A network of interconnected nodes representing agents in a DeFi ecosystem, showcasing influence and connections.

Decoding DeFi: How Multi-Agent Systems Can Revolutionize Governance

Jordan Keane in Tech & Innovation December 2025 • 4 min read.

"Explore how Multi-Agent Influence Diagrams (MAIDs) offer new insights into decentralized finance, making governance more transparent and resilient."

Decentralized Finance (DeFi) is changing how we think about financial systems, shifting away from traditional centralized institutions. DeFi governance models empower token holders to shape the future of these platforms, allowing them to vote on critical decisions, from technical tweaks to major strategic shifts. But with increased participation comes greater complexity.

Imagine a system where numerous independent agents, each with their own unique incentives and strategies, interact to influence governance outcomes. This intricate web of interactions makes it challenging to predict how decisions will be made and whether the system will remain robust. To navigate this complexity, a new approach is needed: Multi-Agent Influence Diagrams (MAIDs).

Multi-Agent Influence Diagrams (MAIDs) provide a powerful framework for modeling and analyzing strategic interactions within DeFi governance. By combining Bayesian Networks and Influence Diagrams, MAIDs offer a comprehensive representation of decision-making processes, capturing the influence of individual actions on others and the overall governance outcomes. Let's explore how MAIDs can bring clarity and structure to the often-opaque world of DeFi governance.

MAIDs: A Clear View of DeFi Decision-Making

A network of interconnected nodes representing agents in a DeFi ecosystem, showcasing influence and connections.

MAIDs are graphical models that represent decision-making scenarios involving multiple agents. Unlike traditional methods, MAIDs accommodate the complexities of interactions and dependencies among decision-makers. In a MAID, nodes symbolize variables like decisions, uncertainties, and utilities, while directed edges indicate causal relationships or dependencies. This structure allows for the explicit representation of cooperation, competition, coordination, and negotiation between agents.

Think of MAIDs as a way to visualize the strategic landscape of DeFi governance. Each agent is associated with a subset of nodes representing their local decisions, beliefs, and preferences. By mapping these elements, MAIDs enable the analysis of strategic interactions and prediction of outcomes resulting from the decisions of multiple agents. This approach is similar to how Bayesian networks clarify dependencies between probabilistic variables, making the decision-making process more transparent.

Chance Variables: Represent uncertain events or outcomes that affect each agent's decisions, similar to nodes in Bayesian networks.
Decision Variables: Represent the choices each agent can make.
Utility Variables: Specify a utility function for each agent, reflecting their preferences and goals.
Directed Edges: Indicate causal relationships or dependencies between variables, showing how decisions and uncertainties influence outcomes.

Consider a simple example: a proposal to update a risk parameter in a DeFi lending protocol. Using a MAID, you can model how different token holders (agents) might vote based on their individual risk preferences, beliefs about market conditions, and expectations about the proposal's impact. The MAID would show how each agent's decision influences the overall outcome and the utilities they derive from it. This allows you to identify potential conflicts, predict voting behavior, and assess the overall robustness of the governance process.

The Future of DeFi Governance with MAIDs

As DeFi continues to evolve, the complexity of governance models will only increase. Multi-Agent Influence Diagrams offer a powerful tool for navigating this complexity, providing insights into strategic interactions and helping to design more robust and transparent governance systems. By understanding how different agents interact and influence each other, we can create DeFi protocols that are more resilient, equitable, and sustainable.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2402.15037,

Title: Multi Agent Influence Diagrams For Defi Governance

Subject: cs.gt econ.gn q-fin.ec

Authors: Abhimanyu Nag, Samrat Gupta, Sudipan Sinha, Arka Datta

Published: 22-02-2024

Everything You Need To Know

What are Multi-Agent Influence Diagrams (MAIDs) and how do they improve DeFi governance?

Multi-Agent Influence Diagrams (MAIDs) are graphical models designed to analyze strategic interactions within Decentralized Finance (DeFi) governance. They combine Bayesian Networks and Influence Diagrams to represent decision-making processes comprehensively. In DeFi, where numerous independent agents with varying incentives shape governance outcomes, MAIDs bring clarity by visualizing these interactions. They help identify potential conflicts, predict voting behavior, and assess the overall robustness of the governance process, which leads to more transparent and resilient governance.

How do Multi-Agent Influence Diagrams (MAIDs) work in the context of DeFi?

In the realm of Decentralized Finance (DeFi), MAIDs function by modeling decision-making scenarios involving multiple agents. They use nodes to represent variables like decisions, uncertainties, and utilities, while directed edges show causal relationships. Agents in a MAID have their own subsets of nodes, which helps analyze how their decisions, beliefs, and preferences interact. For instance, in a proposal to update a risk parameter, MAIDs can model how different token holders (agents) might vote, considering their risk preferences and beliefs. This helps in predicting outcomes and understanding the influence of individual actions on governance.

What are the key components of a Multi-Agent Influence Diagram (MAID)?

A Multi-Agent Influence Diagram (MAID) consists of several key components: 'Chance Variables,' which represent uncertain events affecting each agent's decisions; 'Decision Variables,' representing the choices each agent can make; 'Utility Variables,' specifying a utility function for each agent based on their preferences and goals; and 'Directed Edges,' indicating causal relationships or dependencies between variables. These components work together to create a clear view of decision-making in DeFi governance, allowing for the analysis of strategic interactions.

How do MAIDs help in understanding the behavior of different agents in DeFi governance?

MAIDs provide insights into how different agents in Decentralized Finance (DeFi) governance interact by mapping each agent's decisions, beliefs, and preferences through a subset of nodes. They help analyze strategic interactions and predict outcomes resulting from the decisions of multiple agents. For example, in a scenario like a proposal to update a risk parameter in a DeFi lending protocol, the MAID can model how different token holders (agents) might vote based on their individual risk preferences, beliefs about market conditions, and expectations about the proposal's impact. This enables the identification of potential conflicts and an understanding of how each agent's decision influences overall outcomes.

Why are Multi-Agent Influence Diagrams (MAIDs) considered important for the future of DeFi governance?

Multi-Agent Influence Diagrams (MAIDs) are crucial for the future of Decentralized Finance (DeFi) governance because DeFi governance models are becoming increasingly complex. As DeFi continues to evolve, the complexity of governance will only increase. MAIDs provide a powerful tool for navigating this complexity by offering insights into strategic interactions. By understanding how different agents interact and influence each other, MAIDs help in designing more robust, equitable, and sustainable governance systems. This proactive approach is vital to ensure that DeFi protocols remain resilient and transparent as they grow.