Matthew effect
The Matthew effect of accumulated advantage, Matthew principle, or Matthew effect for short, is sometimes summarized by the adage "the rich get richer and the poor get poorer".[1][2] The concept is applicable to matters of fame or status, but may also be applied literally to cumulative advantage of economic capital. In the beginning, Matthew effects were primarily focused on the inequality in the way scientists were recognized for their work. However, Norman Storer, of Columbia University, led a new wave of research. He believed he discovered that the inequality that existed in the social sciences also existed in other institutions.[3]
The term was coined by sociologist Robert K. Merton in 1968[4] and takes its name from the parable of the talents or minas in the biblical Gospel of Matthew. Merton credited his collaborator and wife, sociologist Harriet Zuckerman, as co-author of the concept of the Matthew effect.[5]
The concept is named according to two of the parables of Jesus in the synoptic Gospels (Table 2, of the Eusebian Canons).
The concept concludes both synoptic versions of the parable of the talents:
For to every one who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away.
— Matthew 25:29, RSV.
I tell you, that to every one who has will more be given; but from him who has not, even what he has will be taken away.
— Luke 19:26, RSV.
The concept concludes two of the three synoptic versions of the parable of the lamp under a bushel (absent in the version of Matthew):
For to him who has will more be given; and from him who has not, even what he has will be taken away.
— Mark 4:25, RSV.
Take heed then how you hear; for to him who has will more be given, and from him who has not, even what he thinks that he has will be taken away.
— Luke 8:18, RSV.
The concept is presented again in Matthew outside of a parable during Christ's explanation to his disciples of the purpose of parables:
And he answered them, "To you it has been given to know the secrets of the kingdom of heaven, but to them it has not been given. For to him who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away."
— Matthew 13:11–12, RSV.
Sociology of science
In the sociology of science, "Matthew effect" was a term coined by Robert K. Merton to describe how, among other things, eminent scientists will often get more credit than a comparatively unknown researcher, even if their work is similar; it also means that credit will usually be given to researchers who are already famous.[4][6] For example, a prize will almost always be awarded to the most senior researcher involved in a project, even if all the work was done by a graduate student. This was later formulated by Stephen Stigler as Stigler's law of eponymy – "No scientific discovery is named after its original discoverer" – with Stigler explicitly naming Merton as the true discoverer, making his "law" an example of itself.
Merton furthermore argued that in the scientific community the Matthew effect reaches beyond simple reputation to influence the wider communication system, playing a part in social selection processes and resulting in a concentration of resources and talent. He gave as an example the disproportionate visibility given to articles from acknowledged authors, at the expense of equally valid or superior articles written by unknown authors. He also noted that the concentration of attention on eminent individuals can lead to an increase in their self-assurance, pushing them to perform research in important but risky problem areas.[4]
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As credit is valued in science, specific claims of the Matthew effect are contentious. Many examples below exemplify more famous scientists getting credit for discoveries due to their fame, even as other less notable scientists had preempted their work.
Ray Solomonoff ... introduced [what is now known as] "Kolmogorov complexity" in a long journal paper in 1964. ... This makes Solomonoff the first inventor and raises the question whether we should talk about Solomonoff complexity. ...
In science, dramatic differences in the productivity may be explained by three phenomena: sacred spark, cumulative advantage, and search costs minimization by journal editors. The sacred spark paradigm suggests that scientists differ in their initial abilities, talent, skills, persistence, work habits, etc. that provide particular individuals with an early advantage. These factors have a multiplicative effect which helps these scholars succeed later. The cumulative advantage model argues that an initial success helps a researcher gain access to resources (e.g., teaching release, best graduate students, funding, facilities, etc.), which in turn results in further success. Search costs minimization by journal editors takes place when editors try to save time and effort by consciously or subconsciously selecting articles from well-known scholars. Whereas the exact mechanism underlying these phenomena is yet unknown, it is documented that a minority of all academics produce the most research output and attract the most citations.[13]
In education, the term "Matthew effect" has been adopted by psychologist Keith Stanovich to describe a phenomenon observed in research on how new readers acquire the skills to read: early success in acquiring reading skills usually leads to later successes in reading as the learner grows, while failing to learn to read before the third or fourth year of schooling may be indicative of lifelong problems in learning new skills.[14]
This is because children who fall behind in reading would read less, increasing the gap between them and their peers. Later, when students need to "read to learn" (where before they were learning to read), their reading difficulty creates difficulty in most other subjects. In this way they fall further and further behind in school, dropping out at a much higher rate than their peers.
In the words of Stanovich:
Slow reading acquisition has cognitive, behavioral, and motivational consequences that slow the development of other cognitive skills and inhibit performance on many academic tasks. In short, as reading develops, other cognitive processes linked to it track the level of reading skill. Knowledge bases that are in reciprocal relationships with reading are also inhibited from further development. The longer this developmental sequence is allowed to continue, the more generalized the deficits will become, seeping into more and more areas of cognition and behavior. Or to put it more simply – and sadly – in the words of a tearful nine-year-old, already falling frustratingly behind his peers in reading progress, "Reading affects everything you do."[15]
The Matthew effect plays a role in today's educational system.
Students around the United States participate in the SAT every year to then send those scores to the colleges to which they are applying. The distributor of the SAT, the College board, conducted a study based on the income earned by the families of the test takers. The results showed the Matthew effect is prevalent when it comes to a family's economic earnings: "Students from families earning more than $200,000 a year average a combined score of 1,714, while students from families earning under $20,000 a year average a combined score of 1,326."[16]
Not only do students with a wealthier family score better, but statistics show that students with parents that have accomplished more in school perform better as well. A student with a parent with a graduate degree, for example, averages 300 points higher on their SAT compared to a student with a parent with only a high school degree.[17]
Network science
In network science, the Matthew effect is used to describe the preferential attachment of earlier nodes in a network, which explains that these nodes tend to attract more links early on.[18] "Because of preferential attachment, a node that acquires more connections than another one will increase its connectivity at a higher rate, and thus an initial difference in the connectivity between two nodes will increase further as the network grows, while the degree of individual nodes will grow proportional with the square root of time."[19] The Matthew Effect therefore explains the growth of some nodes in vast networks such as the Internet.[20]
Markets with social influence
Social influence often induces a rich-get-richer phenomenon where popular products tend to become even more popular.[21] An example of the Matthew Effect's role on social influence. Salganik, Dodds, and Watts created an experimental virtual market named MUSICLAB. In MUSICLAB, people could listen to music and choose to download the songs they enjoyed the most. The song choices were unknown songs produced by unknown bands. There were two groups tested; one group was given zero additional information on the songs and one group was told the popularity of each song and the number of times it had previously been downloaded.[22] As a result, the group that saw which songs were the most popular and were downloaded the most were then biased to choose those songs as well. The songs that were most popular and downloaded the most stayed at the top of the list and consistently received the most plays. To summarize the experiments findings, the performance rankings had the largest effect boosting expected downloads the most. Download rankings had a decent effect; however, not as impactful as the performance rankings.[23] Also, Abeliuk et al. (2016) proved that when utilizing “performance rankings”, a monopoly will be created for the most popular songs.[24]
Political science
Liberalization in autocracies is more likely to succeed in countries with the advantage of a better starting point concerning political institutions, GDP, and education. These more privileged countries can also carry out key reforms more rapidly, and are able to do so even in areas with no initial advantage.[25]
See also
  1. ^ Gladwell, Malcolm (2008-11-18). Outliers: The Story of Success (1 ed.). Little, Brown and Company. ISBN 978-0-316-01792-3.
  2. ^ Shaywitz, David A. (2008-11-15). "The Elements of Success". The Wall Street Journal. Retrieved 2009-01-12.
  3. ^ Rigney, Daniel (2010). "Matthew Effects in the Economy.” The Matthew Effect: How Advantage Begets Further Advantage. Columbia University Press. pp. pp. 35–52.
  4. ^ a b c Merton, Robert K. (1968). "The Matthew Effect in Science" (PDF). Science. 159 (3810): 56–63. Bibcode​:​1968Sci...159...56M​. doi​:​10.1126/science.159.3810.56​. PMID 17737466. S2CID 3526819.
  5. ^ "The Matthew Effect in Science, II : Cumulative Advantage and the Symbolism of Intellectual Property by Robert K. Merton" (PDF). Retrieved 2019-05-04.
  6. ^ Merton, Robert K (1988). "The Matthew Effect in Science, II: Cumulative advantage and the symbolism of intellectual property" (PDF). ISIS. 79 (4): 606–623. doi:10.1086/354848. S2CID 17167736.
  7. ^ Salganik, Matthew J.; Dodds, Peter S.; Watts, Duncan J. (2006). "Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market" (PDF). Science. 311 (5762): 854–856. Bibcode​:​2006Sci...311..854S​. doi​:​10.1126/science.1121066​. PMID 16469928. S2CID 7310490.
  8. ^ Sorenson, Alan T (2007). "Bestseller Lists and Product Variety" (PDF). Journal of Industrial Economics. 55 (4): 715–738. doi​:​10.1111/j.1467-6451.2007.00327.x​. S2CID 49028945.
  9. ^ van de Rijt, A.; Kang, S.; Restivo, M.; Patil, A. (2014). "Field Experiments of Success-Breeds-Success Dynamics" (PDF). PNAS. 111 (19): 6934–6939. Bibcode​:​2014PNAS..111.6934V​. doi​:​10.1073/pnas.1316836111​. PMC 4024896. PMID 24778230.
  10. ^ Li, Ming; Paul Vitanyi (1997-02-27). An Introduction to Kolmogorov Complexity and Its Applications (2nd ed.). Springer. ISBN 0-387-94868-6.
  11. ^ Petersen, Alexander M.; Jung, Woo-Sung; Yang, Jae-Suk; Stanley, H. Eugene (2011). "Quantitative and Empirical demonstration of the Matthew Effect in a study of Career Longevity". PNAS. 108 (1): 18–23. arXiv:0806.1224. Bibcode​:​2011PNAS..108...18P​. doi​:​10.1073/pnas.1016733108​. PMC 3017158. PMID 21173276.
  12. ^ Bol, T.; de Vaan, M.; van de Rijt, A. (2018). "The Matthew Effect in Science Funding" (PDF). PNAS. 115 (19): 4887–4890. doi​:​10.1073/pnas.1719557115​. PMC 5948972. PMID 29686094.
  13. ^ Serenko, A.; Cox, R.; Bontis, N.; Booker, L. (2011). "The Superstar Phenomenon in the Knowledge Management and Intellectual Capital Academic Discipline" (PDF). Journal of Informetrics. 5: 333–345.
  14. ^ Kempe, C., Eriksson‐Gustavsson, A. L., & Samuelsson, S (2011). "Are There any Matthew Effects in Literacy and Cognitive Development?". Scandinavian Journal of Educational Research. 55 (2): 181–196. doi​:​10.1080/00313831.2011.554699​. S2CID 145163197.
  15. ^ Adams, Marilyn J. (1990). Beginning to Read: Thinking and Learning about Print. Cambridge, MA: MIT Press. pp. 59–60.
  16. ^ Goldfarb, Zachary. “These Four Charts Show How the SAT Favors Rich, Educated Families.” The Washington Post, WP Company, 26 Apr. 2019, www.washingtonpost.com/news/wonk/wp/2014/03/05/these-four-charts-show-how-the-sat-favors-the-rich-educated-families/./
  17. ^ Goldfarb, Zachary. “These Four Charts Show How the SAT Favors Rich, Educated Families.” The Washington Post, WP Company, 26 Apr. 2019, www.washingtonpost.com/news/wonk/wp/2014/03/05/these-four-charts-show-how-the-sat-favors-the-rich-educated-families/.
  18. ^ Barabási, A-L; Albert, R (1999). "Emergence of scaling in random networks". Science. 286 (5439): 509–512. arXiv:cond-mat/9910332. Bibcode​:​1999Sci...286..509B​. doi​:​10.1126/science.286.5439.509​. PMID 10521342. S2CID 524106.
  19. ^ Perc, Matjaž (2014). "The Matthew effect in empirical data". Journal of the Royal Society Interface. 12 (104): 20140378. arXiv:1408.5124. Bibcode​:​2014arXiv1408.5124P​. doi​:​10.1098/rsif.2014.0378​. PMC 4233686. PMID 24990288.
  20. ^ Guadamuz, Andres (2011). Networks, Complexity And Internet Regulation – Scale-Free Law. Edward Elgar. ISBN 9781848443105.
  21. ^ Altszyler, E; Berbeglia, F.; Berbeglia, G.; Van Hentenryck, P. (2017). "Transient dynamics in trial-offer markets with social influence: Trade-offs between appeal and quality". PLOS ONE. 12 (7): e0180040. Bibcode​:​2017PLoSO..1280040A​. doi​:​10.1371/journal.pone.0180040​. PMC 5528888. PMID 28746334.
  22. ^ Berbeglia, Franco, and Pascal Van Hentenryck. Taming the Matthew Effect in Online Markets with Social Influence. Taming the Matthew Effect in Online Markets with Social Influence.
  23. ^ Salganik, Matthew J., et al. Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market. 2006, pp. 1–15, Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market.
  24. ^ Abeliuk, Andrés, et al. “The Benefits of Social Influence in Optimized Cultural Markets.” PLOS ONE, vol. 10, no. 4, 2015, doi:10.1371/journal.pone.0121934.
  25. ^ Lindenfors, Patrik; Wilson, Matthew; Lindberg, Staffan I. (2020-09-25). "The Matthew effect in political science: head start and key reforms important for democratization". Humanities and Social Sciences Communications. 7 (1): 1–4. doi​:​10.1057/s41599-020-00596-7​. ISSN 2662-9992.
Further reading
Last edited on 12 February 2021, at 06:06
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