Information processing apparatus, information processing method, and information processing program

Abstract

To provide a technique for making a decision to maximize a profit in consideration of interests among three parties.SOLUTION: An information processing device calculates an optimum value sequence of a first quantity sequence composed of first quantities sequentially presented by a first user to a second user in a negotiation, and an optimum value of a second quantity presented by the first user to a third user when the negotiation is successful, using an objective function representing a profit or a loss of the first user in the negotiation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Technologies for using information processing devices to make decisions in commercial transactions, etc., have been put to practical use. For example, Patent Document 1 describes a technology for determining a price that maximizes total sales amount by using a demand model that represents the relationship between sales price and demand volume. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2023 / 100315 Summary of the Invention [Problem to be solved by the invention]

[0004] In order to make optimal decisions in commercial transactions, etc., it is often necessary to consider the interests of three parties. For example, in a commercial transaction in which a first user (seller) procures a product from a second user (supplier) and sells it to a third user (buyer), the first user must (1) appropriately negotiate with the second user regarding the procurement price and procurement quantity of the product, taking into consideration the interests between the first user and the second user, and (2) appropriately set the selling price of the product, taking into consideration the interests between the first user and the third user. This is because the first user's profits depend not only on the selling price and demand volume of the product, but also on the procurement price and procurement volume of the product.

[0005] As described above, the technology described in Patent Document 1 maximizes total sales amount using a demand model that represents the relationship between sales price and demand quantity. Therefore, even if the technology described in Patent Document 1 is applied to the above problem, it is not possible to appropriately negotiate with the second user regarding the procurement price and procurement quantity of the product, taking into account the interests of the second user. As a result, it is not possible to maximize the profits of the first user.

[0006] The present disclosure has been made in view of the above-mentioned problems, and an exemplary purpose thereof is to provide a technology for making decisions that maximize profits by taking into account the interests of three parties. [Means for solving the problem]

[0007] An information processing device according to an exemplary aspect of the present disclosure includes a calculation means for calculating an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function representing the first user's profit or loss in the negotiation; a first presentation means for sequentially presenting to the second user in the negotiation the optimal values ​​of each first quantity that constitute the optimal value sequence of the first quantity sequence calculated by the calculation means; and a second presentation means for presenting to the third user the optimal value of the second quantity calculated by the calculation means if the negotiation is successful.

[0008] An information processing method according to an exemplary aspect of the present disclosure includes a calculation process in which a processor calculates an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function representing the first user's profit or loss in the negotiation; a first presentation process in which the processor sequentially presents to the second user in the negotiation the optimal values ​​of the first quantities that constitute the optimal value sequence of the first quantity sequence calculated by the calculation process; and a second presentation process in which the processor presents to the third user the optimal value of the second quantity calculated by the calculation process if the negotiation is successful.

[0009] An information processing program according to an exemplary aspect of the present disclosure causes a processor to execute a calculation process that calculates an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function that represents the first user's profit or loss in the negotiation; a first presentation process that sequentially presents the optimal values ​​of the first quantities that constitute the optimal value sequence of the first quantity sequence calculated by the calculation process to the second user in the negotiation; and a second presentation process that presents the optimal value of the second quantity calculated by the calculation process to the third user if the negotiation is successful. [Effects of the Invention]

[0010] According to an exemplary aspect of the present disclosure, an exemplary effect is provided in that a technology for making decisions that maximize profits by taking into account the interests of three parties can be provided. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 2] FIG. 1 is a flow diagram showing the flow of an information processing method according to the present disclosure. [Figure 3]1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 4] FIG. 1 is a flow diagram showing the flow of an information processing method according to the present disclosure. [Figure 5] 1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 6] FIG. 1 is a flow diagram showing the flow of an information processing method according to the present disclosure. [Figure 7] FIG. 10 is a sequence diagram illustrating an example of an operation of an information processing device according to the present disclosure. [Figure 8] 8 is a table illustrating an optimum value sequence of a first quantity sequence and optimum values ​​of a second quantity calculated in the operation example shown in FIG. 7. [Figure 9] 8 is a diagram illustrating an example of a screen displayed on a terminal device operated by a supplier (second user) in the operation example shown in FIG. 7. FIG. [Figure 10] FIG. 8 is a diagram illustrating an example of a screen displayed on a terminal device operated by a buyer (third user) in the operation example shown in FIG. 7. [Figure 11] 8 is a table illustrating first teacher data stored in a memory in the operation example shown in FIG. 7. [Figure 12] 8 is a table illustrating second teacher data stored in a memory in the operation example shown in FIG. 7. [Figure 13] FIG. 1 is a block diagram illustrating a configuration of a computer that functions as an information processing device according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0012] The following are examples of embodiments of the present invention. However, the present invention is not limited to the exemplary embodiments shown below, and various modifications are possible within the scope of the claims. For example, embodiments obtained by appropriately combining the technologies (part or all of the products or methods) employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, embodiments obtained by appropriately omitting some of the technologies employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, the effects mentioned in the exemplary embodiments shown below are examples of effects expected in the exemplary embodiments, and do not define the scope of the present invention. In other words, embodiments that do not exhibit the effects mentioned in the exemplary embodiments shown below may also be included in the scope of the present invention.

[0013] Exemplary Embodiment 1 A first exemplary embodiment, which is an example of an embodiment of the present invention, will be described in detail with reference to the drawings. This exemplary embodiment is the basic form of each exemplary embodiment described later. The scope of application of each technology employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technology employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise. Furthermore, each technology shown in the drawings referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise.

[0014] (Configuration of information processing device) The configuration of the information processing device 1 will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the configuration of the information processing device 1.

[0015] The information processing device 1 is a device for negotiating between a first user U1 and a second user U2 from the perspective of the first user U1. In the information processing device 1, first quantities ω1, ω2, ..., ω Rand the second quantity p that the first user U1 will offer to the third user U3 if the negotiation is successful are optimized together. Here, R represents the number of rounds of negotiation and is an arbitrary natural number equal to or greater than 1. Note that each first quantity ω r (r is a natural number between 1 and R) may be called "offers," and the first quantity sequence π may be called "offer sequence."

[0016] As a non-limiting example, the first user U1 may be a seller who sells goods, the second user U2 may be a supplier who procures goods, and the third user U3 may be a buyer (customer) who sells goods. In this case, as a non-limiting example, each first quantity ω r (r is a natural number between 1 and R) is the procurement price of the product c r , the procurement amount of that product m r , or a combination thereof (c r ,m r ) In this case, as an example that does not limit the invention, the second quantity p may be the selling price of the product.

[0017] As shown in FIG. 1, the information processing device 1 includes a calculation unit 11, a first presentation unit 12a, and a second presentation unit 12b.

[0018] The calculation unit 11 calculates an optimal value sequence of the first quantity sequence π (each first quantity ω r ω ... r As an example that does not limit the invention, if the objective function F represents the loss of the first user U1, the calculation unit 11 may be means for calculating a value sequence of the first quantity sequence π that minimizes the value of the objective function F and the value of the second quantity p.

[0019] The first presenting unit 12a presents each of the first quantities ω constituting the optimum value sequence of the first quantity sequence π calculated by the calculation unit 11. r That is, the first presenter 12a presents the optimal value of the first quantity ω1 to the second user U2 in the first round of negotiation, the optimal value of the first quantity ω2 to the second user U2 in the second round of negotiation, ..., the optimal value of the first quantity ω R The optimum value of is presented to the second user U2.

[0020] The second presenting unit 12b is a means for presenting the optimal value of the second quantity p calculated by the calculation unit 11 to the third user U3.

[0021] (Flow of information processing method) The flow of the information processing method S1 will be described with reference to Fig. 2. Fig. 2 is a flow chart showing the flow of the information processing method.

[0022] The information processing method S1 is a method for negotiating between a first user U1 and a second user U2 from the perspective of the first user U1. In the information processing method S1, first quantities ω1, ω2, ..., ω R and the second quantity p that the first user U1 will offer to the third user U3 if the negotiation is successful are optimized together.

[0023] 2, the information processing method S1 includes a calculation process S11, a first presentation process S12a, and a second presentation process S12b. The information processing method S1 is executed by, for example, the above-described information processing device 1 or a computer including a processor.

[0024] The calculation process S11 is a calculation of the optimal value sequence of the first quantity sequence π (each first quantity ω rThe calculation process S11 is a process for calculating the optimal value of the second quantity p (a sequence of optimal values ​​of the first quantity sequence π) and the optimal value of the second quantity p using an objective function F that represents a profit or loss of the first user U1 in the negotiation. As an example that does not limit the invention, if the objective function F represents a profit of the first user U1, the calculation process S11 may be a process for calculating a value sequence of the first quantity sequence π and the value of the second quantity p that maximizes the value of the objective function F. Also, as an example that does not limit the invention, if the objective function F represents a loss of the first user U1, the calculation process S11 may be a process for calculating a value sequence of the first quantity sequence π and the value of the second quantity p that minimizes the value of the objective function F. The calculation process S11 is executed, for example, by the calculation unit 11 of the information processing device 1 described above or a processor of a computer.

[0025] The first presentation process S12a is a process of displaying the optimal values ​​of each of the first quantities ω constituting the optimal value sequence of the first quantity sequence π calculated in the calculation process S11. r That is, in the first presentation process S12a, the optimal value of the first quantity ω1 is presented to the second user U2 in the first round of negotiation, the optimal value of the first quantity ω2 is presented to the second user U2 in the second round of negotiation, ..., the optimal value of the first quantity ω R The second user U2 is then notified of the optimum value of the first presentation unit 12a. The first presentation process S12a is executed by, for example, the first presentation unit 12a of the information processing device 1 described above or a processor of a computer.

[0026] The second presentation process S12b is a process for presenting the optimal value of the second quantity p calculated by the calculation process S11 to the third user U3. The second presentation process S12b is executed by, for example, the second presentation unit 12b of the information processing device 1 described above or a processor of a computer.

[0027] (Effects of information processing device and information processing method) The information processing device 1 can make a decision to maximize the profit (or minimize the loss) of the first user U1, taking into consideration the interests between the first user U1 and the second user U2, and the interests between the first user U1 and the third user U3. The decision-making process involves determining the first quantities ω1, ω2, ..., ω that the first user U1 will sequentially present to the second user U3 in negotiation. R and determining the second quantity p that the first user U1 will offer to the third user if the negotiation is successful. The information processing method S1 also provides the same effect.

[0028] Exemplary Embodiment 2 A second exemplary embodiment, which is one example of an embodiment of the present invention, will be described in detail with reference to the drawings. Components having the same functions as those described in the above exemplary embodiment will be assigned the same reference numerals, and their description will be omitted as appropriate. The scope of application of each technology employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technology employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical hindrance occurs. Furthermore, each technology shown in each drawing referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical hindrance occurs.

[0029] (Configuration of information processing device) The configuration of the information processing device 1A will be described with reference to Fig. 3. Fig. 3 is a block diagram showing the configuration of the information processing device 1A.

[0030] The information processing device 1A is obtained by adding a first acquisition unit 13a and a second acquisition unit 13b to the information processing device 1 shown in FIG.

[0031] The first acquisition unit 13a is a means for acquiring the first model Ma. Here, the first model Ma is a model that estimates the success probability P of a negotiation from a first quantity sequence π that the first user U1 presents to the second user U2 in the negotiation. In other words, it is a model that takes the first quantity sequence π that the first user U1 presents to the second user U2 in the negotiation as an input and outputs the success probability P of the negotiation. The first model Ma may have time dependency. In this case, the input of the first model Ma is the first quantity sequence π that the first user U1 presents to the second user U2 in the negotiation and the time at which the negotiation is conducted. Hereinafter, the success probability P output from the first model Ma when the first quantity sequence π is input to the first model Ma will also be referred to as the success probability P(π). When the first model Ma has time dependency, the success probability P output from the first model Ma when the first quantity sequence π and time t are input to the first model Ma will be referred to as the success probability P t It is also written as (π).

[0032] As a non-limiting example, the first model Ma may be a machine-learnable model such as a neural network, a support vector machine, or a random forest. In this case, the machine learning of the first model Ma is performed so that the relationship between the input π of the first model Ma and the output P(π) of the first model Ma reproduces a previously observed relationship between the first quantity sequence π that the first user U1 presents to the second user U2 in a negotiation and the probability P of success of the negotiation. Furthermore, if the first model Ma has time dependency, the machine learning of the first model Ma is performed so that the relationship between the input π,t of the first model Ma and the output P(π) of the first model Ma reproduces a previously observed relationship between the first quantity sequence π that the first user U1 presents to the second user U2 in a negotiation and the probability P of success of the negotiation. t The relationship between (π) is established so as to reproduce the previously observed relationship between the first quantity sequence π that a first user U1 presents to a second user U2 in a negotiation taking place at time t and the probability P of success of the negotiation.

[0033] As an example that does not limit the invention, the first model Ma may be a function including a parameter θ. In this case, the parameter θ is set so that the relationship between the input (argument of the function) π of the first model Ma and the output (value of the function) P(π) of the first model Ma reproduces the previously observed relationship between the first quantity sequence π that the first user U1 presents to the second user U2 in a negotiation and the probability P of success of the negotiation. In addition, if the model Ma has time dependency, the parameter θ is set so that the relationship between the input π,t of the first model Ma and the output P of the first model Ma reproduces the previously observed relationship between the first quantity sequence π that the first user U1 presents to the second user U2 in a negotiation and the probability P of success of the negotiation. t The relationship between (π) is established so as to reproduce the previously observed relationship between the first quantity sequence π that a first user U1 presents to a second user U2 in a negotiation taking place at time t and the probability P of success of the negotiation.

[0034] The success probability P(π) for the first quantity sequence π can be decomposed into the sum of the success probabilities P(π,r) in each round r (P(π) = P(π,1) + P(π,2) + ... + P(π,R)). Here, the success probability P(π,r) is the probability of success of the first quantity ω presented by the first user U1 in the rth round. r represents the probability that the negotiation will be successful when the second user U2 accepts the first quantity sequence π. The first model Ma may be configured to output R success probabilities P(π,1), P(π,2), ..., P(π,R) for each of the first quantities ωr that make up the first quantity sequence π, instead of outputting one success probability P(π) for the first quantity sequence π.

[0035] As a non-limiting example, the success probability P(π,r) in each round r is expressed by the logarithm function p(x)=e x / (1+e x ), and the utility function u(ω r ) and P(π,r)=p(u(ω r )) can be expressed as the first quantity ω r is the procurement price of the product c r and the amount of goods procured m r When the combination of r ) is, for example, u(ω r )=(c r -c0)mr +a0max(t+2-T,0), where c0 is the minimum procurement price at which the first user U1 can secure a profit, and a0 is a non-negative real number. The utility function u(ω r ) represents sales, and the second term represents the compromise term associated with the end of the sales period. In this case, the first model Ma is r )) as the first quantity ω r and is estimated using a Gaussian process or the like as a function of time t.

[0036] The second acquisition unit 13b is a means for acquiring the second model Mb. Here, the second model Mb is a model that estimates the probability Q of obtaining a reaction from a third quantity D that represents the reaction of the third user U3. In other words, the second model Mb is a model that receives the third quantity D that represents the reaction of the third user U3 as an input and outputs the probability Q of obtaining the reaction. The second model Mb can be regarded as a function that represents the probability distribution of the third quantity D (random variable). The second model Mb may be a model that depends on the second quantity p that the first user U1 presents to the third user U3. In this case, the inputs of the second model Mb are the second quantity p that the first user U1 presents to the third user U3 and the third quantity D that represents the reaction of the third user U3 to the second quantity p. Furthermore, the second model Mb may have time dependency. In this case, the inputs of the second model Mb are time t and the third quantity D that represents the reaction of the third user U3 at that time t. Hereinafter, the probability Q of an output from the second model Mb when the third quantity sequence D is input to the second model Mb will also be referred to as probability Q(D). In addition, if the second model Mb depends on the second quantity p, the probability Q of an output from the second model Mb when the second quantity p and the third quantity D are input to the second model Mb will be referred to as probability Q p Also, when the second model Mb has time dependency, the probability Q of the output from the second model Mb when the time t and the third quantity sequence D are input to the second model Mb is expressed as the probability Q t Also, when the second model Mb depends on both the time t and the second quantity p, the probability Q of the output from the second model Mb when the time t, the second quantity p, and the third quantity D are input to the second model Mb is expressed as the probability Qt,p Also written as (D).

[0037] As an example that does not limit the invention, the second model Mb may be a machine-learnable model such as a neural network, a support vector machine, or a random forest. In this case, the machine learning of the second model Mb is performed so that the relationship between the input D of the second model Mb and the output Q(D) of the second model Mb reproduces a previously observed relationship between a third quantity D representing a reaction of the third user U3 and the probability Q of obtaining that reaction. Furthermore, if the second model Mb depends on the second quantity p, the machine learning of the second model Mb is performed by calculating the input p, D of the second model Mb and the output Q(D) of the second model Mb for each second quantity p. p The relationship between (D) and (D) is performed so as to reproduce the previously observed relationship between the third quantity D representing the reaction of the third user U3 and the probability Q of obtaining the reaction. In addition, when the second model Mb has time dependency, the machine learning of the second model Mb is performed by reproducing the relationship between the input D of the second model Mb and the output Q of the second model Mb at each time t. t (D) is made to replicate a previously observed relationship between a third quantity D representing a response of a third user U3 and the probability Q of obtaining that response.

[0038] As an example that does not limit the invention, the second model Mb may be a function including a parameter λ. In this case, the parameter λ is set so that the relationship between the input (function argument) D of the second model Mb and the output (function value) Q(D) of the second model Mb reproduces the previously observed relationship between the third quantity D representing the reaction of the third user U3 and the probability Q of obtaining that reaction. Furthermore, if the second model Mb depends on the second quantity p, the machine learning of the second model Mb calculates the relationship between the input (function argument) D of the second model Mb and the output (function value) Q of the second model Mb for each second quantity p. pThe relationship between (D) and (D) is set so as to reproduce the previously observed relationship between the third quantity D representing the reaction of the third user U3 and the probability Q of obtaining that reaction. In addition, when the second model Mb has time dependency, the parameter λ is set so as to reproduce the relationship between the input (argument of the function) D of the second model Mb and the output (value of the function) Q of the second model Mb at each time t. t (D) is made to replicate a previously observed relationship between a third quantity D representing a response of a third user U3 and the probability Q of obtaining that response.

[0039] In the calculation unit 11 of the information processing device 1A, using the first model Ma acquired by the first acquisition unit 13a and the second model Mb acquired by the second acquisition unit 13b, the value sequence of the first quantity sequence π and the value of the second quantity p that maximize or minimize the value of the objective function F are calculated as the optimal value sequence of the first quantity sequence π and the optimal value of the second quantity p, respectively.

[0040] (Flow of information processing method) The flow of the information processing method S1A will be described with reference to Fig. 4. Fig. 4 is a flow diagram showing the flow of the information processing method S1A.

[0041] The information processing method S1A is obtained by adding a first acquisition process S13a and a second acquisition process S13b to the information processing method S1 shown in Fig. 2. The information processing method S1A is executed by, for example, the above-described information processing device 1A or a computer including a processor.

[0042] The first acquisition process S13a is a process for acquiring a first model Ma. As described above, the first model Ma is a model that estimates a success probability P of a negotiation from a first sequence of quantities π that a first user U1 presents to a second user U2 in a negotiation. In other words, the first acquisition process S13a is a model that takes the first sequence of quantities π that a first user U1 presents to a second user U2 in a negotiation as input and outputs the success probability P of the negotiation. The first acquisition process S13a is executed by, for example, the first acquisition unit 13a of the information processing device 1A described above or a processor of a computer.

[0043] The second acquisition process S13b is a process for acquiring the second model Mb. As described above, the second model Mb is a model that estimates the probability Q of obtaining a reaction from the third quantity D that represents the reaction of the third user U3. In other words, the second acquisition process S13b is a model that receives the third quantity D that represents the reaction of the third user U3 as an input and outputs the probability Q of obtaining the reaction. The second acquisition process S13b is executed by, for example, the second acquisition unit 13b of the information processing device 1A described above or a processor of a computer.

[0044] In the calculation process S11 of the information processing method S1A, the first model Ma acquired in the first acquisition process S13a and the second model Mb acquired in the second acquisition process S13b are used to calculate the value sequence of the first quantity sequence π and the value of the second quantity p that maximize or minimize the value of the objective function F as the optimal value sequence of the first quantity sequence π and the optimal value of the second quantity p, respectively.

[0045] The first acquisition process S13a and the second acquisition process S13b may be executed in any order. The second acquisition process S13b may be executed after the first acquisition process S13a, or the second acquisition process S13b may be executed before the first acquisition process S13a. Furthermore, the first acquisition process S13a and the second acquisition process S13b may be executed in parallel.

[0046] (Effects of information processing device and information processing method) According to the information processing device 1A, it is possible to make a decision to maximize the profit (or minimize the loss) of the first user U1, taking into consideration the interests between the first user U1 and the second user U2, and the interests between the first user U1 and the third user U3. The decision-making content includes first quantities ω1, ω2, ..., ω R and determining the second quantity p that the first user U1 will offer to the third user if the negotiation is successful. The information processing method S1A also provides a similar effect.

[0047] Furthermore, according to the information processing device 1A, the above-mentioned decision-making is performed using the first model Ma and the second model Mb. Therefore, by improving the accuracy of the first model Ma and the second model Mb, the accuracy of the above-mentioned decision-making can be improved. The same effect can be obtained by the information processing method S1A.

[0048] (Objective function example 1) As a non-limiting example, the first user U1 may be a seller who sells goods, the second user U2 may be a supplier from which the goods are procured, and the third user U3 may be a buyer to whom the goods are sold. In this case, as a non-limiting example, each first quantity ω r is the procurement price of the product, c r and the procurement amount of that product m r Combination with (c r ,m r ), the second quantity p is the selling price of the product, and the third quantity D is the quantity demanded of the product.

[0049] In this case, as a first specific example that does not limit the invention, the objective function F can be defined by the following formula (1).

[0050] F(p,π)=E D [min(D,m(π))pm(π)c(π)]P(π)…(1) Here, m(π) is the procurement amount when the negotiation in which the first user U1 presents the first quantity sequence π to the second user U2 is successful, for example, the first quantity ω R Procurement amount included in m R Furthermore, c(π) is the procurement price when the negotiation in which the first user U1 presents the first quantity sequence π to the second user U2 is successful, and for example, the first quantity ω R Procurement price included in c R Furthermore, P(π) is the probability of success in negotiation in which the first user U1 presents the first quantity sequence π to the second user U2, estimated using the first model Ma. Furthermore, E D[·] is the demand quantity D as a random variable (more precisely, the probability Q(D) or Q estimated using the second model Mb). p (D) represents the expected value when it is regarded as a random variable with a probability distribution.

[0051] On the right side of the above formula (1), min(D, m(π)) represents the sales amount (number of units) of the product, and p represents the sales price (unit price) of the product. Therefore, min(D, m(π))p represents the income of the first user U1. Also, on the right side of the above formula (1), m(π) represents the procurement amount (number of units) of the product, and c(π) represents the procurement price (unit price) of the product. Therefore, m(π)c(π) represents the expenditure of the first user U1. Therefore, E on the right side of the above formula D [min(D,m(π))pm(π)c(π)] represents the expected profit of the first user U1 if the negotiation is successful. The entire right side of the above equation (1), multiplied by the success probability P(π), represents the expected profit of the first user U1.

[0052] As described above, the first model Ma is a first model of the first quantities ω r In this case, the objective function F can be defined by the following formula (1′):

[0053] F(p,π)=Σ{E D [min(D,m r )pm r c r ]P(π,r)}…(1') Here, Σ is the sum over r. The entire right side of the above formula (1′) represents the expected value of the profit of the first user U1, similar to the entire right side of the above formula (1).

[0054] (Objective function example 2) As a non-limiting example, the first user U1 may be a seller who sells goods, the second user U2 may be a supplier from which the goods are procured, and the third user U3 may be a buyer to whom the goods are sold. In this case, as a non-limiting example, each first quantity ω ris the procurement price of the product, c r and the procurement amount of that product m r Combination with (c r ,m r ), the second quantity p may be the selling price of the commodity, the third quantity D may be the demand quantity of the commodity, and the fourth quantity n may be the inventory quantity of the commodity.

[0055] In this case, as a second specific example that does not limit the invention, the objective function F can be defined by the following formula (2).

[0056] F(p,π)=E D [min(D,n+m(π))pm(π)c(π)]P(π)…(2) The objective function F(p,π) defined by the above formula (2) is obtained by replacing the sales volume min(D,m(π)) in the objective function F(p,π) defined by the above formula (1) with the sales volume min(D,n+m(π)). Therefore, the entire right side of the above formula (2) represents the expected profit of the first user U1, taking into account the inventory amount of the product.

[0057] As described above, the first model Ma is a first model of the first quantities ω r In this case, the objective function F can be defined by the following formula (2'). r The success probability P(π,r) corresponding to is the probability that the negotiation will be successful when the second user U2 accepts the first quantity ωr proposed by the first user U1 in the rth round of the negotiation, as described above.

[0058] F(p,π)=Σ{E D [min(D,n+m r )pm r c r ]P(π,r)}…(2') Here, Σ is the sum over r. The entire right side of the above formula (2′) represents the expected profit of the first user U1, taking into account the inventory of the product, just like the entire right side of the above formula (2).

[0059] (Objective function example 3) In this specific example, a first user U1 negotiates with a second user U2 at times t=t1, t2, ..., tS. Here, S is an arbitrary natural number equal to or greater than 2, which represents the number of negotiations. Let π be the first quantity sequence that the first user U1 presents to the second user U2 in the negotiation at time ts (s is a natural number equal to or greater than 1 and equal to or less than S). ts and the first quantity sequence π ts The first quantity that constitutes ω ts;1 ,ω ts;2 ,…,ω ts;Rts Here, R ts is the number of rounds of negotiation at time ts. Also, the second quantity that the first user U1 offers to the third user U3 if the negotiation at time ts is successful is p ts It is written as follows.

[0060] As a non-limiting example, the first user U1 may be a seller who sells goods, the second user U2 may be a supplier from which the goods are procured, and the third user U3 may be a buyer to whom the goods are sold. In this case, as a non-limiting example, each first quantity ω ts;r is the product's time t ts Procurement price in c ts;r and the time of the product ts Procurement volume m ts;r Combination with (c ts;r ,m ts;r ) and the second quantity p ts is the selling price of the product at time ts, and the third quantity D ts is the quantity demanded for that product at time ts, and the fourth quantity n ts may be the inventory amount of the product at time ts.

[0061] In this case, as a third specific example that does not limit the invention, the objective function F(p t1 ,p t2 ,…,p tS ,π t1 ,π t2 ,…,π tS) can be defined by the following formula (3) and formula (4).

[0062] F ts (p ts ,π ts )=E Dts [min(D ts ,n ts +m ts (π ts ))p ts -m ts (π ts )c ts (π ts )]P ts (π ts )…(3) F(p t1 ,p t2 ,…,p tS ,π t1 ,π t2 ,…,π tS )=F t1 (p t1 ,π t1 )+F t2 (p t2 ,π t2 )+…+F tS (p tS ,π tS )…(4) where m ts (π ts ) is the first quantity sequence π ts The procurement quantity when the negotiation at time ts is successful is, for example, the first quantity ω ts;R Procurement amount included in m ts;R Also, c ts (π ts ) is the first quantity sequence π ts is the procurement price when the negotiation at time ts is successful. For example, the first quantity ω ts;R Procurement price included in c R;ts Also, P ts (π ts ) is the time-dependent first model Ma estimated by the first user U1 to the second user U2, and the first quantity sequence π tsis the probability of success in negotiation at time ts when E Dts [·] is the demand D ts is a random variable (more precisely, the probability Q estimated using the second model Mb) ts (D) or Q ts,p (D) represents the expected value when considered as a random variable with a probability distribution. The inventory quantity n at time ts ts is the initial inventory quantity n at time t1 t1 and the recurrence formula n ts =max(n ts-1 +m ts-1 -D ts-1 ,0) and is calculated using.

[0063] On the right side of the above equation (3), min(D ts ,n ts +m ts (π ts )) represents the sales volume (number of units) of the product at time ts, and p ts represents the selling price (unit price) of the product at time ts. Therefore, min(D ts ,n ts +m ts (π ts ))p ts represents the income of the first user U1 at time ts, taking into account the inventory of the product. ts (π ts ) represents the procurement quantity (number) of the product at time ts, and c ts (π ts ) represents the procurement price (unit price) of the product at time ts. Therefore, m ts (π ts )c ts (π ts ) represents the expenditure of the first user U1 at time ts. Therefore, E on the right side of the above equation Dts [min(D ts ,n ts +m ts (π ts ))p ts -m ts (π ts )c ts (π ts)] represents the expected profit of the first user U1, taking into account the inventory of the product, if the negotiation at time ts is successful. ts (π ts ), the entire right-hand side of the above equation (3) represents the expected profit of the first user U1 at time ts, taking into account the inventory of the product. Therefore, the right-hand side of the above equation (4) represents the sum of the expected profit of the first user U1 at times t1, t2, ..., ts, taking into account the inventory of the product.

[0064] As described above, the first model Ma is a first quantity sequence π ts Each first quantity ω that constitutes ts;r The success probability P ts (π ts , r) may be output. In this case, a function F tS (p ts ,π ts ) can be defined by the following formula (3'). r The corresponding success probability P ts (π ts ,r) is the time t s is the probability that the negotiation will be successful by the second user U2 accepting the first quantity ωr offered by the first user U1 in the r-th round of negotiation.

[0065] F ts (p ts ,π ts )=Σ{E Dts [min(D ts ,n ts +m ts;r )p ts -m ts;r c ts;r ]P ts (π ts ,r)}…(3') Here, Σ is the sum over r. The entire right side of the above formula (3′) represents the expected value of the profit of the first user U1 at time ts, similar to the entire right side of the above formula (3).

[0066] Exemplary Embodiment 3 A third exemplary embodiment, which is an example of an embodiment of the present invention, will be described in detail with reference to the drawings. Components having the same functions as those described in the above exemplary embodiment will be assigned the same reference numerals, and their description will be omitted as appropriate. The scope of application of each technology employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technology employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise. Furthermore, each technology shown in each drawing referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise.

[0067] (Configuration of information processing device) The configuration of the information processing device 1B will be described with reference to Fig. 5. Fig. 5 is a block diagram showing the configuration of the information processing device 1B.

[0068] The information processing device 1B is obtained by adding a first observation unit 14a, a second observation unit 14b, a first update unit 15a, and a second update unit 15b to the information processing device 1 shown in FIG.

[0069] The first observation unit 14a is a means for observing the success or failure of a negotiation in which the first user U1 presents to the second user U2 the optimal value sequence of the first quantity sequence π calculated by the calculation unit 11. The first observation unit 14a provides the first update unit 15a with first teacher data D1 obtained by combining the optimal value sequence of the first quantity sequence π calculated by the calculation unit 11 with the observed success or failure of the negotiation.

[0070] The second observation unit 14b is a means for observing the value of the third quantity D obtained when the first user presents the optimal value of the second quantity p calculated by the calculation unit 11 to the third user. The second observation unit 14b provides the second update unit 15b with second teacher data D2 obtained by combining the optimal value of the second quantity p calculated by the calculation unit 11 with the observed value of the third quantity D. Note that the optimal value of the second quantity p is required when a model dependent on the second quantity p is used as the second model Mb. Therefore, when a model independent of the second quantity p is used as the second model Mb, teacher data consisting only of the observed value of the third quantity D may be used.

[0071] The first updating unit 15a is configured to update the first model Ma described in the second exemplary embodiment using the first teacher data D1 acquired from the first observing unit 14a. The first model Ma may be updated using a single piece of first teacher data D1 obtained in a single negotiation, or may be updated using a data set consisting of multiple pieces of first teacher data D1 obtained in two or more negotiations.

[0072] The second update unit 15b is configured to update the second model Mb described in the second exemplary embodiment using the second teacher data D2 acquired from the second observation unit 14b. The second model Mb may be updated using a single piece of second teacher data D2 obtained in a single negotiation, or may be updated using a data set consisting of multiple pieces of second teacher data D2 obtained in two or more negotiations.

[0073] The calculation unit 11 of the information processing device 1B uses the first model Ma updated by the first update unit 15a and the second model Mb updated by the second update unit 15b to calculate the value sequence of the first quantity sequence π and the value of the second quantity p that maximize or minimize the value of the objective function F as the optimal value sequence of the first quantity sequence π and the optimal value of the second quantity p, respectively.

[0074] (Flow of information processing method) The flow of the information processing method S1B will be described with reference to Fig. 6. Fig. 3 is a flow diagram showing the flow of the information processing method S1A.

[0075] Information processing method S1B is obtained by adding a first observation process S14a, a second observation process S14b, a first update process S15a, and a second update process S15b to information processing method S1 shown in Fig. 2. Information processing method S1B is executed by, for example, information processing device 1B shown in Fig. 5 or a computer equipped with a processor.

[0076] In the information processing method S1B, the calculation process S11, the first presentation process S12a, the second presentation process S12b, the first observation process S14a, and the second observation process S14b are repeatedly executed until a predetermined termination condition is met, and then the first update process S15a and the second update process S15b are executed.

[0077] The first observation process S14a is a process for observing the success or failure of a negotiation in which a first user U1 presents an optimal value sequence of the first quantity sequence π calculated by the calculation process S11 to a second user U2. First teacher data D1 obtained by combining the optimal value sequence of the first quantity sequence π calculated by the calculation process S11 with the success or failure of the negotiation observed by the first observation process S14a is stored in, for example, a memory (not shown). The first observation process S14a is executed, for example, by a first observation unit 14a of the information processing device 1B shown in FIG. 5 or a processor of a computer.

[0078] The second observation process S14b is a process for observing the value of the third quantity D obtained when the first user presents the optimal value of the second quantity p calculated by the calculation process S11 to the third user. The second training data D2 obtained by combining the optimal value of the second quantity p calculated by the calculation process S11 and the value of the third quantity D observed by the second observation process S14b is stored, for example, in a memory (not shown). The optimal value of the second quantity p is required when a model dependent on the second quantity p is used as the second model Mb. Therefore, when a model independent of the second quantity p is used as the second model Mb, training data consisting only of the observed value of the third quantity D may be used. The second observation process S14b is executed, for example, by the second observation unit 14b of the information processing device 1B shown in FIG. 5 or a processor of a computer.

[0079] The first update process S15a is a process for updating the first model Ma described in the second exemplary embodiment using the first teacher data D1 accumulated in a memory (not shown) until a predetermined termination condition is satisfied. The first update process S15a is executed by, for example, the first update unit 15a of the information processing device 1B shown in FIG. 5 or a processor of a computer.

[0080] The second update process S15b is a process for updating the second model Mb described in the second exemplary embodiment using the second teacher data D2 accumulated in the memory until a predetermined termination condition is satisfied. The second update process S15b is executed by, for example, the second update unit 15b of the information processing device 1B shown in FIG. 5 or a processor of a computer.

[0081] The first update process S15a and the second update process S15b may be executed in any order. The first update process S15a may be executed first, followed by the second update process S15b, or the second update process S15b may be executed first, followed by the first update process S15a. The first update process S15a and the second update process S15b may also be executed in parallel.

[0082] The information processing method S1B is repeatedly performed. In the calculation process S11 included in the information processing method S1B performed the nth time, the first model Ma and the second model Mb updated by the first update process S15a and the second update process S15b included in the information processing method S1B performed the (n-1)th time are used.

[0083] (Effects of information processing device and information processing method) According to the information processing device 1B, it is possible to make a decision to maximize the profit (or minimize the loss) of the first user U1, taking into consideration the interests between the first user U1 and the second user U2, and the interests between the first user U1 and the third user U3. The decision-making content includes first quantities ω1, ω2, ..., ω R and determining the second quantity p that the first user U1 will offer to the third user if the negotiation is successful. The information processing method S1B also provides a similar effect.

[0084] Furthermore, according to the information processing device 1B, in order to improve the accuracy of the first model Ma and the second model Mb used in the decision-making, the first model Ma and the second model Mb used in the decision-making can be updated. Therefore, the accuracy of the decision-making can be improved. The same effect can be obtained by the information processing method S1B.

[0085] (Example of operation of information processing device) As a non-limiting example, the first user U1 may be a seller who sells goods, the second user U2 may be a supplier from which the goods are procured, and the third user U3 may be a buyer to whom the goods are sold. In this case, as a non-limiting example, each first quantity ω r is the procurement price of the product, c r and the procurement amount of that product m r Combination with (c r ,m r ), the second quantity p may be the selling price of the commodity, and the third quantity D may be the quantity demanded of the commodity.

[0086] An example of the operation of the information processing device 1B that can be realized in this case will be described with reference to Fig. 7. Fig. 7 is a sequence diagram showing an example of the operation of the information processing device 1B. In the following description, Figs. 8 to 12 will be referred to as appropriate.

[0087] First, the information processing device 1B calculates the optimal value sequence of the first quantity sequence π and the optimal value of the second quantity p. In the operation example illustrated in Fig. 5, the optimal value sequence of the first quantity sequence π and the optimal value of the second quantity p are calculated as shown in Fig. 8.

[0088] Next, in the first round of negotiation, the information processing device 1B presents the first quantity ω1={procurement price: 100 yen, procurement quantity: 5 units} as an offer to the supplier. In the operation example illustrated in Fig. 5, the supplier rejects this offer and presents a counteroffer ω'1={procurement price: 125 yen, procurement quantity: 5 units} to the seller.

[0089] The first quantity ω1={procurement price: 100 yen, procurement quantity: 5 units} is presented to the supplier, for example, using a screen displayed on a terminal device operated by the supplier. An example of the screen displayed on a terminal device operated by the supplier is shown in FIG. 9. The screen shown in FIG. 9 also allows the supplier to input a counteroffer.

[0090] Next, the information processing device 1B determines whether to accept the counteroffer ω'1 from the supplier. Any algorithm can be used for this determination, but one possible algorithm is to accept the counteroffer ω'1 from the supplier if it is more favorable than the next offered first quantity ω2={procurement price: 110 yen, procurement quantity: 5 units}, and reject it if it is not. In the operational example illustrated in FIG. 5, the counteroffer ω'1 is more unfavorable than the next offered first quantity ω2={procurement price: 110 yen, procurement quantity: 5 units}, so the information processing device 1B rejects the counteroffer ω'1.

[0091] Next, in the second round of negotiation, the information processing device 1B presents the first quantity ω2={procurement price: 110 yen, procurement quantity: 5 units} as an offer to the supplier. In the operation example illustrated in Fig. 5, the supplier rejects this offer and presents a counteroffer ω'2={procurement price: 120 yen, procurement quantity: 4 units} to the seller.

[0092] Next, information processing device 1B determines whether to accept counteroffer ω'2 from the supplier. In the example of operation shown in Fig. 5, counteroffer ω'2 has better terms than the next offered first quantity ω3 = {procurement price: 125 yen, procurement quantity: 4 units}, so information processing device 1B accepts counteroffer ω'2. This means that the negotiation is successful.

[0093] Next, if the negotiation is successful, the information processing device 1B presents the second quantity p={selling price: 500 yen} to the buyer. In the operation example illustrated in Fig. 5, it is assumed that the buyer who confirmed this selling price purchases three products.

[0094] The second quantity p={selling price: 500 yen} is presented to the buyer, for example, using a screen displayed on a terminal device operated by the buyer. An example of the screen displayed on the terminal device operated by the buyer is shown in Figure 10.

[0095] Finally, the information processing device 1B observes the negotiation result. Then, the information processing device 1B associates the observed negotiation result = {Success} with the first quantity sequence π = {ω1 = {Procurement price: 110 yen, Procurement quantity: 5 units}, ω2 = {Procurement price: 110 yen, Procurement quantity: 5 units}, ω3 = {Procurement price: 125 yen, Procurement quantity: 4 units}} and stores the result as first training data D1 in the memory of the information processing device 1B. An example of the first training data D1 stored in the memory of the information processing device 1B is shown in FIG. 11.

[0096] Furthermore, information processing device 1B observes a third quantity D. Then, information processing device 1B associates the observed third quantity D={demand quantity: 3 units} with the second quantity p={selling price: 500 yen} and stores it in memory as second training data D2. An example of the second training data D2 stored in the memory of information processing device 1B is shown in FIG. 12.

[0097] [Other application examples] In this embodiment, we consider a case in which the first user U1, who is a seller, negotiates the procurement price and procurement quantity of goods with the second user U2, who is a supplier, and presents the selling price of the goods to the third user U3, who is a buyer, but the scope of application of this disclosure is not limited to this.

[0098] For example, the present disclosure can be applied to a case where a forwarder negotiates the cargo loading price and loading quantity with a second user U2, which is an air cargo company, and presents the cargo shipping fee to a third user U3, which is a shipper. In this case, the above-described exemplary embodiments can be applied by replacing (1) the procurement price of the product with the cargo loading price, (2) the procurement quantity of the product with the cargo loading quantity, (3) the sales price of the product with the cargo shipping fee, (4) the demand quantity of the product (the number of products that the buyer wants to purchase) with the cargo demand quantity (the number of packages that the shipper wants to transport), and (5) the inventory quantity of the product with the cargo loading quantity that has already been secured.

[0099] In this case, in the above-mentioned formula (1), min(D,m(π))×p, which represents the income of the first user U1, may be replaced with D×p, and a penalty term according to the overbooking amount Dm(π) may be added to the formula (1). Similarly, in the above-mentioned formula (2), min(D,n+m(π))×p, which represents the income of the first user U1, may be replaced with D×p, and a penalty term according to the overbooking amount D-(n+m(π)) may be added to the formula (2). Similarly, in the above-mentioned formula (3), min(D ts ,n ts +m ts (π ts ))×pts D ts ×p and the overbooking amount D ts -(n ts +m ts (π ts )) may be added to equation (3).

[0100] [Software implementation example] Some or all of the functions of the information processing devices 1, 1A, 1B (hereinafter also referred to as "each of the above devices") may be realized by hardware such as an integrated circuit (IC chip), or by software.

[0101] In the latter case, each of the above devices is realized by, for example, a computer that executes instructions of a program, which is software that realizes each function. An example of such a computer (hereinafter referred to as computer C) is shown in Figure 13. Figure 13 is a block diagram showing the hardware configuration of computer C that functions as each of the above devices.

[0102] The computer C includes at least one processor C1 and at least one memory C2. The memory C2 stores a program P for causing the computer C to operate as each of the above-mentioned devices. In the computer C, the processor C1 reads and executes the program P from the memory C2, thereby realizing the functions of each of the above-mentioned devices.

[0103] The processor C1 may be, for example, a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a tensor processing unit (TPU), a quantum processor, a microcontroller, or a combination thereof. The memory C2 may be, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or a combination thereof.

[0104] The computer C may further include a RAM (Random Access Memory) for expanding the program P during execution and for temporarily storing various data. The computer C may also include a communication interface for transmitting and receiving data to and from other devices. The computer C may also include an input / output interface for connecting input / output devices such as a keyboard, mouse, display, and printer.

[0105] Furthermore, the program P can be recorded on a non-transitory tangible recording medium M that can be read by the computer C. Such a recording medium M can be, for example, a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit. The computer C can acquire the program P via such a recording medium M. The program P can also be transmitted via a transmission medium. Such a transmission medium can be, for example, a communication network or broadcast waves. The computer C can also acquire the program P via such a transmission medium.

[0106] Furthermore, the functions of each of the devices may be realized by a single processor provided in a single computer, by multiple processors provided in a single computer working in cooperation, or by multiple processors provided in each of multiple computers working in cooperation. Furthermore, the programs for causing each of the devices to realize the functions may be stored in a single memory provided in a single computer, or may be distributed and stored in multiple memories provided in a single computer, or may be distributed and stored in multiple memories provided in each of multiple computers.

[0107] [Appendix A] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0108] (Appendix A1) a calculation means for calculating an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function that represents a profit or loss of the first user in the negotiation; a first presentation means for sequentially presenting to the second user in the negotiation the optimal values ​​of the first quantities that constitute the optimal value sequence of the first quantity sequence calculated by the calculation means; and a second presentation means for presenting the optimal value of the second quantity calculated by the calculation means to the third user if the negotiation is successful. Information processing device.

[0109] (Appendix A2) a first acquisition means for acquiring a first model for estimating the probability of success of the negotiation from the first quantity sequence; and a second acquisition means for acquiring a second model that estimates a probability of obtaining a reaction from a third quantity that represents a reaction of the third user, the objective function includes the first quantity, the second quantity, and the third quantity as variables; the calculation means calculates, using the first model and the second model, a value sequence of the first quantity sequence and values ​​of the second quantity that maximize or minimize the value of the objective function as an optimal value sequence of the first quantity sequence and optimal values ​​of the second quantity; 10. The information processing device according to claim 1,

[0110] (Appendix A3) a first observation means for observing whether the negotiation is successful or not when the optimal value sequence of the first quantity sequence calculated by the calculation means is sequentially presented to the second user; a first updating means for updating a first model that estimates the probability of success of the negotiation from the first quantity sequence using a combination of an optimal value sequence of the first quantity sequence calculated by the calculating means and the success or failure of the negotiation observed by the first observing means; a second observation means for observing a value of a third quantity representing a reaction of the third user; and second updating means for updating a second model that estimates the probability of obtaining the response from the third quantity using the third quantity observed by the second observing means, the objective function includes the first quantity, the second quantity, and the third quantity as variables; the calculation means calculates, using the first model and the second model, a value sequence of the first quantity sequence and values ​​of the second quantity that maximize or minimize the value of the objective function as an optimal value sequence of the first quantity sequence and optimal values ​​of the second quantity; 10. The information processing device according to claim 1,

[0111] (Appendix A4) The first user is a seller who sells products, the second user is a supplier from which the product is procured, the third user is a buyer to whom the product is sold, the first quantity is a procurement price of the commodity and a procurement amount of the commodity; The second quantity is the selling price of the product, The third quantity is the demand quantity of the commodity. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing information.

[0112] (Appendix A5) one or both of the first model and the second model have time dependence; The objective function is a function representing the profit or loss of the first user in the negotiation performed at each time, and is a sum of functions including the first quantity, the second quantity, and the third quantity as variables. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing information.

[0113] (Appendix A6) the function includes the first quantity, the second quantity, the third quantity, and the fourth quantity as variables; The first user is a seller who sells products, the second user is a supplier from which the product is procured, the third user is a buyer to whom the product is sold, the first quantity is a procurement price of the commodity and a procurement amount of the commodity; The second quantity is the selling price of the product, The third quantity is the demand quantity of the commodity. The fourth quantity is the inventory amount of the product. 10. The information processing device according to claim 9, wherein the information processing device is a

[0114] (Appendix A7) One or both of the first model and the second model are models constructed by machine learning. An information processing device according to any one of appendices A2 to A6.

[0115] [Appendix B] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0116] (Appendix B1) a calculation process in which at least one processor calculates an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function that represents a profit or loss of the first user in the negotiation; a first presentation process in which the at least one processor sequentially presents to the second user the optimal values ​​of each first quantity that constitute the optimal value sequence of the first quantity sequence calculated by the calculation process; a second presentation process in which the at least one processor presents the optimal value of the second quantity calculated by the calculation process to the third user if the negotiation is successful; Information processing methods.

[0117] (Appendix B2) a first acquisition process in which the at least one processor acquires a first model for estimating a probability of success of the negotiation from the first quantity sequence; a second acquisition process in which the at least one processor acquires a second model that estimates a probability of obtaining a reaction from a third quantity representing a reaction of the third user, the objective function includes the first quantity, the second quantity, and the third quantity as variables; In the calculation process, the at least one processor calculates, using the first model and the second model, a value sequence of the first quantity sequence and values ​​of the second quantities that maximize or minimize the value of the objective function as an optimal value sequence of the first quantity sequence and optimal values ​​of the second quantities. 1. The information processing method described in Appendix B1.

[0118] (Appendix B3) a first observation process in which the at least one processor observes whether the negotiation is successful or not when optimal value sequences of the first quantity sequence calculated by the calculation process are sequentially presented to the second user; a first update process in which the at least one processor updates a first model that estimates the probability of success of the negotiation from the first quantity sequence using a combination of an optimal value sequence of the first quantity sequence calculated by the calculation process and the success or failure of the negotiation observed by the first observation process; a second observing process in which the at least one processor observes a value of a third quantity representing a reaction of the third user; a second update process in which the at least one processor updates a second model that estimates the probability of obtaining the response from the third quantity using the third quantity observed by the second observation means; the objective function includes the first quantity, the second quantity, and the third quantity as variables; In the calculation process, the at least one processor calculates, using the first model and the second model, a value sequence of the first quantity sequence and values ​​of the second quantities that maximize or minimize the value of the objective function as an optimal value sequence of the first quantity sequence and optimal values ​​of the second quantities. 1. The information processing method described in Appendix B1.

[0119] (Appendix B4) The first user is a seller who sells products, the second user is a supplier from which the product is procured, the third user is a buyer to whom the product is sold, the first quantity is a procurement price of the commodity and a procurement amount of the commodity; The second quantity is the selling price of the product, The third quantity is the demand quantity of the commodity. 1. An information processing method according to Appendix B2 or B3.

[0120] (Appendix B5) one or both of the first model and the second model have time dependence; The objective function is a function representing the profit or loss of the first user in the negotiation performed at each time, and is a sum of functions including the first quantity, the second quantity, and the third quantity as variables. 1. An information processing method according to Appendix B2 or B3.

[0121] (Appendix B6) the function includes the first quantity, the second quantity, the third quantity, and the fourth quantity as variables; The first user is a seller who sells products, the second user is a supplier from which the product is procured, the third user is a buyer to whom the product is sold, the first quantity is a procurement price of the commodity and a procurement amount of the commodity; The second quantity is the selling price of the product, The third quantity is the demand quantity of the commodity. The fourth quantity is the inventory amount of the product. The information processing method described in Appendix B5.

[0122] (Appendix B7) One or both of the first model and the second model are models constructed by machine learning. 1. An information processing method according to any one of Appendices B2 to B7.

[0123] [Appendix C] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0124] (Appendix C1) The processor a calculation process for calculating an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function that represents a profit or loss of the first user in the negotiation; a first presentation process for sequentially presenting the optimal values ​​of each first quantity constituting the optimal value sequence of the first quantity sequence calculated by the calculation process to the second user in the negotiation; and executing a second presentation process of presenting the optimal value of the second quantity calculated by the calculation process to the third user if the negotiation is successful. Information processing program.

[0125] (Appendix C2) the processor, a first acquisition process for acquiring a first model for estimating the probability of success of the negotiation from the first quantity sequence; and a second acquisition process for acquiring a second model that estimates the probability of obtaining a reaction from a third quantity that represents the reaction of the third user. the objective function includes the first quantity, the second quantity, and the third quantity as variables; The calculation process uses the first model and the second model to calculate a value sequence of the first quantity sequence and values ​​of the second quantity that maximize or minimize the value of the objective function as an optimal value sequence of the first quantity sequence and an optimal value of the second quantity. An information processing program as described in Appendix C1.

[0126] (Appendix C3) the processor, a first observation process for observing whether the negotiation is successful or not when the optimal value sequence of the first quantity sequence calculated by the calculation process is sequentially presented to the second user; a first update process for updating a first model that estimates the probability of success of the negotiation from the first quantity sequence using a combination of an optimal value sequence of the first quantity sequence calculated by the calculation process and the success or failure of the negotiation observed by the first observation process; a second observation process for observing a value of a third quantity representing a reaction of the third user; and further executing a second update process for updating a second model that estimates the probability of obtaining the response from the third quantity using the third quantity observed by the second observation means; the objective function includes the first quantity, the second quantity, and the third quantity as variables; In the calculation process, the processor uses the first model and the second model to calculate a value sequence of the first quantity sequence and values ​​of the second quantities that maximize or minimize the value of the objective function as an optimal value sequence of the first quantity sequence and optimal values ​​of the second quantities. An information processing program as described in Appendix C1.

[0127] (Appendix C4) The first user is a seller who sells products, the second user is a supplier from which the product is procured, the third user is a buyer to whom the product is sold, the first quantity is a procurement price of the commodity and a procurement amount of the commodity; The second quantity is the selling price of the product, The third quantity is the demand quantity of the commodity. An information processing program according to Appendix C2 or C3.

[0128] (Appendix C5) one or both of the first model and the second model have time dependence; The objective function is a function representing the profit or loss of the first user in the negotiation performed at each time, and is a sum of functions including the first quantity, the second quantity, and the third quantity as variables. An information processing program according to Appendix C2 or C3.

[0129] (Appendix C6) the function includes the first quantity, the second quantity, the third quantity, and the fourth quantity as variables; The first user is a seller who sells products, the second user is a supplier from which the product is procured, the third user is a buyer to whom the product is sold, the first quantity is a procurement price of the commodity and a procurement amount of the commodity; The second quantity is the selling price of the product, The third quantity is the demand quantity of the commodity. The fourth quantity is the inventory amount of the product. An information processing program as described in Appendix C5.

[0130] (Appendix C7) One or both of the first model and the second model are models constructed by machine learning. An information processing program according to any one of appendices C2 to C6.

[0131] [Appendix D] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0132] (Appendix D1) at least one processor, a calculation process for calculating an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function that represents a profit or loss of the first user in the negotiation; a first presentation process for sequentially presenting the optimal values ​​of each first quantity constituting the optimal value sequence of the first quantity sequence calculated by the calculation process to the second user in the negotiation; and executing a second presentation process of presenting the optimal value of the second quantity calculated by the calculation process to the third user if the negotiation is successful. Information processing device.

[0133] The information processing device may further include a memory, and the memory may store a program for causing the at least one processor to execute each of the processes.

[0134] (Appendix D2) the at least one processor: a first acquisition process for acquiring a first model for estimating the probability of success of the negotiation from the first quantity sequence; and further executing a second acquisition process of acquiring a second model that estimates the probability of obtaining the reaction from a third quantity that represents the reaction of the third user; the objective function includes the first quantity, the second quantity, and the third quantity as variables; In the calculation process, the at least one processor calculates, using the first model and the second model, a value sequence of the first quantity sequence and values ​​of the second quantities that maximize or minimize the value of the objective function as an optimal value sequence of the first quantity sequence and optimal values ​​of the second quantities. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing information.

[0135] (Appendix D3) the at least one processor: a first observation process for observing whether the negotiation is successful or not when the optimal value sequence of the first quantity sequence calculated by the calculation process is sequentially presented to the second user; a first update process for updating a first model that estimates the probability of success of the negotiation from the first quantity sequence using a combination of an optimal value sequence of the first quantity sequence calculated by the calculation process and the success or failure of the negotiation observed by the first observation process; a second observation process for observing a value of a third quantity representing a reaction of the third user; and further executing a second update process for updating a second model that estimates the probability of obtaining the response from the third quantity using the third quantity observed by the second observation means; the objective function includes the first quantity, the second quantity, and the third quantity as variables; In the calculation process, the at least one processor calculates, using the first model and the second model, a value sequence of the first quantity sequence and values ​​of the second quantities that maximize or minimize the value of the objective function as an optimal value sequence of the first quantity sequence and optimal values ​​of the second quantities. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing information.

[0136] (Appendix D4) The first user is a seller who sells products, the second user is a supplier from which the product is procured, the third user is a buyer to whom the product is sold, the first quantity is a procurement price of the commodity and a procurement amount of the commodity; The second quantity is the selling price of the product, The third quantity is the demand quantity of the commodity. An information processing device according to appendix D2 or D3.

[0137] (Appendix D5) one or both of the first model and the second model have time dependence; The objective function is a function representing the profit or loss of the first user in the negotiation performed at each time, and is a sum of functions including the first quantity, the second quantity, and the third quantity as variables. An information processing device according to appendix D2 or D3.

[0138] (Appendix D6) the function includes the first quantity, the second quantity, the third quantity, and the fourth quantity as variables; The first user is a seller who sells products, the second user is a supplier from which the product is procured, the third user is a buyer to whom the product is sold, the first quantity is a procurement price of the commodity and a procurement amount of the commodity; The second quantity is the selling price of the product, The third quantity is the demand quantity of the commodity. The fourth quantity is the inventory amount of the product. 10. The information processing device according to claim 9, wherein said information processing device is a

[0139] (Appendix D7) One or both of the first model and the second model are models constructed by machine learning. An information processing device according to any one of appendices D2 to D6.

[0140] [Appendix E] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0141] (Appendix E1) A program that causes a computer to function as an information processing device, The computer, a calculation process for calculating an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function that represents a profit or loss of the first user in the negotiation; a first presentation process for sequentially presenting the optimal values ​​of each first quantity constituting the optimal value sequence of the first quantity sequence calculated by the calculation process to the second user in the negotiation; and executing a second presentation process of presenting the optimal value of the second quantity calculated by the calculation process to the third user if the negotiation is successful. A non-transitory recording medium on which an information processing program is recorded. [Explanation of symbols]

[0142] 1, 1A, 1B Information processing equipment 11 Calculation section 12a 1st presentation part 12b Second presentation part 13a 1st acquisition part 13b 2nd Acquisition Part 14a First Observation Section 14b Second Observation Section 15a 1st update part 15b 2nd update part

Claims

1. a calculation means for calculating an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function that represents a profit or loss of the first user in the negotiation; a first presentation means for sequentially presenting to the second user in the negotiation the optimal values ​​of the first quantities that constitute the optimal value sequence of the first quantity sequence calculated by the calculation means; and second presentation means for presenting the optimal value of the second quantity calculated by the calculation means to the third user if the negotiation is successful. Information processing device.

2. a first acquisition means for acquiring a first model for estimating the probability of success of the negotiation from the first quantity sequence; and a second acquisition means for acquiring a second model that estimates a probability of obtaining a reaction from the third user based on a third quantity that represents the reaction of the third user, the objective function includes the first quantity, the second quantity, and the third quantity as variables; the calculation means calculates, using the first model and the second model, a value sequence of the first quantity sequence and values ​​of the second quantity that maximize or minimize the value of the objective function as an optimal value sequence of the first quantity sequence and optimal values ​​of the second quantity. The information processing device according to claim 1 .

3. a first observation means for observing whether the negotiation is successful or not when the optimal value sequence of the first quantity sequence calculated by the calculation means is sequentially presented to the second user; a first updating means for updating a first model that estimates the probability of success of the negotiation from the first quantity sequence using a combination of an optimal value sequence of the first quantity sequence calculated by the calculating means and the success or failure of the negotiation observed by the first observing means; a second observation means for observing a value of a third quantity representing a reaction of the third user; a second updating means for updating a second model that estimates the probability of obtaining the response from the third quantity using the third quantity observed by the second observing means; the objective function includes the first quantity, the second quantity, and the third quantity as variables; the calculation means calculates, using the first model and the second model, a value sequence of the first quantity sequence and values ​​of the second quantity that maximize or minimize the value of the objective function as an optimal value sequence of the first quantity sequence and optimal values ​​of the second quantity. The information processing device according to claim 1 .

4. The first user is a seller who sells products, the second user is a supplier from which the product is procured, the third user is a buyer to whom the product is sold, the first quantity is a procurement price of the commodity and a procurement amount of the commodity; the second quantity is the selling price of the product; The third quantity is the demand quantity of the commodity.

4. The information processing device according to claim 2 or 3.

5. one or both of the first model and the second model have time dependence; The objective function is a function representing a profit or loss of the first user in the negotiation performed at each time, and is a sum of functions including the first quantity, the second quantity, and the third quantity as variables.

4. The information processing device according to claim 2 or 3.

6. the function includes the first quantity, the second quantity, the third quantity, and the fourth quantity as variables; The first user is a seller who sells products, the second user is a supplier from which the product is procured, the third user is a buyer to whom the product is sold, the first quantity is a procurement price of the commodity and a procurement amount of the commodity; the second quantity is the selling price of the product; The third quantity is the demand quantity of the commodity. The fourth quantity is the inventory amount of the product. The information processing device according to claim 5 .

7. One or both of the first model and the second model are models constructed by machine learning.

4. The information processing device according to claim 2 or 3.

8. a calculation process in which a processor calculates an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function that represents a profit or loss of the first user in the negotiation; a first presentation process in which the processor sequentially presents the optimal values ​​of the first quantity that constitute the optimal value sequence of the first quantity sequence calculated by the calculation process to the second user during the negotiation; a second presentation process in which the processor presents the optimal value of the second quantity calculated by the calculation process to the third user if the negotiation is successful. Information processing methods.

9. The processor a calculation process for calculating an optimal value sequence of a first quantity sequence consisting of first quantities that a first user will sequentially present to a second user in a negotiation, and an optimal value of a second quantity that the first user will present to a third user if the negotiation is successful, using an objective function that represents a profit or loss of the first user in the negotiation; a first presentation process for sequentially presenting the optimal values ​​of the first quantity that constitute an optimal value sequence of the first quantity sequence calculated by the calculation process to the second user during the negotiation; and executing a second presentation process of presenting the optimal value of the second quantity calculated by the calculation process to the third user if the negotiation is successful. Information processing program.

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