BEHAVIORAL ECONOMICS BASED FRAMEWORK FOR OPTIMAL AND STRATEGIC DECISION-MAKING IN A CIRCULAR ECONOMY

20230169434

17/539130

November 30, 2021

A method of providing optimal and personalized business decision making that leverages behavioral economics principles and machine learning techniques is discussed herein. The method may include collecting or simulating data relating to behavioral characteristics of a plurality of stakeholders and analyzing the collected data to construct behavioral economics and machine learning based models related to a business problem. These models can be used to optimize and personalize business interventions to influence consumers' purchasing behavior to achieve the best business outcome (in B2C use cases) and de-bias distorted information sharing in supply chains (in B2B use cases). By contrast, traditional consumer and supply chain analytics solutions lack behavioral insights and often lead to sub-optimal decision making because economic optimization approach alone is not adequate for decision making where behavioral biases are present.

1. A method of providing optimal and personalized business action recommendations, comprising: collecting data relating to characteristics of a plurality of stakeholders, the collected data comprising at least data associated with a behavioral economics model related to a business problem; generating, based on the collected data, simulated stakeholder interaction data related to the business problem; training, based on the simulated stakeholder interaction data, a machine-learning-based recommendation engine; and providing a business action recommendation for a stakeholder interaction, where the business action recommendation is provided by the trained machine-learning-based recommendation engine based on a state of the stakeholder interaction.

2. The method of claim 1, wherein: the machine-leaning-based recommendation engine comprises a reinforcement-learning-based recommendation engine, the business action recommendation is a behavioral intervention recommendation, training the reinforcement-learning-based recommendation engine comprises training a recommendation policy that associates an input stakeholder-interaction state with a behavioral interaction recommendation, and training the reinforcement-learning-based recommendation engine comprises updating the recommendation policy based on additional stakeholder interaction data as it is received.

3. The method of claim 1, wherein the machine-learning-based recommendation engine comprises a statistical-regression-based recommendation engine for de-biasing projection data received from at least one stakeholder based on at least one of (1) a trust model or (2) a comparison of historical projection data received from the at least one stakeholder and received outcome data associated with the historical projection data.

4. The method of claim 1, wherein the machine-learning-based recommendation engine comprises a neural-network-based recommendation engine for identifying at least one of a set of stakeholder segments or a set of stakeholder micro-segments.

5. The method of claim 1, wherein the collected data further comprises at least one of demographic data, socio-economic data, firmographic data, sales data, or other stakeholder profile data that is readily accessible.

6. The method of claim 1, wherein the data associated with the behavioral economics model related to the business problem comprises at least one of behavioral-economics-informed survey response data or historical interaction data related to a particular behavioral economics model, wherein a behavioral-economics-informed survey used to generate the behavioral-economics-informed survey response data comprises at least one of a set of survey questions or a set of conjoint analysis questions, wherein the behavioral-economics-informed survey relates to at least one of attitudes towards a product, perceived attitudes of other people towards the product, an importance of different features of the product, or a value associated with one or more features of the product.

7. The method of claim 1, wherein the machine-learning-based recommendation engine further comprises a machine-learning-based stakeholder segmentation engine, the method further comprising: identifying, using the machine-learning-based stakeholder segmentation engine, stakeholder segments; and training the machine-learning-based recommendation engine further comprises training the machine-learning-based recommendation engine based on the identified stakeholder segments.

8. The method of claim 1, further comprising modeling at least one of a demand curve for a product or a price elasticity for the product.

9. The method of claim 1, wherein training the machine-learning-based recommendation engine comprises training the machine-learning-based recommendation engine to generate a recommendation policy, wherein the recommendation policy comprises an association between a set of input stakeholder interaction states and a set of recommended business actions, wherein each stakeholder interaction state in the set of input stakeholder interaction states is associated with one recommended business action in the set of recommended business actions, and wherein providing the business action recommendation for the stakeholder interaction based on the state of the stakeholder interaction comprises providing the recommended business action associated with the state of the stakeholder interaction.

10. The method of claim 9, the method further comprising: implementing the business action recommendation: collecting data relating to a stakeholder response to the business action recommendation; and updating the recommendation policy based on the collected data relating to the stakeholder response to the implemented business action recommendation.

11. A computer-readable medium storing computer executable code for providing business action recommendations, the code when executed by a processor causes the processor to: collect data relating to characteristics of a plurality of stakeholders, the collected data comprising at least data associated with a behavioral economics model related to a business problem; generate, based on the collected data, simulated stakeholder interaction data related to the business problem; train, based on the simulated stakeholder interaction data, a machine-learning-based recommendation engine; and provide a business action recommendation for a stakeholder interaction, where the business action recommendation is provided by the trained machine-learning-based recommendation engine based on a state of the stakeholder interaction.

12. The computer-readable medium of claim 11, wherein the machine-learning-based recommendation engine comprises at least one of a reinforcement-learning-based recommendation engine, a statistical-regression-based recommendation engine, or a neural-network-based recommendation engine.

13. The computer-readable medium of claim 11, wherein the collected data further comprises at least one of demographic data, socio-economic data, firmographic data, sales data, or other stakeholder profile data that is readily accessible.

14. The computer-readable medium of claim 11, wherein the data associated with the behavioral economics model related to the business problem comprises at least one of behavioral-economics-informed survey response data or historical interaction data related to a particular behavioral economics model, wherein a behavioral-economics-informed survey used to generate the behavioral-economics-informed survey response data comprises at least one of a set of survey questions or a set of conjoint analysis questions, wherein the behavioral-economics-informed survey relates to at least one of attitudes towards a product, perceived attitudes of other people towards the product, an importance of different features of the product, or a value associated with one or more features of the product.

15. The computer-readable medium of claim 11, wherein the machine-learning-based recommendation engine further comprises a machine-learning-based stakeholder segmentation engine, the code when executed by the processor further causes the processor to: identify, using the machine-learning-based stakeholder segmentation engine, stakeholder segments; and train the machine-learning-based recommendation engine by training the machine-learning-based recommendation engine based on the identified stakeholder segments.

16. The computer-readable medium of claim 11, further comprising modeling at least one of a demand curve for a product or a price elasticity for the product.

17. The computer-readable medium of claim 11, wherein training the machine-learning-based recommendation engine comprises training the machine-learning-based recommendation engine to generate a recommendation policy, wherein the recommendation policy comprises an association between a set of input stakeholder interaction states and a set of recommended business actions, wherein each stakeholder interaction state in the set of input stakeholder interaction states is associated with one recommended business action in the set of recommended business actions, and wherein providing the business action recommendation for the stakeholder interaction based on the state of the stakeholder interaction comprises providing the recommended business action associated with the state of the stakeholder interaction.

18. The computer-readable medium of claim 17, the code when executed by the processor further causes the processor to: implement the business action recommendation; collect data relating to a stakeholder response to the business action recommendation; and update the recommendation policy based on the collected data relating to the stakeholder response to the implemented business action recommendation.

19. A system comprising: at least one processor; and a computer-readable medium storing computer executable code for providing business action recommendations code, the code when executed by the at least one processor causes the at least one processor to: collect data relating to characteristics of a plurality of stakeholders, the collected data comprising at least data associated with a behavioral economics model related to a business problem; generate, based on the collected data, simulated stakeholder interaction data related to the business problem; train, based on the simulated stakeholder interaction data, a machine-learning-based recommendation engine; and provide a business action recommendation for a stakeholder interaction, where the business action recommendation is provided by the trained machine-learning-based recommendation engine based on a state of the stakeholder interaction.

20. The system of claim 19, the code when executed by the processor further causes the processor to: implement the business action recommendation; collect data relating to a stakeholder response to the business action recommendation; and update the machine-learning-based recommendation engine based on the collected data relating to the stakeholder response to the implemented business action recommendation.

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edspap.20230169434