Token Utility Model
DPSN employs a token-based economic model designed to create a sustainable, efficient, and scalable ecosystem. DPSN tokens serve three primary functions within the network:
Gas Fees: Subscribers use tokens to pay for message reception, ensuring efficient resource allocation.
Topic Purchases: Publishers acquire tokens to buy rights for message dissemination on specific channels.
Mining Rewards: Node runners receive tokens as compensation for maintaining network infrastructure.
This multi-faceted utility creates a circular economy where token flow directly correlates with network activity, content creation, and infrastructure support. The DPSN pricing model, built on game theory principles, aims to balance these utilities by:
Maintaining competitive pricing for subscribers
Ensuring sustainable compensation for node runners
Allowing for network scalability without economic disruption
By optimizing the interplay between token utility and pricing, DPSN creates a self-regulating system that aligns the interests of all participants while fostering network growth and efficiency.
Given:
p₀ = maximum competitive price per message
e₀ = base infrastructure cost per hour for node runners
m = messages sent per hour
Let's define:
p = actual price per message (needs to be determined)
n = number of node runners
α = percentage of revenue allocated to node runners (0 < α < 1)
Constraints:
Subscriber benefit: p < p₀
Node runner revenue: (α × p × m) / n > e₀
Objective:
Maximize total network value (V) = Subscriber Surplus + Node Runner Profit
where
Subscriber Surplus is the difference between the maximum subscribers are willing to pay (p₀) and the actual price (p), and
Node Runner Profit is the compensation to node runners based on message volume and infrastructure costs.
V = (p₀ - p)m + (α × p × m - n × e₀)
Optimal Solution:
To find the optimal p and α, we need to maximize V subject to the constraints.Solving the node runner profit constraint:
Optimal α (minimizing p while ensuring node runner profit):
Substituting into the objective function:
To maximize V, we want p to be as low as possible while still satisfying: p > (n × e₀) / (α × m)Therefore, the optimal price p* is just above this lower bound:p* = (n × e₀) / (α × m) + εWhere ε is a small positive number to ensure strict inequality.
Final Model:
Optimal price:
p* = (n × e₀) / (α × m) + ε
Optimal revenue share:
α* = (n × e₀) / (p* × m)
This model ensures:
p* < p₀ (subscriber benefit)
(α* × p* × m) / n > e₀ (node runner profit)
The network can adjust n and α to find the optimal balance between competitive pricing and node runner incentives.
How the Model Balances the Ecosystem:
Subscribers benefit by paying less than the maximum they are willing to (p₀), while still ensuring the network's long-term viability.
Node Runners are compensated above their costs (e₀) to keep the network operational and scalable.
Network Growth is supported by adjusting n (number of nodes) and α (revenue share) to ensure sustainable scalability without economic disruption.
Examples
This self-regulating model fosters a circular token economy, aligning incentives across participants (subscribers, publishers, and node runners) and ensuring that as the network grows, both performance and economic sustainability are maintained.
Examples of various sample configurations below:
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