AI-driven Bonding Curve portfolio risk in-depth exploration

Elaine, Jereyme｜Author

Sissi@TEDAO｜Translator

Translator’s Introduction:

This translation will introduce the acquisition Innovative proposals funded by Token Engineering Commons ("TEC") in the spring of 2024. TEC is one of the important communities in the world that supports and promotes Token Engineering. It is committed to creating and maintaining a sustainable ecosystem and providing support and collaboration platform for community members through its forum and other resources.

This project uses reinforcement learning and agent-based modeling and simulation technology to optimize the bonding curve mechanism in the token ecosystem. By exploring and responding to potential malicious strategies under different PAMM & SAMM bonding curve combinations, the project aims to significantly improve the economic security of the token system. In addition, the project is also committed to promoting the popularization and practice of Token Engineering so that more people can understand and participate in this cutting-edge technology, paving the way for building a more secure and sustainable token ecosystem.

1. Proposal details

1.1 Background overview

The bonding curve is an integral part of the token ecosystem. It controls token price fluctuations and provides necessary liquidity. security and dynamic token supply. By mathematizing the relationship between multiple elements in a token ecosystem, the bonding curve also opens the door to “engineering control” of the token ecosystem.

As early as 2018, the IncentiveAI team proposed the concept of using AI-agent for mechanism optimization. By observing the behavior of greedy Machine Learning agents, we can identify possible user behaviors after the system is deployed to the real environment. Compare the difference between real behavior and expected behavior to continuously optimize mechanism design. They also applied this concept to the bonding curve research of the Ocean protocol. Unfortunately, the project did not end up being implemented on a large scale, and no project code can be found for reference or operation.

Since 2023, BCRG (Bonding Curve Research Group) has conducted relatively comprehensive research, development, education and application of bonding curve, especially in PAMM (Primary Automated Market Maker) and SAMM (Secondary Automated Market Maker) on the joint study of bonding curve. However, according to BCRG's description in Modeling & Simulating bonding curves, perhaps due to resource constraints, it has not yet entered into deeper research such as malicious strategy exploration, penetration testing, and hypothesis analysis.

Our team has long been focused on exploring the field of Token Engineering and is committed to using agent-based modeling and simulation to solve the design and optimization problems of complex systems.

1.2 Project Introduction

In this proposal, we aim to inherit the concept of Incentive AI and explore potential attacks under different PAMM and SAMM bonding curve combinations through AI-agent trained by reinforcement learning. Malicious strategies of attackers, and through further comparative analysis and behavior space exploration, to find relatively stable and high-quality bonding curve parameter combinations, in order to continuously optimize the mechanism design of the protocol, narrow the gap between expected behavior and real behavior, and reduce tokens Economic security risks to ecosystems.

Specifically, in the selection of PAMM bonding curve, we select the four most common types: Linear, Exponential, Power and Sigmoid; in the selection of SAMM bonding curve, we select the most common constant product ( There are two types: e.g Uniswap) and hybrid (e.g Curve), resulting in 8 combinations of PAMM and SAMM. We will use agent-based modeling and simulation methods to conduct experiments, use AI-agent to explore the set of potential malicious strategies for each scheme and the probability of their occurrence, and use the simulation results to visually display the consequences of malicious strategies on the system. It is possible to explore relatively scientific malicious attack response strategies and bonding curve mechanism optimization solutions through experiments.

At the same time, we have applied for Holobit’s premium account sponsorship. We will use this advanced modeling and simulation platform to make our model construction details and the entire experimental process fully transparent.

• Possible innovations

I. Introduce reinforcement learning into Token Engineering to form a set of AI-agent and agent-based modeling and Simulation protocol mechanism optimization method;

II. This method is universal, implementable, and reusable, and may be helpful to the economic security of the entire token ecosystem;

III. Thanks to the powerful tool Holobit, this model can be understood, used, and verifiable by the public.

• The short-term goal of the project‍

#I. Use AI-agent to explore potential malicious strategies under different PAMM and SAMM bonding curve combinations, and identify Identify possible risks under various mechanism combinations, and explore corresponding risk response strategies and mechanism optimization plans;

II. Provide a relatively scientific and rigorous research method for the development of bonding curve;

III. Based on the experimental results, several suggestions are put forward to improve the economic security of the token ecosystem from the perspective of bonding curve.

• Long-term goals of the project‍

By combining the popularization of AI’s Agent-based modeling and simulation methods with the promotion of Token Engineering, it is possible for everyone to become a Token Engineer, thereby creating a “community-driven decentralization” "To build a more anti-fragile and sustainable token ecosystem" to lay a solid foundation, further promote Token Engineering, and accelerate its theoretical and practical development.

2. Expected results

Using the Holobit tool for agent-based modeling, it is expected to deliver the following results:

• An agent that introduces AI-agent The currency economic chain off-chain simulation model includes 8 experimental plans for PAMM and SAMM combinations. At the same time, the model is completely transparent and can be read, used, and verifiable by everyone; Research report on malicious attack strategies (including modeling process, experimental content, vulnerability risks and optimization solutions).

• 3. Alignment of mission and values

Convenience: Holobit supports public sharing, and the modeling logic is simple, achieving visualization and intuition. Make sure everyone can read, use, and verify it. Therefore, this model can be opened as a public good and can be accessed and tested by everyone, such as the case of the Terra/ LUNA ecosystem that has been given.

• Education: Through detailed models and simulation tutorials, the project can help the public gain a deeper understanding of how bonding curves work and their key role in the token ecosystem; through agent-based modeling and simulation, projects can show the public how to analyze and deal with dynamic relationships and potential risks in complex systems. This skill is widely applicable and is also a key skill for studying Token Engineering. If this set of methodologies and tools can be popularized in the community through this model, it can further promote the popularity, development and practical application of Token Engineering.

• Transparency: Only when the public can understand it can it be truly transparent. This model does not involve code. The modeling mechanism and experimental process are visualized through the Holobit tool. Through modeling and experiments, not only the mechanism of the model is made transparent, but also the risks of the mechanism design are made transparent, and specific repair suggestions are given.

• Community-driven: The community can fork this model to conduct various experiments, not only limited to bonding curve, but also used for research on governance, growth, etc. More importantly, this set of methodologies and tools can also be reused on other protocols. Everyone can disclose their research results in the community, disclose the loopholes and optimization areas of a certain token ecosystem, and truly realize the community Driven self-regulation.

• Aligned with Token Engineering principles: After mastering this set of methods and tools, everyone can conduct economic security audits of the protocol based on these skills. Therefore, it becomes possible to "complete the token project in a decentralized manner", and we can pool the power of collective wisdom to build a more anti-fragile and sustainable token ecosystem.

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