Matched Funding

Overview

This wiki page describes the ‘Matched Funding’ scheme, in which the contribution of the Neurons’ Fund to SNS swaps scales in line with direct participation, allowing for a more accurate reflection of market signals.

The Matching function, f

The scheme is implemented through a matching function f, where the input x represents the amount of direct participation, and the output f(x) denotes the corresponding contribution from the Neurons' Fund (NF).

The function f is designed to have three distinct phases and will be a continuous function to ensure a smooth transition between these phases. Importantly, the rules for these phases will be globally consistent, applicable to all SNS launches.

• Initial Lag Phase (I): The function starts at 0, and grows slowly until it reaches a set threshold. This design encourages projects to accumulate enough direct participation before receiving substantial contributions from the NF.
• Growth Phase (II): After crossing the threshold, the NF's contribution increases at a faster rate, signifying more significant support for projects that have demonstrated viability through direct participation.
• Saturation Phase (III): Beyond a certain point, f(x) will level off and will not surpass 10% of the NF's total maturity, which ensures that no single SNS will excessively deplete the NF's resources.
• Bounding Condition: The matching function f(x) is bounded by g(x)=x, meaning that it will never exceed a 1:1 ratio with x. In practical terms, the NF's contribution will always be less than or equal to the amount of direct participation.
• Audability: The shape of f (which can change over time due to varying NF size) should be audible.

These design principles aim to create a fair and sustainable system for allocating NF contributions to various SNS initiatives.

Benefits of matched funding

Better Reflection of Market Signals

The matched funding system is designed to closely align with market sentiment. Specifically, a project that successfully raises more direct contributions will correspondingly receive a greater contribution from the Neurons' Fund (NF), up to a predetermined threshold.

Simpler Decision-making for NF NNS Neurons

The automated adjustment feature in the NF's contributions lessens the decision-making burden on NF NNS neurons. As a result, these neurons have fewer instances where they need to opt out, making the process more efficient.

Improved Incentives for Projects

The matching system provides a more compelling incentive structure for projects. Knowing that increased direct funding will be matched (up to a point) by the NF, encourages projects to be more proactive in their fundraising efforts.

Detailed specification of the matching function

Graph of the matching function

Cap

The contribution should be capped by 10% of the NF maturity at proposal execution time and also by a global NF contribution cap being equivalent to 1M USD and thus 333k ICP. In other words we have cap = min (10% of NF maturity, global NF contribution cap)

The global NF contribution cap should be a configurable parameter of the NNS and eventually it should be specified in units of XDR.

Thresholds

For specifying the shape of the matching function f, we define the following thresholds

• t_1: Up to this point, a project receives no contribution from the NF.
• t_2: Projects get a 2:1 contribution from the NF.
• t_3: Projects receive a 1:1 contribution from the NF.
• t_4 = 2 * cap: Projects get a 2:1 contribution from the NF. (and afterwards the contribution of the NF remains flat).

The thresholds should be configurable parameters of the NNS. For example they could be part of the NetworkEconomics record.

Eventually, it should be possible to specify these thresholds in XDR and then convert them to ICP thresholds at the execution of the SNS swap proposal. For the initial implementation, it is sufficient to have these thresholds in ICP.

For the time being, the following values are suggested

• t_1: 100k USD corresponding to 33k ICP
• t_2: 300k USD corresponding to 100k ICP
• t_3: 500k USD corresponding to 167k ICP
  

Matching function polynomials

The matching should be realized by set of polynomials

• f_1: for function values between t_1 and t_2
• f_2: for function values between t_2 and t_3
• f_3: for function values between t_3 and t_4.

Formulas for the polynomials can be derived from constraints listed above on the functions f and f’.