Cleaner Fish Cooperation Game Theory
ISEF Category: Animal Sciences
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Subcategory: Systematics and Evolution · Difficulty: Advanced · Setup: Home Setup · Time: 1 to 2 Months
The Hook
Some cleaner fish earn food by removing parasites, but they can also cheat by taking a bite of mucus. That makes them a real test case for cooperation under pressure. You can turn that tug-of-war into a simulation and ask when honesty wins.
What Is It?
Cleaner fish mutualism is a partnership where one animal helps another and gets paid for it. In this case, the cleaner fish removes parasites from a client fish, and both sides can benefit. Evolutionary game theory studies what happens when each side can choose a strategy, like cooperate, cheat, or walk away.
Think of it like a group project. If one student does all the work and the others slack off, the system starts to fall apart. Your model asks the same question for fish: when does cooperation stay stable, and when does cheating spread because it brings a short-term gain?
Why This Is a Good Topic
This is a strong science fair topic because you can test clear predictions with a simulation, then compare them to real behavioral data from published studies. It connects to a real biology problem, how cooperation survives when cheating pays off. You can learn model design, data matching, and basic statistics without needing a wet lab.
Research Questions
- How does changing the cheater payoff alter the long-run share of cooperation in a cleaner-fish model?
- What is the effect of different punishment strengths on the stability of cooperation?
- Does adding partner choice reduce cheating over repeated rounds?
- To what extent do parameter values from published datasets match the model's predicted cooperation rate?
- Which update rule, imitation or best response, produces behavior closest to the published dataset?
- How does encounter rate change the balance between cooperation and cheating?
Basic Materials
- Laptop or desktop computer.
- Python installed, or a free browser notebook such as Google Colab.
- Spreadsheet software for tracking runs and summary data.
- Published cleaner-fish behavior dataset or digitized values from a paper.
- Graphing tool for plotting cooperation rates across simulations.
Advanced Materials
- Access to a statistics-capable desktop or university workstation.
- Python with NumPy, pandas, SciPy, and matplotlib.
- R with ggplot2 and lme4 for sensitivity checks and mixed models.
- Downloaded raw behavioral datasets from multiple cleaner-fish studies.
- Reference manager for tracking papers, assumptions, and citations.
Software & Tools
- Python: Runs the simulation, fits parameters, and plots cooperation curves.
- Jupyter Notebook: Keeps code, notes, and figures in one reproducible file.
- R: Helps you test model outputs against published behavioral data.
- WebPlotDigitizer: Extracts numbers from graphs when a paper does not share raw data.
Experiment Steps
- Define the cooperation and cheating rules, including what each strategy gains and loses.
- Choose one published cleaner-fish dataset and decide which behavior metric you will match.
- Build a baseline simulation, then check whether it reproduces the main pattern in the data.
- Test one assumption at a time, such as payoff size, partner choice, or encounter rate.
- Compare model output with the dataset using the same summary measure and a clear statistical test.
Common Pitfalls
- Treating all cheating as the same behavior, which hides the difference between mucus biting and small-scale defection.
- Using one published graph as proof, which leaves you with no check against a second dataset.
- Copying parameter values from different studies without converting them to the same scale, which breaks the comparison.
- Running too few stochastic repeats, which makes random noise look like a stable evolutionary result.
- Matching your model to raw counts instead of the exact summary statistic from the paper, which makes the validation unfair.
What Makes This Competitive
A stronger project does more than show that cooperation can emerge. It compares multiple rules, like partner choice, punishment, or memory, against the same dataset and asks which one explains the data best. You can raise the level again by fitting the model on one study and testing it on a second one, then reporting uncertainty, not just a best-fit line.
Project Variations
- Swap in a different cleaner-fish species or client species and test whether the same game rules still fit.
- Compare partner choice, punishment, and repeated encounters to see which mechanism best reduces cheating.
- Fit the model to one paper, then test it on a second paper to check whether the result transfers across datasets.
Learn More
- PubMed: Search review articles and original studies on cleaner-fish mutualisms, cooperation, and evolutionary game theory.
- NCBI Bookshelf: Find free chapters on animal behavior, evolution, and population modeling.
- OpenStax Biology 2e: Review natural selection, animal behavior, and basic population dynamics.
- MIT OpenCourseWare: Look for free lectures on evolutionary game theory, probability, and mathematical modeling.
- Google Scholar: Search the original cleaner-fish papers and follow their citation trail to related datasets.