Negotiation Scenario Modeling for Multi-Party Path Prediction
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Solution Overview
Problem
Complex multi-party negotiations are challenging due to the influence of multiple actors, hidden agendas, and deep uncertainty in human behavior, leading to unpredictable outcomes and potential risks.
Innovation Solution
A decision-making analysis (DMA) computer system that simulates negotiations to determine optimal paths by analyzing stakeholders, strategies, and tactics, using advanced horizon scanning and deep searching to predict and manage uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional negotiation analysis methods are used, then the analysis process is simple, but the ability to handle complex multi-party situations with multiple actors and hidden agendas is insufficient
Solution Approach 1:
The system segments the complex negotiation analysis into distinct modules: stakeholder identification, strategy generation, simulation execution, and outcome evaluation. Each module handles specific aspects of the complex multi-party situation independently, making the overall system manageable while maintaining high adaptability to complex scenarios
Solution Approach 2:
The system introduces a simulation engine as an intermediary that virtualizes the negotiation process. This intermediary layer allows the system to model complex interactions between multiple actors with hidden agendas without requiring direct analysis of all possible real-world scenarios, reducing computational complexity while maintaining analytical depth
2Measurement precision
If comprehensive simulation of multiple stakeholders and strategies is performed, then the prediction accuracy is improved, but the computation time increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-identifying key stakeholders and pre-generating potential strategies before the actual simulation runs. This preliminary analysis narrows down the simulation space to the most critical scenarios, maintaining high prediction accuracy while reducing the number of simulations required and thus decreasing computation time
Solution Approach 2:
The system dynamically adjusts simulation parameters such as the number of stakeholders to model, the depth of strategy trees, and the frequency of simulation runs based on the complexity of the specific negotiation situation. This adaptive parameter adjustment allows the system to maintain prediction accuracy while optimizing computation time for different scenarios
3Reliability
If the system analyzes all possible strategies and tactics, then the completeness of the analysis is improved, but the ease of operation deteriorates due to overwhelming complexity
Solution Approach 1:
The system implements feedback mechanisms that automatically adjust the analysis scope based on user inputs and preliminary results. The interface provides real-time feedback showing the impact of including or excluding specific stakeholders or strategies, allowing users to interactively refine the analysis scope while maintaining comprehensive coverage of critical factors
Solution Approach 2:
The system creates virtual copies of the negotiation scenario in the simulation environment, allowing users to experiment with different strategies and outcomes without manipulating the actual complex real-world situation. This virtual copying simplifies the user interface by providing a controlled, repeatable simulation environment that maintains analytical completeness
Data Source
AI summary
A decision-making analysis computer device for advanced horizon scanning and deep searching is provided. The computer device includes at least one processor in communication with at least one memory. The at least one processor programmed to: a) receive one or more inputs for a model associated with an issue; b) create a scenario generator; c) execute the scenario generator with the one or more inputs to generate a plurality of scenarios; d) execute a plurality of runs of the model with the plurality of scenarios as inputs to generate a plurality of outputs; and/or e) categorize the plurality of outputs of the model of the plurality of the plurality of runs of the model.