Matthew effect

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.

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][7] 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]

In education, the term "Matthew effect" has been adopted by psychologist Keith Stanovich and popularised by education theorist Anthony Kelly to describe a phenomenon observed in research on how new readers acquire the skills to read. Effectively, 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]

This effect has been used successfully in legal cases, such as Brody v. Dare County Board of Education.[16] Such cases argue that early education intervention is essential for disabled children, and that failing to do so negatively impacts those children.[17]

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."[5] The Matthew Effect therefore explains the growth of some nodes in vast networks such as the Internet.[19]

The Meta-Matthew Effect refers to the potential for biased learning algorithms to perpetuate and amplify the Matthew Effect, a phenomenon where those who are already successful are more likely to continue to be successful. In the field of Artificial Intelligence (AI), biased datasets can lead to biased AI systems that perpetuate and amplify social inequalities. This effect can have negative consequences for society, leading to discrimination and injustice.

Research has shown that biased algorithms can have significant impacts, particularly in areas such as criminal justice and hiring practices. For example, an AI algorithm used in criminal justice systems was found to be biased against Black defendants, leading to longer sentences and higher bail amounts. Another study found that a hiring algorithm was biased against women, leading to fewer female candidates being selected for interviews.

To address this issue, it is crucial to ensure that AI datasets are diverse, representative, and free from biases. Additionally, AI developers must ensure that their algorithms are transparent and explainable, allowing for scrutiny and accountability.

As AI becomes increasingly integrated into our daily lives, it is essential to consider the potential for bias and work to address these issues to create a more just and equitable society.[20]

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 is an experiment by Salganik, Dodds, and Watts in which they 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 experiment's 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]