Agreeable subset

Last updated

An agreeable subset is a subset of items that is considered, by all people in a certain group, to be at least as good as its complement. Finding a small agreeable subset is a problem in computational social choice. [1] [2]

Contents

An example situation in which this problem arises is when a family goes on a trip and has to decide which items to take. Since their car is limited in size, they cannot pick all items, so they have to agree on a subset of items which are most important. If they manage to find a subset of items such that all family members agree that it is at least as good as the subset of items remaining at home, then this subset is called agreeable.

Another use case is when the citizens in some city want to elect a committee from a given pool of candidates, such that all citizens agree that the subset of elected candidates is at least as good as the subset of non-elected ones. Subject to that, the committee size should be as small as possible.

Definitions

Agreeable subset

There is a set S containing m objects. There are n agents who have to choose a subset of S. Each agent is characterized by a preference-relation on subsets of S. The preference-relation is assumed to be monotone - an agent always weakly prefers a set to all its subsets. A subset T of S is called agreeable if all agents prefer T to S\T.

If an agent's preference relation is represented by a subadditive utility function u, then for any agreeable subset T, u(T) ≥ u(S)/2. [2]

As an example, suppose there are two objects - bread and wine, and two agents - Alice and George. The preference-relation of Alice is {bread,wine} > {bread} > {wine} > {}. If the preference-relation of George is the same, then there are two agreeable subsets: {bread,wine} and {bread}. But if George's preference-relation is {bread,wine} > {wine} > {bread} > {}, then the only agreeable subset is {bread,wine}.

Necessarily-agreeable subset

If the agents' preference relations on the subsets are given, it is easy to check whether a subset is agreeable. But often, only the agents' preference relations on individual objects are given. In this case, it is often assumed that the agents' preferences are not only monotone but also responsive. A subset T of S is called necessarily agreeable if all agents prefer T to S\T according to the responsive set extension of their preferences on individual objects.

A closely related property of subsets is:

To satisfy property (*), the subset T should contain the best object in S; at least two of the three best objects in S; at least three of the five best objects in S; etc.

If a subset T satisfies (*) for all agents, then it is necessarily-agreeable. The converse implication holds if the agents' preference relations on indivisible objects are strict. [3] [4]

Worst-case bounds on agreeable subset size

What is the smallest agreeable subset that we can find?

Agreeable subsets

Consider first a single agent. In some cases, an agreeable subset should contain at least objects. An example is when all m objects are identical. Moreover, there always exists an agreeable subset containing objects. This follows from the following lemma:

(this is because S\V1 contains V2 and S\V2 contains V1 and the preferences are monotone).

This can be generalized: For any n agents and m objects, there always exists an agreeable subset of size , and it is tight (for some preferences this is the smallest size of an agreeable subset). The proof for two agents is constructive. The proof for n agents uses a Kneser graph. Let , and let G be the Kneser graph , that is, the graph whose vertices are all subsets of m-k objects, and two subsets are connected iff they are disjoint. If there is a vertex V such that all agents prefer S\V to V, then S\V is an agreeable subset of size k. Otherwise, we can define a color for each agent and color each vertex V of G with an agent who prefers V to S\V. By the theorem on chromatic number of Kneser graphs, the chromatic number of G is ; this means that, in the n-coloring just defined, there are two adjacent vertices with the same color. In other words, there are two disjoint subsets such that, a single agent i prefers each of them to its complement. But this contradicts the above lemma. Hence there must be an agreeable subset of size k. [2] :Thm.1

When there are at most three agents, and their preferences are responsive, an agreeable subset of size can be computed in polynomial time, using polynomially-many queries of the form "which of these two subsets is better?". [2] :Thm.2-3

When there are any number of agents with additive utilities, or a constant number of agents with monotone utilities, an agreeable subset of size can be found in polynomial time using results from consensus halving. [5]

Necessarily-agreeable subsets

When there are two agents with responsive preferences, a necessarily-agreeable subset of size exists and can be computed in polynomial time.

When there are n ≥ 3 agents with responsive preferences, a necessarily-agreeable subset of this size might not exist. However, there always exists a necessarily-agreeable subset of size , and such a set can be computed in polynomial time. On the other hand, for every m which is a power of 3, there exist ordinal preferences of 3 agents such that every necessarily-agreeable subset has size at least . Both proofs use theorems on Discrepancy of permutation hypergraphs.

There exists a randomized algorithm that computes a necessarily-agreeable subset of size . [2] :Thm.4-6

Computing a smallest agreeable subset

In many cases, there may exist an agreeable subset that is much smaller than the worst-case upper bound.

For agents with general monotone preferences, there is no algorithm that computes a smallest agreeable set using a polynomial number of queries. Moreover, for every constant c, there is no algorithm that makes at most mc/8 queries and finds an agreeable subset with expected size at most m/(c log m) of the minimum, even with only one agent. This is tight: there exists a polynomial-time algorithm that finds an agreeable subset with size at most O(m / log m) of the minimum.

Even for agents with additive utilities, deciding whether there exists an agreeable subset of size m/2 is NP-hard; the proof is by reduction from the balanced partition problem. For any fixed of additive agents, there exists a pseudopolynomial time for this problem; but if the number of agents is not fixed, then the problem is strongly NP-hard. There exists a polynomial-time O(log n) approximation algorithm. [2] :Thm.7-13

Extensions

See also

Related Research Articles

<span class="mw-page-title-main">Knapsack problem</span> Problem in combinatorial optimization

The knapsack problem is the following problem in combinatorial optimization:

<span class="mw-page-title-main">Pigeonhole principle</span> If there are more items than boxes holding them, one box must contain at least two items

In mathematics, the pigeonhole principle states that if n items are put into m containers, with n > m, then at least one container must contain more than one item. For example, of three gloves, at least two must be right-handed or at least two must be left-handed, because there are three objects but only two categories of handedness to put them into. This seemingly obvious statement, a type of counting argument, can be used to demonstrate possibly unexpected results. For example, given that the population of London is greater than the maximum number of hairs that can be on a human's head, the principle requires that there must be at least two people in London who have the same number of hairs on their heads.

<span class="mw-page-title-main">Time complexity</span> Estimate of time taken for running an algorithm

In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to be related by a constant factor.

In number theory, a formula for primes is a formula generating the prime numbers, exactly and without exception. Formulas for calculating primes do exist, however, they are computationally very slow. A number of constraints are known, showing what such a "formula" can and cannot be.

In mathematics, in the areas of order theory and combinatorics, Dilworth's theorem characterizes the width of any finite partially ordered set in terms of a partition of the order into a minimum number of chains. It is named for the mathematician Robert P. Dilworth.

The art gallery problem or museum problem is a well-studied visibility problem in computational geometry. It originates from the following real-world problem:

"In an art gallery, what is the minimum number of guards who together can observe the whole gallery?"

<span class="mw-page-title-main">Dedekind number</span> Combinatorial sequence of numbers

In mathematics, the Dedekind numbers are a rapidly growing sequence of integers named after Richard Dedekind, who defined them in 1897. The Dedekind number M(n) is the number of monotone boolean functions of n variables. Equivalently, it is the number of antichains of subsets of an n-element set, the number of elements in a free distributive lattice with n generators, and one more than the number of abstract simplicial complexes on a set with n elements.

In modular arithmetic, Barrett reduction is a reduction algorithm introduced in 1986 by P.D. Barrett.

Fair item allocation is a kind of the fair division problem in which the items to divide are discrete rather than continuous. The items have to be divided among several partners who potentially value them differently, and each item has to be given as a whole to a single person. This situation arises in various real-life scenarios:

Envy-free (EF) item allocation is a fair item allocation problem, in which the fairness criterion is envy-freeness - each agent should receive a bundle that they believe to be at least as good as the bundle of any other agent.

Maximin share (MMS) is a criterion of fair item allocation. Given a set of items with different values, the 1-out-of-n maximin-share is the maximum value that can be gained by partitioning the items into parts and taking the part with the minimum value. An allocation of items among agents with different valuations is called MMS-fair if each agent gets a bundle that is at least as good as his/her 1-out-of-n maximin-share. MMS fairness is a relaxation of the criterion of proportionality - each agent gets a bundle that is at least as good as the equal split ( of every resource). Proportionality can be guaranteed when the items are divisible, but not when they are indivisible, even if all agents have identical valuations. In contrast, MMS fairness can always be guaranteed to identical agents, so it is a natural alternative to proportionality even when the agents are different.

When allocating objects among people with different preferences, two major goals are Pareto efficiency and fairness. Since the objects are indivisible, there may not exist any fair allocation. For example, when there is a single house and two people, every allocation of the house will be unfair to one person. Therefore, several common approximations have been studied, such as maximin-share fairness (MMS), envy-freeness up to one item (EF1), proportionality up to one item (PROP1), and equitability up to one item (EQ1). The problem of efficient approximately fair item allocation is to find an allocation that is both Pareto-efficient (PE) and satisfies one of these fairness notions. The problem was first presented at 2016 and has attracted considerable attention since then.

In computer science, pseudopolynomial time number partitioning is a pseudopolynomial time algorithm for solving the partition problem.

Proportional item allocation is a fair item allocation problem, in which the fairness criterion is proportionality - each agent should receive a bundle that they value at least as much as 1/n of the entire allocation, where n is the number of agents.

In economics and computer science, the house allocation problem is the problem of assigning objects to people with different preferences, such that each person receives exactly one object. The name "house allocation" comes from the main motivating application, which is assigning dormitory houses to students. Other commonly used terms are assignment problem and one-sided matching. When agents already own houses, the problem is often called a housing market. In house allocation problems, it is assumed that monetary transfers are not allowed; the variant in which monetary transfers are allowed is known as rental harmony.

Mathematics of apportionment describes mathematical principles and algorithms for fair allocation of identical items among parties with different entitlements. Such principles are used to apportion seats in parliaments among federal states or political parties. See apportionment (politics) for the more concrete principles and issues related to apportionment, and apportionment by country for practical methods used around the world.

Fair division among groups is a class of fair division problems, in which the resources are allocated among groups of agents, rather than among individual agents. After the division, all members in each group consume the same share, but they may have different preferences; therefore, different members in the same group might disagree on whether the allocation is fair or not. Some examples of group fair division settings are:

Balanced number partitioning is a variant of multiway number partitioning in which there are constraints on the number of items allocated to each set. The input to the problem is a set of n items of different sizes, and two integers mk. The output is a partition of the items into m subsets, such that the number of items in each subset is at most k. Subject to this, it is required that the sums of sizes in the m subsets are as similar as possible.

The welfare maximization problem is an optimization problem studied in economics and computer science. Its goal is to partition a set of items among agents with different utility functions, such that the welfare – defined as the sum of the agents' utilities – is as high as possible. In other words, the goal is to find an item allocation satisfying the utilitarian rule.

References

  1. Suksompong, Warut (2016-07-09). "Assigning a small agreeable set of indivisible items to multiple players". Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. IJCAI'16. New York, New York, USA: AAAI Press: 489–495. arXiv: 1606.08077 . doi:10.1016/j.artint.2018.10.001. ISBN   978-1-57735-770-4.
  2. 1 2 3 4 5 6 Manurangsi, Pasin; Suksompong, Warut (2019-03-01). "Computing a small agreeable set of indivisible items". Artificial Intelligence. 268: 96–114. arXiv: 1606.08077 . doi:10.1016/j.artint.2018.10.001. ISSN   0004-3702. S2CID   124836295.
  3. Brams, Steven J.; Kilgour, D. Marc; Klamler, Christian (2011). "The undercut procedure: An algorithm for the envy-free division of indivisible items" (PDF). Social Choice and Welfare. 39 (2–3): 615. doi:10.1007/s00355-011-0599-1. S2CID   253844146.
  4. Aziz, Haris; Gaspers, Serge; MacKenzie, Simon; Walsh, Toby (2015). "Fair assignment of indivisible objects under ordinal preferences". Artificial Intelligence. 227: 71–92. arXiv: 1312.6546 . doi:10.1016/j.artint.2015.06.002. S2CID   1408197.
  5. Goldberg, Paul W.; Hollender, Alexandros; Igarashi, Ayumi; Manurangsi, Pasin; Suksompong, Warut (2020). Chen, Xujin; Gravin, Nikolai; Hoefer, Martin; Mehta, Ruta (eds.). Consensus Halving for Sets of Items. Lecture Notes in Computer Science. Vol. 12495. Cham: Springer International Publishing. pp. 384–397. arXiv: 2007.06754 . doi:10.1007/978-3-030-64946-3_27. ISBN   978-3-030-64946-3.{{cite book}}: |journal= ignored (help)
  6. Gourvès, Laurent (2019-04-01). "Agreeable sets with matroidal constraints". Journal of Combinatorial Optimization. 37 (3): 866–888. doi:10.1007/s10878-018-0327-1. ISSN   1573-2886. S2CID   254654045.