

2. The state transition determination unit determines whether the state of the consumer represented by each node has transitioned from an initial state to the first state based on a value indicating an amount of information that the consumer represented by each node acquires from information sources other than the consumer represented by the node connected to the first network and the second network of each node.

2. The information processing apparatus according to claim 1, wherein:

3. The state transition determination unit determines whether a state of a specific consumer among the consumers in the second state has transitioned from the second state to a third state based on a preset probability.

3. The information processing apparatus according to claim 2.

4. The specific consumer is a consumer that represents the consumers indicated by the nodes that make up each of the sets.

4. The information processing apparatus according to claim 3.

5. The first network generation unit generates the first network so that each node constituting the first network is a complete graph connected to all other nodes.

2. The information processing apparatus according to claim 1, wherein:

6. The state transition determination unit repeats a trial of determining whether each consumer has transitioned from the first state to the second state a plurality of times, and increases a value indicating the amount of information possessed by each consumer for each trial, thereby simulating the transition of each consumer's state based on information propagated among the plurality of consumers over time.

5. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

7. An information processing method performed by an apparatus including a node generation unit, a first network generation unit, a second network generation unit, and a state transition determination unit, a step of generating a plurality of nodes representing a plurality of consumers by the node generating unit; a step in which the first network generation unit divides the plurality of nodes into a plurality of sets and generates a first network in which the nodes included in each set are connected to each other; a step of generating a second network in which nodes included in different sets are connected by the second network generation unit; and a step in which the state transition determination unit determines whether the state of the consumer indicated by each node has transitioned from a first state to a second state based on a value indicating the amount of information possessed by the consumer indicated by the node to which each node is connected in the first network and a value indicating the amount of information possessed by the consumer indicated by the node to which each node is connected in the second network.

1. An information processing method comprising:

Images