#%%
total_rent = 5400
total_rent
#%%
values = pd.DataFrame({
# Room : 1 2 3 4
'Alice' : [30, 20, 0, 200 ],
'Bob' : [201, 32, 23, 0 ],
'Caitlin' : [31, 204, 29, 0 ],
'Dave' : [32, 26, 212, 0 ]
}).T
values
#%%
rental_harmony(total_rent,values)
#%%
import random
total_rent = 0
def random_values(n,m=100) :
letters = [chr(i + ord('A')) for i in range(0,n)]
return pd.DataFrame({letters[i] : random.sample(range(0, m), n) for i in range(0,n)}).T
#%%
n=5
envy_free = True
trials = 0
while envy_free and trials < 1000:
if trials % 100 == 0 :
print (str(trials) + ' trials completed')
total_rent = 5400
total_rent
#%%
#Have each housemate choose their least-favorite room, and assign it
#a marginal value of zero dollars. Then, ask them to imagine living in
#that room and paying slightly-less-than-average rent. How much extra
#would they be willing to pay to move into each other room?
#Enter those values here:
values = pd.DataFrame({
# Room : 1 2 3 4
'Alice' : [30, 20, 0, 200 ],
'Bob' : [201, 32, 23, 0 ],
'Caitlin' : [31, 204, 29, 0 ],
'Dave' : [32, 26, 212, 0 ]
}).T
values
#%%
#Compute the (room,price) assignment that is maximally-far from
#creating ency between any two housemates; this assignment
#necessarily also maximizes the total utility of the group,
#measured in marginal dollars:
(solution,envies,envy_free) = rental_harmony(total_rent,values)
solution
#%%
envies
#%%
envy_free
