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Metafunding: Fund PGF Mechanisms & Research
Test test proposal
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Commons Dataset
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Audiomoth Data from the Philippines
Data much rich and cool
Simocracy: Generative Agentic Impact Evaluators
Quadratic funding under constrained budget is suboptimal
We analyze the efficiency of Quadratic Funding (QF) under individual budget
constraints, extending the framework of Weyl et al. (2019). QF’s optimality
requires participants to equalize marginal utilities across projects—an assumption
that fails when budgets are binding, as shown through utility function analysis
and a proof by contradiction. Using 5.2 million donations from 23 Gitcoin
Grants rounds and multiple Octant Epochs, we document a shift from early
donors supporting multiple projects with larger amounts to recent cohorts making
small, single-project contributions. These patterns produce peaked preference
distributions, a power law in projects supported, and an exponential decay
in donation amounts. Our results indicate that, under realistic budget limits
and concentrated preferences, QF may deviate from its theoretical efficiency,
underscoring the need for mechanism designs that address behavioral and
informational constraints.
Quadratic funding under constrained budget is suboptimal
We analyze the efficiency of Quadratic Funding (QF) under individual budget
constraints, extending the framework of Weyl et al. (2019). QF’s optimality
requires participants to equalize marginal utilities across projects—an assumption
that fails when budgets are binding, as shown through utility function analysis
and a proof by contradiction. Using 5.2 million donations from 23 Gitcoin
Grants rounds and multiple Octant Epochs, we document a shift from early
donors supporting multiple projects with larger amounts to recent cohorts making
small, single-project contributions. These patterns produce peaked preference
distributions, a power law in projects supported, and an exponential decay
in donation amounts. Our results indicate that, under realistic budget limits
and concentrated preferences, QF may deviate from its theoretical efficiency,
underscoring the need for mechanism designs that address behavioral and
informational constraints.
Quadratic funding under constrained budget is suboptimal
We analyze the efficiency of Quadratic Funding (QF) under individual budget
constraints, extending the framework of Weyl et al. (2019). QF’s optimality
requires participants to equalize marginal utilities across projects—an assumption
that fails when budgets are binding, as shown through utility function analysis
and a proof by contradiction. Using 5.2 million donations from 23 Gitcoin
Grants rounds and multiple Octant Epochs, we document a shift from early
donors supporting multiple projects with larger amounts to recent cohorts making
small, single-project contributions. These patterns produce peaked preference
distributions, a power law in projects supported, and an exponential decay
in donation amounts. Our results indicate that, under realistic budget limits
and concentrated preferences, QF may deviate from its theoretical efficiency,
underscoring the need for mechanism designs that address behavioral and
informational constraints.
Quadratic funding under constrained budget is suboptimal
We analyze the efficiency of Quadratic Funding (QF) under individual budget
constraints, extending the framework of Weyl et al. (2019). QF’s optimality
requires participants to equalize marginal utilities across projects—an assumption
that fails when budgets are binding, as shown through utility function analysis
and a proof by contradiction. Using 5.2 million donations from 23 Gitcoin
Grants rounds and multiple Octant Epochs, we document a shift from early
donors supporting multiple projects with larger amounts to recent cohorts making
small, single-project contributions. These patterns produce peaked preference
distributions, a power law in projects supported, and an exponential decay
in donation amounts. Our results indicate that, under realistic budget limits
and concentrated preferences, QF may deviate from its theoretical efficiency,
underscoring the need for mechanism designs that address behavioral and
informational constraints.
Quadratic funding under constrained budget is suboptimal
We analyze the efficiency of Quadratic Funding (QF) under individual budget
constraints, extending the framework of Weyl et al. (2019). QF’s optimality
requires participants to equalize marginal utilities across projects—an assumption
that fails when budgets are binding, as shown through utility function analysis
and a proof by contradiction. Using 5.2 million donations from 23 Gitcoin
Grants rounds and multiple Octant Epochs, we document a shift from early
donors supporting multiple projects with larger amounts to recent cohorts making
small, single-project contributions. These patterns produce peaked preference
distributions, a power law in projects supported, and an exponential decay
in donation amounts. Our results indicate that, under realistic budget limits
and concentrated preferences, QF may deviate from its theoretical efficiency,
underscoring the need for mechanism designs that address behavioral and
informational constraints.
Quadratic funding under constrained budget is suboptimal
We analyze the efficiency of Quadratic Funding (QF) under individual budget
constraints, extending the framework of Weyl et al. (2019). QF’s optimality
requires participants to equalize marginal utilities across projects—an assumption
that fails when budgets are binding, as shown through utility function analysis
and a proof by contradiction. Using 5.2 million donations from 23 Gitcoin
Grants rounds and multiple Octant Epochs, we document a shift from early
donors supporting multiple projects with larger amounts to recent cohorts making
small, single-project contributions. These patterns produce peaked preference
distributions, a power law in projects supported, and an exponential decay
in donation amounts. Our results indicate that, under realistic budget limits
and concentrated preferences, QF may deviate from its theoretical efficiency,
underscoring the need for mechanism designs that address behavioral and
informational constraints.
Generative Agentic Impact Evaluators: Next Steps for Filecoin
This paper proposes the development of generative agentic evaluators to enhance funding decision-making within the Filecoin ecosystem. We outline a multi-agent evaluation system where digital twins of key stakeholder personas—including Storage Providers, Data Clients, Token Stakeholders, Developers, and Community Projects—can assess proposals for both proactive and retroactive funding programs. These agentic evaluators, constructed through structured interviews and grounded in stakeholder values, would simulate human judgment to reduce reviewer workload, surface value-specific disagreements, and provide pluralistic, interpretable feedback to decision-makers. We further envision a comprehensive Ideal Project Evaluation and Negotiation System enabling dynamic dialogue between project agents and evaluator agents, with cumulative memory and transparent documentation. This approach represents a step toward scaling funding systems through agentic, pluralistic deliberation while maintaining trust and interpretability in the evaluation process.
Simocracy: Generative Agentic Impact Evaluators
Simocracy: Generative Agentic Impact Evaluator
Simocracy: Generative Agentic Impact Evaluators
Simocracy: Generative Agentic Impact Evaluators
Ecocert Test 1
This is an ecocert test
Poor Economic Breakfast 2nd Edition
# **Impact Certificate**
**Event**: Poor Economics Breakfast, 2nd Edition
**Location**: Funding the Commons Berlin Residency
## *Certificate of Participation*
---
### **Description**:
The 2nd edition of Poor Economics Breakfast at the Berlin Residency dived into the economic lives of the underprivileged. Attendees explored 'impact evaluation', learning to measure the effectiveness of interventions.
This gathering was more than a discussion. It aimed to enact tangible change, recognizing that everyone deserves an opportunity to improve their life quality.
Participation signifies a dedication to understanding poverty's complexities and to ensuring effective socio-economic strategies for the marginalized.
---
**For a better world,**
**Organized by**: GainForest
**Date**: 13th September 2023
Ethereum’s long-term health depends on the sustainability and effectiveness of its Public Goods Funding (PGF) mechanisms — systems like Quadratic Funding and RetroPGF that allocate resources to the infrastructure, tools, and communities the ecosystem relies on. While these models have proven their potential in catalyzing growth and strengthening resilience, adoption and impact remain constrained by systemic challenges: PGF is often perceived as charity rather than high-ROI investment, there is little formal academic engagement to validate or improve designs, data quality is insufficient for robust analysis, and evaluator capacity limits scale and credibility. Without dedicated research and infrastructure, PGF risks becoming a series of isolated experiments with diminishing engagement, as recent Gitcoin trends show. Metafunding proposes to address this by creating a Gitcoin Grants domain devoted to the rigorous study, refinement, and scaling of PGF mechanisms. Leveraging a mechanism that is inspired by academic peer-review, the initiative will aim to bridge academia and crypto, generate open datasets, and establish the evaluation capacity needed to turn PGF from a niche experiment into a trusted, scalable funding layer for Ethereum and beyond.
Quadratic funding under constrained budget is suboptimal
This paper examines the optimality of Quadratic Funding (QF) mechanisms under individual budget constraints through both theoretical analysis and large-scale empirical data. Building on Weyl et al. (2019), we show that QF’s efficiency depends critically on the assumption that participants possess sufficiently large budgets to equalize marginal utilities across multiple public goods. When individual budgets are binding, this first-order condition may not be satisfiable, leading to systematically suboptimal allocations. We formalize this limitation via utility function analysis and a proof by contradiction. Empirically, we analyze 5.2 million donations from 23 Gitcoin Grants rounds and multiple Octant Epochs, revealing a marked shift in donor behavior over time. While early participants supported multiple projects with larger contributions, recent cohorts—driven by outreach and marketing strategies—tend to make small ($1–10) contributions to a single project. This concentration of support produces highly peaked preference distributions, challenging QF’s core assumption of distributed positive utilities across projects. The resulting funding patterns exhibit a power law in the number of projects supported and an exponential decay in contribution amounts, with the rate parameter λ serving as a concise descriptor of community giving behavior. These findings suggest that under realistic budget constraints and concentrated preferences, QF may fail to achieve its theoretical efficiency, and that evolving donor demographics can further exacerbate this divergence. We argue that mechanism design must adapt to these behavioral realities—incorporating donor education, information structure, and incentive alignment—to sustain QF’s effectiveness in public goods provision.
Simocracy: Generative Agentic Impact Evaluators
Decentralized funding ecosystems face a critical bottleneck: as funding scales grow, human evaluators struggle with burnout, inconsistency, and the cognitive demands of assessing diverse impact claims. We introduce Generative Agentic Impact Evaluators—LLM-based digital twins that simulate individual evaluators' reasoning and value judgments. Through structured interviews with five participants at the Impact Evaluator Research Retreat in Iceland, we captured each person's evaluation philosophy and constructed their digital counterparts. When tested on value-laden funding scenarios, these digital twins demonstrated remarkable variation in fidelity: the most successful achieved 95.0% agreement with their human counterpart, while overall system accuracy reached 76.7%—more than double random chance. Our findings reveal that generative models can meaningfully replicate evaluative reasoning when participants articulate consistent values, offering a path toward scalable, pluralistic evaluation systems that preserve individual perspectives while reducing human cognitive load. However, model performance depends critically on interview quality and participant consistency, highlighting both the promise and current limitations of AI-mediated governance. A working demonstration is available at simocracy.org.
Decentralized funding ecosystems face a critical bottleneck: as funding scales grow, human evaluators struggle with burnout, inconsistency, and the cognitive demands of assessing diverse impact claims. We introduce Generative Agentic Impact Evaluators—LLM-based digital twins that simulate individual evaluators' reasoning and value judgments. Through structured interviews with five participants at the Impact Evaluator Research Retreat in Iceland, we captured each person's evaluation philosophy and constructed their digital counterparts. When tested on value-laden funding scenarios, these digital twins demonstrated remarkable variation in fidelity: the most successful achieved 95.0% agreement with their human counterpart, while overall system accuracy reached 76.7%—more than double random chance. Our findings reveal that generative models can meaningfully replicate evaluative reasoning when participants articulate consistent values, offering a path toward scalable, pluralistic evaluation systems that preserve individual perspectives while reducing human cognitive load. However, model performance depends critically on interview quality and participant consistency, highlighting both the promise and current limitations of AI-mediated governance. A working demonstration is available at simocracy.org.
Quadratic funding under constrained budget is suboptimal
This paper examines the optimality of Quadratic Funding (QF) mechanisms under individual budget constraints through both theoretical analysis and large-scale empirical data. Building on Weyl et al. (2019), we show that QF’s efficiency depends critically on the assumption that participants possess sufficiently large budgets to equalize marginal utilities across multiple public goods. When individual budgets are binding, this first-order condition may not be satisfiable, leading to systematically suboptimal allocations. We formalize this limitation via utility function analysis and a proof by contradiction. Empirically, we analyze 5.2 million donations from 23 Gitcoin Grants rounds and multiple Octant Epochs, revealing a marked shift in donor behavior over time. While early participants supported multiple projects with larger contributions, recent cohorts—driven by outreach and marketing strategies—tend to make small ($1–10) contributions to a single project. This concentration of support produces highly peaked preference distributions, challenging QF’s core assumption of distributed positive utilities across projects. The resulting funding patterns exhibit a power law in the number of projects supported and an exponential decay in contribution amounts, with the rate parameter λ serving as a concise descriptor of community giving behavior. These findings suggest that under realistic budget constraints and concentrated preferences, QF may fail to achieve its theoretical efficiency, and that evolving donor demographics can further exacerbate this divergence. We argue that mechanism design must adapt to these behavioral realities—incorporating donor education, information structure, and incentive alignment—to sustain QF’s effectiveness in public goods provision.
Quadratic funding under constrained budget is suboptimal
This paper examines the optimality of Quadratic Funding (QF) mechanisms under individual budget constraints through both theoretical analysis and large-scale empirical data. Building on Weyl et al. (2019), we show that QF’s efficiency depends critically on the assumption that participants possess sufficiently large budgets to equalize marginal utilities across multiple public goods. When individual budgets are binding, this first-order condition may not be satisfiable, leading to systematically suboptimal allocations. We formalize this limitation via utility function analysis and a proof by contradiction. Empirically, we analyze 5.2 million donations from 23 Gitcoin Grants rounds and multiple Octant Epochs, revealing a marked shift in donor behavior over time. While early participants supported multiple projects with larger contributions, recent cohorts—driven by outreach and marketing strategies—tend to make small ($1–10) contributions to a single project. This concentration of support produces highly peaked preference distributions, challenging QF’s core assumption of distributed positive utilities across projects. The resulting funding patterns exhibit a power law in the number of projects supported and an exponential decay in contribution amounts, with the rate parameter λ serving as a concise descriptor of community giving behavior. These findings suggest that under realistic budget constraints and concentrated preferences, QF may fail to achieve its theoretical efficiency, and that evolving donor demographics can further exacerbate this divergence. We argue that mechanism design must adapt to these behavioral realities—incorporating donor education, information structure, and incentive alignment—to sustain QF’s effectiveness in public goods provision.
Quadratic funding under constrained budget is suboptimal
This paper examines the optimality of Quadratic Funding (QF) mechanisms under individual budget constraints through both theoretical analysis and large-scale empirical data. Building on Weyl et al. (2019), we show that QF’s efficiency depends critically on the assumption that participants possess sufficiently large budgets to equalize marginal utilities across multiple public goods. When individual budgets are binding, this first-order condition may not be satisfiable, leading to systematically suboptimal allocations. We formalize this limitation via utility function analysis and a proof by contradiction. Empirically, we analyze 5.2 million donations from 23 Gitcoin Grants rounds and multiple Octant Epochs, revealing a marked shift in donor behavior over time. While early participants supported multiple projects with larger contributions, recent cohorts—driven by outreach and marketing strategies—tend to make small ($1–10) contributions to a single project. This concentration of support produces highly peaked preference distributions, challenging QF’s core assumption of distributed positive utilities across projects. The resulting funding patterns exhibit a power law in the number of projects supported and an exponential decay in contribution amounts, with the rate parameter λ serving as a concise descriptor of community giving behavior. These findings suggest that under realistic budget constraints and concentrated preferences, QF may fail to achieve its theoretical efficiency, and that evolving donor demographics can further exacerbate this divergence. We argue that mechanism design must adapt to these behavioral realities—incorporating donor education, information structure, and incentive alignment—to sustain QF’s effectiveness in public goods provision.
Quadratic funding under constrained budget is suboptimal
This paper examines the optimality of Quadratic Funding (QF) mechanisms under individual budget constraints through both theoretical analysis and large-scale empirical data. Building on Weyl et al. (2019), we show that QF’s efficiency depends critically on the assumption that participants possess sufficiently large budgets to equalize marginal utilities across multiple public goods. When individual budgets are binding, this first-order condition may not be satisfiable, leading to systematically suboptimal allocations. We formalize this limitation via utility function analysis and a proof by contradiction. Empirically, we analyze 5.2 million donations from 23 Gitcoin Grants rounds and multiple Octant Epochs, revealing a marked shift in donor behavior over time. While early participants supported multiple projects with larger contributions, recent cohorts—driven by outreach and marketing strategies—tend to make small ($1–10) contributions to a single project. This concentration of support produces highly peaked preference distributions, challenging QF’s core assumption of distributed positive utilities across projects. The resulting funding patterns exhibit a power law in the number of projects supported and an exponential decay in contribution amounts, with the rate parameter λ serving as a concise descriptor of community giving behavior. These findings suggest that under realistic budget constraints and concentrated preferences, QF may fail to achieve its theoretical efficiency, and that evolving donor demographics can further exacerbate this divergence. We argue that mechanism design must adapt to these behavioral realities—incorporating donor education, information structure, and incentive alignment—to sustain QF’s effectiveness in public goods provision.
Quadratic funding under constrained budget is suboptimal
This paper examines the optimality of Quadratic Funding (QF) mechanisms under individual budget constraints through both theoretical analysis and large-scale empirical data. Building on Weyl et al. (2019), we show that QF’s efficiency depends critically on the assumption that participants possess sufficiently large budgets to equalize marginal utilities across multiple public goods. When individual budgets are binding, this first-order condition may not be satisfiable, leading to systematically suboptimal allocations. We formalize this limitation via utility function analysis and a proof by contradiction. Empirically, we analyze 5.2 million donations from 23 Gitcoin Grants rounds and multiple Octant Epochs, revealing a marked shift in donor behavior over time. While early participants supported multiple projects with larger contributions, recent cohorts—driven by outreach and marketing strategies—tend to make small ($1–10) contributions to a single project. This concentration of support produces highly peaked preference distributions, challenging QF’s core assumption of distributed positive utilities across projects. The resulting funding patterns exhibit a power law in the number of projects supported and an exponential decay in contribution amounts, with the rate parameter λ serving as a concise descriptor of community giving behavior. These findings suggest that under realistic budget constraints and concentrated preferences, QF may fail to achieve its theoretical efficiency, and that evolving donor demographics can further exacerbate this divergence. We argue that mechanism design must adapt to these behavioral realities—incorporating donor education, information structure, and incentive alignment—to sustain QF’s effectiveness in public goods provision.
Decentralized funding ecosystems face a critical bottleneck: as funding scales grow, human evaluators struggle with burnout, inconsistency, and the cognitive demands of assessing diverse impact claims. We introduce Generative Agentic Impact Evaluators—LLM-based digital twins that simulate individual evaluators' reasoning and value judgments. Through structured interviews with five participants at the Impact Evaluator Research Retreat in Iceland, we captured each person's evaluation philosophy and constructed their digital counterparts. When tested on value-laden funding scenarios, these digital twins demonstrated remarkable variation in fidelity: the most successful achieved 95.0% agreement with their human counterpart, while overall system accuracy reached 76.7%—more than double random chance. Our findings reveal that generative models can meaningfully replicate evaluative reasoning when participants articulate consistent values, offering a path toward scalable, pluralistic evaluation systems that preserve individual perspectives while reducing human cognitive load. However, model performance depends critically on interview quality and participant consistency, highlighting both the promise and current limitations of AI-mediated governance. A working demonstration is available at simocracy.org.
Decentralized funding ecosystems face a critical bottleneck: as funding scales grow, human evaluators struggle with burnout, inconsistency, and the cognitive demands of assessing diverse impact claims. We introduce Generative Agentic Impact Evaluators—LLM-based digital twins that simulate individual evaluators' reasoning and value judgments. Through structured interviews with five participants at the Impact Evaluator Research Retreat in Iceland, we captured each person's evaluation philosophy and constructed their digital counterparts. When tested on value-laden funding scenarios, these digital twins demonstrated remarkable variation in fidelity: the most successful achieved 95.0% agreement with their human counterpart, while overall system accuracy reached 76.7%—more than double random chance. Our findings reveal that generative models can meaningfully replicate evaluative reasoning when participants articulate consistent values, offering a path toward scalable, pluralistic evaluation systems that preserve individual perspectives while reducing human cognitive load. However, model performance depends critically on interview quality and participant consistency, highlighting both the promise and current limitations of AI-mediated governance. A working demonstration is available at simocracy.org.
Decentralized funding ecosystems face a critical bottleneck: as funding scales grow, human evaluators struggle with burnout, inconsistency, and the cognitive demands of assessing diverse impact claims. We introduce Generative Agentic Impact Evaluators—LLM-based digital twins that simulate individual evaluators' reasoning and value judgments. Through structured interviews with five participants at the Impact Evaluator Research Retreat in Iceland, we captured each person's evaluation philosophy and constructed their digital counterparts. When tested on value-laden funding scenarios, these digital twins demonstrated remarkable variation in fidelity: the most successful achieved 95.0% agreement with their human counterpart, while overall system accuracy reached 76.7%—more than double random chance. Our findings reveal that generative models can meaningfully replicate evaluative reasoning when participants articulate consistent values, offering a path toward scalable, pluralistic evaluation systems that preserve individual perspectives while reducing human cognitive load. However, model performance depends critically on interview quality and participant consistency, highlighting both the promise and current limitations of AI-mediated governance. A working demonstration is available at simocracy.org.
Decentralized funding ecosystems face a critical bottleneck: as funding scales grow, human evaluators struggle with burnout, inconsistency, and the cognitive demands of assessing diverse impact claims. We introduce Generative Agentic Impact Evaluators—LLM-based digital twins that simulate individual evaluators' reasoning and value judgments. Through structured interviews with five participants at the Impact Evaluator Research Retreat in Iceland, we captured each person's evaluation philosophy and constructed their digital counterparts. When tested on value-laden funding scenarios, these digital twins demonstrated remarkable variation in fidelity: the most successful achieved 95.0% agreement with their human counterpart, while overall system accuracy reached 76.7%—more than double random chance. Our findings reveal that generative models can meaningfully replicate evaluative reasoning when participants articulate consistent values, offering a path toward scalable, pluralistic evaluation systems that preserve individual perspectives while reducing human cognitive load. However, model performance depends critically on interview quality and participant consistency, highlighting both the promise and current limitations of AI-mediated governance. A working demonstration is available at simocracy.org.