The struggle for reputation

This chapter discusses what reputation is and why and how we are struggling for it. Reputation can be considered a form of currency, and our reputation determines whether or not other people invest in us, buy from us, or give us some kind of reward. Indirect reciprocity is an efficient evolutionary mechanism that has led to the emergence of reputation. In the Internet age, digital reputation plays a particularly important role. After a brief discussion about the measurement of reputation, the chapter turns to the rules of the ranking games that scientists and artists play. The rules for these players are better elaborated than the rules for other communities. The illusion and manipulation of objectivity is discussed and related to two of the most prestigious awards: the Nobel Prize and the Academy Award. A recurring perspective in the book, namely the navigation between objectivity and subjectivity, is analyzed, here in the context of the nomination and selection processes for each award. The author then turns to digital reputation. Since a big industry has emerged with the goal of making websites more visible, the search engine manipulation effect and its possible impact is discussed.

Choices, games, laws, and the Web

This chapter starts with the notions of “objective reality” and “subjective reality.” Objectivity attempts to represent the outside world without bias or presuppositions, while subjectivity results from personal cognition or preferences. The chapter discusses the mechanisms by which people make choices. Research conducted in the last 60 years has resulted in a shift in our understanding of human decision making from the concept of rationality to a new model that acknowledges the role of cognitive biases. Individual choices and preferences are aggregated to form social preferences, and this chapter reviews some techniques behind this aggregation. It also explains that preference ranking does not always imply a unique result because it is possible to get a cyclic pathway, as in the rock, paper, scissors game. Elements of this game appeared in both ancient religious systems and in the US governmental system. Then the chapter turns to the famous PageRank algorithm, which made Google what it is today. The algorithm is able to produce a relevant ranking of websites within a very reasonable time. The algorithm could produce different results, and rank reversal may happen in real-world situations. Ranking many elements based on some characteristic features, such as words based on the frequency of their occurrence, can use statistical methods. In many real cases, the distribution of these features strongly deviates from the bell curve, and models instead a skew distribution, technically called a power law distribution.

Social ranking in animal and human societies

This chapter studies how social ranking in humans emerged as the result of an evolutionary process. It starts with the story of the discovery of pecking order among chickens by a Norwegian boy. Both animals and humans need a healthy balance between cooperation and competition to ensure evolutionarily efficient strategies. The biological machinery behind social ranking is discussed. There are two distinct mechanisms for navigating the social ladder: dominance and prestige. Dominance, an evolutionarily older strategy, is based on the ability to intimidate other members in the group by physical size and strength. The group members don’t accept dominance-based social rank freely, only by coercion. Members of a colony fight, and the winners of these fights will be accepted as “dominants” and the losers as “subordinates.” The naturally formed hierarchy serves as a way to prevent superfluous fighting and injuries within a colony. Prestige, as a strategy, is evolutionarily younger and is based on skills and knowledge as appraised by the community. Prestige hierarchies are maintained by the consent of the community, without pressure being applied by particular members. The mechanisms of forming and maintaining social hierarchies are described. Social structures, both hierarchies and network organizations, are reviewed. Discussion of these structures is carried over to social and political history and the tension between democracy and authoritarianism.

Inspired by your wish list

The majority of our purchasing decisions are being influenced by the opinion of the Web. Recommendation systems use algorithms to suggest items for us to buy, and we are often better off if we follow their advice. Nobody can force us to use Amazon, TripAdvisor, or Netflix, but we do if we trust them. While any such system can be gamed, and some illustrative examples of gaming are given, fake reviews and other tricks can be filtered, and recommendation systems can help us make better choices. Some examples are given to help readers understand the scope and limits of adopting recommendation systems.

Ranking games

This chapter starts with a discussion of the magic power of round numbers and left-digit effects, and it then deals with two popular ranking games concerning colleges and countries. A recurring theme in our complex world pertains to the question of whether it is possible to summarize the performance of an organization faithfully with a single score. Schools are complex social organizations that serve a variety of purposes, and measuring their progress toward these goals is obviously tricky. Students, admissions offices, and college administrators are the major stakeholders in the college ranking game. Ranking colleges not only provides a passive mirror of the institutional landscape but also drives changes within the institutions. Similarly, ranking countries both reflects the current state and motivates change. While some believe that the idea of the nation-state is outdated and a source of conflict, countries remain a primary means of controlling people, organizing society, and managing the distribution of wealth. Measuring corruption and freedom within countries is difficult, and the ranking of countries based on these measurements is discussed.

The ignorant, the manipulative, and the difficulties of measuring society

While making objective rankings sounds like an appealing goal, there are at least two reasons why we may not have objectivity: ignorance and manipulation. Persons with less knowledge suffer from illusory superiority due to their cognitive bias, a phenomenon called the Dunning–Kruger effect. Omnipresent in society is not only ignorance but also manipulation. Manipulators have the intention of gaining personal advantage by adopting different tricks, and the chapter summarizes these tricks. Then the importance and the difficulties of measuring society are discussed. According to Campbell’s law, use of quantitative indicators will distort and corrupt the social processes they are intended to monitor. “Rank and yank” refers to an annual performance review process by which a company ranks its employees against one another, and it looks as though CEOs have the difficult problem of avoiding unhealthy dog-eat-dog situations in the workplace. The chapter then discusses how a particular social metric, the credit score, is calculated and how objective the result is. It argues that social scientists and computer scientists should cooperate to generate “ethical algorithms” to avoid social prejudice. Further, it is argued that quantification is an indispensable part of an evaluation process. Metrics are not a silver bullet, because they might be manipulated, but careful measurement is better than purely subjective opinion.

Comparison, ranking, rating, and lists

In this chapter, four basic concepts—comparison, ranking, rating, and lists—are introduced, and a number of questions are discussed. Why do we compare ourselves with others? Is comparison the “thief of joy” or the driving force toward future successes? Are we born with the desire to compare ourselves with others, or do we learn in childhood that we should demonstrate that we are better or stronger than others? Is it true that “the grass is always greener on the other side of the fence”? How are ratings of graduate school applicants prepared? How do we rate our pain in a medical office? Why do we have the top-10 mania, and why do we love listicles? Ranking of mathematicians and the rating of chess players are used to illustrate the main concepts.

Epilogue

The closing chapter summarizes the book’s statements about the reality, illusion, and manipulation of objectivity. Whether we like it or not, ranking is with us and will remain with us for the foreseeable future. Computer scientists design ranking algorithms, and modern computers can process huge datasets with these algorithms. We are not always happy with the results, so we might ask whether, when, and how the results of a ranking algorithm should be controlled by content curators. Recent public debates about the use and misuse of data reinforce the message of the book that we need the combination of human and computational intelligence.

Prologue

The book begins with three personal stories about the author’s early encounters with comparison, rating, and ranking. The stories demonstrate that ranking might reflect the reality of objectivity in certain cases, while in other cases objectivity is only an illusion. In addition, objectivity might even be manipulated. The first story tells why the only boy with a soccer ball in a grade-school class in postwar Budapest led his class’s popularity list. Then the author describes how subjective ratings of soccer players were aggregated to arrive at an “objective score” for each player at the end of the season. Finally, the author uses a folktale to show how the strongest member of a group can become a self-nominated judge and manipulate what ought to be a collective decision.