How patra-arya/anisogamy works
anisogamy is a computational reproduction of a game-theoretic model for the evolution of sperm–egg size dimorphism. The main workflow evaluates survival and reproductive fitness, computes a best response, iterates that response, and classifies the resulting equilibrium as isogamy or anisogamy. Parameter sweeps and plotting extend the analysis with bifurcation results, best-response maps, and cobweb diagrams. The README documents alternative survival functions and a grid-search best response. Wiring below follows the sampled source; no separate external service or persistent store is evident.
Main entry points: plot.py Maps and diagrams · dynamics.py Regime classification · best_response.py Best response · fitness.py Log fitness
Components 10
Survival and fitness
- Survival functions
src/anisogamy/survival.py - Log fitness
src/anisogamy/fitness.py - Isogamous ESS
src/anisogamy/fitness.py
Strategy optimization
- Best response
src/anisogamy/best_response.py - Grid-search response
src/anisogamy/best_response.py
Evolutionary dynamics
- Response iteration
src/anisogamy/dynamics.py - Regime classification
src/anisogamy/dynamics.py - Bifurcation sweep
src/anisogamy/dynamics.py
Analysis and plots
- Maps and diagrams
src/anisogamy/plot.py
Other components
- Researcher
Connections 10
- Researcher → Regime classification: requests classification
- Researcher → Maps and diagrams: requests plots
- Regime classification → Response iteration: runs trajectory
- Response iteration → Best response: updates strategy
- Best response → Log fitness: ranks candidates
- Log fitness → Survival functions: evaluates survival
- Bifurcation sweep → Regime classification: classifies parameters
- Maps and diagrams → Best response: samples responses
- Maps and diagrams → Response iteration: builds cobweb
- Maps and diagrams → Regime classification: plots regimes