WO2024218971A1 - 情報処理装置、情報処理方法、および情報処理プログラム - Google Patents

情報処理装置、情報処理方法、および情報処理プログラム Download PDF

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WO2024218971A1
WO2024218971A1 PCT/JP2023/015959 JP2023015959W WO2024218971A1 WO 2024218971 A1 WO2024218971 A1 WO 2024218971A1 JP 2023015959 W JP2023015959 W JP 2023015959W WO 2024218971 A1 WO2024218971 A1 WO 2024218971A1
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incentive
user
information processing
policy
history data
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French (fr)
Japanese (ja)
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秀明 金
健 倉島
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NTT Inc
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Nippon Telegraph and Telephone Corp
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

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  • This invention relates to an information processing device, an information processing method, and an information processing program.
  • Non-Patent Document 1 discloses that the effect of incentives varies depending on the method of providing the incentives.
  • the amount of incentive given each time (daily, weekly, etc.) is assumed to be either constant, monotonically decreasing, or monotonically increasing, but it is thought that the effect of the incentive also varies depending on the internal state of the person, which changes from day to day. Therefore, it may be difficult to administer effective incentives using simple incentive giving methods.
  • incentives such as cash or coupons
  • costs are directly linked to costs, so it is desirable to achieve high cost-effectiveness, i.e., to achieve large effects with less incentive.
  • the object of this invention is to address the above-mentioned issues, and to provide technology that can identify the most effective incentive measures for an individual to achieve their desired behavior, based on the individual's principles of behavior regarding incentives.
  • one aspect of the present invention is an information processing device that includes an acquisition unit that acquires behavioral history data having a series of incentive amounts at each observation time for each user and conditions for optimizing an incentive policy, a parameter estimation unit that estimates parameter values of a behavioral model for each user based on the behavioral history data, the behavioral model having self-efficacy and a reference point as internal variables that vary over time depending on the success or failure of past actions, an optimization unit that calculates an optimal incentive policy for each user based on the estimated parameter values and the conditions, and an output unit that outputs the optimal incentive policy.
  • the present invention it is possible to identify the most effective incentive measures for an individual to achieve his or her target behavior, based on the individual's behavioral principles for incentives. Furthermore, by using cost-effective incentive measures, businesses can support each user in achieving their target behavior at a lower cost. This allows businesses to increase profits or set lower service fees.
  • FIG. 1 is a block diagram showing an example of a hardware configuration of an information processing apparatus according to the first embodiment.
  • FIG. 2 is a block diagram showing the software configuration of the information processing apparatus according to the first embodiment in relation to the hardware configuration shown in FIG.
  • FIG. 3 is a flowchart showing an example of a parameter estimation operation of the information processing device.
  • FIG. 4 is a flowchart showing an example of an operation of the information processing device to calculate an optimal incentive policy.
  • FIG. 1 is a block diagram showing an example of a hardware configuration of an information processing device 1 according to the first embodiment.
  • the information processing device 1 is realized by a computer such as a PC (Personal Computer).
  • the information processing device 1 includes a control unit 11, an input/output interface 12, and a storage unit 13.
  • the control unit 11, the input/output interface 12, and the storage unit 13 are connected to each other via a bus so as to be able to communicate with each other.
  • the control unit 11 controls the information processing device 1.
  • the control unit 11 has a hardware processor such as a central processing unit (CPU).
  • the input/output interface 12 is an interface that enables the transmission and reception of information between the input device 2 and the output device 3.
  • the input/output interface 12 may include a wired or wireless communication interface.
  • the information processing device 1 and the input device 2 and output device 3 may transmit and receive information via a network such as a LAN or the Internet.
  • the memory unit 13 is a storage medium.
  • the memory unit 13 is configured by combining non-volatile memory that can be written to and read from at any time, such as a HDD (Hard Disk Drive) or SSD (Solid State Drive), non-volatile memory such as ROM (Read Only Memory), and volatile memory such as RAM (Random Access Memory).
  • the memory unit 13 has a program storage area and a data storage area in its storage area.
  • the program storage area stores application programs necessary for executing various processes, in addition to the OS (Operating System) and middleware.
  • the input device 2 includes, for example, a keyboard or a pointing device that allows the owner of the information processing device 1 (for example, an assignor, an administrator, or a supervisor) to input instructions to the information processing device 1.
  • the input device 2 may also include a reader for reading data to be stored in the storage unit 13 from a memory medium such as a USB memory, or a disk device for reading such data from a disk medium.
  • the input device 2 may further include an image scanner.
  • the output device 3 includes a display that displays output data to be presented to the owner from the information processing device 1, a printer that prints the output data, and the like.
  • the output device 3 may also include a writer for writing data to be input to another information processing device 1 such as a PC or smartphone onto a memory medium such as a USB memory, and a disk device for writing such data onto a disk medium.
  • FIG. 2 is a block diagram showing the software configuration of the information processing device 1 according to the first embodiment in relation to the hardware configuration shown in FIG.
  • the storage unit 13 includes an acquired data storage unit 131 , a parameter storage unit 132 , and an optimized incentive policy storage unit 133 .
  • the acquired data storage unit 131 stores various data acquired by the acquisition unit 111 of the control unit 11, which will be described later.
  • the data stored in the acquired data storage unit 131 may be acquired by importing behavioral history data, conditions, etc. from the outside via the input device 2, or may include data generated by the control unit 11. The behavioral history data and conditions will be described later.
  • the parameter storage unit 132 stores the parameter values of the behavioral model estimated by the parameter estimation unit 112, which will be described later.
  • the behavioral model and the parameter values of the behavioral model will be described later.
  • the optimized incentive policy storage unit 133 stores the optimal incentive policy calculated by the optimization unit 113, which will be described later.
  • the optimal incentive policy will be described later.
  • the control unit 11 includes an acquisition unit 111, a parameter estimation unit 112, an optimization unit 113, and an output control unit 114. These functional units are realized by the hardware processor executing an application program stored in the memory unit 13.
  • the acquisition unit 111 acquires necessary data and stores it in the acquired data storage unit 131.
  • the acquisition unit 111 includes a behavior history data acquisition unit 1111 and a condition acquisition unit 1112.
  • the behavioral history data acquisition unit 1111 acquires behavioral history data for each user from the input device 2 via the input/output interface 12, and stores the acquired behavioral history data in the acquired data storage unit 131.
  • the behavioral history data acquisition unit 1111 may acquire behavioral history data for one user separately, or may acquire behavioral history data for multiple users at once in a form that can be distinguished from one another.
  • the behavioral history data acquisition unit 1111 may also output a signal indicating that behavioral history data has been acquired to the parameter estimation unit 112. Details of the acquired behavioral history data will be described later.
  • the condition acquisition unit 1112 acquires conditions for each user from the input device 2 via the input/output interface 12, and stores the acquired conditions in the acquired data storage unit 131.
  • the condition acquisition unit 1112 may also acquire conditions for one user separately, or may acquire conditions for multiple users at once in a form that allows them to be distinguished from one another.
  • the condition acquisition unit 1112 may also output a signal indicating that the conditions have been acquired to the optimization unit 113. The details of the acquired conditions will be described later.
  • the parameter estimation unit 112 estimates, for each user, parameter values of a mathematical model (behavioral model) that inputs an incentive amount and outputs the degree of achievement of a target behavior, based on the behavior history data stored in the acquired data storage unit 131.
  • the behavior model has self-efficacy and a reference point as internal variables.
  • the parameter estimation unit 112 stores the estimated parameter values in the parameter storage unit 132.
  • the incentive amount, target behavior, behavior model, self-efficacy, and reference point will be described in detail later.
  • the optimization unit 113 calculates an optimal incentive policy based on the parameter values estimated by the parameter estimation unit 112 and the conditions stored in the acquired data storage unit 131.
  • the incentive policy is a function that takes as input the time, the self-efficacy at the time, the reference point, the remaining budget out of the total budget available for the incentive policy, and explanatory variables, and outputs the amount of incentive to be presented next time.
  • the optimization unit 113 calculates this optimal incentive policy for each user.
  • the optimization unit 113 also stores the calculated optimal incentive policy in the optimized incentive policy storage unit 133.
  • the method of calculating the optimal incentive policy and the details of the explanatory variables will be described later.
  • the output control unit 114 After the parameter values for a given user are estimated based on the behavioral history data of the given user, the output control unit 114 outputs the optimal incentive policy stored in the optimized incentive policy storage unit 133 to the output device 3 via the input/output interface 12 in response to acquiring the conditions from the input device 2. In addition, after the optimal incentive policy is calculated based on the parameter values and conditions for a given user, the output control unit 114 may output the optimal incentive policy for a given user stored in the optimized incentive policy storage unit 133 to the output device 3 via the input/output interface 12 in response to an operation by the user of the information processing device 1.
  • FIG. 3 is a flowchart showing an example of a parameter estimation operation of the information processing device 1.
  • the control unit 11 of the information processing device 1 reads out and executes a program stored in the storage unit 13, thereby implementing the operation of this flowchart.
  • the operation may be started at any timing. For example, it may be started automatically at regular time intervals, or may be started in response to an operation by the owner of the information processing device 1.
  • the behavioral history data acquisition unit 1111 acquires behavioral history data from the input device 2 via the input/output interface 12. For example, the user may input behavioral history data to the input device 2. Alternatively, the behavioral history data acquisition unit 1111 may acquire behavioral history data stored in an external server or the like via the input/output interface 12. Then, the behavioral history data acquisition unit 1111 stores the acquired behavioral history data in the acquired data storage unit 131. Furthermore, the behavioral history data acquisition unit 1111 may output a signal indicating that the behavioral history data has been acquired to the parameter estimation unit 112. Alternatively, the behavioral history data acquisition unit 1111 may output the behavioral history data to the parameter estimation unit 112.
  • the behavior history data includes various information for each user at each observation time.
  • the behavior history data includes a user ID (hereinafter, denoted as u), the total number of users (hereinafter, denoted as U), the duration of a target behavior (target behavior) of user u (hereinafter, denoted as Tu ), and a series of observed values of the target behavior of user u at each observation time (hereinafter, ), and a series of incentive amounts presented to user u at each observation time (hereinafter, ), the sequence of explanatory variables at each observation time of user u (hereinafter,
  • the observed value ⁇ y u t ⁇ of the target behavior is a numerical value that evaluates the success or failure of the target behavior, and takes the value 0 (failure) or 1 (success).
  • the explanatory variable ⁇ e u t ⁇ is information such as the day of the week and weather that may affect the user's target behavior other than the incentive.
  • the incentive amount ⁇ a u t ⁇ may be, for example, money or points.
  • the behavior history data may be data resulting from acquiring the above-mentioned information for each user using, for example, a behavior observation device including a sensor.
  • the reference point effect is taken into consideration in the effect of incentives.
  • the reference point effect is a phenomenon in which, when a certain amount of incentive (price, number of points, etc.) is presented as compensation for achieving a target behavior, if the amount of incentive previously acquired is less than normal, the effect becomes relatively stronger, whereas if the amount of incentive previously acquired is more than normal, the effect becomes relatively weaker.
  • the reference point is also a psychological standard point for the amount of incentive that is formed based on the amount of incentive that has been acquired in the past; if a larger amount of incentive has been acquired in the past than usual, a higher reference point is formed, whereas if a smaller amount of incentive has been acquired in the past than usual, a lower reference point is formed.
  • the reference point may be formed not only by the amount of incentive previously acquired, but also by the amount of incentive previously offered.
  • the specific relationship between the reference point and the amount of incentive previously acquired or offered may be the average, median, weighted average, maximum, minimum, etc., of the amount of incentive previously acquired.
  • the parameter estimation unit 112 estimates parameter values.
  • the parameter estimation unit 112 acquires the behavioral history data stored in the acquired data storage unit 131.
  • the parameter estimation unit 112 may use the received behavioral history data. Then, the parameter estimation unit 112 estimates, for each user u, parameter values of a behavioral model in which the incentive amount included in the behavioral history data is input and the degree of achievement of the target behavior is output.
  • the behavioral model has self-efficacy (hereinafter, denoted as x u t ) and a reference point (hereinafter, denoted as r u t ) as internal variables.
  • Self-efficacy is proposed as a precursor of human behavior in social cognitive theory, and is known to increase through achievement experiences, i.e., experiences of achieving past goals.
  • self-efficacy is assumed to vary over time depending on the success or failure of past actions, and follows the following equation:
  • ⁇ u represents the forgetting rate.
  • the forgetting rate is, for example, a value indicating how much of something that has been memorized can be retained over time.
  • the self-efficacy at the next observation time is larger if the interval between the next observation time and the current observation time is close, and if the target behavior has been achieved (succeeded), this is taken into account.
  • ⁇ u represents the forgetting rate
  • motivation can be expressed as follows, as being determined by self-efficacy, the amount of incentive presented, and explanatory variables.
  • r ut , ⁇ u h ) is a function representing the sensitivity of user u to the incentive amount, and has a parameter value ⁇ u h .
  • the sensitivity to the incentive amount depends on the reference point r ut , and examples thereof include the following.
  • h0 , h1 , ⁇ , and ⁇ are each any positive real number, estimated from behavioral history data.
  • the function form of the sensitivity to the incentive amount differs asymmetrically depending on the magnitude of the reference point and the incentive amount, which reflects the asymmetry of the value function for gains and losses in prospect theory (a psychological effect called loss aversion, in which human decision-making is more strongly influenced by losses than by gains).
  • the function form of the sensitivity to the incentive amount is not limited to formula (4).
  • ⁇ u e ) is a function representing the degree of influence of user u on the explanatory variables and has a parameter value ⁇ u e . It is assumed that the observed value y u t of the target behavior for each user at time t is probabilistically generated from the following binomial distribution P(y u t ) based on the motivation.
  • ⁇ u ⁇ ) is a non-negative function that satisfies the following condition and has a parameter value ⁇ u ⁇ :
  • the behavior model defined above is a user-specific parameter value (hereinafter, denoted as ⁇ u )
  • the parameter values are estimated by the parameter estimation unit 112 based on the maximum likelihood estimation method shown in the following equation.
  • the parameter estimation unit 112 estimates a parameter value ⁇ u of a behavior model for each user based on the behavior history data.
  • step ST103 the parameter estimation unit 112 stores the estimated parameter values in the parameter storage unit 132.
  • FIG. 4 is a flowchart showing an example of the operation of the information processing device 1 to calculate an optimal incentive policy.
  • the control unit 11 of the information processing device 1 reads out and executes a program stored in the storage unit 13, thereby implementing the operation of this flowchart.
  • the operation may be started at any timing. For example, it may be started automatically at regular time intervals, or may be started in response to an operation by the owner of the information processing device 1. Alternatively, the operation may be executed after estimating the above-mentioned parameter values.
  • the condition acquisition unit 1112 acquires conditions from the input device 2 via the input/output interface 12. For example, the user may input the conditions to the input device 2. Alternatively, the behavior history data acquisition unit 1111 may acquire conditions stored in an external server or the like via the input/output interface 12. The condition acquisition unit 1112 then stores the acquired conditions in the acquired data storage unit 131. Furthermore, the condition acquisition unit 1112 may output a signal indicating that the conditions have been acquired to the optimization unit 113. Alternatively, the condition acquisition unit 1112 may output the conditions to the optimization unit 113.
  • the conditions are the length of the target period (hereinafter, denoted as ⁇ u ), the total budget used for incentives during the target period (hereinafter, denoted as B), and the series of explanatory variables during the target period (hereinafter,
  • the objective function is composed of an objective function (hereinafter, denoted as Z) for evaluating the optimality of the incentive policy.
  • the incentive policy that maximizes the expected value of the objective function is defined as the optimal incentive policy.
  • the objective function Z is, for example, , the weighted sum of the total number of successes and the total amount of incentives paid etc., where c is a weight. It is needless to say that the objective function Z is not limited to the above example.
  • step ST202 the optimization unit 113 acquires the parameter values stored in the parameter storage unit 132. Having received a signal indicating that the conditions have been acquired, the optimization unit 113 acquires the parameter values stored in the parameter storage unit 132. Furthermore, the optimization unit 113 acquires the conditions stored in the acquired data storage unit 131. Furthermore, when the conditions are received directly from the condition acquisition unit 1112, the optimization unit 113 may use the received conditions.
  • the optimization unit 113 calculates an optimal incentive policy.
  • the optimization unit 113 calculates an optimal incentive policy based on reinforcement learning theory for each user u ⁇ 1, 2, ..., U ⁇ .
  • the incentive policy is defined by a function f u that takes as input time t, self-efficacy x u t at time t, reference point r u t , remaining available budget (hereinafter, b u t ) of the total budget at time t, and explanatory variable e u t at time t, and outputs an incentive amount a u t to be presented at time t , and is expressed by the following formula.
  • the optimal incentive policy is the policy that maximizes the expected value of the objective function Z as described above, and is expressed by the following equation.
  • E[ ⁇ ] represents the expected value.
  • the state V u t at time t is expressed as
  • the state V u t follows the following Markov decision process (hereinafter, referred to as MDP):
  • MDP Markov decision process
  • the state V u t at time t has self-efficacy, reference point, remaining budget, explanatory variables, and observed values of behavior as functions.
  • an observed value y u t of a target behavior when an incentive amount a u t is presented is generated probabilistically according to formula (3).
  • formula (11) is a case where the reference point follows formula (2-1).
  • the reference point may follow formula (2-2).
  • a measure for maximizing the expected value of the objective function Z can be obtained by, for example, solving the Bellman optimal equation.
  • the incentive measure f * that satisfies formula (10) can also be obtained by solving the Bellman optimal equation.
  • the method for solving the Bellman optimal equation may be, for example, a Deep Q Network using a neural network.
  • This Deep Q Network using a neural network is described in, for example, non-patent document "Volodymyr Mnih et al., “Playing Atari with Deep Reinforcement Learning”, arXiv, 2013” and the like.
  • the optimized incentive policy f u * is, for example, an action value function approximated by a neural network when the Bellman optimal equation is solved using a Deep Q Network.
  • the optimization unit 113 stores the calculated optimal incentive policy in the optimized incentive policy storage unit 133.
  • the optimization unit 113 may output a signal indicating that the optimal incentive policy has been stored in the optimized incentive policy storage unit 133 to the output control unit 114.
  • the optimization unit 113 may output the optimal incentive policy directly to the output control unit 114.
  • the output control unit 114 outputs the optimal incentive policy.
  • the output control unit 114 receives a signal indicating that the optimal incentive policy has been stored in the optimized incentive policy storage unit 133 from the optimization unit 113
  • the output control unit 114 acquires the optimal incentive policy f u* from the optimized incentive policy storage unit 133.
  • the output control unit 114 may use the received optimal incentive policy.
  • the output control unit 114 outputs the optimal incentive policy f u* to the output device 3 via the input/output interface 12.
  • the optimal incentive policy f u* output to the output device 3 as shown in formula (10) is a parameter value of the neural network model.
  • the user can obtain the optimal incentive policy f u* from the output device 3.
  • parameters of a behavioral model having internal variables of self-efficacy and a psychological reference point (reference point) for the amount of incentive formed by the amount of incentive acquired in the past are estimated from observed data. This makes it possible to identify the most cost-effective incentive measure for each individual to achieve the target behavior. Furthermore, by using the cost-effective incentive measure, businesses can support the achievement of the target behavior of each user at a lower cost. This allows businesses to increase profits or set lower service fees.
  • the present invention is not limited to the above embodiment.
  • an example of solving the Bellman optimal equation using Deep Q Network has been shown, but the present invention is not limited to this.
  • the Bellman optimal equation may be solved by approximation using a multilayer perceptron. That is, a general method can be applied as a method of solving the Bellman optimal equation.
  • the method described in the above embodiment can be stored as a program (software means) that can be executed by a calculator (computer) on a storage medium such as a magnetic disk (floppy disk, hard disk, etc.), optical disk (CD-ROM, DVD, MO, etc.), semiconductor memory (ROM, RAM, flash memory, etc.), and can also be distributed by transmitting it via a communication medium.
  • the program stored on the medium also includes a setting program that configures the software means (including not only execution programs but also tables and data structures) that the computer executes.
  • the computer that realizes this device reads the program stored in the storage medium, and in some cases, configures the software means using the setting program, and executes the above-mentioned processing by controlling the operation of this software means.
  • the storage medium referred to in this specification is not limited to one for distribution, but also includes storage media such as magnetic disks and semiconductor memories installed inside the computer or in devices connected via a network.
  • this invention is not limited to the above-described embodiment, and various modifications can be made in the implementation stage without departing from the gist of the invention. Furthermore, the embodiments may be implemented in combination as appropriate as possible, in which case the combined effects can be obtained. Furthermore, the above-described embodiment includes inventions at various stages, and various inventions can be extracted by appropriate combinations of the multiple constituent elements disclosed.

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170206337A1 (en) * 2016-01-19 2017-07-20 Conduent Business Services, Llc System for disease management through recommendations based on influencer concepts for behavior change
JP2019191984A (ja) * 2018-04-26 2019-10-31 ヤフー株式会社 情報処理装置、情報処理方法、およびプログラム
JP2022163957A (ja) * 2021-04-15 2022-10-27 ケイスリー株式会社 行動支援システム、行動支援方法及び行動支援プログラム
WO2022239178A1 (ja) * 2021-05-13 2022-11-17 日本電信電話株式会社 インセンティブ最適化方法、インセンティブ最適化装置、及びプログラム
WO2023042382A1 (ja) * 2021-09-17 2023-03-23 日本電信電話株式会社 情報処理装置、インセンティブ方策算出方法およびプログラム

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170206337A1 (en) * 2016-01-19 2017-07-20 Conduent Business Services, Llc System for disease management through recommendations based on influencer concepts for behavior change
JP2019191984A (ja) * 2018-04-26 2019-10-31 ヤフー株式会社 情報処理装置、情報処理方法、およびプログラム
JP2022163957A (ja) * 2021-04-15 2022-10-27 ケイスリー株式会社 行動支援システム、行動支援方法及び行動支援プログラム
WO2022239178A1 (ja) * 2021-05-13 2022-11-17 日本電信電話株式会社 インセンティブ最適化方法、インセンティブ最適化装置、及びプログラム
WO2023042382A1 (ja) * 2021-09-17 2023-03-23 日本電信電話株式会社 情報処理装置、インセンティブ方策算出方法およびプログラム

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