Incentive Design & Data Compensation
This research project is part of our "Piloting Progress" phase, where we focus on early adoption, user testing, and refining our MVP.
What is Incentive Design & Data Compensation?
Incentive design within the CEIS ecosystem refers to the mechanisms that drive user participation, data contribution, and AI-agentic transactions through a structured compensation model. Data compensation ensures that users are fairly rewarded for their contributions in a way that maintains economic equilibrium and long-term network sustainability.
This process is crucial for aligning the interests of users, suppliers, and AI agents—ensuring that participation in the ecosystem remains rewarding, sustainable, and scalable as adoption grows.
Why It’s Important to the CEIS Ecosystem
Balances supply and demand for data – AI agents rely on a steady influx of user data to optimize financial decisions and product recommendations. Proper incentive mechanisms ensure that users stay engaged and contribute high-quality, actionable data.
Prevents economic imbalances – Without proper incentive structures, the system could experience over-compensation (inflationary effects) or under-compensation (leading to disengagement).
Creates a fair and transparent economy – Users earn compensation proportional to the value their data provides, ensuring equitable distribution of rewards.
Description of the Protocol
The Incentive & Compensation Protocol (ICP) governs how users earn rewards based on their data contributions, engagement levels, and network participation. The system operates through:
Adaptive incentive structures – Compensation dynamically adjusts based on user activity, data quality, and real-time network demand.
Tiered reward mechanisms – Users are compensated in multiple ways, including direct payouts, data-backed credits, and investment multipliers tied to ecosystem growth.
AI-driven optimization – AI models continuously refine incentive structures to balance user participation and long-term economic viability.
Transaction-based compensation – Users earn Personal Growth Income (PGI) as AI agents facilitate commerce, investment, and collaborative economic interactions.
Objectives: Testing & Optimization
Phase 1 is focused on validating and optimizing the incentive system to ensure it remains scalable, sustainable, and aligned with user behavior. Key research questions include:
How do different incentive models impact user participation and data contribution rates?
What are the most effective ways to balance short-term user engagement with long-term network stability?
How can AI-driven reward systems be fine-tuned to ensure fair compensation while preventing economic saturation?
How do different tiers of user activity correlate with network growth and AI efficiency?
By refining these mechanisms, Phase 1 will establish an economically viable and self-sustaining compensation framework that optimizes AI-agentic participation while ensuring fair and equitable rewards for all stakeholders.
Supervisors
Leon Tsvasman Ph.D.
Yingbo Li, Ph.D.
Laurence Levin, Ph.D.
Mohyeddin Kassar, Ph.D.
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