Pareto efficiency

Jump to navigation Jump to search

Pareto efficiency or Pareto optimality as it was previously referred to is a concept of efficiency in exchange whereby an individual or preference criterion cannot be made better off without making at least one individual or preference criterion worse off. For Pareto efficiency to hold, it must be that productive efficiency holds and exchange efficiency must hold; for a given bundle of goods, one cannot redistribute them so that the utility of one individual is increased without reducing the utility of another individual. [1] 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.

Edgeworth Pareto efficient point.pdf

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.[2]

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 greater utility 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 non Pareto dominated solutions for a given search space in a multi objective optimisation function, conventionally shown graphically. It also is also known as the Pareto front or Pareto set.[3]

Pareto originally used the word "optimal" for the concept, but as it describes a situation where a limited number of people will be made better off under finite resources, and it does not take equality or social well-being into account, it is in effect a definition of and better captured by 'efficiency'.[1]


An allocation is Pareto efficient if there is no alternative allocation which leads to at least one participant's well-being increasing without reducing any other participant's well-being. If there is a transfer that satisfies this condition, the new reallocation is a Pareto improvement. When no Pareto improvements are possible, the allocation is Pareto efficient. [2]

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 .[3] 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.[4] 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. [5]

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.[6]


Weak Pareto efficiency[edit]

Weak Pareto efficiency is a situation that cannot be strictly improved for every individual.[7] A feasible allocation is efficient iff it is not possible to make anyone better off without making someone else worse off.[8]

Formally, a strong Pareto improvement is defined as a situation in which all agents are strictly better-off. In other words, an allocation is Strongly Pareto efficient iff it is not Pareto dominated by feasible allocation.[9] 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-efficient 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.[10]

Constrained Pareto efficiency [edit]

Under the definition of Pareto efficiency there arises some issues in attaining a set of feasible allocations in part due to the fact that information in can be costly as well as markets may be incomplete.[11] When a market is incomplete it is well known that a competitive equilibrium will generally not lead to Pareto efficient allocations. In order to deal with this Peter Diamond and James Mirrlees theorised Constrained Pareto efficiency; A restriction of the Pareto efficient conditions to the attainable set of allocations within a market. [12]

Constrained Pareto efficiency 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.[13]: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 behaviour; "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 efficient".

Fractional Pareto efficiency[edit]

Fractional Pareto efficiency is a strengthening of Pareto-efficiency in the context of fair item allocation. An allocation of indivisible items is fractionally Pareto-efficient (fPE or 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-efficiency, which only considers domination by feasible (discrete) allocations.[14][15] For the allocation of indivisible goods it is true that the Fractional Pareto efficiency and envy freeness is achieved when one maximises the Nash Welfare over all feasible allocations of goods.[16]

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-efficient, since any other discrete allocation (without splitting items) makes someone worse-off.
  • However, it is not fractionally-Pareto-efficient, 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).

Ex-ante and Ex-post Pareto efficiency[edit]

When the decision process is random, such as in fair random assignment, there is a difference between ex-post and ex-ante Pareto-efficiency: If consumer choices are made to maximise the expected utility under a budget constraint and the allocation achieved is such that the expected utility of one trader cannot be increased without another traders expected utility decreasing then this allocation is ex-ante Pareto efficient. In contrast to this, an ex-post Pareto efficient allocation is when no redistribution of real goods will increase the realised utility of one trader without decreasing the realised utility of another trader. In general the distinction between ex-ante and ex-post efficiency is that an ex-ante efficient allocation is one which is forecast and an ex-post efficient allocation is one which has been realised with real goods.[17]

If some lottery L is ex-ante PE, then it is also ex-post PE. Proof: suppose that one of the ex-post outcomes x of L is Pareto-dominated by some other outcome y. Then, by moving some probability mass from x to y, one attains another lottery L' which ex-ante Pareto-dominates L.

The opposite is not true: ex-ante PE is stronger that ex-post PE. For example, suppose there are two objects - a car and a house. Alice values the car at 2 and the house at 3; George values the car at 2 and the house at 9. Consider the following two lotteries:

  1. With probability 1/2, give car to Alice and house to George; otherwise, give car to George and house to Alice. The expected utility is (2/2+3/2)=2.5 for Alice and (2/2+9/2)=5.5 for George. Both allocations are ex-post PE, since the one who got the car cannot be made better-off without harming the one who got the house.
  2. With probability 1, give car to Alice. Then, with probability 1/3 give the house to Alice, otherwise give it to George. The expected utility is (2+3/3)=3 for Alice and (9*2/3)=6 for George. Again, both allocations are ex-post PE.

While both lotteries are ex-post PE, the lottery 1 is not ex-ante PE, since it is Pareto-dominated by lottery 2.

Approximate Pareto-efficiency[edit]

An outcome is ε-Pareto-efficient if there is a different outcome which improves all players by at least an ε factor.Given some ε>0, an outcome is called ε-Pareto-efficient if no other outcome gives all agents at least the same utility, and one agent a utility at least (1+ε) higher. This captures the notion that improvements smaller than (1+ε) are negligible and should not be considered a breach of efficiency. in an ε-Pareto-efifcient outcome, all players can simultaneously improve their outcome by a factor of at least ε. In an ε-Pareto-efficient outcome, it is impossible to improve all players simultaneously by more than ε [18]

Pareto-efficiency and welfare-maximization[edit]

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[19] 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.[20]

Use in engineering[edit]

The notion of Pareto efficiency has been used in engineering.[21]: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.[22]: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 frontier[edit]

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.[23]: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 substitution[edit]

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.[24] 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.[25]


Algorithms for computing the Pareto frontier of a finite set of alternatives have been studied in computer science and power engineering.[26] They include:

Use in public policy[edit]

The modern microeconomic theory drew inspirations heavily from Pareto efficiency. Since Pareto showed that the equilibrium achieved through competition would optimize resource allocation, it is effectively corroborating Adam Smith's "invisible hand" notion. More specifically, it motivated the debate over "market socialism" in the 1930s.[34]

Use in biology[edit]

Pareto optimisation has also been studied in biological processes.[35]: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.[36]:166–169 Genes near the Pareto frontier were also shown to evolve more slowly (indicating that they are providing a selective advantage).[37]

Common misconceptions[edit]

It would be incorrect to treat Pareto efficiency as equivalent to societal optimization,[38]: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.[39]: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.[40]:95–132


This section will introduce criticisms from the most radical to more moderate ones.

Some commentators contest that Pareto efficiency could potentially serve as an ideological tool. With it implying that capitalism is self-regulated thereof, it is likely that the embedded structural problems such as unemployment would be treated as deviating from the equilibrium or norm, and thus neglected or discounted.[34]

Pareto efficiency does not require a totally equitable distribution of wealth, which is another aspect that draws in criticism.[41]: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.[42]: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.[43]:92–94

Lastly, it is proposed that Pareto efficiency to some extent inhibited discussion of other possible criteria of efficiency. As the scholar Lockhood argues, one possible reason is that any other efficiency criteria established in the neoclassical domain will reduce to Pareto efficiency at the end.[34]

See also[edit]


  1. ^ Pindyck, Robert (12 September 2017). Microeconomics (9 ed.). Pearson Education. p. 616. Retrieved 20 April 2021.
  2. ^ Pindyck, Robert (12 September 2017). Microeconomics (9 ed.). Pearson Education. p. 616. Retrieved 20 April 2021.
  3. ^ 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
  4. ^ Debreu, Gerard (15 July 1954). "Valuation Equilibrium and Pareto Optimum". PNAS. 40. No 7: 588–592. Retrieved 20 April 2021.
  5. ^ Stiglitz, Joseph (May 1981). "The Allocation role of the stock market Pareto Optimality and Competition". The Journal of Finance. 36 (2): 235–255. Retrieved 21 April 2021.
  6. ^ Feldman, Allan (22 September 2006). "Welfare Economics" (PDF). Brown University. Retrieved 20 April 2021.
  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. ^ Nachbar, John (9 April 2018). "Efficiency and Competitive equilbrium" (PDF). Retrieved 21 April 2021. Cite journal requires |journal= (help)
  9. ^ Nachbar, John (9 April 2018). "Efficiency and Competitive equilbrium" (PDF). Retrieved 21 April 2021. Cite journal requires |journal= (help)
  10. ^ 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
  11. ^ Stiglitz, Joseph (2 May 1981). "The Allocation role of the stock market Pareto Optimality and Competition". The Journal of Finance. 36 (2). Retrieved 23 April 2021.
  12. ^ Forsythe Suchanek, Robert Gerry (2 June 1987). "Decentralised constrained optimal allocations in stock ownership economies: An impossibility theorem". International Economic Review. 28 (2). Retrieved 23 April 2021.
  13. ^ Magill, M., & Quinzii, M., Theory of Incomplete Markets, MIT Press, 2002, p. 104.
  14. ^ 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.
  15. ^ Sandomirskiy, Fedor; Segal-Halevi, Erel (2020-09-13). "Efficient Fair Division with Minimal Sharing". arXiv:1908.01669 [cs.GT].
  16. ^ Caragiannis Kurokawa Moulin Procaccia Shah Wang, Ioannis David Hervé Ariel Nisarg Junxing (September 2019). "The unreasonable fairness of Maximum Nash Welfare". ACM transactions on Economics and Computation. 7 (3). Retrieved 23 April 2021.
  17. ^ Starr, Ross (February 1973). "Optimal Production and Allocation Under Uncertainty". The Quarterly Journal of Economics. 87 (1): 81–95. Retrieved 25 April 2021.
  18. ^ Aumann,Yonatan, Dombb, Yair (October 2010). "Pareto Efficiency and Approximate Pareto Efficiency in Routingand Load Balancing Games". ResearchGate. Retrieved 25 April 2021.
  19. ^ 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.
  20. ^ 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.
  21. ^ Goodarzi, E., Ziaei, M., & Hosseinipour, E. Z., Introduction to Optimization Analysis in Hydrosystem Engineering (Berlin/Heidelberg: Springer, 2014), pp. 111–148.
  22. ^ Jahan, A., Edwards, K. L., & Bahraminasab, M., Multi-criteria Decision Analysis, 2nd ed. (Amsterdam: Elsevier, 2013), pp. 63–65.
  23. ^ 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.
  24. ^ Just, Richard E. (2004). The welfare economics of public policy : a practical approach to project and policy evaluation. Hueth, Darrell L., Schmitz, Andrew. Cheltenham, UK: E. Elgar. pp. 18–21. ISBN 1-84542-157-4. OCLC 58538348.
  25. ^ Appendix 1 Necessary Conditions for Pareto Optimality (PDF). Retrieved 26 April 2021.
  26. ^ 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.
  27. ^ 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.
  28. ^ 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.
  29. ^ 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.
  30. ^ 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.
  31. ^ 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.
  32. ^ "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.
  33. ^ 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.
  34. ^ a b c Lockwood, B. (2008). The New Palgrave Dictionary of Economics (2nd ed.). London: Palgrave Macmillan. ISBN 978-1-349-95121-5.
  35. ^ 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.
  36. ^ Eiben, A. E., & Smith, J. E., Introduction to Evolutionary Computing (Berlin/Heidelberg: Springer, 2003), pp. 166–169.
  37. ^ Seward, E. A., & Kelly, S., "Selection-driven cost-efficiency optimization of transcripts modulates gene evolutionary rate in bacteria", Genome Biology, Vol. 19, 2018.
  38. ^ Drèze, J., Essays on Economic Decisions Under Uncertainty (Cambridge: Cambridge University Press, 1987), pp. 358–364
  39. ^ Backhaus, J. G., The Elgar Companion to Law and Economics (Cheltenham, UK / Northampton, MA: Edward Elgar, 2005), pp. 10–15.
  40. ^ 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.
  41. ^ Bhushi, K., ed., Farm to Fingers: The Culture and Politics of Food in Contemporary India (Cambridge: Cambridge University Press, 2018), p. 222.
  42. ^ Wittman, D., Economic Foundations of Law and Organization (Cambridge: Cambridge University Press, 2006), p. 18.
  43. ^ Sen, A., Rationality and Freedom (Cambridge, MA / London: Belknep Press, 2004), pp. 92–94.

Further reading[edit]