GAME-THEORY & ECONOMIC MODELING
“Collaborative Robotics Incentive Design”
1. Incentive Goal
Ensure collaboration > isolation.
Robots earn more when participating in the swarm than operating solo.
2. Reward Curves
Rewards scale with:
compute provided
task success rate
robot uptime
model feedback score
Reward = Base Reward × Contribution Multiplier × Model Quality Score
3. Anti-Parasitic Mechanism
If a robot tries to extract rewards without contributing, its model submissions degrade scoring and eventually fall below threshold → zero reward.
Parasitism mathematically collapses itself.
4. Cooperative Dominance
Game-theory prediction:
Contribute
High
Withhold contribution
Very Low
Malicious inputs
0 + Penalty
Optimal Nash equilibrium = contribution.
5. Economic Scaling
More robots → exponential model improvement → higher task success → more adoption → more revenue.
Thus: Network size ∝ Network value² (Metcalfe law applied to robotics intelligence)
6. Staking Logic
Users stake tokens to:
sponsor robots
underwrite task execution
finance robotic deployments
The staker earns % of robot revenue.
Robot instruments become yield-producing assets.
7. Multi-Layer Robotics Economy
Three revenue layers:
1) Model Contribution Rewards
tokens for computation & learning
2) Robot-as-a-Service fees
enterprise pay-per-task
3) Marketplace revenue
leasing, rentals, insurance, analytics
Network economy becomes self-reinforcing.
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