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| 1 | +# Metrics and measures |
| 2 | +This is what should be measured each run. These runs can then be further aggregated for final metrics. |
| 3 | +### Fixed parameter runs |
| 4 | +- Population count (mean and std after warmup) |
| 5 | +- Cluster size distribution (means and stds after warmup) |
| 6 | +### Evolution runs |
| 7 | +- Population count (over time after warmup) |
| 8 | +- Cluster size distribution (over time after warmup) |
| 9 | +- Prey death rate (mean and std over time after warmup) |
| 10 | +# Experiments |
| 11 | +These phases should be completed sequentially, deepening our understanding at each step. The different experiments in each phase should be completed with data from the same runs. |
| 12 | +### Phase 1: finding the critical point |
| 13 | +- Create bifurcation diagram of mean population count, varying prey death rate |
| 14 | + - Look for critical transition |
| 15 | +- Create log-log plot of cluster size distribution, varying prey death rate |
| 16 | + - Look for power-law |
| 17 | +### Phase 2: sensitivity analysis |
| 18 | +- Show correlation between critical prey death rate and post-evolution prey death rate, varying other parameters |
| 19 | + - Look for self-organized criticality: an SOC-system should move towards the critical point regardless of other parameters |
| 20 | +- Show sensitivity of hydra effect varying other parameters |
| 21 | +### Phase 3: perturbation analysis |
| 22 | +- Create autocorrelation plot of mean population count, following perturbations around the critical point |
| 23 | + - Look for critical slowing down: perturbations to states closer to the critical point should more slowly return to the steady state |
| 24 | +### Phase 4: model extensions |
| 25 | +- Investigate whether hydra effect and SOC still occur with diffusion and directed movement |
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