Digitalization has led to the production of massive amounts of data in every field, making it easier to develop models for evaluation. Today, rankings based on such data can be generated, determining who performs best in any given area. From the university you choose to attend, the books you read, and the films and series you watch, to the most profitable investment options and the hotels where you stay, recommendation systems increasingly guide our lives.
In every domain, such as economy, science, art, education, reputation, fame, elections, voting, consumption habits and more, rankings that show where individuals stand in the competition or inform us how to behave have become indispensable parts of our lives.
Everything is being drawn into the scope of measurement and thus condemned to be digitized. To measure qualitative aspects, assumptions must be made; factors that could influence the measurement and their respective weights are identified; and numerical values are assigned to qualitative features using constructed algorithms.
In other words, everything qualitative in any field is now being measured in some form and subsequently ranked. Although it is commonly assumed that "what cannot be measured cannot be improved," in reality, every measurement attempt aims to capture and ultimately control the area being measured. What cannot be measured or digitized gradually loses its value and becomes marginalized.
It is increasingly observed that institutions engaged in measurement and evaluation across all fields are growing in number, creating a domain of legitimacy and gradually beginning to shape their respective areas.
Today, investments are made based on the "neutral" reports issued by these companies, countries are ranked across different categories and attract international investments according to their position in these rankings, and decisions about which educational institutions to attend or which products to consume are increasingly determined by published rankings and recommendation lists.
For example, despite rising objections from many countries and the academic world regarding the published university ranking lists and the deformations caused by these rankings, countries continue to strive to place their universities in global rankings.
Today, it is the rankings that define meaning. Therefore, in the higher education market, institutions must climb the rankings to be regarded as important by others. As a result, the widespread listing, ranking and rating activities across all fields are transforming every domain into arenas where game theory is effectively at play.
Subjecting quality to measurement inevitably brings about distortions in the domain being measured. Two famous laws help explain these distortions: Campbell’s Law and Goodhart’s Law.
Campbell’s Law states that, "The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more it will distort and corrupt the social processes it is intended to monitor."
Goodhart’s Law, on the other hand, is expressed as, "When a social or economic indicator or other measure becomes a target for the purpose of conducting social or economic policy, it ceases to be a good measure and loses the information content it originally had."
Therefore, efforts to digitize a non-quantitative social domain are problematic not only in terms of the questionable outcomes they produce but also because of the distortions and deformations they inevitably cause within that domain.
On the other hand, since the game continues to be played according to these rules, the increase and deepening of inequalities in every field has become inevitable. Indicators already point to this trend.
Data distributions in any domain no longer follow a normal distribution but instead follow a power law distribution.
The citation distributions of scientific publications, the price distributions of artists’ works, income distributions and similar metrics are all increasingly distributed according to power laws and display significant skewness.
Those who have previously achieved success in any domain tend to continue increasing their success. In other words, distributions are becoming increasingly asymmetric.
In fact, the deviation from the normal distribution was identified long ago by the renowned sociologist Robert Merton through a mechanism he developed based on a verse from the Gospel of Matthew, which he termed the "Matthew Effect."
According to this mechanism, small differences in all fields are amplified over time, and advantage continuously breeds further advantage, leading to an accumulation of benefits. In other words, those who were successful in the past have an increased probability of remaining successful in the future.
According to these rules, the chances of success for newcomers are increasingly dependent on behaving in ways that reinforce the advantages of those already dominating the playing field.
Therefore, competition within each field does not occur under equal conditions. To explain this situation, Erdi draws upon the work of the renowned scientist Albert-Laszlo Barabasi, who has made significant contributions to the understanding of network structures, and references his famous statement: "Success is not about you. It's about us."
Thus, the rules of the success game lie not merely in individual effort but in the distribution of success indicators, which are defined beyond personal control. Success is not solely the result of an individual's effort; it is the outcome of a value attribution and meaning-making process that emerges from the interactions of many different institutions and network structures within society.
Consequently, while society grants the reward of success to a small number of individuals who strive for it, it withholds it from the vast majority. Moreover, most actors within any given field are often unaware of the underlying rules governing this process.
Barabasi’s book "The Formula: The Universal Laws of Success" offers important insights into how success is achieved in modern times, the patterns underlying it, how these patterns relate to networks, and how success is rewarded.
Barabasi formulates his first law as follows: “Performance drives success, but when performance can’t be measured, networks drive success.” However, this statement is somewhat incomplete when considered in the full context of the book, because performance alone does not guarantee success. It is only when performance is socially valued, through networks and societal recognition, that it can be transformed into success. In other words, success emerges when performance is embedded within a broader social context.
However, in fields where performance is particularly difficult to measure, such as art, it is networks that entirely determine success. The value attribution and pricing processes that define success are shaped by numerous actors and the networks in which they are embedded: art galleries, collectors, museums, art dealers, critics and more.
As Barabási emphasizes, two artists who start from the same point and possess similar talents can be rewarded differently depending on the choices they make and the trajectories they follow, resulting in varying levels of visibility. Barabasi notes that although there are many galleries and museums in the art world, the rewards are determined by only a small number of them, and he underlines their central importance. In other words, once an artist's career intersects with these few central institutions that define success, success becomes almost inevitable.
Today, similar central hubs exist across all fields, and these hubs play a crucial role in increasing visibility. People tend to read bestselling books, listen to the most-streamed music and watch the most-viewed films, thus reinforcing the consumption of already popular items. As a result, objects with a high number of initial connections or consumption instances continue to accumulate even more, increasing their chances of becoming central hubs within the network. In other words, prior success significantly increases the likelihood of future success.
Barabasi’s second law is stated as: "Performance is bounded, but success is unbounded." In other words, while human performance has natural limits in every field, once that performance activates the right networks, the resulting success and the rewards it brings can be limitless.
Thus, the number of citations a successful scientist receives, the number of books a bestselling novelist sells, the number of albums sold by a popular musician, or the price of a single painting by a renowned artist can rise to abnormal levels that are incomparable to the achievements of others in the same field. In short, the magnitude of success and reward becomes highly concentrated in a few hands, often to the disadvantage of others with similar levels of performance.
Barabasi refines his first and second laws of success with a third law, which he formulates as: “Previous success × fitness = future success.”
In this formulation, prior success alone does not determine future success; fitness becomes a critical variable. This means that a new player entering the game can catch up with, or even surpass, established, successful competitors if their fitness is high enough. Although fitness may appear to be related to performance, it actually represents a form of competitive strength that reduces the impact of rivals in the same field.
As Barabasi explains: "We called this feature ‘fitness,’ borrowing the term from evolution. It was a suitable choice. Because fitness is not synonymous with quality, though it is certainly related to it. Rather than being a value judgment, fitness gives us the ability to detect a product’s inherent capacity to outcompete other products targeting the same buyer, the same audience, or the same fan base."
Barabasi explains the mismatch between the distribution of performance and that of success using the concepts of the normal distribution and the power-law distribution.
Performance follows a normal (bell curve) distribution, meaning that it has natural limits and that there are many individuals whose performance is very close to that of top performers. However, the returns of success, which can be achieved with only marginal differences in performance and thus become decoupled from it, are distributed logarithmically according to a power law. In this model, small differences in performance can lead to disproportionately large differences in success.
Enormous gaps in rewards occur despite minimal differences in performance. In many cases, rewards differ so dramatically that they cannot be justified by the small differences in performance.
For instance, a small number of scientists receive the vast majority of academic citations; a single novel can generate astonishing levels of income; the price of one painting can exceed the total lifetime earnings of tens of thousands of other painters; or the profits of a few companies can surpass the total national income of countries populated by billions of people. This asymmetric distribution of rewards, largely decoupled from actual performance, follows the same dynamics in the arts and entertainment sectors.
As Daniel Markovits highlights in "The Meritocracy Trap: How America’s Foundational Myth Feeds Inequality, Dismantles the Middle Class, and Devours the Elite," "No sector is immune. Even in the arts and entertainment, new technologies allow a few 'superstar' entertainers to capture global audiences, displacing many only slightly less skilled performers who previously served local audiences by being the best entertainment within traveling range.
"In 2017, Beyonce, LeBron James and J. K. Rowling each made nearly $100 million. This is perhaps one hundred times more than their midcentury counterparts. It is roughly 1,000 times more than backup singers, players in the NBA’s development league, and television scriptwriters, all skilled people, near but not at the tops of their professions, make today."
Drawing on the relationship between asymmetric profit generation in the art world and the constructed “star system,” Richard Sennett extends the performance-asymmetric reward distribution dynamic into the realm of politics. He argues that sponsors, who hold significant power in the selection of political candidates, adopt a similar approach: "Rather than focusing their efforts on building and overseeing the distribution system itself, that is, the party, sponsors concentrate on a ‘product’ that is suitable for distribution and marketable: a candidate.”
Modern times, through globalization, have made it possible for everything in the world to be interconnected, dramatically scaling up the size of the game and enabling an avalanche effect in the distribution of success and rewards. In every field, a very small number of units, such as individuals, artists or companies, can transform into hubs with nearly unlimited connections, absorbing the entirety of rewards associated with success in that domain.
As global civilization has vastly increased the number of connections in every area, this “unbounded connectivity” has shifted reward distribution from a normal distribution to one modeled by a power law.
This represents a new condition brought about by the hyper-social networks created through globalization. Thus, the boundlessness of success and the rewards it brings is not a natural law; it is a consequence of the system of modern civilization, which amplifies social inequality. And the game is played accordingly.
