Capital Allocation in the Crypto Economy

Source

This is sourced from the Capital Allocation Pattern Language document.

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A work in progress by Benjamin Life and associates.

Description

Core Structure of Capital Allocation

"This pattern language organizes capital allocation into three primary components, each with distinct subcategories and relationships.

1. Allocators (Who Allocates Capital?)

Allocators determine how resources are distributed within a system. They can be categorized based on their decision-making process:

A. Decision-Making Models

1. Crowdsourced Decision-Making: Allocations determined by collective intelligence (e.g., Quadratic Funding, Participatory Budgeting, Conviction Voting).
2. Algorithmic & Automated Allocation: Programmatic mechanisms govern resource flows (e.g., AutoPGF, Bonding Curves, Roundabout Production).
3. Market-Driven Allocation: Uses pricing mechanisms, speculation, or risk assessments (e.g., Harberger Taxes, Investment DAOs, Social Impact Bonds).

B. Systemic Allocators

1. Community-Driven Allocators: Localized funding pools managed by members (e.g., ROSCAs, Community Currencies).
2. Institutional Allocators: Foundations, governments, and DAOs that deploy structured funding mechanisms.
3. Hybrid Allocators: Mixes human governance with algorithmic enforcement (e.g., Investment DAOs, Quadratic Funding Operators).

2. Allocations (How is Capital Distributed?)

Allocations define the way capital moves through a system, classified by timing, liquidity, and flow mechanisms.

A. Temporal Models

1. Preemptive Funding: Capital is deployed before work is done (e.g., Participatory Budgeting, Investment DAOs).
2. Ongoing Funding: Continuous capital flow based on real-time feedback (e.g., Bounties, Streaming Payments, ROSCAs).
3. Retroactive Funding: Resources are allocated after impact is demonstrated (e.g., RetroPGF, Hypercerts, Social Impact Bonds).

B. Liquidity & Capital Flow Models

1. One-Time Disbursement: Lump-sum grants or bounties.
2. Continuous & Streaming Payments: Auto-adjusting fund flows (e.g., AutoPGF, Revnets).
3. Circular & Reinvestment Models: Funds recirculate within an ecosystem (e.g., Community Currencies, Bonding Curves).

3. Signals (How Are Allocation Decisions Made?)

Signals provide data points that inform allocators and guide allocation mechanisms.

A. Verification Models

1. Reputation-Based: Trust networks assess eligibility (e.g., Revnets, UBI, Conviction Voting).
2. Data-Driven & Attestation-Based: Impact metrics and oracle data validate claims (e.g., Hypercerts, Social Impact Bonds).
3. Hybrid Verification: Combines qualitative and quantitative assessments (e.g., DAO-based grant approvals, Investment DAOs).

B. Output Signals

1. Performance Metrics: Assess effectiveness of past allocations.
2. Condition-Based Signals: Dynamic allocation triggers based on real-world events (e.g., climate oracles for ReFi funding).
3. Network Signals: Peer validation and social capital assessments (e.g., quadratic voting weight adjustments).

Examples

II. Application to Capital Allocation Mechanisms

This pattern language can be used to break down the structure of several key capital allocation mechanisms:

1. Quadratic Funding

1. Allocator Type: Crowdsourced Allocators (community-driven decision-making).
2. Allocation Model: Preemptive Funding with pooled matching funds.
3. Signals Used: Reputation-Based Signals (weighted community votes to determine fund distribution).

2. Tunable Quadratic Funding

1. Allocator Type: Hybrid Allocators (mix of community voting and algorithmic adjustments).
2. Allocation Model: Preemptive or Ongoing Funding with adjustable match weighting.
3. Signals Used: Reputation-Based and Condition-Based Signals (adjusting funding weights dynamically based on predefined criteria).

3. AutoPGF (Automated Public Goods Funding)

1. Allocator Type: Algorithmic Allocators (smart contract-driven disbursements).
2. Allocation Model: Ongoing Funding with continuous distribution based on predefined formulas.
3. Signals Used: Data-Driven Signals (onchain metrics and external data oracles trigger fund releases).

4. RetroPGF (Retroactive Public Goods Funding)

1. Allocator Type: Hybrid Allocators (community reviewers + algorithmic fund allocation).
2. Allocation Model: Retroactive Funding based on verified past contributions.
3. Signals Used: Data-Driven & Attestation-Based Signals (Hypercerts, reputation scores, impact metrics)."

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