Pareto efficiency

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Pareto efficiency or Pareto optimality is a situation where no individual or preference criterion can be better off without making at least one individual or preference criterion worse off or without any loss thereof. The concept is named after Vilfredo Pareto (1848–1923), Italian 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 if there's no scope for further Pareto improvement.

The Pareto frontier is the set of all Pareto efficient allocations, conventionally shown graphically. It also is variously known as the Pareto front or Pareto set.[1]

"Pareto efficiency" is considered as a minimal notion of efficiency that does not necessarily result in a socially desirable distribution of resources: it makes no statement about equality, or the overall well-being of a society.[2][3]:46–49

In addition to the context of efficiency in allocation, the concept of Pareto efficiency also arises in the context of efficiency in production vs. x-inefficiency: a set of outputs of goods is Pareto efficient if there is no feasible re-allocation of productive inputs such that output of one product increases while the outputs of all other goods either increase or remain the same.[4]:459

Besides economics, the notion of Pareto efficiency has been applied to the selection of alternatives in engineering and biology. Each option is first assessed, under multiple criteria, and then a subset of options is ostensibly identified with the property that no other option can categorically outperform the specified option. It is a statement of impossibility of improving one variable without harming other variables in the subject of multi-objective optimization (also termed Pareto optimization).


Formally, an allocation is Pareto optimal if there is no alternative allocation where improvements can be made to at least one participant's well-being without reducing any other participant's well-being. If there is a transfer that satisfies this condition, the new reallocation is called a "Pareto improvement". When no Pareto improvements are possible, the allocation is a "Pareto optimum".

The formal presentation of the concept in an economy is the following: Consider an economy with   agents and   goods. Then an allocation  , where   for all i, is Pareto optimal if there is no other feasible allocation   where, for utility function   for each agent  ,   for all   with   for some  .[5] Here, in this simple economy, "feasibility" refers to an allocation where the total amount of each good that is allocated sums to no more than the total amount of the good in the economy. In a more complex economy with production, an allocation would consist both of consumption vectors and production vectors, and feasibility would require that the total amount of each consumed good is no greater than the initial endowment plus the amount produced.

Under the assumptions of the first welfare theorem, a competitive market leads to a Pareto-efficient outcome. This result was first demonstrated mathematically by economists Kenneth Arrow and Gérard Debreu.[citation needed] However, the result only holds under the assumptions of the theorem: markets exist for all possible goods, there are no externalities; markets are perfectly competitive; and market participants have perfect information.

In the absence of perfect information or complete markets, outcomes will generally be Pareto inefficient, per the Greenwald-Stiglitz theorem.[6]

The second welfare theorem is essentially the reverse of the first welfare-theorem. It states that under similar, ideal assumptions, any Pareto optimum can be obtained by some competitive equilibrium, or free market system, although it may also require a lump-sum transfer of wealth.[5]

Weak Pareto efficiencyEdit

Weak Pareto optimality is a situation that cannot be strictly improved for every individual.[7]

Formally, a strong Pareto improvement is defined as a situation in which all agents are strictly better-off (in contrast to just "Pareto improvement", which requires that one agent is strictly better-off and the other agents are at least as good). A situation is weak Pareto-optimal if it has no strong Pareto-improvements.

Any strong Pareto-improvement is also a weak Pareto-improvement. The opposite is not true; for example, consider a resource allocation problem with two resources, which Alice values at 10, 0 and George values at 5, 5. Consider the allocation giving all resources to Alice, where the utility profile is (10,0):

  • It is a weak-PO, since no other allocation is strictly better to both agents (there are no strong Pareto improvements).
  • But it is not a strong-PO, since the allocation in which George gets the second resource is strictly better for George and weakly better for Alice (it is a weak Pareto improvement) - its utility profile is (10,5).

A market doesn't require local nonsatiation to get to a weak Pareto-optimum.[8]

Constrained Pareto efficiency Edit

Constrained Pareto optimality is a weakening of Pareto-optimality, accounting for the fact that a potential planner (e.g., the government) may not be able to improve upon a decentralized market outcome, even if that outcome is inefficient. This will occur if it is limited by the same informational or institutional constraints as are individual agents.[9]:104

An example is of a setting where individuals have private information (for example, a labor market where the worker's own productivity is known to the worker but not to a potential employer, or a used-car market where the quality of a car is known to the seller but not to the buyer) which results in moral hazard or an adverse selection and a sub-optimal outcome. In such a case, a planner who wishes to improve the situation is unlikely to have access to any information that the participants in the markets do not have. Hence, the planner cannot implement allocation rules which are based on the idiosyncratic characteristics of individuals; for example, "if a person is of type A, they pay price p1, but if of type B, they pay price p2" (see Lindahl prices). Essentially, only anonymous rules are allowed (of the sort "Everyone pays price p") or rules based on observable behavior; "if any person chooses x at price px, then they get a subsidy of ten dollars, and nothing otherwise". If there exists no allowed rule that can successfully improve upon the market outcome, then that outcome is said to be "constrained Pareto-optimal".

Fractional Pareto efficiencyEdit

Fractional Pareto optimality is a strengthening of Pareto-optimality in the context of fair item allocation. An allocation of indivisible items is fractionally Pareto-optimal (fPO) if it is not Pareto-dominated even by an allocation in which some items are split between agents. This is in contrast to standard Pareto-optimality, which only considers domination by feasible (discrete) allocations.[10]

As an example, consider an item allocation problem with two items, which Alice values at 3, 2 and George values at 4, 1. Consider the allocation giving the first item to Alice and the second to George, where the utility profile is (3,1):

  • It is Pareto-optimal, since any other discrete allocation (without splitting items) makes someone worse-off.
  • However, it is not fractionally-Pareto-optimal, since it is Pareto-dominated by the allocation giving to Alice 1/2 of the first item and the whole second item, and the other 1/2 of the first item to George - its utility profile is (3.5, 2).

Pareto-efficiency and welfare-maximizationEdit

Suppose each agent i is assigned a positive weight ai. For every allocation x, define the welfare of x as the weighted sum of utilities of all agents in x, i.e.:


Let xa be an allocation that maximizes the welfare over all allocations, i.e.:


It is easy to show that the allocation xa is Pareto-efficient: since all weights are positive, any Pareto-improvement would increase the sum, contradicting the definition of xa.

Japanese neo-Walrasian economist Takashi Negishi proved[11] that, under certain assumptions, the opposite is also true: for every Pareto-efficient allocation x, there exists a positive vector a such that x maximizes Wa. A shorter proof is provided by Hal Varian.[12]

Use in engineeringEdit

The notion of Pareto efficiency has been used in engineering.[13]:111–148 Given a set of choices and a way of valuing them, the Pareto frontier or Pareto set or Pareto front is the set of choices that are Pareto efficient. By restricting attention to the set of choices that are Pareto-efficient, a designer can make tradeoffs within this set, rather than considering the full range of every parameter.[14]:63–65

Example of a Pareto frontier. The boxed points represent feasible choices, and smaller values are preferred to larger ones. Point C is not on the Pareto frontier because it is dominated by both point A and point B. Points A and B are not strictly dominated by any other, and hence lie on the frontier.
A production-possibility frontier. The red line is an example of a Pareto-efficient frontier, where the frontier and the area left and below it are a continuous set of choices. The red points on the frontier are examples of Pareto-optimal choices of production. Points off the frontier, such as N and K, are not Pareto-efficient, since there exist points on the frontier which Pareto-dominate them.

Pareto frontierEdit

For a given system, the Pareto frontier or Pareto set is the set of parameterizations (allocations) that are all Pareto efficient. Finding Pareto frontiers is particularly useful in engineering. By yielding all of the potentially optimal solutions, a designer can make focused tradeoffs within this constrained set of parameters, rather than needing to consider the full ranges of parameters.[15]:399–412

The Pareto frontier, P(Y), may be more formally described as follows. Consider a system with function  , where X is a compact set of feasible decisions in the metric space  , and Y is the feasible set of criterion vectors in  , such that  .

We assume that the preferred directions of criteria values are known. A point   is preferred to (strictly dominates) another point  , written as  . The Pareto frontier is thus written as:


Marginal rate of substitutionEdit

A significant aspect of the Pareto frontier in economics is that, at a Pareto-efficient allocation, the marginal rate of substitution is the same for all consumers. A formal statement can be derived by considering a system with m consumers and n goods, and a utility function of each consumer as   where   is the vector of goods, both for all i. The feasibility constraint is   for  . To find the Pareto optimal allocation, we maximize the Lagrangian:


where   and   are the vectors of multipliers. Taking the partial derivative of the Lagrangian with respect to each good   for   and   and gives the following system of first-order conditions:


where   denotes the partial derivative of   with respect to  . Now, fix any   and  . The above first-order condition imply that


Thus, in a Pareto-optimal allocation, the marginal rate of substitution must be the same for all consumers.[citation needed]


Algorithms for computing the Pareto frontier of a finite set of alternatives have been studied in computer science and power engineering.[16] They include:

Use in biologyEdit

Pareto optimisation has also been studied in biological processes.[24]:87–102 In bacteria, genes were shown to be either inexpensive to make (resource efficient) or easier to read (translation efficient). Natural selection acts to push highly expressed genes towards the Pareto frontier for resource use and translational efficiency.[25]:166–169 Genes near the Pareto frontier were also shown to evolve more slowly (indicating that they are providing a selective advantage).[26]


It would be incorrect to treat Pareto efficiency as equivalent to societal optimization,[27]:358–364 as the latter is a normative concept that is a matter of interpretation that typically would account for the consequence of degrees of inequality of distribution.[28]:10–15 An example would be the interpretation of one school district with low property tax revenue versus another with much higher revenue as a sign that more equal distribution occurs with the help of government redistribution.[29]:95–132

Pareto efficiency does not require a totally equitable distribution of wealth.[30]:222 An economy in which a wealthy few hold the vast majority of resources can be Pareto efficient. A simple example is the distribution of a pie among three people. The most equitable distribution would assign one third to each person. However the assignment of, say, a half section to each of two individuals and none to the third is also Pareto optimal despite not being equitable, because none of the recipients could be made better off without decreasing someone else's share; and there are many other such distribution examples. An example of a Pareto inefficient distribution of the pie would be allocation of a quarter of the pie to each of the three, with the remainder discarded.[31]:18

The liberal paradox elaborated by Amartya Sen shows that when people have preferences about what other people do, the goal of Pareto efficiency can come into conflict with the goal of individual liberty.[32]:92–94

See alsoEdit


  1. ^ proximedia. "Pareto Front". Retrieved October 8, 2018.
  2. ^ Sen, A. (October 1993). "Markets and freedom: Achievements and limitations of the market mechanism in promoting individual freedoms" (PDF). Oxford Economic Papers. 45 (4): 519–541. doi:10.1093/oxfordjournals.oep.a042106. JSTOR 2663703.
  3. ^ Barr, N. (2012). "3.2.2 The relevance of efficiency to different theories of society". Economics of the Welfare State (5th ed.). Oxford University Press. pp. 46–49. ISBN 978-0-19-929781-8.
  4. ^ Black, J. D., Hashimzade, N., & Myles, G., eds., A Dictionary of Economics, 5th ed. (Oxford: Oxford University Press, 2017), p. 459.
  5. ^ a b Mas-Colell, A.; Whinston, Michael D.; Green, Jerry R. (1995), "Chapter 16: Equilibrium and its Basic Welfare Properties", Microeconomic Theory, Oxford University Press, ISBN 978-0-19-510268-0
  6. ^ Greenwald, B.; Stiglitz, J. E. (1986). "Externalities in economies with imperfect information and incomplete markets". Quarterly Journal of Economics. 101 (2): 229–64. doi:10.2307/1891114. JSTOR 1891114.
  7. ^ Mock, William B T. (2011). "Pareto Optimality". Encyclopedia of Global Justice. pp. 808–809. doi:10.1007/978-1-4020-9160-5_341. ISBN 978-1-4020-9159-9.
  8. ^ Markey‐Towler, Brendan and John Foster. "Why economic theory has little to say about the causes and effects of inequality", School of Economics, University of Queensland, Australia, 21 February 2013, RePEc:qld:uq2004:476
  9. ^ Magill, M., & Quinzii, M., Theory of Incomplete Markets, MIT Press, 2002, p. 104.
  10. ^ 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.
  11. ^ Negishi, Takashi (1960). "Welfare Economics and Existence of an Equilibrium for a Competitive Economy". Metroeconomica. 12 (2–3): 92–97. doi:10.1111/j.1467-999X.1960.tb00275.x.
  12. ^ Varian, Hal R. (1976). "Two problems in the theory of fairness". Journal of Public Economics. 5 (3–4): 249–260. doi:10.1016/0047-2727(76)90018-9. hdl:1721.1/64180.
  13. ^ Goodarzi, E., Ziaei, M., & Hosseinipour, E. Z., Introduction to Optimization Analysis in Hydrosystem Engineering (Berlin/Heidelberg: Springer, 2014), pp. 111–148.
  14. ^ Jahan, A., Edwards, K. L., & Bahraminasab, M., Multi-criteria Decision Analysis, 2nd ed. (Amsterdam: Elsevier, 2013), pp. 63–65.
  15. ^ Costa, N. R., & Lourenço, J. A., "Exploring Pareto Frontiers in the Response Surface Methodology", in G.-C. Yang, S.-I. Ao, & L. Gelman, eds., Transactions on Engineering Technologies: World Congress on Engineering 2014 (Berlin/Heidelberg: Springer, 2015), pp. 399–412.
  16. ^ Tomoiagă, Bogdan; Chindriş, Mircea; Sumper, Andreas; Sudria-Andreu, Antoni; Villafafila-Robles, Roberto (2013). "Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II". Energies. 6 (3): 1439–55. doi:10.3390/en6031439.
  17. ^ Nielsen, Frank (1996). "Output-sensitive peeling of convex and maximal layers". Information Processing Letters. 59 (5): 255–9. CiteSeerX doi:10.1016/0020-0190(96)00116-0.
  18. ^ Kung, H. T.; Luccio, F.; Preparata, F.P. (1975). "On finding the maxima of a set of vectors". Journal of the ACM. 22 (4): 469–76. doi:10.1145/321906.321910. S2CID 2698043.
  19. ^ Godfrey, P.; Shipley, R.; Gryz, J. (2006). "Algorithms and Analyses for Maximal Vector Computation". VLDB Journal. 16: 5–28. CiteSeerX doi:10.1007/s00778-006-0029-7. S2CID 7374749.
  20. ^ Kim, I. Y.; de Weck, O. L. (2005). "Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation". Structural and Multidisciplinary Optimization. 31 (2): 105–116. doi:10.1007/s00158-005-0557-6. ISSN 1615-147X. S2CID 18237050.
  21. ^ Marler, R. Timothy; Arora, Jasbir S. (2009). "The weighted sum method for multi-objective optimization: new insights". Structural and Multidisciplinary Optimization. 41 (6): 853–862. doi:10.1007/s00158-009-0460-7. ISSN 1615-147X. S2CID 122325484.
  22. ^ "On a Bicriterion Formulation of the Problems of Integrated System Identification and System Optimization". IEEE Transactions on Systems, Man, and Cybernetics. SMC-1 (3): 296–297. 1971. doi:10.1109/TSMC.1971.4308298. ISSN 0018-9472.
  23. ^ Mavrotas, George (2009). "Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems". Applied Mathematics and Computation. 213 (2): 455–465. doi:10.1016/j.amc.2009.03.037. ISSN 0096-3003.
  24. ^ Moore, J. H., Hill, D. P., Sulovari, A., & Kidd, L. C., "Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing", in R. Riolo, E. Vladislavleva, M. D. Ritchie, & J. H. Moore, eds., Genetic Programming Theory and Practice X (Berlin/Heidelberg: Springer, 2013), pp. 87–102.
  25. ^ Eiben, A. E., & Smith, J. E., Introduction to Evolutionary Computing (Berlin/Heidelberg: Springer, 2003), pp. 166–169.
  26. ^ Seward, E. A., & Kelly, S., "Selection-driven cost-efficiency optimization of transcripts modulates gene evolutionary rate in bacteria", Genome Biology, Vol. 19, 2018.
  27. ^ Drèze, J., Essays on Economic Decisions Under Uncertainty (Cambridge: Cambridge University Press, 1987), pp. 358–364
  28. ^ Backhaus, J. G., The Elgar Companion to Law and Economics (Cheltenham, UK / Northampton, MA: Edward Elgar, 2005), pp. 10–15.
  29. ^ Paulsen, M. B., "The Economics of the Public Sector: The Nature and Role of Public Policy in the Finance of Higher Education", in M. B. Paulsen, J. C. Smart, eds. The Finance of Higher Education: Theory, Research, Policy, and Practice (New York: Agathon Press, 2001), pp. 95–132.
  30. ^ Bhushi, K., ed., Farm to Fingers: The Culture and Politics of Food in Contemporary India (Cambridge: Cambridge University Press, 2018), p. 222.
  31. ^ Wittman, D., Economic Foundations of Law and Organization (Cambridge: Cambridge University Press, 2006), p. 18.
  32. ^ Sen, A., Rationality and Freedom (Cambridge, MA / London: Belknep Press, 2004), pp. 92–94.

Further readingEdit