Neuro-symbolic compliance monitors (SMT-solvers enforcing causal DAG constraints derived from pulsar timing data-sharing workflows) will reduce coalition-based deviations in CSIRO Data61’s radio astronomy collaborations by ≥30% compared to unilateral Nash equilibrium enforcement.

In a simulated (and subsequently live-piloted) multi-institutional radio astronomy data-sharing environment modeled on CSIRO Data61 pulsar-timing collaboration workflows, a neuro-symbolic compliance monitor — defined as an SMT-solver (e.g., Z3) that enforces hard constraints derived from a causal DAG of legitimate data-provenance/access/embargo relationships, coupled to a learned (neural) policy proposer for candidate actions — will reduce the rate of coalition-based deviations (defined as any subset of ≥2 agents jointly violating an agreed data-sharing/embargo/attribution rule to their mutual benefit) by ≥30% relative to a baseline of unilateral Nash-equilibrium enforcement (agents individually optimize under a static incentive mechanism with no cross-agent constraint propagation), measured over a fixed benchmark of repeated data-sharing games with identical agent population, payoff structure, and stochastic task arrival process, at p<0.05 significance with the reduction persisting across ≥3 independent random seeds and ≥2 distinct coalition-formation topologies.

Design: Randomized, seeded, controlled simulation study with staged real-world pilot.

• Stage A (Simulation, primary test): Agent-based multi-agent simulation of N=6–10 synthetic institutions participating in repeated pulsar-timing data-sharing games (≥500 rounds/game, ≥200 game instances per condition). Two arms: (1) neuro-symbolic monitor (SMT + causal DAG + learned proposer), (2) unilateral Nash-equilibrium baseline (independent Q-learning/best-response agents under static mechanism). Randomize coalition-formation topology (star, ring, random Erdős–Rényi) and payoff asymmetry across 2×3 factorial design.
• Stage B (Semi-synthetic replay): Replay of anonymized/synthetic CSIRO Data61-style workflow logs (pulsar timing array data release schedules, embargo events) with injected synthetic deviation attempts calibrated to historical incident rates (if available) or literature-derived base rates (~8–15% deviation incidence assumed from mechanism-design literature; to be confirmed with domain SMEs).
• Stage C (Live pilot, optional/conditional on A+B success): Shadow-mode deployment alongside one real CSIRO Data61 pulsar-timing collaboration workflow for 90 days, monitor-only (no enforcement authority), comparing flagged deviations against ground-truth post-hoc audit.

# Stage A pseudocode

class CausalDAGConstraints:
    def __init__(self, dag_spec):  # SME-elicited YAML/JSON
        self.solver = z3.Solver()
        self.encode(dag_spec)

    def check_action(self, agent_id, proposed_action, world_state):
        self.solver.push()
        self.solver.add(world_state.to_smt())
        self.solver.add(proposed_action.to_smt())
        result = self.solver.check()
        self.solver.pop()
        return result == z3.sat  # True = compliant

class NeuroSymbolicMonitor:
    def __init__(self, dag_constraints, policy_net):
        self.constraints = dag_constraints
        self.policy = policy_net  # small transformer, self-play trained

    def propose_and_verify(self, agent_id, world_state):
        candidate = self.policy.sample_action(agent_id, world_state)
        if self.constraints.check_action(agent_id, candidate, world_state):
            return candidate
        else:
            return self.repair(candidate, world_state)  # SMT-guided repair/veto

class NashBaseline:
    def __init__(self, payoff_matrix):
        self.agents = [BestResponseAgent(payoff_matrix) for _ in range(N)]

    def step(self, world_state):
        return [a.best_response(world_state) for a in self.agents]

# Experiment loop
for topology in [star, ring, random_graph]:
    for seed in [1,2,3]:
        for condition in [NeuroSymbolicMonitor, NashBaseline]:
            env = DataSharingGame(topology, seed, N_agents=8)
            results = run_games(env, condition, n_games=200, n_rounds=500)
            log_metrics(results)  # deviation_rate, fp_rate, latency

compute_effect_size(neuro_symbolic_results, baseline_results)
bootstrap_ci(n_resamples=10000)