Weighted Tree Similarity Based Optimization for Agent Matching in Multi Agent Markets
Journal of Computer and Communication Networks
Received On : 10 March 2025
Revised On : 12 July 2025
Accepted On : 30 July 2025
Published On : 06 September 2025
Volume 01, 2025
Pages : 187-196
Abstract
The article analyzes the nature of multi-agent markets and suggests a new framework based on hierarchical matching and decision architecture. Furthermore, the framework adopts weighted tree similarity algorithms to obtain a more detailed perspective of the agents’ interaction and preferences. The article describes the theoretical background of fuzzy similarity measures based on trees and introduces a new weighted tree similarity algorithm that enables assessment of the agent preferences and characteristics by employing fuzzy representations and hierarchy. Therefore, it fosters better and specific matching decisions that can be made. In addition, the article examines whether and how the Stackelberg equilibrium, a form of game theory, can help to enhance the efficiency of the interactions and decisions of the agents. The main conclusions in this article include the creation of the new weighted tree similarity algorithm for multi-agent markets, the creation of the hierarchical matching and decision system, and the verification that the proposed system significantly improves the efficiency of interactions between agents in the market. The effectiveness of these conclusions is supported by case and empirical evidence, which further emphasizes the applicability and the possibility of the projected framework in real-world market scenarios. This is beneficial in providing significant information concerning the improvement of the market forces and decision making.
Keywords
Multi-Agent Markets, Weighted Tree Similarity, Multi-Agent Systems, Hierarchical Matching, Fuzzy Representations, Game Theory, Market Efficiency.
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The authors confirm contribution to the paper as follows:
Conceptualization: Anna Recchi and Minu Balakrishnan; Methodology: Anna Recchi; Writing-Original Draft Preparation: Anna Recchi and Minu Balakrishnan; Investigation: Anna Recchi and Minu Balakrishnan; Supervision: Minu Balakrishnan; Validation: Anna Recchi and Minu Balakrishnan; Writing- Reviewing and Editing: Anna Recchi and Minu Balakrishnan; All authors reviewed the results and approved the final version of the manuscript.
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Cite this Article
Anna Recchi and Minu Balakrishnan, “Weighted Tree Similarity Based Optimization for Agent Matching in Multi Agent Markets”, Journal of Computer and Communication Networks, pp. 187-196, 2025, doi: 10.64026/JCCN/2025018.