Decentralized energy market AI agents with physics-constrained stochastic control and cryptographically verifiable trajectories will reduce wildfire ignition risk by 30% through adaptive, resource-allocated sensor fusion, when governed by a mechanism-design model that enforces compute budgets proportional to observed tissue-scale mechanical stress in fire-prone vegetation zones.

In a defined wildland-urban interface (WUI) test region of 50-200 km², a network of decentralized AI sensing/control agents — each (a) governed by a physics-constrained stochastic control policy (fuel moisture, wind, mechanical stress in vegetation), (b) allocated compute/sensing budget proportional to real-time tissue-scale mechanical stress readings in monitored vegetation, and (c) producing cryptographically signed and chain-verifiable trajectory logs — will reduce a defined wildfire ignition-risk index (composite of time-to-detection, false-negative rate on ignition precursors, and missed-anomaly rate) by ≥30% relative to a static/centralized sensor-scheduling baseline, measured over a minimum 12-month field or high-fidelity simulation deployment, with statistical significance p<0.05 and effect size confirmed via bootstrapped confidence intervals.

Phase 0 (simulation-only, 3 months): Build/adapt a coupled fire-behavior + sensor-network simulator (e.g., FARSITE/ELMFIRE or CAWFE coupled with an agent-based sensor model) to generate synthetic ignition scenarios with ground-truth mechanical stress fields.

Phase 1 (hardware-in-loop, 6 months): Deploy a 20-50 node sensor testbed (moisture, strain gauges, weather, low-res thermal/optical) in one real WUI test site (partnering with a fire science station, e.g., a USFS experimental forest) running the actual agent software stack, including cryptographic trajectory logging on a lightweight permissioned ledger (e.g., Hyperledger Fabric or a rollup on a low-fee L2).

Phase 2 (field validation, 12 months minimum, spans 1 fire season): Compare treatment (decentralized+crypto+physics-constrained) vs. control (static round-robin polling, centralized scheduler) arms across paired similar-risk zones, using historical ignition/near-miss events and injected synthetic anomalies (controlled prescribed-burn or surrogate stress events) for detection-time and false-negative measurement.

Minimum viable test (fast/cheap version): Phase 0 simulation only, using publicly available fire behavior datasets + synthetic sensor injection, to obtain a directional effect-size estimate before committing to field deployment.

# High-level architecture

class VegetationAgent:
    def __init__(self, sensor_suite, physics_model, crypto_key):
        self.stress_estimator = TissueStressModel(physics_model)
        self.policy = ConstrainedStochasticController(
            constraints=["fuel_moisture_bounds", "wind_stress_coupling"])
        self.crypto_key = crypto_key

    def step(self, obs):
        stress = self.stress_estimator.infer(obs)
        budget = MechanismDesignAllocator.request_budget(stress)
        action = self.policy.act(obs, budget, constraints=self.physics_constraints)
        trajectory_commit = sign(hash(obs, action), self.crypto_key)
        Ledger.append(trajectory_commit)
        return action

class MechanismDesignAllocator:
    # proportional-share auction: budget_i ∝ stress_i / sum(stress_j)
    # incentive-compatibility proof via VCG-style payment rule
    def request_budget(stress_report):
        return proportional_share(stress_report, total_compute_pool)

class Ledger:
    # permissioned chain / Merkle-anchored log
    # periodic root anchoring to public chain for tamper-evidence
    def append(commit): ...
    def verify(trajectory): ...

# Simulation loop
for episode in fire_season_scenarios:
    agents = deploy_agent_network(topology, sensor_config)
    for t in timesteps:
        for agent in agents:
            action = agent.step(env.observe(agent))
            env.apply(action)
        env.advance_fire_physics(dt)
    log_ignition_risk_index(env, agents)

# Evaluation
compare(treatment_run_logs, baseline_run_logs,
        metric="composite_ignition_risk_index",
        test="bootstrap_CI + mixed_effects_model")