Key Points
A simulation analysis of how voting behavior trends (hot/cold) influence funding allocation. “Hot” voters are defined as those who concentrate votes on a small number of projects, while “cold” voters are those who spread votes across many projects. The analysis simulates funding allocation excluding these types of voters and evaluates the impact by comparing it to the original allocation.
Background
In FIL-RetroPGF-1, each badgeholder (voter) had 100 votes and cast them across multiple projects. This analysis classifies badgeholders’ voting behavior based on the concept of “temperature” and examines how such tendencies affected overall funding allocation. Specifically, it identifies “hot” voters who concentrate votes on a small number of projects and “cold” voters who spread votes across many projects, then simulates scenarios excluding these groups.
Analysis Method
Dataset
An anonymized dataset of all votes cast by badgeholders in the FIL-RetroPGF-1 round.
Intervation / Explanatory Variable
Excluding the top 10% of “hot” badgeholders (those who concentrated votes on fewer projects) and the top 10% of “cold” badgeholders (those who spread votes across many projects) from the dataset.
Dependent Variable
Funding allocation to projects
Identification Strategy
A counterfactual analysis comparing three scenarios: the original funding allocation, allocation excluding “hot” badgeholders, and allocation excluding “cold” badgeholders.
Results
- Excluding the top 10% of “hot” badgeholders had a very small impact on funding allocation.
- In contrast, excluding the top 10% of “cold” badgeholders led to a disproportionately large reduction in the number of projects receiving funding.
- This is likely because “cold” badgeholders helped more projects reach the quorum requirement, enabling funding to be distributed more widely. In addition, their low votes (e.g., 0, 1, 2) lowered the average scores of projects, further contributing to broader distribution of funds.