migration: economics: PiRC Adaptive Economic Engine

economics/ai_central_bank_enhanced.py

@@ -0,0 +1,22 @@
+
+def stabilize_ippr(current_ippr: float, supply: int, target: int = 314_000_000) -> float:
+    error = (target - supply) / target
+    updated = current_ippr * (1 + 0.05 * error)
+    return max(0.0, updated)
+
+
+def run_policy_path(start_ippr=0.02, start_supply=300_000_000, years=10):
+    ippr = start_ippr
+    supply = start_supply
+    history = []
+
+    for year in range(1, years + 1):
+        ippr = stabilize_ippr(ippr, supply)
+        supply = int(supply * (1 + ippr * 0.2))
+        history.append({"year": year, "ippr": round(ippr, 6), "supply": supply})
+
+    return history
+
+
+if __name__ == "__main__":
+    print(run_policy_path())

economics/ai_economic_stabilizer.py

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+import numpy as np
+
+class EconomicStabilizer:
+
+    def __init__(self):
+        self.target_liquidity = 50
+        self.reward_multiplier = 1.0
+
+    def update(self, liquidity, transaction_volume):
+
+        if liquidity < self.target_liquidity:
+            self.reward_multiplier *= 1.05
+
+        elif liquidity > self.target_liquidity * 1.5:
+            self.reward_multiplier *= 0.95
+
+        if transaction_volume > 1000:
+            self.reward_multiplier *= 0.98
+
+        return self.reward_multiplier
+
+
+def simulate():
+
+    stabilizer = EconomicStabilizer()
+
+    liquidity_levels = np.random.normal(50, 10, 100)
+    volumes = np.random.normal(800, 200, 100)
+
+    multipliers = []
+
+    for l, v in zip(liquidity_levels, volumes):
+        multipliers.append(stabilizer.update(l, v))
+
+    return multipliers
+
+
+if __name__ == "__main__":
+    results = simulate()
+    print("Simulation multipliers:", results[:10])

economics/ai_human_economy_simulator.py

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+"""
+AI + Human Economy Simulator
+Models future Pi ecosystem workforce economy
+"""
+
+import random
+import statistics
+from dataclasses import dataclass, field
+from typing import List
+
+
+@dataclass
+class Task:
+    difficulty: float
+    ai_accuracy: float
+    reward: float
+
+
+@dataclass
+class HumanWorker:
+    skill: float
+    tasks_completed: int = 0
+    earnings: float = 0.0
+
+
+@dataclass
+class AISystem:
+    accuracy: float
+
+
+@dataclass
+class EconomyState:
+    humans: List[HumanWorker]
+    ai: AISystem
+    tasks: List[Task]
+    reward_pool: float = 0
+
+
+class HumanAIEconomySimulator:
+
+    def __init__(self, human_count=1000):
+        humans = [
+            HumanWorker(skill=random.uniform(0.4, 1.0))
+            for _ in range(human_count)
+        ]
+
+        self.state = EconomyState(
+            humans=humans,
+            ai=AISystem(accuracy=0.75),
+            tasks=[]
+        )
+
+    def generate_tasks(self, n=500):
+        tasks = []
+        for _ in range(n):
+            difficulty = random.uniform(0.2, 1.0)
+            reward = difficulty * random.uniform(0.5, 2.0)
+
+            tasks.append(Task(
+                difficulty=difficulty,
+                ai_accuracy=self.state.ai.accuracy,
+                reward=reward
+            ))
+
+        self.state.tasks = tasks
+
+    def ai_attempt(self, task):
+        success = random.random() < (self.state.ai.accuracy - task.difficulty * 0.3)
+        return success
+
+    def human_attempt(self, worker, task):
+        probability = worker.skill - task.difficulty * 0.4
+        success = random.random() < probability
+
+        if success:
+            worker.tasks_completed += 1
+            worker.earnings += task.reward
+            self.state.reward_pool += task.reward
+
+        return success
+
+    def run_round(self):
+
+        for task in self.state.tasks:
+
+            if self.ai_attempt(task):
+                continue
+
+            worker = random.choice(self.state.humans)
+            self.human_attempt(worker, task)
+
+    def summary(self):
+
+        earnings = [h.earnings for h in self.state.humans]
+
+        return {
+            "total_rewards": sum(earnings),
+            "avg_worker_income": statistics.mean(earnings),
+            "median_worker_income": statistics.median(earnings),
+            "top_worker": max(earnings),
+            "tasks_completed": sum(h.tasks_completed for h in self.state.humans)
+        }
+
+
+if __name__ == "__main__":
+
+    sim = HumanAIEconomySimulator()
+
+    for _ in range(30):
+        sim.generate_tasks(500)
+        sim.run_round()
+
+    print(sim.summary())

economics/autonomous_pi_economy.py

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+import random
+
+years = 10
+
+supply = 1000000000
+liquidity = 50000000
+activity = 100000
+
+for year in range(1, years+1):
+
+    activity_growth = random.uniform(0.05,0.20)
+    liquidity_growth = random.uniform(0.03,0.15)
+
+    activity *= (1 + activity_growth)
+    liquidity *= (1 + liquidity_growth)
+
+    fees = activity * 0.01
+    rewards = fees * 1.2
+
+    supply += rewards
+
+    print("Year:",year)
+    print("Supply:",int(supply))
+    print("Liquidity:",int(liquidity))
+    print("Activity:",int(activity))
+    print("Fees:",int(fees))
+    print("Rewards:",int(rewards))
+    print("--------------------")

economics/economic_model.md

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+# PiRC Economic Model
+
+## Overview
+
+The PiRC economic model defines the relationship between token supply,
+liquidity growth, economic activity, and reward distribution.
+
+The objective is to create a sustainable economic loop within the Pi ecosystem.
+
+Core variables:
+
+S = token supply
+L = liquidity
+A = economic activity
+F = protocol fees
+R = rewards distributed
+
+The PiRC loop can be expressed as:
+
+S → L → A → F → R → S
+
+This reflexive loop ensures that reward issuance is linked to real economic activity.
+
+---
+
+## Economic Flow
+
+1 Pioneer Supply increases available tokens.
+
+2 Liquidity providers deposit tokens into liquidity pools.
+
+3 Economic activity generates transaction fees.
+
+4 Fees are partially routed to the treasury.
+
+5 Rewards are distributed to participants.
+
+---
+
+## Economic Stability
+
+To prevent inflation, the protocol introduces several constraints:
+
+reward_emission ≤ fee_generation × emission_multiplier
+
+Where:
+
+emission_multiplier ∈ [0.5 , 2.0]
+
+These bounds ensure that reward emissions remain tied to real activity.
+
+---
+
+## Long-Term Objective
+
+The model attempts to stabilize the ecosystem by aligning:
+
+• token incentives  
+• liquidity incentives  
+• user participation

economics/global_pi_economy_simulator.py

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+import random
+from dataclasses import dataclass
+
+@dataclass
+class EconomyState:
+
+    pioneers: int
+    apps: int
+    transactions: int
+    circulating_pi: float
+    price: float
+
+
+class GlobalPiEconomySimulator:
+
+    def __init__(self):
+
+        self.state = EconomyState(
+            pioneers=17000000,
+            apps=200,
+            transactions=1000000,
+            circulating_pi=2000000000,
+            price=0.5
+        )
+
+    def simulate_growth(self):
+
+        new_users = int(self.state.pioneers * random.uniform(0.01, 0.05))
+        new_apps = int(self.state.apps * random.uniform(0.02, 0.1))
+
+        self.state.pioneers += new_users
+        self.state.apps += new_apps
+
+    def simulate_activity(self):
+
+        self.state.transactions = int(
+            self.state.pioneers *
+            random.uniform(0.05, 0.3)
+        )
+
+    def price_model(self):
+
+        demand = self.state.transactions * 0.00001
+        supply = self.state.circulating_pi
+
+        self.state.price = demand / supply * 100000
+
+    def run_year(self):
+
+        self.simulate_growth()
+        self.simulate_activity()
+        self.price_model()
+
+    def summary(self):
+
+        return vars(self.state)
+
+
+if __name__ == "__main__":
+
+    sim = GlobalPiEconomySimulator()
+
+    for year in range(10):
+
+        sim.run_year()
+
+        print("YEAR", year)
+        print(sim.summary())

economics/liquidity_model.md

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+# Liquidity Model
+
+## Liquidity Objective
+
+Liquidity stabilizes token markets and supports trading activity.
+
+Liquidity growth function:
+
+Lt+1 = Lt + αD − βW
+
+Where:
+
+D = deposits
+W = withdrawals
+α = liquidity growth factor
+β = liquidity decay factor
+
+---
+
+## Liquidity Incentives
+
+Liquidity providers receive rewards proportional to:
+
+• deposited capital
+• duration of liquidity provision
+• trading volume supported

economics/merchant_pricing_sim.py

@@ -0,0 +1,20 @@
+import statistics
+
+
+def stable_price(kraken, kucoin, binance, phi=0.05):
+    median_price = statistics.median([kraken, kucoin, binance])
+    return round(median_price * (1 + phi), 6)
+
+
+def simulate_quotes(quotes):
+    computed = [stable_price(k, ku, b) for k, ku, b in quotes]
+    return {
+        "samples": len(computed),
+        "avg_stable_price": round(sum(computed) / len(computed), 6) if computed else 0,
+        "latest_stable_price": computed[-1] if computed else 0,
+    }
+
+
+if __name__ == "__main__":
+    sample_quotes = [(0.81, 0.79, 0.83), (0.84, 0.82, 0.85), (0.88, 0.87, 0.89)]
+    print(simulate_quotes(sample_quotes))