Team Lampros Labs DAO - LTIPP Research Bounty Proposals

Proposed Research Topic -

We, members of the team Lampros Labs DAO, would like to work on the below-mentioned research topics -

1. Comprehensive Analysis of Incentive Program Effectiveness and Value Generation for LTIPP Participating Protocols on Arbitrum

2. Effects of Growth Incentives on Non-Recipients Protocol in STIP & BSTIP

Research Project 1 -

Title - Comprehensive Analysis of Incentive Program Effectiveness and Value Generation for LTIPP Participating Protocols on Arbitrum

Objective - To conduct an in-depth investigation into the effectiveness of the Long-Term Incentive Pilot Program (LTIPP) for protocols participating in the Arbitrum ecosystem, evaluating their value generation, user acquisition, and engagement, while exploring innovative mechanisms and strategies for optimizing incentive program design and value capture.

Sub-Research Questions - (Recommended by Council)

Sector Growth, User Interaction, and Incentive Effectiveness - (Wintermute ARB, GMX/Saurabh)

  • Identify which sectors (e.g., DeFi, gaming) witnessed the largest growth in user activity and user base during the incentive programs.
  • Analyze user retention rates and interaction patterns within each sector, categorizing actions as reward-driven or genuine long-term usage.
  • Evaluate different ways incentives are utilized across the Arbitrum ecosystem and their effectiveness in creating sticky liquidity and long-term user engagement.

Mercenary Users and Multi-Protocol Engagement - (Wintermute ARB)

  • Determine the percentage of users/wallets engaging with multiple protocols simultaneously to maximize rewards from incentive programs.
  • Analyze wallet activity across different protocols, identify patterns of multi-protocol engagement, and quantify the extent of mercenary behaviour.
  • Investigate the impact of mercenary users on the overall ecosystem and propose strategies to mitigate potential negative effects.

Reward/User Ratio and Market Demand - (Wintermute ARB)

  • Identify protocols with the highest and lowest rewards per user during the incentive programs.
  • Analyze user activity trends in relation to reward levels, and identify inflection points in user behavior based on the reward/user ratio.
  • Evaluate the correlation between reward levels, user engagement, and market demand for various protocols and their offerings.

User Actions with ARB Rewards and Unintended Incentivized Actions - (Wintermute ARB, 404 DAO)

  • Investigate how users utilize the ARB rewards received from various protocols and their subsequent actions.
  • Track the flow of ARB rewards, categorize actions (e.g., selling, reinvesting, adding to liquidity pools), and analyze behaviour by protocol cohorts.
  • Identify any unintended incentivized actions resulting from the current incentive programs and propose adjustments to mitigate such behaviours.

Funding Mechanisms and Dollar-Cost Average of Incentives - (GMX/Saurabh)

  • Explore and assess the various funding mechanisms employed for distributing incentive funds across the ecosystem.
  • Research different funding mechanisms, evaluate their effectiveness in terms of capital efficiency and value distribution, and propose new mechanisms if applicable.
  • Calculate the average cost of $1 ARB spent on incentive programs and the corresponding return on investment for the Arbitrum ecosystem.

High-Velocity Incentive Systems and Total Value Returned (TVR) - (404 DAO)

  • Identify incentive systems that enable the highest velocity of value generation and circulation per ARB token distributed.
  • Analyze different incentive systems, measure their value generation velocity using appropriate metrics, and identify the most effective systems for optimizing value capture.
  • Define and compute the Total Value Returned (TVR) metric for each protocol participating in incentive programs, and compare results to assess the effectiveness of incentive programs in driving value creation and retention.

Why this is an important topic to research -

  • Incentive programs like LTIPP involve a lot of resources, so we need to understand if they are effective and valuable for participating protocols.
  • By analyzing user behaviour, engagement, and value generation, we can optimize incentive program designs to achieve better results.
  • Understanding funding mechanisms and sustainability is crucial for the long-term success of incentive programs and protocol growth.
  • Insights into cross-protocol interactions and network effects can help foster collaborations and ecosystem growth.
  • Identifying best practices and recommendations will benefit not just LTIPP participants but the entire Arbitrum ecosystem.
  • Evaluating reward distribution and allocation strategies can help protocols attract and retain users/liquidity more effectively.
  • Assessing protocol competitiveness and differentiation through incentives can inform better positioning strategies.
  • Uncovering unintended consequences allows for course-correction and risk mitigation.
  • Quantifying the value captured by protocols can guide future investment and incentive budgeting decisions.
  • The research provides a framework for measuring the ROI and cost-effectiveness of incentive programs.

We have divided our work in Milestones and created a roadmap to work on this research bounty. Sharing it here for reference.

Milestone Breakdown of this Research by Lampros Labs DAO.

How you will present this information to the DAO -

  • We will create a detailed final report with clear explanations, visuals (charts, graphs), and actionable insights.
  • The report will be accompanied by interactive dashboards showcasing key metrics and data visualizations.
  • All data, code, and methodologies used in the research will be open-sourced in a public data repository.
  • The report, dashboards, and data repository will be shared publicly for transparency and knowledge-sharing.

Research Project 2 –

Title - Effects of Growth Incentives on Non-Recipients Protocol

Objective -

  • Study the spillover effects of incentive programs on protocols within the same category that did not receive incentives.
  • Focus on a specific category like decentralized exchanges (DEXes) or lending protocols that received incentives in STIP Round 1 or STIP Backfund, and compare the user(DAU, MAU), TVL, and fee growth of non-recipient protocols within the same category.

Why this is an important topic to research -

  • Incentive programs aim to boost growth, but their impact on non-recipient protocols in the same category is unclear.
  • Understanding spillover effects can help assess the overall effectiveness of incentive programs on the ecosystem.
  • Insights into user behaviour, TVL, and fee trends for non-recipients can inform future incentive program designs.
  • Identifying potential negative consequences or unfair advantages for non-recipients is crucial.
  • The research can guide protocols on strategies to benefit from or mitigate the effects of incentive programs.

How you will present this information to the DAO -

  • We will create a comprehensive report with clear explanations, visuals (charts, graphs), and key findings.
  • The report will be accompanied by interactive dashboards showcasing relevant metrics and data visualizations.
  • All data, code, and methodologies used in the research will be open-sourced in a public data repository.
  • The report, dashboards, and data repository will be shared publicly for transparency and knowledge-sharing.

Research Team Information -

Team background -

About Team Lampros Labs DAO -

At Lampros Labs DAO, our mission is to foster innovation and support the growth of the Arbitrum ecosystem. We are dedicated to building a decentralized future by contributing to projects that enhance the Arbitrum network’s scalability, security, and user experience. We aim to foster a collaborative environment for Arbitrum, bringing in a new user base and spreading awareness about the Arbitrum ecosystem.

Our team is dedicated to data analysis and visualization, to make complex data simple to understand for every user of Arbitrum. Through our expertise in data engineering, statistical analysis, machine learning, and blockchain analytics, we aim to provide insightful and actionable insights that can drive informed decision-making and optimize the Arbitrum ecosystem’s growth and development.

Contributions done by Members of Lampros Labs DAO in Arbitrum Ecosystem -

Lampros Labs DAO | Arbitrum Contributions

Team Members -

@Euphoria -

Euphoria is a management postgraduate from a Tier 1 institute, specializing in data and analysis, project management, and marketing. He excels in data visualization and analysis, leveraging his expertise to derive insightful conclusions from complex datasets. With a strong background in project management, Euphoria is adept at coordinating team efforts and ensuring seamless execution of research initiatives.

@Blueweb -

With powerful experience in the blockchain industry, Chain-L has developed and built various projects across multiple ecosystems. He possesses a deep understanding of the underlying technologies and has worked extensively with Layer 2 solutions, including Arbitrum, Optimism, and Polygon. He led the numbaNERDs task leaderboard while encouraging other data analysts to contribute to the DAO. Chain-L is skilled in analyzing on-chain data, and tracking key metrics like user growth, Total Value Locked (TVL), and engagement levels.

@ARDev097 -

ARDev is skilled in data warehousing, ETL processes, and database management systems like MySQL and PostgreSQL. He is an expert in integrating on-chain data sources, data cleaning, and ensuring data quality. ARDev is proficient in using blockchain data analysis tools like Dune Analytics, creating interactive Dune Dashboards, and developing spellbooks for analyzing user behaviour and interaction patterns within the Arbitrum ecosystem.

@jason42 -

Jason is highly skilled in statistical analysis and data mining techniques. He is proficient in using programming languages like Python for exploratory data analysis and identifying patterns within complex datasets. Jason has extensive experience with econometric modelling and quantitative analysis, which are critical for assessing financial mechanisms and cost-effectiveness.

Why your team is the best fit to research this topic -

Our team, Lampros Labs DAO, is the ideal choice to conduct this research due to our extensive hands-on experience and proven expertise in analyzing user behaviour, interaction patterns, and incentive program dynamics within the Arbitrum ecosystem. We have completed numerous projects involving data collection, analysis, and impact assessments specific to Arbitrum, including Sybil/impact analysis, delegate voting patterns, and governance participation studies.

This domain knowledge, coupled with our skills in handling large datasets and understanding the intricacies of incentive program mechanics, makes us uniquely qualified to deliver comprehensive and actionable insights for this bounty program.

We are familiar with the Arbitrum incentive programs, including STIP Round 1 and STIP Backfund and have closely followed and studied the various incentive programs implemented on Arbitrum, giving us the necessary context and knowledge to effectively evaluate their impact on the ecosystem.

We have extensive experience in analyzing decentralized exchange (DEX) and lending protocol data on Arbitrum. We have consistently worked on extracting and processing on-chain data specific to these protocol categories, giving us a deep understanding of the underlying metrics and patterns.

Some of the relevant tasks that we have completed for the Arbitrum ecosystem are -

Collusion analysis on the arbitrum governance forum (User Activity and Collaboration)

Understanding Factors That Lead To Successful Grant Applications

Collusion analysis on the arbitrum governance forum (Analyzing Sentiment Dynamics and Engagement Patterns)

Analysis of Delegates Voting Pattern

Analyzing Governance Participation Data

LTIPP Voting Dashboard

STIP-Bridge Voting Dashboard

Budget -

Requested budget -

We are requesting a budget of 27,724 ARB for both the research.

Cost breakdown (including both the research topics) -

  • Research and Data Gathering - 5,000 ARB
  • Data Analysis, Data Visualization & Dashboard Creation - 15,000 ARB
  • Platform Subscription (Dune) - $999 * 3 months - 2724 ARB (1ARB ~ 1.1 USD).
  • Report Creation and Compilation - 5,000 ARB

Hi @Euphoria,

thanks for putting together a great research bounty proposal! Both research projects are well-rounded with clear deliverables and goals which we appreciate. Our only concern is that there is a considerable amount of research questions and deliverables within scope and we wouldn’t want sections receiving inadequate research time due to the workload being so large. However, the deliverables table should certainly help with this.

1 Like

Hey @WintermuteGovernance,

Thank you for your comments, we will look into all questions once again and re-distribute tasks among team members expected to work. If there are inconsistencies in what we will be able to answer in the given timeframe, we will eliminate a few questions to focus well on the rest.

Thanks for your application @Euphoria. We appreciate the approach to tackle multiple of the Council’s research questions as well as providing a milestone breakdown with deliverables. Overall, the budget seems very reasonable but we have some questions on research project 2.
You state the objective:

Can you provide more details on the proposed scope? Will you be addressing one specific category or do you plan to address all subcategories that received incentives?

1 Like

Hi @404DAO,

Thank you for your comments!

Regarding research project 2, our primary focus will be on specific categories such as DEXes and lending protocols. We believe a comprehensive study requires an in-depth understanding of all protocols within a category. Therefore, we will initially limit our scope to one committed category. However, if time permits, we are open to expanding our research to include additional subcategories.