Data Shapley Framework for Multi-Stakeholder Decision Modeling
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Solution Overview
Problem
Current methods for decision-making in data-rich environments, particularly in multi-stakeholder settings, face challenges in accurately valuing and interpreting data, handling complex preferences, and adapting to real-time market changes, with existing techniques lacking a cohesive framework to integrate Data Shapley with Multi-Criteria Decision Analysis (MCDA) and accounting for irrational beliefs.
Innovation Solution
A multi-stage process combining decision theory, game theory, and Data Shapley to create a framework for real-time decision-making, involving data analytics, machine learning, and decision conferencing to determine valuable information, estimate utility functions, decompose decision processes, and adjust strategies based on new data, allowing for dynamic and proactive decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Data Shapley is used to value data points, then data valuation accuracy is improved, but the complexity of the decision-making framework increases
Solution Approach 1:
The framework segments the decision-making process into distinct modules: data valuation using Data Shapley, utility function estimation using machine learning, and decision optimization using MCDA. This segmentation allows each component to be developed and optimized independently, managing overall system complexity while maintaining high data valuation accuracy.
Solution Approach 2:
The patent introduces utility functions as an intermediary layer between data valuation and decision-making. The utility functions translate complex data valuations into actionable decision criteria, bridging the gap between accurate data pricing and practical decision optimization without requiring direct integration of all complex components.
2Adaptability or versatility
If multi-criteria decision analysis is integrated with Data Shapley, then decision-making comprehensiveness is improved, but the difficulty of implementing the framework increases
Solution Approach 1:
The framework designs utility functions that serve multiple purposes: they encode stakeholder preferences, incorporate data valuations, and guide decision optimization. This multi-functionality reduces implementation difficulty by eliminating the need for separate components for each function, while maintaining comprehensive decision-making capabilities.
Solution Approach 2:
The patent employs parameter changes in the utility functions to adapt to different decision-making scenarios and stakeholder preferences. By allowing flexible parameter adjustment rather than requiring rigid structural changes, the framework achieves comprehensive decision-making across diverse applications while simplifying implementation through standard optimization techniques.
3Productivity
If real-time data collection is optimized using Data Shapley, then data utilization efficiency is improved, but the computational resources required increase
Solution Approach 1:
The framework uses partial action by selectively collecting and valuing only the most impactful data points according to Data Shapley calculations, rather than processing all available data. This approach improves data utilization efficiency by focusing computational resources on high-value data while avoiding unnecessary processing of less informative data points.
Solution Approach 2:
The system performs preliminary data valuation using Data Shapley before actual decision-making occurs. By pre-calculating data values and prioritizing data collection accordingly, the framework optimizes which data to collect in real-time, reducing the computational burden during critical decision moments while maintaining high data utilization efficiency.
4Measurement precision
If the framework accounts for irrational beliefs in decision-making, then decision accuracy is improved, but the complexity of modeling preferences increases
Solution Approach 1:
The framework employs self-service by allowing stakeholders to directly express their preferences and beliefs through utility function parameters, rather than requiring complex automated inference of their decision-making processes. This approach captures irrational beliefs and preferences accurately while avoiding the computational complexity of modeling underlying psychological mechanisms.
Data Source
AI summary
Data Shapley is an approach to understand the role of data in a decision-making process. The present invention involves a process to connect Data Shapley to a data analytics and machine learning based decision-making environment through the use of utility functions. In the present invention a problem is structurally analyzed using machine learning and data analytics to determine structural trends. Data is then analyzed using Data Shapley to determine what additional information is needed to make a decision. This allows for the relevant data to be collected to estimate utility functions for participants. Data Shapley is then used again to decompose the decision-making process and look for trends in the process, and machine learning is applied to see if there are commonalities across the criteria in the decision-making process. After this, the decision-making process selects a strategy as the decision. If new information becomes available or an event occurs that makes a change of strategy necessary, then Data Shapley is used to guide the data acquisition and decision-making process. If no new information is available or an event does not occur, event occurrence is dynamically predicted using data analytics and Data Shapley proactively recommends what data streams to monitor and collect.