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The Decreasing Demand procedure is a procedure for fair item allocation. It yields a Pareto-efficient division that maximizes the rank of the agent with the lowest rank. This corresponds to the Rawlsian justice criterion of taking care of the worst-off agent.
The procedure was developed by Dorothea Herreiner and Clemens Puppe. [1]
Each agent is supposed to have a linear ranking on all bundles of items.
The agents are queried in a round-robin fashion: each agent, in turn, reports his next bundle in the ranking, going from the best to the worst.
After each report, the procedure checks whether it is possible to construct a complete partition of the items based on the reports made so far. If it is possible, then the procedure stops and returns one such partition. If there is more than one partition, then a Pareto-efficient one is returned.
The procedure produces "balanced" allocations, that is, allocations which maximize the rank in the preference ordering of the bundle obtained by the worst-off agent. [2] : 308
The procedure requires the agents to rank bundles of items. This is feasible when the number of items is small, but may be difficult when the number of items is large, since the number of bundles grows exponentially with the number of items.
The procedure does not guarantee envy-freeness; see envy-free item assignment for procedures that do guarantee it. However, for two agents, if an envy-free allocation exists, it will be found. [3]
The allocation returned by the Decreasing Demand procedure - the maximin-rank allocation - satisfies certain natural axioms when there are two agents: [3]
Fair item allocation is a kind of a fair division problem in which the items to divide are discrete rather than continuous. The items have to be divided among several partners who 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-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.
Efficiency and fairness are two major goals of welfare economics. Given a set of resources and a set of agents, the goal is to divide the resources among the agents in a way that is both Pareto efficient (PE) and envy-free (EF). The goal was first defined by David Schmeidler and Menahem Yaari. Later, the existence of such allocations has been proved under various conditions.
Rental harmony is a kind of a fair division problem in which indivisible items and a fixed monetary cost have to be divided simultaneously. The housemates problem and room-assignment-rent-division are alternative names to the same problem.
A picking sequence is a protocol for fair item assignment. Suppose m items have to be divided among n agents. One way to allocate the items is to let one agent select a single item, then let another agent select a single item, and so on. A picking-sequence is a sequence of m agent-names, where each name determines what agent is the next to pick an item.
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.
The undercut procedure is a procedure for fair item assignment between two people. It provably finds a complete envy-free item assignment whenever such assignment exists. It was presented by Brams and Kilgour and Klamler and simplified and extended by Aziz.
The AL procedure is a procedure for fair item assignment between two people. It finds an envy-free item assignment of a subset of the items. Moreover, the resulting allocation is Pareto efficient in the following sense: there is no other envy-free allocation which is better for one person and not worse for the other person.
Approximate Competitive Equilibrium from Equal Incomes (A-CEEI) is a procedure for fair item assignment. It was developed by Eric Budish.
Fair random assignment is a kind of a fair division problem.
Random priority (RP), also called Random serial dictatorship (RSD), is a procedure for fair random assignment - dividing indivisible items fairly among people.
A simultaneous eating algorithm(SE) is an algorithm for allocating divisible objects among agents with ordinal preferences. "Ordinal preferences" means that each agent can rank the items from best to worst, but cannot (or does not want to) specify a numeric value for each item. The SE allocation satisfies SD-efficiency - a weak ordinal variant of Pareto-efficiency (it means that the allocation is Pareto-efficient for at least one vector of additive utility functions consistent with the agents' item rankings).
Various experiments have been made to evaluate various procedures for fair division, the problem of dividing resources among several people. These include case studies, computerized simulations, and lab experiments.
Round robin is a procedure for fair item allocation. It can be used to allocate several indivisible items among several people, such that the allocation is "almost" envy-free: each agent believes that the bundle he received is at least as good as the bundle of any other agent, when at most one item is removed from the other bundle. In sports, the round-robin procedure is called a draft.
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 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. So fPO is a stronger requirement than PO: every fPO allocation is PO, but not every PO allocation is fPO.
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:
Fair allocation of items and money is a class of fair item allocation problems in which, during the allocation process, it is possible to give or take money from some of the participants. Without money, it may be impossible to allocate indivisible items fairly. For example, if there is one item and two people, and the item must be given entirely to one of them, the allocation will be unfair towards the other one. Monetary payments make it possible to attain fairness, as explained below.
Ordinal Pareto efficiency refers to several adaptations of the concept of Pareto-efficiency to settings in which the agents only express ordinal utilities over items, but not over bundles. That is, agents rank the items from best to worst, but they do not rank the subsets of items. In particular, they do not specify a numeric value for each item. This may cause an ambiguity regarding whether certain allocations are Pareto-efficient or not. As an example, consider an economy with three items and two agents, with the following rankings: