Risky food simulation #4
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| # initialize model with one agent | ||
| model = RiskyFoodModel(1) | ||
| model.prob_notcontaminated = prob_notcontaminated | ||
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| results = [] | ||
| total_runs = 100 | ||
| for i in range(total_runs): | ||
| results.append(model.get_risky_food_status()) | ||
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| # use counter to tally the results | ||
| result_count = Counter(results) | ||
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| # the expected value is the probability times number of times we ran it | ||
| expected = total_runs * model.prob_notcontaminated | ||
| assert math.isclose( | ||
| result_count[FoodStatus.NOTCONTAMINATED], expected, abs_tol=total_runs * 0.1 | ||
| ) |
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This is nice. 'Testing' takes on an interesting meaning here. One knows (from the scholarship) what expected outcomes of particular models ought to be. These can be written into our implementations directly as tests.
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I wouldn't have thought of using unit tests for checking reproducibility / replicability for known models, but would be interesting to automate (maybe more like integration testing?) that if we are working on a known model with expected outputs. But IDK if we'll be working much with simulations where we know the expected outputs, seems they are less interesting for investigating risk attitudes.
| Every agent gets a parameter `r` between 0 and 1. [or DISCRETE: 8 buckets etc.] | ||
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| EACH ROUND: | ||
| - Nature selects a probability `p` for **N** | ||
| - For each agent: if `r` > `p`, then they choose RISKY; else SAFE | ||
| - Nature flips a coin with bias `p` for **N**, and announces **N** or **C** | ||
| - If **N**: everyone who chose RISKY gets 3, everyone who chose SAFE gets 2 | ||
| - If **C**: everyone who chose RISKY gets 1, everyone SAFE 2 | ||
| - Reproduce in proportion to payoff | ||
| - Either agent gets # of offspring = payoff [they replace–original “dies off”] | ||
| - OR: take the total payoff for RISKYs over total for everyone, there are that proportion of RISKYs in the new population | ||
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| END ROUND |
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I like this semi-formal way of describing a 'round'! Very clear articulation of the steps/rules of the game.
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this is all from Lara! I just added some formatting. very clear, right?
| ## Running the simulation | ||
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| - Install python dependencies as described in the main project readme (requires mesa) | ||
| - To run from the main `simulating-risk` project directory: | ||
| - Configure python to include the current directory in import path; | ||
| for C-based shells, run `setenv PYTHONPATH .` ; for bash, run `export $PYTHONPATH=.` | ||
| - To run interactively with mesa runserver: `mesa runserver simulatingrisk/risky_food/` |
| def step(self): | ||
| # choose food based on the probability not contaminated and risk tolerance | ||
| if self.risk_level > self.model.prob_notcontaminated: | ||
| choice = FoodChoice.RISKY | ||
| else: | ||
| choice = FoodChoice.SAFE | ||
| self.payoff = self.model.payoff(choice) |
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Again I feel like a 1st-person POV might be more legible as a way of describing what's happening
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can you say more, I don't understand?
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oh, read in the wrong order - I see your comments on the other PR now
| self.datacollector = mesa.DataCollector( | ||
| model_reporters={ | ||
| "prob_notcontaminated": "prob_notcontaminated", | ||
| "contaminated": "contaminated", | ||
| "average_risk_level": "avg_risk_level", | ||
| "min_risk_level": "min_risk_level", | ||
| "max_risk_level": "max_risk_level", | ||
| "num_agents": "total_agents", | ||
| }, | ||
| agent_reporters={"risk_level": "risk_level", "payoff": "payoff"}, | ||
| ) |
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seems very verbose / redundant to me, but at least workable enough for now
implements the "risky food" game as described in #3