Independent stochastic growth scenarios

// Two separate synthetic scenarios, 10000 independent repetitions per scenario; paths are matched across scenarios.
// This is a larger CPU workload than repeat_local.aner. No files are written by default.
import aner.parallel

// Synthetic multiplicative stochastic growth model; adapt its assumptions to your research question.
// Parameters: initial level, per-step drift, noise scale, number of steps.
// The abstract level has no domain calibration or empirical predictive claim.
fn simulate_growth_path(parameters: List, iteration: Int64, seed: Int64) -> List {
    let initial_level = cast<Float64>(parameters.get(0))
    let drift = cast<Float64>(parameters.get(1))
    let noise_scale = cast<Float64>(parameters.get(2))
    let steps = cast<Int64>(parameters.get(3))
    var level = initial_level
    var peak = initial_level
    var largest_relative_decline = 0.0
    var step = 0
    while step < steps {
        let shock = parallel.normal(seed: seed, draw: step)
        level = level * (1.0 + drift + noise_scale * shock)
        if level > peak { peak = level }
        let relative_decline = (peak - level) / peak
        if relative_decline > largest_relative_decline { largest_relative_decline = relative_decline }
        step = step + 1
    }
    return List(level / initial_level - 1.0, largest_relative_decline)
}

let execution = parallel.local()
let lower_noise = parallel.repeat(iterations: 10000, task: simulate_growth_path,
    parameters: List(100000.0, 0.0002, 0.01, 64), execution: execution, seed: 42)
let higher_noise = parallel.repeat(iterations: 10000, task: simulate_growth_path,
    parameters: List(100000.0, 0.0002, 0.025, 64), execution: execution, seed: 42)
print(lower_noise.rows()) // 10000
print(higher_noise.rows())     // 10000
print(lower_noise.mean(column: 0))
print(lower_noise.std(column: 0, sample: true))
print(higher_noise.mean(column: 0))
print(higher_noise.std(column: 0, sample: true))
print(lower_noise.seed(iteration: 0) == higher_noise.seed(iteration: 0)) // true: matched stochastic paths
print(lower_noise.summary())
print(higher_noise.summary())
// Save separately to new destinations if needed; repeat runs refuse existing files.
// lower_noise.save_csv("lower_noise-results.csv")
// higher_noise.save_csv("higher_noise-results.csv")