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Artha is a virtual environment replicating and enhancing real-world systems. It integrates quantum-inspired data handling, AI-driven governance, and a unique utility-based economic model for a self-regulating, evolving environment.
Artha operates as:
Artha aims for:
Data constantly moves across nodes, inspired by quantum principles:
Dynamic Caching Code:
import time, random def cache_data(nodes, data): while True: current_node = random.choice(nodes) current_node.store(data) time.sleep(1) current_node.clear()
AI automates economic tasks, learns from interactions, and ensures security.
Learning Rate Equation:
[ L(t) = L_0 e^{-alpha t} ]
Where:
Utility grows with use:
[ U(n) = U_0 beta n^2 ]
Where:
PoV ensures measurable contributions based on real-time data.
PoV Equation:
[ PoV = sum_{i=1}^{N} left( C_i cdot W_i right) ]
Where:
PoV Code:
import time, random def cache_data(nodes, data): while True: current_node = random.choice(nodes) current_node.store(data) time.sleep(1) current_node.clear()
Artha mirrors physical rules:
Data Orbit Code:
class ProofOfValue: def __init__(self): self.contributions = [] def add(self, contribution, weight): self.contributions.append((contribution, weight)) def calculate(self): return sum(c * w for c, w in self.contributions) pov = ProofOfValue() pov.add(100, 0.8) pov.add(50, 1.0) print(pov.calculate())
Data behaves like quantum particles:
PoW ensures security by requiring computational effort to validate actions.
PoW Equation:
[ H(x) leq T ]
Where:
PoW Code:
class DataObject: def __init__(self, mass, radius, velocity): self.mass = mass self.radius = radius self.velocity = velocity def update_position(self, time_step): angle = (self.velocity / self.radius) * time_step return angle data = DataObject(10, 5, 2) angle = data.update_position(1)
Data dynamically transitions between wave and particle states, ensuring security and efficiency.
Velocity Equation:
[ v = frac{2 pi r}{T} ]
Where:
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