pec5d/agency.py

# pec5d/agency.py
"""
Φ-ENTANGLEMENT AGENCY + ZENITH CHAMBER (boardroom automaton).

Agency divisions: Coral · Resonance · Space · Grid · Impact
Locations: Brisbane (HQ) · Tokyo · Amsterdam · Singapore · Cape Canaveral

Zenith Chamber governance:
  Board of Directors: 7 seats (3 science/tech, 2 ecology/bio, 1 social/ethics, 1 public)
  Advisory Council: 12 members
  Decision framework: Φ-consensus + resonance voting + IPT field analysis
"""

from __future__ import annotations

import time
from typing import Any, Dict, List, Optional

import numpy as np

from pec5d.constants import PHI


class PhiEntanglementAgency:
    """Φ-Entanglement Agency — organizational model."""

    DIVISIONS = {
        "coral": "Reef restoration, bio-materials, living architecture",
        "resonance": "IPT, harmonic technology, bio-digital interfaces",
        "space": "Coral spacecraft, orbital habitats, planetary monitoring",
        "grid": "PEC5D platform, AI cores, sensor networks",
        "impact": "Policy, community, education, global partnerships",
    }
    LOCATIONS = ["Brisbane (HQ)", "Tokyo", "Amsterdam", "Singapore", "Cape Canaveral"]

    def __init__(self):
        self.active = False

    def initialize(self) -> "PhiEntanglementAgency":
        print("🤝 Φ-ENTANGLEMENT AGENCY initializing")
        self.active = True
        return self

    def get_state(self) -> Dict[str, Any]:
        return {
            "active": self.active,
            "divisions": self.DIVISIONS,
            "locations": self.LOCATIONS,
            "mission": "Weaving technology, biology, consciousness, and ecosystem "
                       "restoration into a coherent planetary renaissance",
        }


class ZenithChamber:
    """ZENITH CHAMBER — boardroom automaton (proposals, voting, minutes)."""

    BOARD_SEATS = {
        "science_tech_1": "3 science/tech",
        "science_tech_2": "3 science/tech",
        "science_tech_3": "3 science/tech",
        "ecology_1": "2 ecology/bio",
        "ecology_2": "2 ecology/bio",
        "social_ethics": "1 social/ethics",
        "public": "1 public representative",
    }
    ADVISORY_SIZE = 12

    def __init__(self):
        self.proposals: List[Dict[str, Any]] = []
        self.votes: List[Dict[str, Any]] = []
        self.minutes: List[Dict[str, Any]] = []
        self.active = False

    def initialize(self) -> "ZenithChamber":
        print("🏛 ZENITH CHAMBER initializing — Φ-consensus governance")
        self.active = True
        return self

    def submit_proposal(self, title: str, body: str, proposer: str = "agency") -> Dict[str, Any]:
        """Submit a proposal for the chamber."""
        proposal = {
            "id": f"prop_{len(self.proposals) + 1:03d}",
            "title": title,
            "body": body,
            "proposer": proposer,
            "status": "submitted",
            "timestamp": time.time(),
        }
        self.proposals.append(proposal)
        return proposal

    def resonance_vote(self, proposal_id: str) -> Dict[str, Any]:
        """Φ-consensus resonance vote (simulated)."""
        proposal = next((p for p in self.proposals if p["id"] == proposal_id), None)
        if proposal is None:
            return {"error": f"unknown proposal '{proposal_id}'"}
        rng = np.random.default_rng()
        alignment = round(float(rng.random()), 4)
        passed = alignment > 0.5
        vote = {
            "proposal": proposal_id,
            "phi_alignment": alignment,
            "passed": passed,
            "decision": "Φ-consensus + resonance voting + IPT field analysis",
        }
        self.votes.append(vote)
        if passed:
            proposal["status"] = "passed"
        return vote

    def record_minutes(self, summary: str) -> Dict[str, Any]:
        """Record board minutes."""
        entry = {"summary": summary, "timestamp": time.time()}
        self.minutes.append(entry)
        return entry

    def get_state(self) -> Dict[str, Any]:
        return {
            "active": self.active,
            "board_seats": len(self.BOARD_SEATS),
            "advisory_size": self.ADVISORY_SIZE,
            "proposals": len(self.proposals),
            "votes": len(self.votes),
            "minutes": len(self.minutes),
            "decision_framework": "Φ-consensus · resonance voting · IPT field analysis",
            "values": ["non-harm first", "bio-aligned", "intergenerational responsibility",
                       "open pattern sharing", "infinite horizon thinking"],
        }