# data[].attributes.body
The median results reflect the average of the majority of people receive the same results.

# data[].attributes.body
pretty much everyone either got what they wanted or came close to achieving it. Really interesting how the assessments on here differ in terms of what is most fair

# data[].attributes.body
Based on average distribution

# data[].attributes.body
The largest number of people got the highest utility. If price is affordable and fair, this is a got choise to expand. Someone do not afford have more children because of small property, so here is the chance.

# data[].attributes.headline
Alogorithm 1 - most people achieving highest utility

# data[].attributes.subject
Alogorithm 1 - most people achieving highest utility

# data[].attributes.headline
Algorithms seem to have most people in the highest utility ranking 

# data[].attributes.subject
Algorithms seem to have most people in the highest utility ranking 

# data[].attributes.body
It seemed like the average one as opposed to weighted in one particular way. 5 was a close second.

# data[].attributes.body
I picked this one because on the left hand there is an almost even distribution between the 7 properties (or maybe the most popular of them) and on the right, it appears that in ~400 cases, the people got their property at zero distance from the one they voted.

# data[].attributes.body
It looks as if no one received below 4 and 7 and most people received a high utility

# data[].attributes.body
Tightest distribution around mean Utility just indicating that the most participants got the highest Utility

# data[].attributes.body
I was doubting between 1 and 5 and finally opted for 1 as the difference between somebody's individual utility and the average achieved by all participants was the fairest in my opinion. If the exercise allowed to include other values although secondary then I would have probably chosen  as more people got the desired utility and the negative differences between individuals were not 'that bad'

# data[].attributes.body
I don't think there is enough information to determine what algorithm is the most "fair". The nature of housing means you seldom have the chance to house a large amount of people at one go. As properties become available other factors might determine the fairness of who gets the house. Length of time on a waiting list, special needs eg. no stairs. I'm tempted to go with the highest mean utility or the least people with low utility. But in reality the best may be to give as many people as you can the ideal property. Housing isn't a one off and hopefully those that didn't get what they want can be re-homed at some point in the near future.

# data[].attributes.body
Crec que reflexa bé el criteri de proporció acostant-se als resultats donats en la selecció.

# data[].attributes.headline
I chose algorithm 5 because it is the one where most people are on or close to the average utility

# data[].attributes.subject
I chose algorithm 5 because it is the one where most people are on or close to the average utility

# data[].attributes.body
I chose the number two based on the proportionallity -the proportion of the group receiving a positive outcome-

# data[].attributes.body
I picked algorithm 5 based on the distribution

# data[].attributes.body
The highest number of people received their utmost preference with mo one receiving below their 4th preference. That coupled with low standard deviation from the mean makes 2 and 4 the fairest in my judgment.

# data[].attributes.body
My reading would be algorithms 2 and 4 are most fair. most people got a high utility from the allocation, and no one got below a 4 out of 7, whereas on all the others at least someone got below a utility of 4. 1 also seems a pretty good outcome, but 2 and 4 have more people achieving a greater positive distance from the mean.. so why not make quite a few people even happier when no one is really being made that unhappy!

# data[].attributes.body
If I understand correctly, you have 7 property to gives so all 7 should be given hence there should be a good distribution among them. I see Algorith 3 and 5 to have better distribution of the 7 types of unit.

But when we look at the deviation from the mean utlity, Algorith has a lot of people deviating from the mean would Algorith 5 as more people on the mean utility.

# data[].attributes.headline
Algorithm 1 - More utility for individuals?

# data[].attributes.subject
Algorithm 1 - More utility for individuals?

# data[].attributes.body
I'm not sure if I'm reading this correctly, but it seems that algorithm 1 provides a lot of people with the utility of 5 or more, and the distance from the average is very narrow (right hand side).  I'm interpreting this as saying that many people got close to their desired utility AND the variance as a group was low.