Machine-Learning Asset-Exchange Feedback to Reduce Negotiation Iterations
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Solution Overview
Problem
Existing online asset-exchange platforms face inefficiencies due to differing views between offerors and offerees on the importance of offer terms, leading to unnecessary negotiations, over-giving, unmet agreements, and prolonged execution times.
Innovation Solution
An asset-exchange feedback system utilizing machine learning models to analyze historical data and generate attribute-importance scores, providing insights to optimize asset listings and improve negotiation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If offerors manually negotiate and adjust offer terms to appeal to offerees, then the offeror can attempt to make the offer more appealing, but this leads to unnecessary negotiations and prolonged execution time
Solution Approach 1:
The system implements feedback by providing attribute-importance scores to offerors about which offer terms are most important to offerees. This feedback mechanism allows offerors to understand offeree priorities without extensive negotiation, directly reducing negotiation time while maintaining ease of making appealing offers.
Solution Approach 2:
The system performs preliminary analysis of offer attributes and their importance to offerees before the actual negotiation occurs. By pre-calculating attribute-importance scores based on historical data and machine learning, the system prepares information that guides offerors in making effective offers from the start, avoiding unnecessary negotiation iterations.
2Reliability
If offerors significantly alter many terms to obtain acceptance, then the offeror may secure agreement, but this results in over-giving where one party gives up far more than necessary
Solution Approach 1:
The attribute-importance scores provide feedback to offerors about which terms are critical to offeree acceptance. This enables offerors to make targeted concessions only on important terms rather than significantly altering many terms, thus securing agreement while minimizing unnecessary concessions and preserving value.
Solution Approach 2:
The system changes the parameter of offer formulation by providing prioritized attribute importance information. This allows offerors to adjust their strategy from making broad term alterations to making precise, targeted adjustments only where necessary, reducing the total value given up in concessions.
3Adaptability or versatility
If offerors and offerees have differing views on term importance, then each party has their own priorities, but this leads to multiple unnecessary iterations of negotiation
Solution Approach 1:
The system provides feedback in the form of attribute-importance scores that reflect offeree priorities. This feedback bridges the information gap between parties with differing views, allowing offerors to align their offers with offeree preferences more accurately and reducing the number of negotiation iterations needed to reach agreement.
Solution Approach 2:
The machine learning system acts as an intermediary that translates offeree preferences into actionable attribute-importance scores. This intermediary provides a common language and understanding between parties with differing views, enabling more efficient negotiation without requiring multiple iterations to bridge the information gap.
4Productivity
If automated techniques are implemented to reduce inefficiencies, then negotiation efficiency can improve, but this requires complex machine learning models and data processing
Solution Approach 1:
The system enables self-service by providing automated attribute-importance scoring that offerors can use independently. The machine learning models process historical data and provide actionable insights without requiring complex manual analysis, improving negotiation efficiency while managing complexity through automation rather than human expertise.
Data Source
AI summary
An asset-exchange feedback system is implemented for performing asset-exchange feedback operations. The asset-exchange feedback system collects historical asset-listing data from an asset-exchange platform. The historical asset-listing data comprises, for each asset listing of a plurality of previous asset listings, a plurality of asset-listing attributes and a result of the asset listing. The asset-exchange feedback system uses a first machine learning model to determine, based on the historical asset-listing data, a first set of attribute-importance scores. Each attribute-importance score in the first set of attribute-importance scores corresponds to a respective asset-listing attribute in the plurality of asset-listing attributes and indicates an importance of the respective asset-listing attribute to one or more offerees participating in the asset-exchange platform. The asset-exchange feedback system performs an asset-exchange feedback operation based on the first set of attribute-importance scores.