Mycelith Voting System[edit]

The Mycelith Voting System is a decentralized, layered voting mechanism designed for the Seigr ecosystem. Inspired by the resilient and adaptive nature of mycelial networks, Mycelith empowers community-driven decision-making while promoting fairness, adaptability, and ethical governance. The system is engineered to accommodate participants’ evolving insights, rewarding consistency and commitment through a structured, multi-layered approach.

Overview of Mycelith[edit]

The Mycelith Voting System is structured around sequential voting rounds, or layers, that create a gradual decision-making process. Each layer increases the influence weight of votes cast, encouraging participants to engage consistently throughout the entire process. This layered structure aligns with Seigr’s ethos of transparency, resilience, and collaborative evolution, drawing on principles from biological networks that adapt to dynamic conditions.

Key Features[edit]

1. Senary-Scaled Voting Layers: Mycelith operates on a six-layer voting system, each layer assigned a unique scaling factor based on Seigr’s senary (base-6) principles. This scaling rewards consistent participants by amplifying their influence across layers.

2. Weighted Voting through WCAS: Each participant’s influence in the system is weighted by their Weighted Consistency and Alignment Score (WCAS), which accounts for their experience, prior participation, and adherence to ethical standards within the ecosystem.

3. Adaptive Scaling: Mycelith’s influence scaling increases with each layer, allowing participants who commit early and stay consistent to gain more influence. This design mirrors the adaptability of mycelial networks and enhances decision-making by valuing both immediate insights and reinforced decisions.

The layered approach provides a nuanced decision-making model that encourages careful consideration, consistency, and adaptability, aligning closely with Seigr’s core principles.

Structure of the Mycelith Voting System[edit]

Mycelith’s voting process unfolds over six structured layers, denoted as $L_{1}$ to $L_{6}$ . Each layer provides an opportunity for participants to maintain or adjust their votes, with influence weights progressively increasing to reward consistent engagement.

Six Voting Layers Explained[edit]

The six layers are defined as follows:

Layer 1 (Initial): Participants cast an initial vote with minimal influence weight. This layer establishes the starting point for each voter’s stance.
Layer 2 (Observation): Influence weight increases slightly, allowing participants to reaffirm or adjust their initial choice based on early trends.
Layers 3–6 (Commitment Layers): Influence weights increase significantly for participants who maintain their stance, with the highest weight applied in Layer 6. This progression rewards consistency, ensuring that committed participants’ votes have the most impact by the final layer.

The progression of layers allows participants to refine their stance over time, encouraging thoughtful participation and providing a system that values both initial instincts and reinforced choices.

Mathematical Model of Mycelith Voting[edit]

Mycelith employs a senary scaling model to calculate influence across layers. Influence increases exponentially with each layer, allowing for a dynamic scaling that prioritizes commitment and consistency.

Let:

$W_{i}$ represent the base influence of participant $i$ , derived from their WCAS.
$S_{j}$ denote the senary scaling factor for layer $j$ .
$W_{j}^{(i)}$ represent the influence weight of participant $i$ in layer $j$ .

The scaling factor for each layer is computed as: $S_{j}=1.2^{j}$ where $j=1,2,\ldots ,6$ , representing each of the six layers. This exponential factor ensures that influence grows gradually and rewards participants who remain consistent.

The influence of a participant in each layer is then given by: $W_{j}^{(i)}=W_{i}\cdot S_{j}$

This model allows the influence of each participant to be adjusted according to their consistency and the progression of the voting layers.

Consistency Adjustment[edit]

Participants who maintain their stance across all layers receive full influence, while those who switch incur a moderation factor, $\gamma$ , applied to their influence in layers where they change. This adjustment rewards consistent voting behavior:

$\gamma$ is a consistency factor where $0<\gamma <1$ .
If a participant changes their vote between layers, their influence for that layer is reduced by $\gamma$ .

For example, if a participant’s influence for a layer would be $W_{j}^{(i)}$ , but they switched their vote, their moderated influence becomes $\gamma \cdot W_{j}^{(i)}$ .

Aggregating Votes for the Final Decision[edit]

The final outcome $O$ of a proposal is calculated by summing all influence-weighted votes across layers. Let $V_{i}^{(j)}$ represent participant $i$ ’s vote in layer $j$ , where $V_{i}^{(j)}\in \{+1,-1\}$ for binary decisions ("yes" or "no").

The outcome $O$ is determined as follows: $O={\text{sign}}\left(\sum _{j=1}^{6}\sum _{i=1}^{n}W_{j}^{(i)}\cdot V_{i}^{(j)}\right)$ where:

$O=+1$ indicates a "yes" outcome.
$O=-1$ indicates a "no" outcome.

This aggregated result reflects both the influence and consistency of participants, ensuring a fair and adaptive decision-making process.

Example Calculation[edit]

Consider three participants, A, B, and C, with WCAS-derived influence scores. Assume:

Participant A: $W=0.7$ , votes "yes" consistently.
Participant B: $W=0.5$ , switches from "no" to "yes" mid-process.
Participant C: $W=0.4$ , votes "no" consistently.

The senary scaling factors $S_{j}$ for each layer are:

$S_{1}=1.0$ , $S_{2}=1.2$ , $S_{3}=1.44$ , $S_{4}=1.728$ , $S_{5}=2.0736$ , $S_{6}=2.48832$

1. Participant A: Consistently "yes"

   ${\text{Total Influence}}_{A}=0.7\times (1.0+1.2+1.44+1.728+2.0736+2.48832)=7.236$

2. Participant B: Switches from "no" to "yes"

   ${\text{Total Influence}}_{B}=0.5\times (1.0+1.2+1.44)+0.5\times (1.728+2.0736+2.48832)\times 0.5=2.73$

3. Participant C: Consistently "no"

   ${\text{Total Influence}}_{C}=0.4\times (1.0+1.2+1.44+1.728+2.0736+2.48832)=4.136$

Aggregated outcome: $O={\text{sign}}(7.236\cdot (+1)+2.73\cdot (+1)+4.136\cdot (-1))=+1$

The outcome is "yes," reflecting the influence-weighted voting.

Summary of Senary Influence[edit]

By applying senary scaling, Mycelith ensures fair and adaptable voting, rewarding consistency and reflecting Seigr’s values of resilience, ethical governance, and community empowerment.