Pareto efficiency

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

Pareto efficiency or Pareto optimality is a situation where no individual or preference criterion can be made better off without making at least one individual or preference criterion 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.[1]

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".[2]

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.[3]

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).

Overview[edit]

Formally, a state is Pareto-optimal if there is no alternative state 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 state change that satisfies this condition, the new state is called a "Pareto improvement". When no Pareto improvements are possible, the state is a "Pareto optimum".

A special case of a state is an allocation of resources. 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 .[4] 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.[5] 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.[4]

Variants[edit]

Weak Pareto efficiency[edit]

Weak Pareto efficiency 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-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.[8]

Constrained Pareto efficiency [edit]

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.[9]

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 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.[10][11]

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 Pareto efficiency[edit]

When the decision process is random, such as in fair random assignment or random social choice or fractional approval voting, there is a difference between ex-post and ex-ante Pareto efficiency:

  • Ex-post Pareto efficiency means that any outcome of the random process is Pareto-efficient.
  • Ex-ante Pareto efficiency means that the lottery determined by the process is Pareto-efficient with respect to the expected utilities. That is: no other lottery gives a higher expected utility to one agent and at least as high expected utility to all agents.

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' that 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.

Another example involves dichotomous preferences.[12] There are 5 possible outcomes (a, b, c, d, e) and 6 voters. The voters' approval sets are (ac, ad, ae, bc, bd, be). All five outcomes are PE, so every lottery is ex-post PE. But the lottery selecting c, d, e with probability 1/3 each is not ex-ante PE, since it gives an expected utility of 1/3 to each voter, while the lottery selecting a, b with probability 1/2 each gives an expected utility of 1/2 to each voter.

Bayesian Pareto efficiency[edit]

Bayesian efficiency is an adaptation of Pareto efficiency to settings in which players have incomplete information regarding the types of other players.

Ordinal Pareto efficiency[edit]

Ordinal Pareto efficiency is an adaptation of Pareto efficiency to settings in which players report only rankings on individual items, and we do not know for sure how they rank entire bundles.

Pareto efficiency and equity[edit]

Although an outcome may be considered a Pareto improvement, this does not imply that the outcome is satisfying or equitable. It is possible that inequality persists even after a Pareto improvement. Despite the fact that it is frequently used in conjunction with the idea of Pareto optimality, the term "efficiency" refers to the process of increasing societal productivity.[13] It is possible for a society to have Pareto efficiency while also have high levels of inequality. Consider the following scenario: there is a pie and three persons; the most equitable way would be to divide the pie into three equal portions. However, if the pie is divided in half and shared between two people, it is considered Pareto efficient – meaning that the third person does not lose out (despite the fact that he does not receive a piece of the pie). When making judgments, it is critical to consider a variety of aspects, including social efficiency, overall welfare, and issues such as diminishing marginal value.

Pareto efficiency and market failure[edit]

In order to fully understand market failure, one must first comprehend market success, which is defined as the ability of a set of idealized competitive markets to achieve an equilibrium allocation of resources that is Pareto-optimal in terms of resource allocation. According to the definition of market failure, it is a circumstance in which the conclusion of the first fundamental theorem of welfare is erroneous; that is, when the allocations made through markets are not efficient.[14] In a free market, market failure is defined as an inefficient allocation of resources. Due to the fact that it is feasible to improve, market failure implies Pareto inefficiency. For example, excessive consumption of depreciating items (drugs/tobacco) results in external costs to non-smokers, as well as premature death for smokers who do not quit. An increase in the price of cigarettes could motivate people to quit smoking while also raising funds for the treatment of smoking-related ailments.

Approximate Pareto efficiency[edit]

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.

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:

Let xa be an allocation that maximizes the welfare over all allocations:

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[15] 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.[16]

Use in engineering[edit]

The notion of Pareto efficiency has been used in engineering.[17] Given a set of choices and a way of valuing them, the Pareto front (or Pareto set or Pareto frontier) 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 trade-offs within this set, rather than considering the full range of every parameter.[18]

Use in public policy[edit]

Modern microeconomic theory has drawn heavily upon the concept of Pareto efficiency for inspiration. Pareto and his successors have tended to describe this technical definition of optimal resource allocation in the context of it being an equilibrium that can theoretically be achieved within an abstract model of market competition. It has therefore very often been treated as a corroboration of Adam Smith's "invisible hand" notion. More specifically, it motivated the debate over "market socialism" in the 1930s.[2]

However, because the Pareto-efficient outcome is difficult to assess in the real world when issues including asymmetric information, signalling, adverse selection, and moral hazard are introduced, most people do not take the theorems of welfare economics as accurate descriptions of the real world. Therefore, the significance of the two welfare theorems of economics is in their ability to generate a framework that has dominated neoclassical thinking about public policy. That framework is that the welfare economics theorems allow the political economy to be studied in the following two situations: "market failure" and "the problem of redistribution".[19]

Analysis of "market failure" can be understood by the literature surrounding externalities. When comparing the "real" economy to the complete contingent markets economy (which is considered efficient), the inefficiencies become clear. These inefficiencies, or externalities, are then able to be addressed by mechanisms, including property rights and corrective taxes.[19]

Analysis of "the problem with redistribution" deals with the observed political question of how income or commodity taxes should be utilized. The theorem tells us that no taxation is Pareto-efficient and that taxation with redistribution is Pareto-inefficient. Because of this, most of the literature is focused on finding solutions where given there is a tax structure, how can the tax structure prescribe a situation where no person could be made better off by a change in available taxes.[19]

Use in biology[edit]

Pareto optimisation has also been studied in biological processes.[20] 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.[21] Genes near the Pareto frontier were also shown to evolve more slowly (indicating that they are providing a selective advantage).[22]

Common misconceptions[edit]

It would be incorrect to treat Pareto efficiency as equivalent to societal optimization,[23] as the latter is a normative concept, which is a matter of interpretation that typically would account for the consequence of degrees of inequality of distribution.[24] 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.[25]

Criticism[edit]

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.[2]

Pareto efficiency does not require a totally equitable distribution of wealth, which is another aspect that draws in criticism.[26] 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.[27]

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.[28]

Lastly, it is proposed that Pareto efficiency to some extent inhibited discussion of other possible criteria of efficiency. As the scholar Lockhood[clarify] argues, one possible reason is that any other efficiency criteria established in the neoclassical domain will reduce to Pareto efficiency at the end.[2]

See also[edit]

References[edit]

  1. ^ proximedia. "Pareto Front". www.cenaero.be. Archived from the original on February 26, 2020. Retrieved October 8, 2018.
  2. ^ a b c d Lockwood, B. (2008). The New Palgrave Dictionary of Economics (2nd ed.). London: Palgrave Macmillan. ISBN 978-1-349-95121-5.
  3. ^ Black, J. D., Hashimzade, N., Myles, G. (eds.), A Dictionary of Economics, 5th ed. (Oxford: Oxford University Press, 2017), p. 459.
  4. ^ 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.
  5. ^ Gerard, Debreu (1959). "Valuation Equilibrium and Pareto Optimum". Proceedings of the National Academy of Sciences of the United States of America. 40 (7): 588–592. doi:10.1073/pnas.40.7.588. JSTOR 89325. PMC 528000. PMID 16589528.
  6. ^ Greenwald, B.; Stiglitz, J. E. (1986). "Externalities in economies with imperfect information and incomplete markets". Quarterly Journal of Economics. 101 (2): 229–264. 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. ^ Sandomirskiy, Fedor; Segal-Halevi, Erel (September 13, 2020). "Efficient Fair Division with Minimal Sharing". arXiv:1908.01669 [cs.GT].
  12. ^ Bogomolnaia, Anna; Moulin, Hervé; Stong, Richard (June 1, 2005). "Collective choice under dichotomous preferences". Journal of Economic Theory. 122 (2): 165–184. doi:10.1016/j.jet.2004.05.005. ISSN 0022-0531.
  13. ^ Nicola. (2013). Efficiency and Equity in Welfare Economics (1st ed. 2013). Springer Berlin Heidelberg : Imprint: Springer.
  14. ^ Ledyard, J. O. (1989). Market Failure. In: Eatwell, J., Milgate, M., Newman, P. (eds.) Allocation, Information and Markets. The New Palgrave. Palgrave Macmillan, London. doi:10.1007/978-1-349-20215-7_19.
  15. ^ 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.
  16. ^ 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.
  17. ^ Goodarzi, E., Ziaei, M., & Hosseinipour, E. Z., Introduction to Optimization Analysis in Hydrosystem Engineering (Berlin/Heidelberg: Springer, 2014), pp. 111–148.
  18. ^ Jahan, A., Edwards, K. L., & Bahraminasab, M., Multi-criteria Decision Analysis, 2nd ed. (Amsterdam: Elsevier, 2013), pp. 63–65.
  19. ^ a b c Lockwood B. (2008) Pareto Efficiency. In: Palgrave Macmillan (eds.) The New Palgrave Dictionary of Economics. Palgrave Macmillan, London.
  20. ^ 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.
  21. ^ Eiben, A. E., & Smith, J. E., Introduction to Evolutionary Computing (Berlin/Heidelberg: Springer, 2003), pp. 166–169.
  22. ^ Seward, E. A., & Kelly, S., "Selection-driven cost-efficiency optimization of transcripts modulates gene evolutionary rate in bacteria", Genome Biology, Vol. 19, 2018.
  23. ^ Drèze, J., Essays on Economic Decisions Under Uncertainty (Cambridge: Cambridge University Press, 1987), pp. 358–364.
  24. ^ Backhaus, J. G., The Elgar Companion to Law and Economics (Cheltenham, UK / Northampton, MA: Edward Elgar, 2005), pp. 10–15.
  25. ^ 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.
  26. ^ Bhushi, K. (ed.), Farm to Fingers: The Culture and Politics of Food in Contemporary India (Cambridge: Cambridge University Press, 2018), p. 222.
  27. ^ Wittman, D., Economic Foundations of Law and Organization (Cambridge: Cambridge University Press, 2006), p. 18.
  28. ^ Sen, A., Rationality and Freedom (Cambridge, MA / London: Belknep Press, 2004), pp. 92–94.

Further reading[edit]