top of page

Don't miss our early bird ticket sale! Offer ends February 10

Digital Platform Econometrics Research Program 2025

  • Funding the Commons, Open Source Observer, Metagov join forces to initiate a body of work bridging universities, disciplines, and practical applications establishing research in digital platform econometrics.

    ​

    We propose a series of engagements and interventions in 2025 bringing together economists, political scientists, and policy makers together to study the science of blockchain-network economies and incentive programs.


    This proposal suggests a series of programs to bridge the gap between existing academic expertise (e.g. in econometrics) to solving the problems facing crypto foundations today, potentially spurring the creation of a new field: digital platform econometrics.
     

    We have the following goals in this series:

    • Study historical funding programs to understand their impact, potentially uncovering generalizable learnings that can be applied in future programs

    • Gain an understanding of the causal relationship between different forms of incentives and network outcomes

    • Design rigorous experimentation / evaluation methods for new incentive programs

    • Develop new mechanisms to achieve network goals

    • Engage young academics to be interested in the problems in crypto as part of their career
       

    The holy grail is to understand how to design incentive programs that maximize the impact per unit dollar spent for any possible goal that network governance may have.

  • Research Funding
    Funding the Commons, Open Source Observer and Metagov are excited to share announce a call for proposals for research grants in academically rigorous Successful applicants will receive up to $50,000 in funding per researcher, for 6-8 month’s duration of research (as co-defined with funding partner). The DPE grant selection committee will be looking for applications in current and past programs programs operating in the web3 “digital public goods funding” and open source space. We invite proposals that apply economic and econometric methods to policy evaluation, particularly causal inference methods. We are especially interested in research projects that use observational or experimental data, potentially generated in collaboration with a partner Foundation, and address topics related to program effectiveness within grant ecosystems, including developing better evaluation methods and metrics for grant recipients. We especially welcome research demonstrating clear, practical implications for grant-making program design in the blockchain ecosystem.

    Research Incubation Residency (Spring 2025)
    We will kick off the Fellowship timeline with an incubation residency, in which fellows are invited to spend approximately a week of sustained research time occurring across digital and physical space.

    During this time, fellows will have the opportunity to:

    • Engage in deep, focused working time (”hackathon-style”) with their research team

    • Meet directly with funders to understand their needs and opportunities

      • Refine research direction and roadmap

      • Access necessary data in desired form for workflow

      • Perform qualitative interviews and related data gathering as-needed, from data/funding sources

    • Participate in workshops and learning sessions facilitated by top thinkers from academia and industry, ensuring that cross-disciplinary skills are accounted for in guiding research direction

      Ongoing Support of Fellows
      Following the on-site incubation residency, fellows will have access to regular sessions with an advisory council of relevant funders, cross-disciplinary academics and thought leaders to ensure research needs are adequately supported.

      Digital Platform Econometrics Inaugural Summit (~November 2025)
      The year of research production will culminate in a research summit, in which researchers will have the opportunity to showcase their work. This conference will be co-organized with Funding the Commons, Open Source Observer and an academic partner to ensure academic rigor and value created for both fellows and peer researchers establishing the discipline of Digital Platform Econometrics.

  • The Digital Platform Econometrics Research Program aims to activate the emerging field of Digital Platform Econometrics, rigorously analyze the impact of large-scale economic and financial policies implemented by blockchain foundations and DAOs, and encourage cross-disciplinary collaboration uniting data science and econometrics as applied to “digital public goods” funding systems. Research areas of interest include:

    Historical Analysis of Funding Programs
     

    • How have the success rates and long-term sustainability of blockchain projects differed between those funded through ICOs, grants, and venture capital investments?

    • What are the key factors that have contributed to the success or failure of blockchain funding initiatives, and how have these factors evolved over time?

    • To what extent have historical funding approaches influenced the level of decentralization and community engagement in blockchain networks?

      Causal Relationships Between Incentives and Network Outcomes
       

    • How do different types of incentives (monetary, reputation-based, and gamified) impact user participation rates and the overall security of blockchain networks?

    • What is the relationship between the choice of consensus mechanism and the degree of decentralization achieved in blockchain networks?

    • How do varying incentive structures affect user retention and long-term engagement in blockchain ecosystems?

      Experimentation and Evaluation Methods
       

    • How can blockchain simulation models be designed to accurately predict the effects of different incentive mechanisms on network behavior?

    • What are the most effective metrics and evaluation frameworks for assessing the impact of incentive programs on blockchain network performance and user engagement?

    • How can machine learning techniques be leveraged to dynamically optimize incentive mechanisms based on real-time network data and user behavior?

      New Mechanism Design
       

    • What novel consensus mechanisms can be developed to effectively address the blockchain trilemma of scalability, decentralization, and security?

    • How can hybrid incentive models be designed to combine multiple forms of rewards while maintaining network stability and promoting desired user behaviors?

    • What are the potential benefits and challenges of integrating reputation-based systems with traditional blockchain incentive mechanisms?

      Impact of Established Web3 Funding Mechanisms and Methodologies
       

    • What has been the impact of retro funding so far?

      • What has happened that wouldn’t have happened if we didn’t do retro funding rounds?

      • Which retro funding rounds had the most sustained impact?
         

    • What are the short-term and long-term effects of a particular funding round on network goals?

      • How much of this effect is “sticky” vs transient?
         

    • How do we de-tangle the impact of overlapping funding programs (i.e. grants/missions, retro funding, airdrops, internal spend)?

      • e.g. when a project gets support from multiple ecosystems

      • e.g. when a project gets both prospective and retrospective funding
         

    • What types of goals are best satisfied by which funding mechanisms and when should we use each?
       

    • What things does the industry do that doesn’t work and why?
       

    • How can we improve retro funding?

      • How much funding is needed to change behavior in the long-term?

      • How can retro funding be applied to drive/accelerate net-new innovation in the long run (e.g. build tooling that doesn’t exist yet)

  • To be defined with our funding partner - check back for details shortly, and email research@fundingthecommons.io for furhter information

  • To be defined with our funding partner - check back for details shortly, and email research@fundingthecommons.io for furhter information

    Examples:

    Results in greater detail here: Did OP Airdrop 5 Increase User Retention Rates? A Regression Discontinuity Analysis

bottom of page