Efficient Approximately-fair Item Allocation
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Efficient Approximately-fair Item Allocation
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. Setting There is a finite set of objects, denoted by ''M''. There are ''n'' agents. Each agent ''i'' has a value-function ''Vi'', that assigns a value to each subset of objects. The goal is to partitio ...
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Pareto Efficiency
Pareto efficiency or Pareto optimality is a situation where no action or allocation is available that makes one individual better off without making another worse off. The concept is named after Vilfredo Pareto (1848–1923), Italian civil engineer and economist, who used the concept in his studies of economic efficiency and income distribution. The following three concepts are closely related: * Given an initial situation, a Pareto improvement is a new situation where some agents will gain, and no agents will lose. * A situation is called Pareto-dominated if there exists a possible Pareto improvement. * A situation is called Pareto-optimal or Pareto-efficient if no change could lead to improved satisfaction for some agent without some other agent losing or, equivalently, if there is no scope for further Pareto improvement. The Pareto front (also called Pareto frontier or Pareto set) is the set of all Pareto-efficient situations. Pareto originally used the word "optimal" for t ...
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Group Strategyproof
In game theory, an asymmetric game where players have private information is said to be strategy-proof or strategyproof (SP) if it is a weakly-dominant strategy for every player to reveal his/her private information, i.e. given no information about what the others do, you fare best or at least not worse by being truthful. SP is also called truthful or dominant-strategy-incentive-compatible (DSIC), to distinguish it from other kinds of incentive compatibility. An SP game is not always immune to collusion, but its robust variants are; with group strategyproofness no group of people can collude to misreport their preferences in a way that makes every member better off, and with strong group strategyproofness no group of people can collude to misreport their preferences in a way that makes at least one member of the group better off without making any of the remaining members worse off. Examples Typical examples of SP mechanisms are majority voting between two alternatives, second ...
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Envy-graph Procedure
The envy-graph procedure (also called the envy-cycles procedure) is a procedure for fair item allocation. It can be used by several people who want to divide among them several discrete items, such as heirlooms, sweets, or seats in a class. Ideally, we would like the allocation to be envy-free (EF). i.e., to give each agent a bundle that he/she prefers over the bundles of all other agents. However, the items are discrete and cannot be cut, so an envy-free assignment might be impossible (for example, consider a single item and two agents). The envy-graph procedure aims to achieve the "next-best" option -- ''envy-freeness up to at most a single good'' (EF1): it finds an allocation in which the envy of every person towards every other person is bounded by the maximum marginal utility it derives from a single item. In other words, for every two people ''i'' and ''j'', there exists an item such that, if that item is removed, ''i'' does not envy ''j''. The procedure was presented by Lip ...
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Basis Of A Matroid
In mathematics, a basis of a matroid is a maximal independent set of the matroid—that is, an independent set that is not contained in any other independent set. Examples As an example, consider the matroid over the ground-set R2 (the vectors in the two-dimensional Euclidean plane), with the following independent sets: It has two bases, which are the sets , . These are the only independent sets that are maximal under inclusion. The basis has a specialized name in several specialized kinds of matroids: * In a graphic matroid, where the independent sets are the forests, the bases are called the ''spanning forests'' of the graph. * In a transversal matroid, where the independent sets are endpoints of matchings in a given bipartite graph, the bases are called ''transversals''. * In a linear matroid, where the independent sets are the linearly-independent sets of vectors in a given vector-space, the bases are just called ''bases'' of the vector space. Hence, the concept of ...
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Proportional Item Allocation
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. Since the items are indivisible, a proportional assignment may not exist. The simplest case is when there is a single item and at least two agents: if the item is assigned to one agent, the other will have a value of 0, which is less than 1/2. Therefore, the literature considers various relaxations of the proportionality requirement. Proportional allocation An allocation of objects is called proportional (PROP) if every agent ''i'' values his bundle at least 1/''n'' of the total. Formally, for all ''i'' (where ''M'' is the set of all goods): * V_i(X_i) \geq V_i(M)/n. A proportional division may not exist. For example, if the number of people is larger than the number of items, then some people will get no item at all and t ...
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Adjusted Winner Procedure
Adjusted Winner (AW) is a procedure for envy-free item allocation. Given two agents and some goods, it returns a partition of the goods between the two agents with the following properties: # Envy-freeness: Each agent believes that his share of the goods is at least as good as the other share; # Equitable division, Equitability: The "relative happiness levels" of both agents from their shares are equal; # Pareto-optimality: no other allocation is better for one agent and at least as good for the other agent; # At most one good has to be shared between the agents. For two agents, Adjusted Winner is the only Pareto optimal and equitable procedure that divides at most a single good. The procedure can be used in divorce settlements and partnership dissolutions, as well as international conflicts. The procedure was designed by Steven Brams and Alan D. Taylor. It was first published in their book on fair division and later in a stand-alone book. The algorithm has been commercialized t ...
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Envy-free
Envy-freeness, also known as no-envy, is a criterion for fair division. It says that, when resources are allocated among people with equal rights, each person should receive a share that is, in their eyes, at least as good as the share received by any other agent. In other words, no person should feel envy. General definitions Suppose a certain resource is divided among several agents, such that every agent i receives a share X_i. Every agent i has a personal preference relation \succeq_i over different possible shares. The division is called envy-free (EF) if for all i and j: :::X_i \succeq_i X_j Another term for envy-freeness is no-envy (NE). If the preference of the agents are represented by a value functions V_i, then this definition is equivalent to: :::V_i(X_i) \geq V_i(X_j) Put another way: we say that agent i ''envies'' agent j if i prefers the piece of j over his own piece, i.e.: :::X_i \prec_i X_j :::V_i(X_i) 2 the problem is much harder. See envy-free cake-cutting. I ...
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First Welfare Theorem
There are two fundamental theorems of welfare economics. The first states that in economic equilibrium, a set of complete markets, with complete information, and in perfect competition, will be Pareto optimal (in the sense that no further exchange would make one person better off without making another worse off). The requirements for perfect competition are these: # There are no externalities and each actor has perfect information. # Firms and consumers take prices as given (no economic actor or group of actors has market power). The theorem is sometimes seen as an analytical confirmation of Adam Smith's "invisible hand" principle, namely that ''competitive markets ensure an efficient allocation of resources''. However, there is no guarantee that the Pareto optimal market outcome is socially desirable, as there are many possible Pareto efficient allocations of resources differing in their desirability (e.g. one person may own everything and everyone else nothing). The second ...
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Fisher Market
Fisher market is an economic model attributed to Irving Fisher. It has the following ingredients: * A set of m divisible products with pre-specified supplies (usually normalized such that the supply of each good is 1). * A set of n buyers. * For each buyer i=1,\dots,n, there is a pre-specified monetary budget B_i. Each product j has a price p_j; the prices are determined by methods described below. The price of a ''bundle'' of products is the sum of the prices of the products in the bundle. A bundle is represented by a vector x = x_1,\dots,x_m, where x_j is the quantity of product j. So the price of a bundle x is p(x)=\sum_^m p_j\cdot x_j. A bundle is ''affordable'' for a buyer if the price of that bundle is at most the buyer's budget. I.e, a bundle x is affordable for buyer i if p(x)\leq B_i. Each buyer has a preference relation over bundles, which can be represented by a utility function. The utility function of buyer i is denoted by u_i. The ''demand set'' of a buyer is the se ...
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Competitive Equilibrium
Competitive equilibrium (also called: Walrasian equilibrium) is a concept of economic equilibrium introduced by Kenneth Arrow and Gérard Debreu in 1951 appropriate for the analysis of commodity markets with flexible prices and many traders, and serving as the benchmark of efficiency in economic analysis. It relies crucially on the assumption of a competitive environment where each trader decides upon a quantity that is so small compared to the total quantity traded in the market that their individual transactions have no influence on the prices. Competitive markets are an ideal standard by which other market structures are evaluated. Definitions A competitive equilibrium (CE) consists of two elements: * A price function P. It takes as argument a vector representing a bundle of commodities, and returns a positive real number that represents its price. Usually the price function is linear - it is represented as a vector of prices, a price for each commodity type. * An allocation ...
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Fractionally Pareto Optimal
In economics and computer science, Fractional Pareto efficiency or Fractional Pareto optimality (fPO) is a variant of Pareto efficiency used in the setting of fair allocation of discrete objects. An allocation of objects is called ''discrete'' if each item is wholly allocated to a single agent; it is called ''fractional'' if some objects are split among two or more agents. A discrete allocation is called Pareto-efficient (PO) if it is not Pareto-dominated by any discrete allocation; it is called fractionally Pareto-efficient (fPO) if it is not Pareto-dominated by any discrete ''or fractional'' allocation.Barman, S., Krishnamurthy, S. K., & Vaish, R., "Finding Fair and Efficient Allocations", ''EC '18: Proceedings of the 2018 ACM Conference on Economics and Computation'', June 2018. So fPO is a stronger requirement than PO: every fPO allocation is PO, but not every PO allocation is fPO. Formal definitions There is a set of ''n'' agents and a set of ''m'' objects. An ''allocatio ...
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Pseudo-polynomial Time
In computational complexity theory, a numeric algorithm runs in pseudo-polynomial time if its running time is a polynomial in the ''numeric value'' of the input (the largest integer present in the input)—but not necessarily in the ''length'' of the input (the number of bits required to represent it), which is the case for polynomial time algorithms. Michael R. Garey and David S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, 1979. In general, the numeric value of the input is exponential in the input length, which is why a pseudo-polynomial time algorithm does not necessarily run in polynomial time with respect to the input length. An NP-complete problem with known pseudo-polynomial time algorithms is called weakly NP-complete. An NP-complete problem is called strongly NP-complete if it is proven that it cannot be solved by a pseudo-polynomial time algorithm unless . The strong/weak kinds of NP-hardness are defined anal ...
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