Don’t follow the leader: how ranking performance reduces meritocracy

Abstract

In the name of meritocracy, modern economies devote increasing amounts of resources to quantifying and ranking the performance of individuals and organizations. Rankings send out powerful signals, which lead to identifying the actions of top performers as the ‘best practices’ that others should also adopt. However, several studies have shown that the imitation of best practices often leads to a drop in performance. So, should those lagging behind in a ranking imitate top performers or should they instead pursue a strategy of their own? I tackle this question by numerically simulating a stylized model of a society whose agents seek to climb a ranking either by imitating the actions of top performers or by randomly trying out different actions, i.e. via serendipity. The model gives rise to a rich phenomenology, showing that the imitation of top performers increases welfare overall, but at the cost of higher inequality. Indeed, the imitation of top performers turns out to be a self-defeating strategy that consolidates the early advantage of a few lucky—and not necessarily talented—winners, leading to a very unequal, homogenized and effectively non-meritocratic society. Conversely, serendipity favours meritocratic outcomes and prevents rankings from freezing.

Network-based ranking in social systems: three challenges

“…The control parameter q ∈ [0, 1] determines the relative importance of imitation and serendipity for agents' choices. As q increases, the total welfare of the system increases, but so do success inequalities, whereas the correlation between agents' success and talent decreases [61].…”

“…Can we predict how the adoption of an algorithm in a given system will alter the agents' behavior and further influence the evolution of the system? Most studies that aimed to answer this question have focused on agent-based models and network formation models [57,[59][60][61][62]. In the following, we highlight three examples of insights that can be gained from stochastic models of network formation.…”

“…Third, if the agents attempt to mimic the strategies of the highest-ranked individuals, the overall welfare of the society increases, but so do success inequalities [61]. This has been demonstrated with a model where a given agent benefits from a given action due to both the intrinsic payoff associated with the action and her ability to produce a successful outcome out of it.…”

“…Taken together, these studies indicate that agent-based models can shed light on the possible systemic consequences of ranking algorithms on a given system. In particular, agent-level actions motivated by the results of ranking algorithms can result in unhealthy systemic consequences, like reduced social mobility [59] and low correlation between success and merit [57,61]. In future research, we expect investigations of more complex models to deepen our understanding of the impact of rankings on the structure and stability of social systems.…”

Commentary: Teacher Candidates' Responses to Examining Personal Privilege: Nuanced Understandings of the Discourse of Individualism in Critical Multicultural Education

“…The comprehension of meritocracy implies many domains of human life. Some studies discuss the relationship between meritocracy, productivity, and inequalities (Livan, 2019), across reflections and actions since the economic area until debates in education and health fields.…”