N?o existem almo?os grátis / No such thing as a free lunch ...
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"No such thing as a free lunch" or, "There ain't no such thing as a free lunch", "There is no such thing as a free lunch" or other variants) is a popular adage communicating the idea that it is impossible to get something for nothing. The acronyms TANSTAAFL, TINSTAAFL, and TNSTAAFL are also used. The phrase was in use by the 1930s, but its first appearance is unknown.
The "free lunch" in the saying refers to the formerly common practice in American bars of offering a "free lunch" in order to entice drinking customers.
The phrase and the acronym are central to Robert A. Heinlein's 1966 science-fiction novel The Moon Is a Harsh Mistress, which helped popularize it. The free-market economist Milton Friedman also increased its exposure and use by paraphrasing it as the title of a 1975 book, and it is used in economics literature to describe opportunity cost.
Campbell McConnell writes that the idea is "at the core of economics".
Free lunch"
The "free lunch" refers to the once-common tradition of saloons in the United States providing a "free" lunch to patrons who had purchased at least one drink. Many foods on offer were high in salt (e.g., ham, cheese, and salted crackers), so those who ate them ended up buying a lot of beer. Rudyard Kipling, writing in 1891, noted how he
...came upon a bar-room full of bad Salon pictures, in which men with hats on the backs of their heads were wolfing food from a counter. It was the institution of the "free lunch" I had struck. You paid for a drink and got as much as you wanted to eat. For something less than a rupee a day a man can feed himself sumptuously in San Francisco, even though he be a bankrupt. Remember this if ever you are stranded in these parts...
TANSTAAFL, on the other hand, indicates an acknowledgement that in reality a person or a society cannot get "something for nothing". Even if something appears to be free, there is always a cost to the person or to society as a whole, although that may be a hidden cost or an externality. For example, as Heinlein has one of his characters point out, a bar offering a free lunch will likely charge more for its drinks.
Early uses
The earliest known occurrence of the full phrase (except for the "a"), in the form "There ain't no such thing as free lunch", appears as the punchline of a joke related in an article in the El Paso Herald-Post of June 27, 1938 (and other Scripps-Howard newspapers about the same time), entitled "Economics in Eight Words".
In 1945, "There ain't no such thing as a free lunch" appeared in the Columbia Law Review, and "there is no free lunch" appeared in a 1942 article in the Oelwein Daily Register (in a quote attributed to economist Harley L. Lutz) and in a 1947 column by economist Merryle S. Rukeyser.
In 1949, the phrase appeared in an article by Walter Morrow in the San Francisco News (published on 1 June) and in Pierre Dos Utt's monograph TANSTAAFL: A Plan for a New Economic World Order,which describes an oligarchic political system based on his conclusions from "no free lunch" principles.
The 1938 and 1949 sources use the phrase in relating a fable about a king (Nebuchadnezzar in Dos Utt's retelling) seeking advice from his economic advisors. Morrow's retelling, which claims to derive from an earlier editorial reported to be non-existent, but closely follows the story as related in the earlier article in the El Paso Herald-Post, differs from Dos Utt's in that the ruler asks for ever-simplified advice following their original "eighty-seven volumes of six hundred pages" as opposed to a simple failure to agree on "any major remedy". The last surviving economist advises that "There ain't no such thing as free lunch."
In 1950, a New York Times columnist ascribed the phrase to economist (and army general) Leonard P. Ayres of the Cleveland Trust Company: "It seems that shortly before the General's death [in 1946]... a group of reporters approached the general with the request that perhaps he might give them one of several immutable economic truisms that he had gathered from his long years of economic study... 'It is an immutable economic fact,' said the general, 'that there is no such thing as a free lunch.'"
The September 8, 1961, issue of LIFE magazine has an editorial on page 4, "'TANSTAFL', It's the Truth", that closes with an anecdotal farmer explaining this slight variant of TANSTAAFL.
Popularization
In 1966, author Robert A. Heinlein published his novel The Moon Is a Harsh Mistress, in which TANSTAAFL was a central, libertarian theme, mentioned by name and explained. This increased its use in the mainstream.
Edwin G. Dolan used the phrase as the title of his 1971 book TANSTAAFL (There Ain't No Such Thing As A Free Lunch) – A Libertarian Perspective on Environmental Policy.[15]
Meaning
Science
In the sciences, no free lunch means that the universe as a whole is ultimately a closed system. There is no source of matter, energy, or light that draws resources from something else which will not eventually be exhausted. Therefore, the no free lunch argument may also be applied to natural physical processes in a closed system (either the universe as a whole, or any system that does not receive energy or matter from outside). (See Second law of thermodynamics.) The bio-ecologist Barry Commoner used this concept as the last of his famous "Four Laws of Ecology".
According to American theoretical physicist and cosmologist Alan Guth "the universe is the ultimate free lunch", given that in the early stage of its expansion the total amount of energy available to make particles was very large.[16]
Economics
In economics, no free lunch demonstrates opportunity cost. Greg Mankiw described the concept as follows: "To get one thing that we like, we usually have to give up another thing that we like. Making decisions requires trading off one goal against another."
The idea that there is no free lunch at the societal level applies only when all resources are being used completely and appropriately – i.e., when economic efficiency prevails. If not, a 'free lunch' can be had through a more efficient utilization of resources. Or, as Fred Brooks put it, "You can only get something for nothing if you have previously gotten nothing for something." If one individual or group gets something at no cost, somebody else ends up paying for it. If there appears to be no direct cost to any single individual, there is a social cost. Similarly, someone can benefit for "free" from an externality or from a public good, but someone has to pay the cost of producing these benefits. (See Free rider problem and Tragedy of the commons.)
Finance
In mathematical finance, the term is also used as an informal synonym for the principle of no-arbitrage. This principle states that a combination of securities that has the same cash-flows as another security must have the same net price in equilibrium.
Statistics[edit]
In statistics, the term has been used to describe the tradeoffs of statistical learners (e.g., in machine learning) which are unavoidable according to the "No free lunch" theorem. That is, any model that claims to offer superior flexibility in analyzing data patterns usually does so at the cost of introducing extra assumptions, or by sacrificing generalizability in important situations.
Technology
No free lunch is sometimes used as a response to claims of the virtues of free software. Supporters of free software often counter that the use of the term "free" in this context is primarily a reference to a lack of constraint ("libre") rather than a lack of cost ("gratis"). Richard Stallman has described it as "'free' as in 'free speech', not as in 'free beer'".
The prefix "TANSTAA-" (or "TINSTAA-") is used in numerous other contexts as well to denote some immutable property of the system being discussed. For example, "TANSTAANFS" is used by electrical engineering professors to stand for "There Ain't No Such Thing As A Noise-Free System".[citation needed]
Sports
Baseball Prospectus coined the abbreviation "TINSTAAPP", for "There Is No Such Thing As A Pitching Prospect", as many young pitchers hurt their arms before they can be effective at a major league level.
Social policy
Hungarian prime minister Ferenc Gyurcsány used this adage to justify his social reforms in the mid-2000s. As a post-socialist country, Hungary struggled with the illusion of the state as a caring and giving, independent entity, rather than being the embodiment of the community. The saying "there is no free lunch" represented that even if the state provides welfare or something else for the people in need, it is in fact bought or provided by other people of the same community through taxes. Therefore, the state cannot provide everything for everyone, and increased provisions given by the state can only be financed by economic growth, increased taxes or public debt.
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Exceptions
Some exceptions from the "no free lunch" tenet have been put forward, such as the Sun and carbon dioxide.It was argued in particular that metabolism evolved to take advantage of the free lunch provided by the Sun, which also triggers production of vital oxygen in plants.
However, these too fall short in that the viewpoint is an open system, Earth, with "free" inputs from the Sun. When viewed from the larger system context, the Sun/Earth or Solar System, there is no net energy exchange, and still "no free lunch".
No free lunch theorem ...
In mathematical folklore, the "no free lunch" (NFL) theorem (sometimes pluralized) of David Wolpert and William Macready, alludes to the saying "no such thing as a free lunch", that is, there are no easy shortcuts to success. It appeared in the 1997 "No Free Lunch Theorems for Optimization". Wolpert had previously derived no free lunch theorems for machine learning (statistical inference).
In 2005, Wolpert and Macready themselves indicated that the first theorem in their paper "state[s] that any two optimization algorithms are equivalent when their performance is averaged across all possible problems".
The "no free lunch" (NFL) theorem is an easily stated and easily understood consequence of theorems Wolpert and Macready actually prove. It is weaker than the proven theorems, and thus does not encapsulate them. Various investigators have extended the work of Wolpert and Macready substantively. In terms of how the NFL theorem is used in the context of the research area, the no free lunch in search and optimization is a field that is dedicated for purposes of mathematically analyzing data for statistical identity, particularly searchand optimization.
While some scholars argue that NFL conveys important insight, others argue that NFL is of little relevance to machine learning research.
Example
Posit a toy universe that exists for exactly two days and on each day contains exactly one object, a square or a triangle. The universe has exactly four possible histories:
Any prediction strategy that succeeds for history #2, by predicting a square on day 2 if there is a square on day 1, will fail on history #1, and vice versa. If all histories are equally likely, then any prediction strategy will score the same, with the same accuracy rate of 0.5.
Origin
Wolpert and Macready give two NFL theorems that are closely related to the folkloric theorem. In their paper, they state:
We have dubbed the associated results NFL theorems because they demonstrate that if an algorithm performs well on a certain class of problems then it necessarily pays for that with degraded performance on the set of all remaining problems.
The first theorem hypothesizes objective functions that do not change while optimization is in progress, and the second hypothesizes objective functions that may change.
.
The theorem can be equivalently formulated as follows:
Theorem?—?Given a finite set ?? and a finite set ?? of real numbers, assume that ??:??→?? is chosen at random according to uniform distribution on the set ???? of all possible functions from ?? to ??. For the problem of optimizing ?? over the set ??, then no algorithm performs better than blind search.
Here, blind search means that at each step of the algorithm, the element ??∈?? is chosen at random with uniform probability distribution from the elements of ?? that have not been chosen previously.
In essence, this says that when all functions f are equally likely, the probability of observing an arbitrary sequence of m values in the course of optimization does not depend upon the algorithm. In the analytic framework of Wolpert and Macready, performance is a function of the sequence of observed values (and not e.g. of wall-clock time), so it follows easily that all algorithms have identically distributed performance when objective functions are drawn uniformly at random, and also that all algorithms have identical mean performance. But identical mean performance of all algorithms does not imply Theorem 1, and thus the folkloric theorem is not equivalent to the original theorem.
Theorem 2 establishes a similar, but "more subtle", NFL result for time-varying objective functions.
Motivati
The NFL theorems were explicitly not motivated by the question of what can be inferred (in the case of NFL for machine learning) or found (in the case of NFL for search) when the "environment is uniform random". Rather uniform randomness was used as a tool, to compare the number of environments for which algorithm A outperforms algorithm B to the number of environments for which B outperforms A. NFL tells us that (appropriately weighted) there are just as many environments in both of those sets.
This is true for many definitions of what precisely an "environment" is. In particular, there are just as many prior distributions (appropriately weighted) in which learning algorithm A beats B (on average) as vice versa.
This statement about sets of priors is what is most important about NFL, not the fact that any two algorithms perform equally for the single, specific prior distribution that assigns equal probability to all environments.
While the NFL is important to understand the fundamental limitation for a set of problems, it does not state anything about each particular instance of a problem that can arise in practice. That is, the NFL states what is contained in its mathematical statements and it is nothing more than that. For example, it applies to the situations where the algorithm is fixed a priori and a worst-case problem for the fixed algorithm is chosen a posteriori. Therefore, if we have a "good" problem in practice or if we can choose a "good" learning algorithm for a given particular problem instance, then the NFL does not mention any limitation about this particular problem instance. Though the NFL might seem contradictory to results from other papers suggesting generalization of learning algorithms or search heuristics, it is important to understand the difference between the exact mathematical logic of the NFL and its intuitive interpretation.
Theorem?—?For any algorithms a1 and a2, at iteration step m
∑????(??????∣??,??,??1)=∑????(??????∣??,??,??2) ...
Courtesy
The Moon is a Harsh Mistress by Robert A. Heinlein
Milton Friedman
No Free Lunch Theorems https://www.no-free-lunch.org/