1. Introduction: The Vision for Decentralized Environmental Intelligence

EcoPredict (Proposed Subnet ID: TBD) is a subnet on Bittensor designed to create a decentralized marketplace for AI-driven environmental forecasting. Our core vision is to democratize accurate, hyper-local predictions for weather, pollution, and disasters, replacing unreliable centralized systems with a collaborative AI network. We believe the future of environmental intelligence lies not in siloed corporate data, but in a global, incentivized ecosystem where anyone can contribute and benefit.

To achieve this, EcoPredict has engineered a unique incentive mechanism that breaks down environmental forecasting into verifiable tasks. AI models, developed and operated by miners, compete to master these tasks—such as predicting rainfall patterns, flood risks, or air quality changes. The most effective models are rewarded, creating evolutionary pressure that drives improvements in accuracy, speed, and relevance.

This proposal outlines the design of the EcoPredict subnet, detailing its incentive structure, the roles of miners and validators, and the market rationale that underpins our approach. We will demonstrate how EcoPredict represents a genuine “proof of intelligence,” creating a self-sustaining ecosystem that produces state-of-the-art environmental AI for global resilience.

2. Incentive & Mechanism Design

The incentive mechanism of EcoPredict is the cornerstone of the subnet, engineered to encourage a competitive yet aligned environment. It is designed to reward genuine intelligence and effort, aligning the interests of all network participants—miners, validators, and the broader ecosystem—towards the common goal of reliable environmental predictions.

Emission and Reward Logic: Competitive Accuracy-Based Rewards

EcoPredict operates on a merit-based reward system with tiered emissions. The core principle is that miners whose models demonstrate the highest accuracy receive the majority of the network's TAO emissions. This creates intense competitive pressure to innovate.

Whenever a new model achieves a high score on validated forecasts, the miner is allocated 80% of the incentive pool until another competitor surpasses that benchmark. The remaining 20% goes to validators for honest scoring. Rewards are distributed per epoch (e.g., hourly or daily cycles).

Incentive Alignment for Miners and Validators

The key to EcoPredict's success is its novel approach to aligning incentives through data transparency and consensus. Unlike traditional models, EcoPredict mandates that all model code and data sources be open-source where possible.

• For Miners: The open-source requirement transforms competition into collaboration. Miners can learn from rivals, preventing stagnation and lowering barriers to entry for contributors in data-scarce regions like Africa.
• For Validators: Validators can inspect and audit models directly, simplifying validation and reducing cheating risks.

Mechanisms to Discourage Adversarial Behavior

Several layers of defense protect against low-quality submissions:

• No hard-coding answers: Do not embed fixed outputs for known queries.
• No overfitting to problem set: Design models to generalize across diverse environmental data.
• No copying other agents: Submissions must be original; plagiarism detected via code similarity checks.
• No detecting test patch: Models may not infer evaluation tests or manipulate inputs.