Philosophy:Success

From HandWiki
Short description: Meeting or surpassing an intended goal or objective
A Nigerian man receives the smallpox vaccine in February 1969, as part of a global program that successfully eradicated the disease from the human population.

Success is the state or condition of meeting a defined range of expectations. It may be viewed as the opposite of failure. The criteria for success depend on context, and may be relative to a particular observer or belief system. One person might consider a success what another person considers a failure, particularly in cases of direct competition or a zero-sum game. Similarly, the degree of success or failure in a situation may be differently viewed by distinct observers or participants, such that a situation that one considers to be a success, another might consider to be a failure, a qualified success or a neutral situation. For example, a film that is a commercial failure or even a box-office bomb can go on to receive a cult following, with the initial lack of commercial success even lending a cachet of subcultural coolness.[1][2]

It may also be difficult or impossible to ascertain whether a situation meets criteria for success or failure due to ambiguous or ill-defined definition of those criteria. Finding useful and effective criteria, or heuristics, to judge the failure or success of a situation may itself be a significant task.

In American culture

DeVitis and Rich link the success to the notion of the American Dream. They observe that "[t]he ideal of success is found in the American Dream which is probably the most potent ideology in American life"[3] and suggest that "Americans generally believe in achievement, success, and materialism."[4] Weiss, in his study of success in the American psyche, compares the American view of success with Max Weber's concept of the Protestant work ethic.[5]

In biology

Natural selection is the variation in successful survival and reproduction of individuals due to differences in phenotype. It is a key mechanism of evolution, the change in the heritable traits characteristic of a population over generations. Charles Darwin popularized the term "natural selection", contrasting it with artificial selection, which in his view is intentional, whereas natural selection is not. As Darwin phrased it in 1859, natural selection is the "principle by which each slight variation [of a trait], if useful, is preserved".[6] The concept was simple but powerful: individuals best adapted to their environments are more likely to survive and reproduce. As long as there is some variation between them and that variation is heritable, there will be an inevitable selection of individuals with the most advantageous variations. If the variations are heritable, then differential reproductive success leads to a progressive evolution of particular populations of a species, and populations that evolve to be sufficiently different eventually become different species.[7][8]

In education

A student's success within an educational system is often expressed by way of grading. Grades may be given as numbers, letters or other symbols. By the year 1884, Mount Holyoke College was evaluating students' performance on a 100-point or percentage scale and then summarizing those numerical grades by assigning letter grades to numerical ranges. Mount Holyoke assigned letter grades A through E, with E indicating lower than 75% performance. The AE system spread to Harvard University by 1890. In 1898, Mount Holyoke adjusted the grading system, adding an F grade for failing (and adjusting the ranges corresponding to the other letters). The practice of letter grades spread more broadly in the first decades of the 20th century. By the 1930s, the letter E was dropped from the system, for unclear reasons.[9]

Educational systems themselves can be evaluated on how successfully they impart knowledge and skills. For example, the Programme for International Student Assessment (PISA) is a worldwide study by the Organisation for Economic Co-operation and Development (OECD) intended to evaluate educational systems by measuring 15-year-old school pupils' scholastic performance on mathematics, science, and reading.[10] It was first performed in 2000 and then repeated every three years.

Carol Dweck, a Stanford University psychologist, primarily researches motivation, personality, and development as related to implicit theories of intelligence, her key contribution to education the 2006 book Mindset: The New Psychology of Success. Dweck's work presents mindset as on a continuum between fixed mindset (intelligence is static) and growth mindset (intelligence can be developed). Growth mindset is a learning focus that embraces challenge and supports persistence in the face of setbacks. As a result of growth mindset, individuals have a greater sense of free will and are more likely to continue working toward their idea of success despite setbacks.

In business and leadership

Malcolm Gladwell's 2008 book Outliers: The Story of Success suggests that the notion of the self-made man is a myth. Gladwell argues that the success of entrepreneurs such as Bill Gates is due to their circumstances, as opposed to their inborn talent.[11][12]

Andrew Likierman, former Dean of London Business School,[13] argues that success is a relative rather than an absolute term: success needs to be measured against stated objectives and against the achievements of relevant peers: he suggests Jeff Bezos (Amazon) and Jack Ma (Alibaba) have been successful in business "because at the time they started there were many companies aspiring to the dominance these two have achieved".[14] Likierman puts forward four propositions regarding company success and its measurement

  1. There is no single definition of "a successful company" and no single measure of "company success"
  2. Profit and share value cannot be taken directly as measures of company success and require careful interpretation
  3. Judgement is required when interpreting past and present performance
  4. "Company success" reflects an interpretation of key factors: it is not a "fact".[15]

In philosophy of science

Graph of cosmic microwave background spectrum measured by the FIRAS instrument on the COBE, the most precisely measured black body spectrum in nature.[16] The error bars are too small to be seen even in an enlarged image, and it is impossible to distinguish the observed data from the theoretical curve.

Scientific theories are often deemed successful when they make predictions that are confirmed by experiment. For example, calculations regarding the Big Bang predicted the cosmic microwave background and the relative abundances of chemical elements in deep space (see Big Bang nucleosynthesis), and observations have borne out these predictions. Scientific theories can also achieve success more indirectly, by suggesting other ideas that turn out correct. For example, Johannes Kepler conceived a model of the Solar System based on the Platonic solids. Although this idea was itself incorrect, it motivated him to pursue the work that led to the discoveries now known as Kepler's laws, which were pivotal in the development of astronomy and physics.[17]

In probability

The fields of probability and statistics often study situations where events are labeled as "successes" or "failures". For example, a Bernoulli trial is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted.[18] The concept is named after Jacob Bernoulli, a 17th-century Swiss mathematician, who analyzed them in his Ars Conjectandi (1713).[19] The term "success" in this sense consists in the result meeting specified conditions, not in any moral judgement. For example, the experiment could be the act of rolling a single die, with the result of rolling a six being declared a "success" and all other outcomes grouped together under the designation "failure". Assuming a fair die, the probability of success would then be [math]\displaystyle{ 1/6 }[/math].

See also

References

  1. Hunter, I. Q. (2016-09-08) (in en). Cult Film as a Guide to Life: Fandom, Adaptation, and Identity. Bloomsbury Publishing USA. ISBN 978-1-62356-897-9. https://books.google.com/books?id=2H6hDAAAQBAJ&q=%22commercial+failure%22. 
  2. Mathijs, Ernest; Sexton, Jamie (2019-11-22) (in en). The Routledge Companion to Cult Cinema. Routledge. ISBN 978-1-317-36223-4. https://books.google.com/books?id=rNi_DwAAQBAJ. 
  3. DeVitis & Rich 1996, p. 4.
  4. DeVitis & Rich 1996, p. 5.
  5. Weiss 1969, p. 17.
  6. Darwin 1859, p. 61
  7. Darwin 1859, p. 5
  8. Hall, Brian K.; Hallgrímsson, Benedikt (2008). Strickberger's Evolution (4th ed.). Jones and Bartlett. pp. 4–6. ISBN 978-0-7637-0066-9. OCLC 796450355. https://books.google.com/books?id=jrDD3cyA09kC&pg=PA4. 
  9. Schinske, Jeffrey; Tanner, Kimberly (2014). "Teaching More by Grading Less (or Differently)". CBE: Life Sciences Education 13 (2): 159–166. doi:10.1187/cbe.CBE-14-03-0054. ISSN 1931-7913. PMID 26086649. 
  10. "About PISA". http://www.oecd.org/pisa/aboutpisa/. 
  11. "'Outliers' Puts Self-Made Success To The Test" (in en). NPR. 2008-11-18. https://www.npr.org/templates/story/story.php?storyId=97117414. 
  12. Cowley, Jason (2008-11-23). "Review: Outliers: The Story of Success by Malcolm Gladwell" (in en). The Guardian. http://www.theguardian.com/books/2008/nov/23/outliers-story-success-malcolm-gladwell. 
  13. The Chartered Governance Institute, Sir Andrew Likierman, accessed 9 January 2022
  14. Likierman, A., Sir Andrew Likierman of London Business School on good leaders, published 19 October 2014, accessed 6 November 2021
  15. Likierman, A. (2006), "Measuring Company Success", in Performance Management: Public and Private
  16. White, M. (1999). "Anisotropies in the CMB". UCLA. Bibcode1999dpf..conf.....W. 
  17. Olenick, R. P.; Apostol, T. M.; Goodstein, D. L. (1986). The Mechanical Universe: Introduction to Mechanics and Heat. Cambridge University Press. ISBN 0-521-30429-6. 
  18. Papoulis, A. (1984). "Probability, Random Variables, and Stochastic Processes". Probability, Random Variables, and Stochastic Processes (2nd ed.). New York: McGraw-Hill. pp. 57–63. 
  19. James Victor Uspensky: Introduction to Mathematical Probability, McGraw-Hill, New York 1937, page 45

Sources

Further reading