Predator-Prey Preset
Use Sample Usage 1. Suggested: 20 steps, 3 episodes. Expected outputs: prey/predator trends and extinction signal.
Quick Start Presets
Policy-Firm Preset
Use Sample Usage 2. Suggested: 24 steps, 4 episodes. Expected outputs: welfare/compliance variation and inequality profile.
Sample Usage 1 (3 Layers)
Component Roles → Key Parameters
- L1 Climate seasonal driver + shocks: seasonal amplitude = 0.4 (condition: 0.4 or 0.8), period = 12, shock probability = 0.1
- L1 Landscape patch quality + graph: number of patches = 12, habitat fragmentation = 0.2, movement cost = 0.2 (condition: 0.2 or 0.5)
- L2 Prey density growth + risk foraging: initial prey = 40, intrinsic growth rate = 0.55, carrying capacity = 120, risk level = 0.55, foraging sensitivity (beta) = 0.3, visibility scaling (gamma) = 0.2
- L2 Predator consumption response: initial predator = 9, conversion rate = 0.3, mortality rate = 0.1
- L2 Movement dispersal on graph: dispersal rate = 0.35 in [0, 1]
- L3 Aggregator per-step & episode metrics: extinction threshold = 1.0
Component Purpose → Parameters
- Evolution (NSGA-II) multi-objective search: population size = 24, generations = 6-8, weights = (-1, -1)
- Schema trait genes: risk in [0, 1], dispersal in [0, 1]
- Scenarios environment grid: seasonal amplitude in {0.4, 0.8}, habitat fragmentation in {0.2, 0.5}
- Participants policies/traits: baseline risk/dispersal = (0.55, 0.35), evolved = champion
- Scoring biomass metric: PREY.PREY.mean_x (mean prey population)
- Voting episode winner rule: choose maximum score (per episode)
Editable. Click a cell for detailed parameters.
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Run limits: max 500 steps, 20 episodes, 64 scenarios, 45s runtime.
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