**GPU-accelerated agent-based molecular pathway simulations (cancer therapy design) will exhibit coalition-based equilibrium deviations (beyond Nash) when modeling drug-resistant subclonal interactions, where rare cell populations act as coordinated "deviator coalitions" to evade therapeutic pressure.**

In GPU-accelerated agent-based models (ABMs) of tumor subclonal populations under therapeutic drug pressure, rare drug-resistant subclones will exhibit emergent "coalition-like" behaviors — statistically correlated, mutually reinforcing strategy shifts (e.g., synchronized phenotype switching, paracrine signaling coordination, or metabolic resource-sharing) — that produce population-level equilibria deviating from the Nash equilibrium predicted by an equivalent non-cooperative game-theoretic model of the same pathway network. Specifically: the empirical steady-state distribution of resistant vs. sensitive subclone fractions under fixed drug dosing will differ from the Nash-predicted distribution by an effect size ≥0.2 (Cohen's d) in ≥60% of parameter regimes tested, and this deviation will be reducible (partially explained, R²≥0.3) by a coalition-formation model (e.g., cooperative game / core-based solution concept) fit to the same simulation trajectories.

Minimum viable test (MVT):

1. Build a spatial ABM (2D lattice, ~5,000–50,000 agents) of a single canonical resistance pathway (e.g., EGFR/MAPK with a known bypass mechanism, or AR-pathway prostate cancer resistance) with 3 agent types: drug-sensitive, resistant, and intermediate/persister.
2. Implement a paracrine signaling channel (diffusible factor) enabling local cooperative benefit (e.g., resistant cells secreting a factor that protects neighboring sensitive cells — a documented biological phenomenon).
3. Run the ABM under fixed dosing to steady state (GPU-accelerated, e.g., via CUDA/JAX/Taichi) for 200 parameter combinations (dose × diffusion range × cooperation strength × initial resistant fraction).
4. In parallel, formulate an equivalent non-cooperative evolutionary game (replicator dynamics / normal-form game) using the same fitness payoffs stripped of spatial/signaling coupling; compute its Nash equilibrium analytically or via best-response iteration.
5. Compare ABM steady-state distributions to Nash predictions; fit a cooperative-game solution concept (Shapley value allocation, core stability test) to ABM trajectories.
6. Run ablation controls: (a) zero signaling range, (b) randomized/shuffled signaling (breaks coordination but preserves magnitude), (c) increasing agent count 10×/100× to test finite-size effects.

# GPU ABM core (pseudocode, JAX/Taichi-style)
class SubcloneABM:
    agents: array[N, state]   # state: {type, position, fitness, resistance_level}
    signal_field: grid[X,Y]   # diffusible paracrine factor concentration

    def step(dt):
        signal_field = diffuse(signal_field, D) + secrete(agents)
        local_signal = sample(signal_field, agents.position)
        fitness = base_fitness(agents.type, drug_dose) + coop_bonus(local_signal)
        agents = reproduce_and_die(agents, fitness, dt)   # vectorized on GPU
        agents = mutate(agents, mutation_rate)
        return agents, signal_field

# Nash equilibrium comparator
def compute_nash(payoff_matrix):
    return replicator_dynamics_fixed_point(payoff_matrix)  # or best-response iteration

# Coalition-fit analysis
def fit_coalition_model(abm_trajectories):
    shapley_values = compute_shapley(abm_trajectories, characteristic_function)
    core_stability = test_core(abm_trajectories)
    r2 = compare_variance_explained(abm_trajectories, shapley_values, null_model)
    return r2, shapley_values

# Main sweep
for dose, diffusion_range, coop_strength, init_frac in parameter_grid(200):
    for seed in range(30):
        traj = run_abm(dose, diffusion_range, coop_strength, init_frac, seed, steps=10000)
        nash_pred = compute_nash(payoff_from_params(dose, coop_strength))
        deviation[dose, diffusion_range, coop_strength, init_frac, seed] = effect_size(traj[-1], nash_pred)
fit_coalition_model(all_trajectories)
run_ablations(['zero_range','shuffled_signal','scale_10x','scale_100x'])