Egalitarian_social_choice_rule

Egalitarian rule

Egalitarian rule

Rawlsian decision rule for social choice

In social choice and operations research, the egalitarian rule (also called the max-min rule or the Rawlsian rule) is a rule saying that, among all possible alternatives, society should pick the alternative which maximizes the minimum utility of all individuals in society. It is a formal mathematical representation of the egalitarian philosophy. It also corresponds to John Rawls' principle of maximizing the welfare of the worst-off individual.[1]

Definition

Let be a set of possible `states of the world' or `alternatives'. Society wishes to choose a single state from . For example, in a single-winner election, may represent the set of candidates; in a resource allocation setting, may represent all possible allocations.

Let be a finite set, representing a collection of individuals. For each , let be a utility function, describing the amount of happiness an individual i derives from each possible state.

A social choice rule is a mechanism which uses the data to select some element(s) from which are `best' for society. The question of what 'best' means is the basic question of social choice theory. The egalitarian rule selects an element which maximizes the minimum utility, that is, it solves the following optimization problem:

Leximin rule

Often, there are many different states with the same minimum utility. For example, a state with utility profile (0,100,100) has the same minimum value as a state with utility profile (0,0,0). In this case, the egalitarian rule often uses the leximin order, that is: subject to maximizing the smallest utility, it aims to maximize the next-smallest utility; subject to that, maximize the next-smallest utility, and so on.

For example, suppose there are two individuals - Alice and George, and three possible states: state x gives a utility of 2 to Alice and 4 to George; state y gives a utility of 9 to Alice and 1 to George; and state z gives a utility of 1 to Alice and 8 to George. Then state x is leximin-optimal, since its utility profile is (2,4) which is leximin-larger than that of y (9,1) and z (1,8).

The egalitarian rule strengthened with the leximin order is often called the leximin rule, to distinguish it from the simpler max-min rule.

The leximin rule for social choice was introduced by Amartya Sen in 1970,[1] and discussed in depth in many later books.[2][3][4][5]: sub.2.5  [6]

Properties

Pareto inefficiency

The leximin rule is Pareto-efficient if the outcomes of every decision are known with perfect certainty. However, by Harsanyi's utilitarian theorem, any leximin function is Pareto-inefficient for a society that must make tradeoffs under uncertainty: There exist situations in which every person in a society would be better-off (ex ante) if they were to take a particular bet, but the leximin rule will reject it (because some person might be made worse off ex post).

Pigou-Dalton property

The leximin rule satisfies the Pigou–Dalton principle, that is: if utility is "moved" from an agent with more utility to an agent with less utility, and as a result, the utility-difference between them becomes smaller, then resulting alternative is preferred.

Moreover, the leximin rule is the only social-welfare ordering rule which simultaneously satisfies the following three properties:[5]: 266 

  1. Pareto efficiency;
  2. Pigou-Dalton principle;
  3. Independence of common utility pace - if all utilities are transformed by a common monotonically-increasing function, then the ordering of the alternatives remains the same.

Egalitarian resource allocation

The egalitarian rule is particularly useful as a rule for fair division. In this setting, the set represents all possible allocations, and the goal is to find an allocation which maximizes the minimum utility, or the leximin vector. This rule has been studied in several contexts:

See also

References

  1. [1]
    Sen, Amartya (2017-02-20). Collective Choice and Social Welfare. Harvard University Press. doi:10.4159/9780674974616. ISBN 978-0-674-97461-6.
  2. [2]
    D'Aspremont, Claude; Gevers, Louis (1977). "Equity and the Informational Basis of Collective Choice". The Review of Economic Studies. 44 (2): 199–209. doi:10.2307/2297061. ISSN 0034-6527. JSTOR 2297061.
  3. [3]
    Kolm, Serge-Christophe (2002). Justice and Equity. MIT Press. ISBN 978-0-262-61179-4.
  4. [4]
    Moulin, Herve (1991-07-26). Axioms of Cooperative Decision Making. Cambridge University Press. ISBN 978-0-521-42458-5.
  5. [5]
    Herve Moulin (2004). Fair Division and Collective Welfare. Cambridge, Massachusetts: MIT Press. ISBN 9780262134231.
  6. [6]
    Bouveret, Sylvain; Lemaître, Michel (2009-02-01). "Computing leximin-optimal solutions in constraint networks". Artificial Intelligence. 173 (2): 343–364. doi:10.1016/j.artint.2008.10.010. ISSN 0004-3702.
  7. [7]
    Nicosia, Gaia; Pacifici, Andrea; Pferschy, Ulrich (2017-03-16). "Price of Fairness for allocating a bounded resource". European Journal of Operational Research. 257 (3): 933–943. arXiv:1508.05253. doi:10.1016/j.ejor.2016.08.013. ISSN 0377-2217. S2CID 14229329.
  8. [8]
    Imai, Haruo (1983). "Individual Monotonicity and Lexicographic Maxmin Solution". Econometrica. 51 (2): 389–401. doi:10.2307/1911997. ISSN 0012-9682. JSTOR 1911997.
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