Add files via upload
This commit is contained in:
Peter Norvig authored and 
GitHub committed
1 parent
802de5d6e7
commit
952675ffc7
1 file changed
+283
-143
+283
-143
@@ -13,13 +13,15 @@
|
||||
"- Hospitals in turn state which students they prefer. \n",
|
||||
"- An algorithm finds a stable matching.\n",
|
||||
"\n",
|
||||
"Each actor has **preferences** for who they would prefer to be matched with. In the default way of stating the problem, preferences are expressed as an **ordering**: each actor rates the possible matches on the other side from most preferred to least preferred. But we will go beyond that, allowing each actor to say more: to express their preference for each possible match as a **utility**: a number between 0 and 1. For example actor $A$ on one side could say that they would like to be paired with actor β on the other side with utility 0.9 (meaning a very desireable match) and with actor γ on the other side with utility 0.1 (meaning an undesireable match). The algorithm we present actually pays attention only to the ordering of preferences, but we will use the utilities to analyze how well each side does, on average.\n",
|
||||
"Each actor has **preferences** for who they would like to be matched with. A matching is **stable** if it is **not** the case that there is an actor from one side and an actor from the other side who both have a higher preference for each other than they have for who they are currently matched with. \n",
|
||||
"\n",
|
||||
"In the default way of stating the problem, preferences are expressed as an **ordering**: each actor rates the possible matches on the other side from most preferred to least preferred. But in this notebook we will allow each actor to say more: to express their preference for each possible match as a **utility**: a number between 0 and 1. For example actor $A$ on one side could say that they would like to be paired with actor β on the other side with utility 0.9 (meaning a very desireable match) and with actor γ on the other side with utility 0.1 (meaning an undesireable match). The matching algorithm we present actually pays attention only to the ordering of preferences, but we will use the utilities to analyze how well each side does, on average.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"A matching is **stable** if it is **not** the case that there is an actor from one side and an actor from the other side who both have a higher preference for each other than they have for who they are currently matched with. \n",
|
||||
"\n",
|
||||
"# Gale-Shapley Matching Algorithm\n",
|
||||
"\n",
|
||||
"The **[Gale-Shapley Stable Matching Algorithm](https://en.wikipedia.org/wiki/Gale%E2%80%93Shapley_algorithm)** (*Note: David Gale was my father's [PhD advisor](https://www.genealogy.math.ndsu.nodak.edu/id.php?id=10282&fChrono=1).*) works as follows: one side is chosen to be the **proposers** and the other side the **acceptors**. Until everyone has been matched the algorithm repeats the following steps:\n",
|
||||
"The **[Gale-Shapley Stable Matching Algorithm](https://en.wikipedia.org/wiki/Gale%E2%80%93Shapley_algorithm)** (*Note: David Gale was my father's [PhD advisor](https://www.genealogy.math.ndsu.nodak.edu/id.php?id=4381).*) works as follows: one side is chosen to be the **proposers** and the other side the **acceptors**. Until everyone has been matched the algorithm repeats the following steps:\n",
|
||||
"- An unmatched proposer, $p$, proposes a match to the highest-ranked acceptor, $a$, that $p$ has not yet proposed to.\n",
|
||||
"- If $a$ is unmatched, then $a$ tentatively accepts the proposal to be a match.\n",
|
||||
"- If $a$ is matched, but prefers $p$ to their previous match, then $a$ breaks the previous match and tentatively accepts $p$.\n",
|
||||
@@ -42,33 +44,33 @@
|
||||
"from statistics import mean, stdev\n",
|
||||
"from typing import *\n",
|
||||
"import random\n",
|
||||
"import itertools \n",
|
||||
"flatten = itertools.chain.from_iterable\n",
|
||||
"\n",
|
||||
"# Types\n",
|
||||
"ID = int # ID number for an actor\n",
|
||||
"Utility = float # Real number form 0 to 1 indicating degree of preference\n",
|
||||
"Match = Tuple[ID, ID] # A pair of actors who are matched\n",
|
||||
"PrefArray = List[List[Utility]] # Preference of each actor on a side for each actor on other side\n",
|
||||
"\n",
|
||||
"ID = int\n",
|
||||
"Match = Tuple[ID, ID]\n",
|
||||
"\n",
|
||||
"def stable_matching(P, A) -> Set[Match]:\n",
|
||||
"def stable_matching(P: PrefArray, A: PrefArray) -> Set[Match]:\n",
|
||||
" \"\"\"Compute a stable match, a set of (p, a) pairs.\n",
|
||||
" P and A are square preference arrays: P[p][a] is how much p likes a;\n",
|
||||
" A[a][p] is how much a likes p. Stable means there is no (p, a)\n",
|
||||
" such that both prefer each other over the partner they are matched with.\"\"\"\n",
|
||||
" ids = range(len(P)) # ID numbers of all actors on (either) side\n",
|
||||
" ids = range(len(P)) # ID numbers of all actors on either side\n",
|
||||
" unmatched = set(ids) # Members of P that are not yet matched to anyone\n",
|
||||
" matched = {} # {a: p} mapping of who acceptors are matched with\n",
|
||||
" proposals = [sorted(ids, key=lambda a: P[p][a]) \n",
|
||||
" for p in ids] # proposals[p] is an ordered list of who p should propose to\n",
|
||||
" while unmatched:\n",
|
||||
" p = next(iter(unmatched)) # p is an arbitrary unmatched Proposer\n",
|
||||
" p = unmatched.pop() # p is an arbitrary unmatched Proposer\n",
|
||||
" a = proposals[p].pop() # a is p's most preferred remaining acceptor\n",
|
||||
" if a not in matched:\n",
|
||||
" unmatched.remove(p)\n",
|
||||
" if a not in matched: # first proposal to a; a tentatively accepts\n",
|
||||
" matched[a] = p\n",
|
||||
" elif A[a][p] > A[a][matched[a]]:\n",
|
||||
" elif A[a][p] > A[a][matched[a]]: # a breaks previous engagement and accepts\n",
|
||||
" unmatched.add(matched[a])\n",
|
||||
" unmatched.remove(p)\n",
|
||||
" matched[a] = p\n",
|
||||
" else:\n",
|
||||
" unmatched.add(p) # proposal declined; p remains unmatched\n",
|
||||
" return {(p, a) for (a, p) in matched.items()}"
|
||||
]
|
||||
},
|
||||
@@ -86,7 +88,7 @@
|
||||
"\n",
|
||||
"# Preference Arrays\n",
|
||||
"\n",
|
||||
"Let's define some preference arrays. `I` is the identity matrix: it says that every proposer number $i$ likes acceptor $i$ best, and dislikes the others equally. `X` is the same as the identity matrix for indexes 0, 1, and 2, but it says that the actor with index 3 would be happy with any of 2, 3, or 4, and actor 4 prefers 3."
|
||||
"Let's define some preference arrays. `I` is the identity matrix: it says that every proposer number $i$ likes acceptor $i$ best, and dislikes the others equally. "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -99,37 +101,46 @@
|
||||
" [0, 1, 0, 0, 0],\n",
|
||||
" [0, 0, 1, 0, 0],\n",
|
||||
" [0, 0, 0, 1, 0],\n",
|
||||
" [0, 0, 0, 0, 1]]\n",
|
||||
"\n",
|
||||
"X = [[1, 0, 0, 0, 0],\n",
|
||||
" [0, 1, 0, 0, 0],\n",
|
||||
" [0, 0, 1, 0, 0],\n",
|
||||
" [0, 0, 1, 0, 0],\n",
|
||||
" [0, 0, 0, 1, 0]]"
|
||||
" [0, 0, 0, 0, 1]]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"I = [[.9, .4, .3, .2, .1],\n",
|
||||
" [.1, .9, .4, .3, .2],\n",
|
||||
" [.2, .1, .9, .4, .3],\n",
|
||||
" [.3, .2, .1, .9, .4],\n",
|
||||
" [.4, .3, .2, .1, .9]]\n",
|
||||
"\n",
|
||||
"M = [[.9, .4, .3, .2, .1],\n",
|
||||
" [.1, .9, .4, .3, .2],\n",
|
||||
" [.2, .1, .9, .4, .3],\n",
|
||||
" [.1, .2, .3, .4, .9],\n",
|
||||
" [.9, .4, .3, .2, .1]]"
|
||||
"If both sides have this same preference array, it should result in a stable matching of (*i*, *i*) pairs:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)}"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stable_matching(I, I)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can measure the average utility for both sides:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -141,16 +152,58 @@
|
||||
" mean(A[a][p] for (p, a) in matching))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(1, 1)"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean_utilities(I, I)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let's see what happens when `I` is the proposing side, and when `X` is the proposing side:"
|
||||
"Perfect! Both sides achieve the maximum utility, 1.\n",
|
||||
"\n",
|
||||
"Next, preference array `X` is almost the same as the identity matrix, except that proposer 3 prefers 4, not 3, and proposer 4 prefers 3, not 4."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X = [[1, 0, 0, 0, 0],\n",
|
||||
" [0, 1, 0, 0, 0],\n",
|
||||
" [0, 0, 1, 0, 0],\n",
|
||||
" [0, 0, 0, 0, 1],\n",
|
||||
" [0, 0, 0, 1, 0]]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We see that the way that 3 and 4 get matched depends on which side is the proposer:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -159,7 +212,7 @@
|
||||
"{(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)}"
|
||||
]
|
||||
},
|
||||
"execution_count": 44,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -170,16 +223,43 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(0.9, 0.64)"
|
||||
"{(0, 0), (1, 1), (2, 2), (3, 4), (4, 3)}"
|
||||
]
|
||||
},
|
||||
"execution_count": 45,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stable_matching(X, I)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The proposers always do better (or no worse than) the acceptors in mean utility:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(1, 0.6)"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -190,36 +270,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{(0, 0), (1, 1), (2, 2), (3, 4), (4, 3)}"
|
||||
"(1, 0.6)"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stable_matching(X, I)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(0.76, 0.64)"
|
||||
]
|
||||
},
|
||||
"execution_count": 47,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -232,20 +292,106 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"When `I` is the proposing side, every actor in `I` gets their first-choice match.\n",
|
||||
"\n",
|
||||
"Likewise, when `X` is the proposer, every actor in `X` gets a first-choice match.\n",
|
||||
"\n",
|
||||
"We can measure the average utility to each side for any matching:"
|
||||
"Similar results hold when we use preference arrays that have utilities that are spread out between 0 and 1; it is still better to be the proposing side:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Is it Fair?\n",
|
||||
"A = [[.9, .4, .3, .2, .1],\n",
|
||||
" [.1, .9, .4, .3, .2],\n",
|
||||
" [.2, .1, .9, .4, .3],\n",
|
||||
" [.3, .2, .1, .9, .4],\n",
|
||||
" [.4, .3, .2, .1, .9]]\n",
|
||||
"\n",
|
||||
"We see that in both cases, the proposers get 100% of their maximum possible utility, and the acceptors gets only 60% (averaged over all five acceptors). Is this a problem? If the Gale-Shapley algorithm is used in high-stakes applications like matching medical residents to hospitals, does it make a big difference which side is the proposers? I want to address that question with some experiments."
|
||||
"B = [[.9, .4, .3, .2, .1],\n",
|
||||
" [.1, .9, .4, .3, .2],\n",
|
||||
" [.2, .1, .9, .4, .3],\n",
|
||||
" [.1, .2, .3, .4, .9],\n",
|
||||
" [.9, .4, .3, .2, .1]]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)}"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stable_matching(A, B)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(0.9, 0.64)"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean_utilities(A, B)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{(0, 0), (1, 1), (2, 2), (3, 4), (4, 3)}"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stable_matching(B, A)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(0.76, 0.64)"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean_utilities(B, A)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -254,27 +400,29 @@
|
||||
"source": [
|
||||
"# Preferences with Common and Private Values\n",
|
||||
"\n",
|
||||
"I will create a bunch of randomized preference arrays and get a feeling for how they perform. But I don't want them to be completely random; I want them to reflect, in a very abstract way, some properties of the real world:\n",
|
||||
"If the Gale-Shapley algorithm is used in high-stakes applications like matching medical residents to hospitals, does it make a big difference which side is the proposers? In the toy examples above, it does make a difference; in some cases the acceptors got only 60% of their possible utility, while the proposers got up to 100%. \n",
|
||||
"\n",
|
||||
"I will address the question by examining some randomized preference arrays and getting a feeling for how they perform. But I don't want them to be completely random; I want them to reflect, in a very abstract way, some properties of the real world:\n",
|
||||
"- Some choices have intrinsic properties that make them widely popular (or unpopular). For example, Massachusetts General Hospital is considered an excellent choice by many aspiring residents. The amount of utility that is commonly agreed upon is called the **common value** of a choice.\n",
|
||||
"- Some choices have idiosyncratic properties that appeal only to specific choosers. For example, you might really want to be a resident at your hometown hospital, even if it is not highly-regarded by others. This is the **private value** of a choice.\n",
|
||||
"- Some choices have idiosyncratic properties that appeal only to specific actors. For example, you might really want to be a resident at your hometown hospital, even if it is not highly-regarded by others. This is the **private value** of a choice.\n",
|
||||
"- In real world situations there is usually a mix of common and private value.\n",
|
||||
"\n",
|
||||
"The function call `preferences(N, 0.75)`, for example, creates an NxN array of preferences, where each preference is 75% common value and 25% individual value. I implement individual value as being proportional to the ID number (`a` in the code):"
|
||||
"The function call `preferences(N, 0.75)`, for example, creates an NxN array of preferences, where each preference is 75% common value and 25% individual value. I implement individual value as being proportional to the ID number (`a` in the code); that is equivalent to assuming that the hospitals (say) are sorted in increasing order of quality by ID number."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def preferences(N=25, c=0.75):\n",
|
||||
" \"\"\"Create an NxN preference array, weighted: c × common + (1 - c) × random.\"\"\"\n",
|
||||
" return [[round(c * (a + 0.5) / N + (1 - c) * random.uniform(0, 1), 4)\n",
|
||||
" for a in range(N)] \n",
|
||||
" return [[round(c * (a + 0.5) / N + (1 - c) * random.uniform(0, 1), 3)\n",
|
||||
" for a in range(N)] \n",
|
||||
" for p in range(N)]\n",
|
||||
"\n",
|
||||
"random.seed(42)"
|
||||
"random.seed(99) # For reproducability"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -286,22 +434,22 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[[0.3554, 0.1196, 0.3161, 0.3616, 0.6897, 0.7312, 0.9104],\n",
|
||||
" [0.0792, 0.3181, 0.1935, 0.3593, 0.5741, 0.4061, 0.5637],\n",
|
||||
" [0.3607, 0.3796, 0.2888, 0.5446, 0.7261, 0.3961, 0.8672],\n",
|
||||
" [0.3848, 0.2773, 0.2563, 0.7286, 0.4897, 0.4392, 0.5126],\n",
|
||||
" [0.4595, 0.409, 0.5821, 0.6149, 0.5895, 0.8794, 0.6536],\n",
|
||||
" [0.3117, 0.5218, 0.4878, 0.6809, 0.6101, 0.7451, 0.4872],\n",
|
||||
" [0.1497, 0.2518, 0.2185, 0.3664, 0.3719, 0.5318, 0.7821]]"
|
||||
"[[0.238, 0.207, 0.268, 0.374, 0.701, 0.518, 0.656],\n",
|
||||
" [0.378, 0.376, 0.648, 0.495, 0.532, 0.699, 0.573],\n",
|
||||
" [0.487, 0.294, 0.373, 0.591, 0.398, 0.723, 0.889],\n",
|
||||
" [0.205, 0.581, 0.443, 0.637, 0.556, 0.594, 0.599],\n",
|
||||
" [0.04, 0.209, 0.673, 0.517, 0.552, 0.843, 0.767],\n",
|
||||
" [0.524, 0.286, 0.286, 0.71, 0.402, 0.789, 0.762],\n",
|
||||
" [0.527, 0.579, 0.509, 0.269, 0.333, 0.63, 0.874]]"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -319,22 +467,22 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[[0.3648, 0.3702, 0.2095, 0.267, 0.9367, 0.648, 0.6091],\n",
|
||||
" [0.1711, 0.7291, 0.1634, 0.3795, 0.9895, 0.64, 0.5569],\n",
|
||||
" [0.6846, 0.8429, 0.776, 0.229, 0.0321, 0.3155, 0.2677],\n",
|
||||
" [0.211, 0.9429, 0.8764, 0.3147, 0.6554, 0.3956, 0.9145],\n",
|
||||
" [0.4589, 0.2649, 0.2466, 0.5614, 0.2627, 0.5846, 0.8978],\n",
|
||||
" [0.3994, 0.2193, 0.9975, 0.5095, 0.0909, 0.0471, 0.1096],\n",
|
||||
" [0.6274, 0.7921, 0.4222, 0.0635, 0.3816, 0.9961, 0.5291]]"
|
||||
"[[0.081, 0.425, 0.086, 0.31, 0.006, 0.33, 0.523],\n",
|
||||
" [0.201, 0.787, 0.055, 0.211, 0.258, 0.404, 0.764],\n",
|
||||
" [0.354, 0.695, 0.922, 0.131, 0.864, 0.6, 0.747],\n",
|
||||
" [0.608, 0.408, 0.096, 0.144, 0.174, 0.178, 0.146],\n",
|
||||
" [0.208, 0.561, 0.771, 0.798, 0.846, 0.117, 0.822],\n",
|
||||
" [0.186, 0.072, 0.288, 0.586, 0.048, 0.216, 0.399],\n",
|
||||
" [0.822, 0.399, 0.414, 0.371, 0.093, 0.88, 0.923]]"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -352,7 +500,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -365,7 +513,7 @@
|
||||
" [0.1, 0.3, 0.5, 0.7, 0.9]]"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -383,42 +531,42 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.5000444"
|
||||
"0.4996304"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean(flatten(preferences(100)))"
|
||||
"mean(map(mean, preferences(100, c=0.8)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.50186629"
|
||||
"0.5004173"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean(flatten(preferences(100, c=0.25)))"
|
||||
"mean(map(mean, preferences(100, c=0.2)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -430,7 +578,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -441,27 +589,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[([[0.1692, 0.6815, 0.6931],\n",
|
||||
" [0.2352, 0.7386, 0.7107],\n",
|
||||
" [0.2157, 0.6245, 0.7099]],\n",
|
||||
" [[0.095, 0.6114, 0.6695],\n",
|
||||
" [0.3564, 0.4007, 0.4181],\n",
|
||||
" [0.4574, 0.7469, 0.7934]]),\n",
|
||||
" ([[0.5066, 0.2609, 0.439],\n",
|
||||
" [0.0884, 0.6074, 0.4683],\n",
|
||||
" [0.5632, 0.6722, 0.7142]],\n",
|
||||
" [[0.1663, 0.6358, 0.8687],\n",
|
||||
" [0.4716, 0.7222, 0.8577],\n",
|
||||
" [0.2024, 0.2663, 0.5462]])]"
|
||||
"[([[0.181, 0.736, 0.7], [0.207, 0.734, 0.599], [0.572, 0.745, 0.715]],\n",
|
||||
" [[0.537, 0.72, 0.784], [0.281, 0.256, 0.582], [0.452, 0.657, 0.554]]),\n",
|
||||
" ([[0.464, 0.575, 0.662], [0.23, 0.416, 0.821], [0.342, 0.381, 0.658]],\n",
|
||||
" [[0.471, 0.517, 0.603], [0.201, 0.365, 0.482], [0.102, 0.347, 0.479]])]"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -476,12 +616,12 @@
|
||||
"source": [
|
||||
"# Histograms of Acceptor/Proposer Utility\n",
|
||||
"\n",
|
||||
"Now we're readsy to answer the original question: how much worse is it to be an acceptor rather than a proposer? The function `show` displays two overlapping histograms of mean utilities: one for acceptors and one for proposers. "
|
||||
"Now we're ready to answer the original question: how much worse is it to be an acceptor rather than a proposer? The function `show` displays two overlapping histograms of mean utilities: one for acceptors and one for proposers. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -506,7 +646,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -515,12 +655,12 @@
|
||||
"text": [
|
||||
"10,000 examples with N = 25 actors, common value ratio c = 0.0\n",
|
||||
" Acceptors: 0.729 ± 0.063\n",
|
||||
" Proposers: 0.870 ± 0.035\n"
|
||||
" Proposers: 0.871 ± 0.035\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -546,7 +686,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -560,7 +700,7 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -577,7 +717,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -586,12 +726,12 @@
|
||||
"text": [
|
||||
"10,000 examples with N = 50 actors, common value ratio c = 0.5\n",
|
||||
" Acceptors: 0.647 ± 0.013\n",
|
||||
" Proposers: 0.661 ± 0.011\n"
|
||||
" Proposers: 0.661 ± 0.012\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -617,7 +757,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -631,7 +771,7 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -648,7 +788,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -662,7 +802,7 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAATFUlEQVR4nO3df2ydV33H8feXhsLoKPnRtApJurQi/Oim0RarOEOaWjJG2zHSiVYUTRC6oGioTAyBRrv9MTaYAkhboWKq5BFYihilKkMNrGPLgis2rYG60Ka0gcRkneOla0x/bayCLuO7P+4xdeJr+8a+v3z8fklX93nOc659npP44+PzPPfcyEwkSXV5Xq8bIElqP8NdkipkuEtShQx3SaqQ4S5JFVrW6wYAnHXWWblhw4ZeN0OSFpX77rvvh5m5utmxvgj3DRs2MDIy0utmSNKiEhH/PtMxp2UkqUKGuyRVyHCXpAoZ7pJUIcNdkipkuEtShQx3SaqQ4S5JFTLcJalCffEOVakWN+052LT8fW94eZdboqXOkbskVchwl6QKOS0jzWZ4R/Pyy27sbjukU+TIXZIqZLhLUoUMd0mqkHPuEsw8ty4tUi2N3CNieUTcERHfi4gDEbEpIlZGxJ6IOFSeV5S6ERE3R8RoROyPiIs7ewqSpJO1Oi3zSeBrmflK4NXAAeAGYG9mbgT2ln2AK4CN5bEduKWtLZYkzWnOaZmIOBP4VeCdAJn5LPBsRGwBLi3VdgF3Ax8EtgC3ZmYC+8qof01mPtr21kvz0OxdpINjj89Yf9P5qzrZHKkjWhm5nw9MAJ+NiO9ExKcj4gzgnMnALs9nl/prgSNTXj9eyk4QEdsjYiQiRiYmJhZ0EpKkE7US7suAi4FbMvMi4H94bgqmmWhSltMKMocycyAzB1avXt1SYyVJrWnlbplxYDwzv1n276AR7o9NTrdExBrg2JT666e8fh1wtF0NlrrtnsPTp2z2HT/oYmDqa3OO3DPzP4EjEfGKUrQZeBjYDWwtZVuBO8v2buAd5a6ZQeBp59slqbtavc/994DPR8TpwGHgOhq/GG6PiG3AGHBNqXsXcCUwCjxT6kqSuqilcM/M+4GBJoc2N6mbwPULbJckaQFcfkCSKmS4S1KFXFtGmqeZPlJP6geGuzQPg2ND08r2nbu9By2RmnNaRpIq5MhdS8vwjlnXkZFqYbhLbdJsqgacrlFvGO6qkx++oSXOcJe6YKY7a1yfRp3iBVVJqpAjd6nDnItXLzhyl6QKGe6SVCHDXZIqZLhLUoW8oKpFr9lthoNjj7Pp/FU9aI3UHwx3VavZZ59KS4XTMpJUIcNdkipkuEtShQx3SaqQ4S5JFTLcJalChrskVailcI+IRyLiwYi4PyJGStnKiNgTEYfK84pSHhFxc0SMRsT+iLi4kycgSZruVEbul2XmhZk5UPZvAPZm5kZgb9kHuALYWB7bgVva1VhJUmsW8g7VLcClZXsXcDfwwVJ+a2YmsC8ilkfEmsx8dCENlWozODYEw02WSLjsxu43RtVpdeSewD9GxH0RMfkJA+dMBnZ5PruUrwWOTHnteCk7QURsj4iRiBiZmJiYX+slSU21OnJ/XWYejYizgT0R8b1Z6kaTspxWkDkEDAEMDAxMOy5Jmr+Wwj0zj5bnYxHxZeAS4LHJ6ZaIWAMcK9XHgfVTXr4OONrGNkvVaLa42b7jB/3gbC3YnNMyEXFGRLx4chv4deC7wG5ga6m2FbizbO8G3lHumhkEnna+XZK6q5WR+znAlyNisv7fZObXIuJe4PaI2AaMAdeU+ncBVwKjwDPAdW1vtSRpVnOGe2YeBl7dpPxxYHOT8gSub0vrpBYMjg31uglS3/EdqpJUIcNdkipkuEtShQx3SaqQ4S5JFVrI2jKSOsA1Z9QOjtwlqUKGuyRVyGkZLR7DO3rdAmnRMNy1qDRbaEvSdE7LSFKFDHdJqpDhLkkVMtwlqUKGuyRVyHCXpAp5K6TUh/xsVS2UI3dJqpDhLkkVMtwlqUKGuyRVyHCXpAoZ7pJUoZZvhYyI04AR4D8y800RcR5wG7AS+Dbw9sx8NiJeANwKvAZ4HHhrZj7S9parXi7t25Sf0KRTcSoj9/cCB6bsfwy4KTM3Ak8C20r5NuDJzHwZcFOpJ0nqopbCPSLWAb8BfLrsB/B64I5SZRdwVdneUvYpxzeX+pKkLml15P4J4A+An5b9VcBTmXm87I8Da8v2WuAIQDn+dKl/gojYHhEjETEyMTExz+ZLkpqZM9wj4k3Ascy8b2pxk6rZwrHnCjKHMnMgMwdWr17dUmMlSa1p5YLq64A3R8SVwAuBM2mM5JdHxLIyOl8HHC31x4H1wHhELANeAjzR9pZLkmY058g9M2/MzHWZuQG4Fvh6Zv42MAxcXaptBe4s27vLPuX41zNz2shdktQ5C1kV8oPAbRHxEeA7wM5SvhP4XESM0hixX7uwJqpas9zy6AdhSwtzSuGemXcDd5ftw8AlTer8GLimDW2TJM2T67mrpxyhS53h8gOSVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXI+9ylRaTZ+wL2HT/I+97w8h60Rv3MkbskVchwl6QKGe6SVCHDXZIqZLhLUoW8W0aqwE17Dk4r8w6apc2RuyRVyHCXpAoZ7pJUIefcpUVucGxoWtm+c7f3oCXqJ4a7uqLZBb/BMT9iT+oUp2UkqUKGuyRVyHCXpAoZ7pJUoTnDPSJeGBHfiogHIuKhiPiTUn5eRHwzIg5FxBcj4vRS/oKyP1qOb+jsKUiSTtbKyP0nwOsz89XAhcDlETEIfAy4KTM3Ak8C20r9bcCTmfky4KZST5LURXOGezb8qOw+vzwSeD1wRynfBVxVtreUfcrxzRERbWuxJGlOLc25R8RpEXE/cAzYA/wAeCozj5cq48Dasr0WOAJQjj8NrGryNbdHxEhEjExMTCzsLCRJJ2gp3DPz/zLzQmAdcAnwqmbVynOzUXpOK8gcysyBzBxYvXp1q+2VJLXglN6hmplPRcTdwCCwPCKWldH5OuBoqTYOrAfGI2IZ8BLgifY1WVIrmr0rGFwKeKlo5W6Z1RGxvGz/HPBrwAFgGLi6VNsK3Fm2d5d9yvGvZ+a0kbskqXNaGbmvAXZFxGk0fhncnplfjYiHgdsi4iPAd4Cdpf5O4HMRMUpjxH5tB9otSZrFnOGemfuBi5qUH6Yx/35y+Y+Ba9rSOknSvLgqpFShZssAg0sBLyUuPyBJFTLcJalChrskVcg5d3Xe8A4/dUnqMkfuklQhw12SKmS4S1KFDHdJqpAXVNU+wzt63QJJhSN3SaqQ4S5JFTLcJalCzrmrre457JuV+p0f4rE0OHKXpAo5cpeWEJcCXjocuUtShQx3SaqQ4S5JFTLcJalChrskVchwl6QKGe6SVKE5wz0i1kfEcEQciIiHIuK9pXxlROyJiEPleUUpj4i4OSJGI2J/RFzc6ZOQJJ2olZH7ceD9mfkqYBC4PiIuAG4A9mbmRmBv2Qe4AthYHtuBW9reaknSrOYM98x8NDO/Xbb/GzgArAW2ALtKtV3AVWV7C3BrNuwDlkfEmra3XJI0o1Oac4+IDcBFwDeBczLzUWj8AgDOLtXWAkemvGy8lEmSuqTlcI+Inwe+BPx+Zv7XbFWblGWTr7c9IkYiYmRiYqLVZkiSWtBSuEfE82kE++cz829L8WOT0y3l+VgpHwfWT3n5OuDoyV8zM4cycyAzB1avXj3f9kuSmphzVciICGAncCAz/2LKod3AVuCj5fnOKeXviYjbgNcCT09O36ge9+z8QK+bIGkWrSz5+zrg7cCDEXF/KftDGqF+e0RsA8aAa8qxu4ArgVHgGeC6trZYkjSnOcM9M/+F5vPoAJub1E/g+gW2S5K0AH5YhyTAj9+rjeEuyU9oqpBry0hShQx3SaqQ4S5JFXLOXdKMBseGYHjV9AOX3dj9xuiUOHKXpAoZ7pJUIcNdkipkuEtShQx3SaqQd8todsM7et0CSfPgyF2SKmS4S1KFnJbRrO45/HivmyBpHhy5S1KFDHdJqpDhLkkVcs5d0qyaXnc5/AE2ne+CYv3MkbskVciRu6R5aTai33f8oJ+52icMd/1Msw9IHuxBOyQtnOGuhuEdDI55T7tUC+fcJalCc4Z7RHwmIo5FxHenlK2MiD0Rcag8ryjlERE3R8RoROyPiIs72XhJUnOtjNz/Grj8pLIbgL2ZuRHYW/YBrgA2lsd24Jb2NFOSdCrmDPfM/AbwxEnFW4BdZXsXcNWU8luzYR+wPCLWtKuxkqTWzHfO/ZzMfBSgPJ9dytcCR6bUGy9l00TE9ogYiYiRiYmJeTZDktRMu++WiSZl2axiZg4BQwADAwNN66gzmt7y6J0yUlXmO3J/bHK6pTwfK+XjwPop9dYBR+ffPEnSfMw33HcDW8v2VuDOKeXvKHfNDAJPT07fSJK6Z85pmYj4AnApcFZEjAN/DHwUuD0itgFjwDWl+l3AlcAo8AxwXQfaLEmaw5zhnplvm+HQ5iZ1E7h+oY2SJC2M71CVpAq5tsxS4xoy0pJguEtqm8GxIe7ZeWLZvnO3A7gUcJcZ7rUa3tHrFkjqIefcJalChrskVchwl6QKGe6SVCHDXZIq5N0yknpjpju6Lruxu+2olOFesXsO+2Yl9Y+Tl5oeHHucTeev6lFr6me4S+qowbGhXjdhSTLcF7vhHY7QJU3jBVVJqpAj98XEJQVUmWZ/de47ftB1aNrAkbskVchwl6QKOS2ziHjhVFKrDHdJfefke+InORffOsNdUl+Z6b74yQ/9UGsM9z4006hlsMvtkPqNI/rWeUFVkirkyL0P+XZtSQtluHdJsz8nDXGpdc7Fn5qOhHtEXA58EjgN+HRmfrQT36cfzRTizpdLnTE4NgTDLa4uuYSWE257uEfEacBfAm8AxoF7I2J3Zj7c7u/VTqdLine truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -681,7 +821,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We see that there is very little difference between the two sides. So the conclusion is: when there is a lot of common value, the Gale-Shapley Matching Algorithm is fair. So it is probably okay to use it for matching medical residents, because there is a lot of common value in the perception of the quality of hospitals, and likewise for the quality of students.\n",
|
||||
"We see that there is very little difference between the two sides. So the conclusion is: when there is a lot of common value, the Gale-Shapley Matching Algorithm is almost fair: the proposers have an advantage, but it is minimal. So it is probably okay to use the algorithm for matching medical residents, because there is a lot of common value in the perception of the quality of hospitals, and likewise for the quality of students.\n",
|
||||
"\n",
|
||||
"But when there is mostly private value, the algorithm is unfair, favoring the proposers over the acceptors."
|
||||
]
|
||||
@@ -689,7 +829,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@@ -703,9 +843,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.7"
|
||||
"version": "3.9.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Reference in new issue
Block a user