Smart Contract Pricing Model

  9 minute read  

Smart Contract Pricing Model

Guiding Principles

Youseddit’s pricing for verified quote pairs (linked initiator and response quotes) must be clear and simple for journalists and writers. While the exact formula remains internal, the principles guiding it should be easy to grasp. This document outlines those principles.

We base pricing on three factors:

1. Time: How recent is the response quote (and the exchange it belongs to)?
2. Category: What is the topic?
3. Relevance: How prominent is the source providing the response quote? What is the quality of the response? How scarce is this type of information?

See a mock newspaper example demonstrating how verified response quotes (linked to their initiator context) might appear in a publication.

Philosophy: Empowering Journalists

While the underlying mechanics involve several factors, the goal for the journalist is simple: focus on securing and verifying impactful quote exchanges. The YouSeddit pricing model is designed to just work behind the scenes, automatically assessing the multifaceted value of your verified quote pair based on its timeliness, topic, and relevance.

We handle the complexity so you don’t have to. The system aims to intelligently capture the market value derived from your journalistic instincts – whether that’s breaking news, securing a response from a key figure, uncovering a unique perspective, or providing deep insight within a verified exchange. The result should feel intuitive: valuable journalistic work in securing and verifying quote pairs translates directly into fair compensation through the smart contract, empowering you to pursue the next story.

1. Time

New information costs more, reflecting the economic principle of the diminishing marginal utility of information over time. Like news, quote pairs lose value as they age. Prices for the pair will decrease from a set maximum for fresh exchanges to a minimum for older ones.

Next Step: Define a clear formula for this price decay over time for the quote pair, including maximum and minimum values.

2. Category

Quote pairs are categorized by the topic of the exchange. Topics with broad impact or high current interest (e.g., global economics, AI breakthroughs) result in a higher price for the pair than niche or local topics, reflecting media theories like Agenda-Setting.

Next Step: Create a defined list of categories and their corresponding price tiers or multipliers for quote pairs.

3. Relevance (Influence / Lippmann)

The price reflects the source’s (the individual providing the response quote) potential influence on public perception, or what Walter Lippmann termed the “pseudo-environment”. Quote pairs featuring responses from figures who significantly shape this pseudo-environment within a given category command higher prices than those from less influential individuals.

Next Step: Develop a system to rank sources’ influence within categories to set price tiers for the quote pairs they are part of.

3b. Relevance (Dialogue Quality / UDQA)

A different approach assesses relevance based on the intrinsic quality of the response quote itself using a structured rubric like the Universal Dialogue Quality Assessment (UDQA) framework. This focuses on measurable aspects like responsiveness, clarity, helpfulness, accuracy, and safety within the context of the initiator-response exchange.

• Focus on Content: This view prioritizes the substance and construction of the response quote, independent of the source’s fame.
• Quantifiable Rubric: UDQA provides specific dimensions (Responsiveness, Clarity, Helpfulness, Accuracy, Safety) that can potentially be assessed, even automatically, to generate a quality score.

This perspective suggests pricing for the quote pair could reflect not just who the source is, but how well their response quote communicates and contributes value based on the UDQA criteria within the exchange.

Next Step: Develop and evaluate methods (potentially NLP-based) for reliably scoring response quotes against the UDQA dimensions within their exchange context.

Alternative Perspective: Inverse Relevance Pricing

An alternative viewpoint argues that source relevance should exert inverse pressure on the price of the quote pair. The strongest form suggests that exchanges involving less prominent individuals might hold unique value and should therefore be more accessible (i.e., lower priced).

• Discovery Value (Weak Ties): Less prominent sources often offer novel perspectives in their responses. Lowering the cost barrier for these quote pairs encourages journalists to uncover potentially groundbreaking insights from less central sources.
• Supply, Demand, and Saturation: Highly influential figures generate a vast volume of public statements. Consequently, the marginal value and demand for one additional verified quote pair involving such a figure may be lower compared to a rare, unique exchange with a less-covered source. Pricing could reflect this dynamic.
• Amplifying Diverse Voices: Pricing that favors less “relevant” sources could actively promote journalistic diversity, giving platform to underrepresented viewpoints and challenging dominant narratives shaped by the usual influential figures.
• Incentivizing Deeper Reporting: Making quote pairs involving less-known experts or individuals cheaper could incentivize journalists to move beyond readily available soundbites and pursue more in-depth, original exchanges.

This perspective suggests that while source influence is a factor, a pricing model for quote pairs could strategically reduce the cost associated with lower-ranked source relevance to foster a richer information ecosystem.

Next Step: Evaluate the strategic implications of both direct and inverse source relevance pricing models for quote pairs on the YouSeddit platform.

Synthesized Pricing Formula (Conceptual)

While defining precise metrics for qualitative aspects like “dialogic quality” is complex, we can structure a conceptual formula to integrate the discussed factors, similar in approach to Multi-Attribute Utility Theory (MAUT) used in decision analysis. This formula serves as a framework for further definition:

$$ \text{Price} = \text{BasePrice} \times F_{\text{time}}(t) \times F_{\text{category}}(c) \times F_{\text{relevance}}(l, b, s) $$

Where:

• BasePrice: A constant minimum price for any verified quote pair.
• F_time(t): A time decay function. Value starts high for new quote pairs (t=0, based on exchange time) and decreases over time t, approaching a minimum multiplier > 0.
  • Example: Exponential decay: $$ F_{\text{time}}(t) = \max(\text{mult}{\text{min}}, \text{mult}{\text{init}} \times e^{-\lambda t}) $$ (where \lambda is the decay rate)
  • Requires Definition: initial_multiplier, min_multiplier, decay_rate.
• F_category(c): A multiplier based on the quote pair’s topic category c. Higher impact categories yield higher multipliers.
  • Requires Definition: A mapping of categories c to specific multiplier values (>= 1).
• F_relevance(l, b, s): A composite function combining different relevance aspects:
  • l: Lippmann Influence metric (source’s prominence/impact).
  • b: UDQA Quality metric (response quote’s intrinsic dialogue quality score within the exchange).
  • s: Supply/Scarcity metric (inverse of quote pair availability/source saturation).
  • Conceptual Structure: $$ F_{\text{relevance}}(l, b, s) \approx (1 + w_L \cdot \text{norm}(l) + w_B \cdot \text{norm}(b)) \times (1 + w_S \cdot \text{norm}(s)) $$
    • norm(): Normalization function to bring metrics l, b, s to a comparable scale (e.g., 0 to 1).
    • w_L, w_B, w_S: Weighting factors determining the relative impact of Lippmann influence, UDQA quality, and Supply/Scarcity. Note that s represents scarcity here (inverse of saturation), so higher s increases the price.
    • This structure allows weighting the source’s influence (l), the response quote’s quality (b), and the quote pair’s rarity/scarcity (s).
  • Requires Definition: Precise metrics for l, b, s; normalization methods; weighting factors w_L, w_B, w_S. Defining b objectively is particularly challenging.

Key Challenge: The primary challenge lies in developing objective, quantifiable metrics for l, b, and s, and agreeing on the appropriate weighting factors (w_L, w_B, w_S) to align with Youseddit’s strategic goals.

Example: Price Decay Over Time

Let’s illustrate the time decay function F_time(t) with hypothetical values:

• Base Price = $3
• Initial Time Multiplier (mult_init) = 5.0 (Quote pair is 5x base price when new)
• Minimum Time Multiplier (mult_min) = 0.5 (Quote pair price never drops below 50% of base * other factors)
• Decay Rate (\lambda) = 0.05 (per day)
• Category Multiplier (F_category) = 1.5 (e.g., for a high-impact topic)
• Relevance Multiplier (F_relevance) = 2.0 (e.g., for a quote pair with a high-influence source, high-quality response, and high scarcity)

Using the formula: $$ F_{\text{time}}(t) = \max(0.5, 5.0 \times e^{-0.05 t}) $$

And the overall price formula: $$ \text{Price}(t) = 3 \times F_{\text{time}}(t) \times 1.5 \times 2.0 = 9 \times F_{\text{time}}(t) $$

Here’s how the price might decay over the first 90 days:

Day (t)	F_time(t) (approx)	Price(t) (approx)
0	5.00	$45.00
7	3.52	$31.68
14	2.48	$22.32
30	1.12	$10.08
60	0.50 (hits min)	$4.50
90	0.50	$4.50

Note: This is a simplified example. The actual decay rate, multipliers, and the point at which the minimum is reached would be determined by platform policy and market analysis. This table provides data points that could be used to generate a visual chart of the price decay curve.

Steelman Justification for the Formula

The strongest argument for adopting this multi-faceted formula rests on its potential to create a nuanced and balanced marketplace for verified information, moving beyond simplistic pricing models.

• Reflects Complex Value, Simply: It acknowledges that a quote pair’s value isn’t monolithic. The formula aims to capture this complexity automatically, providing a fair valuation for the linked initiator and response.
• Balances Market Reality with Quality: By incorporating both source influence (l) and response quality (b), the formula attempts to balance the market’s tendency to overvalue fame with a mechanism to reward substantive, insightful response quotes, regardless of the source’s prominence.
• Mitigates Information Saturation: The scarcity factor (s) addresses the diminishing marginal value of exchanges involving over-exposed figures or topics, preventing inflated prices for readily available information and potentially incentivizing the discovery of less common quote pairs.
• Adaptability through Weighting: The weighting factors (w_L, w_B, w_S) provide crucial flexibility. YouSeddit can strategically adjust these weights over time to fine-tune the market dynamics, perhaps initially emphasizing influence (w_L) but gradually increasing the weight of quality (w_B) or scarcity (w_S) as the platform matures and seeks to differentiate itself.
• Encourages Desired Outcomes: Depending on the weighting, the formula can be tuned to actively encourage specific journalistic behaviors, such as seeking out diverse sources (high w_S), rewarding high-quality responses (high w_B), or prioritizing timely exchanges (high initial F_time).

While acknowledging the significant challenge in objectively measuring b (response quality), this formula provides the most comprehensive framework discussed for capturing the multifaceted nature of quote pair value.

Projected Long-Term Influence

Implementing a pricing model this complex could have significant long-term effects as YouSeddit gains traction:

• Shaping Journalistic Incentives: If the weighting favors quality (w_B) and scarcity (w_S), journalists might be incentivized to seek out less prominent but more insightful sources, potentially diversifying media narratives. Conversely, a heavy emphasis on influence (w_L) could reinforce the focus on established figures.
• Influencing Public Discourse: By potentially making diverse or high-quality quote pairs more accessible or valuable, the model could subtly influence which sources and responses gain prominence in news produced using YouSeddit content.
• Creating a Market for “Quality”: If the b metric (response quote quality) can be implemented effectively, YouSeddit could pioneer a market that explicitly values the substance of the response within its verified context.
• Potential for Gaming/Manipulation: As the model becomes understood, actors might attempt to manipulate the metrics (e.g., artificially boosting perceived influence l, optimizing quotes for perceived quality b). Constant vigilance and refinement of the metrics would be necessary.
• Setting Industry Standards: A successful implementation could influence how other platforms or news organizations value and source information, potentially leading to broader adoption of more nuanced valuation methods.
• Complexity Barrier: The model’s complexity could also be a barrier to adoption or understanding for some users, requiring clear communication and potentially simplified interfaces.

Ultimately, the long-term influence will depend heavily on the specific implementation details, particularly the definition and weighting of the relevance components (l, b, s), and the platform’s ability to measure them accurately and resist manipulation.

User Control

How much control users (journalists, buyers) should have over these pricing factors within the smart contract system needs further discussion.