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Fix bug in tutorial to calculate MRS with potential trade #33

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May 5, 2023
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Fix bug in tutorial to calculate MRS with potential trade #33

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4 changes: 2 additions & 2 deletions examples/sugarscape_g1mt/run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,8 +88,8 @@ def assess_results(results, single_agent):
params = {
"width": 50,
"height": 50,
"vision_min": range(1, 3),
"metabolism_max": [3, 5],
"vision_min": range(1, 4),
"metabolism_max": [2, 3, 4, 5],
}

results_batch = mesa.batch_run(
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18 changes: 18 additions & 0 deletions examples/sugarscape_g1mt/sugarscape_g1mt/model.py
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Expand Up @@ -187,6 +187,24 @@ def step(self):

# collect model level data
self.datacollector.collect(self)
"""
Mesa is working on updating datacollector agent reporter
so it can collect information on specific agents from
mesa.time.RandomActivationByType.

Please see issue #1419 at
https://github.com/projectmesa/mesa/issues/1419
(contributions welcome)

Below is one way to update agent_records to get specific Trader agent data
"""
# Need to remove excess data
# Create local variable to store trade data
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What about projectmesa/mesa#1419 (comment) ? Which doesn't expose the internal wiring of the DataCollector.

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My concern with putting it in the model collector is the point of the lesson is to talk about the agent reporter. Showing the internal wiring of datacollector, I think is more consistent with the learning objectives as I show the source code in various lessons.. This leads back to your second comment that we would need to build a class for collecting DataCollectorRABT.

So in I would prefer to show the internal wiring... I don't know.. what do you think?

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@rht rht May 3, 2023 •
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I suppose it is fine for now as long as there is a comment that says this part is still a work in progress, with a link to #1419.

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Comment added

agent_trades = self.datacollector._agent_records[self.schedule.steps]
# Get rid of all None to reduce data storage needs
agent_trades = [agent for agent in agent_trades if agent[2] is not None]
# Reassign the dictionary value with lean trade data
self.datacollector._agent_records[self.schedule.steps] = agent_trades

def run_model(self, step_count=1000):
for i in range(step_count):
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24 changes: 13 additions & 11 deletions examples/sugarscape_g1mt/sugarscape_g1mt/trader_agents.py
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Expand Up @@ -140,16 +140,16 @@ def is_starved(self):

return (self.sugar <= 0) or (self.spice <= 0)

def calculate_MRS(self):
def calculate_MRS(self, sugar, spice):
"""
Helper function for self.trade()
Helper function for
- self.trade()
- self.maybe_self_spice()

Determines what trader agent is needs and can give up
Determines what trader agent needs and can give up
"""

return (self.spice / self.metabolism_spice) / (
self.sugar / self.metabolism_sugar
)
return (spice / self.metabolism_spice) / (sugar / self.metabolism_sugar)

def calculate_sell_spice_amount(self, price):
"""
Expand Down Expand Up @@ -205,8 +205,10 @@ def maybe_sell_spice(self, other, price, welfare_self, welfare_other):
welfare_self < self.calculate_welfare(self_sugar, self_spice)
) and (welfare_other < other.calculate_welfare(other_sugar, other_spice))

# trade criteria #2 is their mrs crossing
mrs_not_crossing = self.calculate_MRS() > other.calculate_MRS()
# trade criteria #2 is their mrs crossing with potential trade
mrs_not_crossing = self.calculate_MRS(
self_sugar, self_spice
) > other.calculate_MRS(other_sugar, other_spice)

if not (both_agents_better_off and mrs_not_crossing):
return False
Expand All @@ -229,9 +231,9 @@ def trade(self, other):
assert other.sugar > 0
assert other.spice > 0

# calculate marginal rate of subsitution in Growing Artificial Socieites p. 101
mrs_self = self.calculate_MRS()
mrs_other = other.calculate_MRS()
# calculate marginal rate of substitution in Growing Artificial Societies p. 101
mrs_self = self.calculate_MRS(self.sugar, self.spice)
mrs_other = other.calculate_MRS(other.sugar, other.spice)

# calculate each agents welfare
welfare_self = self.calculate_welfare(self.sugar, self.spice)
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