arXiv subjects

Arnout van de Rijt

Publications and source records attributed to Arnout van de Rijt.

6 recordsLinked to original sources

arXiv2026

Births are difficult to predict even with rich survey and full-population register data

Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child within three years - through a near-ideal setting for prediction: a data challenge where 147 researchers predicted births for Dutch residents aged 18-45, using survey data and full-population registers. Methods ranged from logistic regression to a large language model and transformers. Predictions were moderately accurate (best F1: register 0.59, survey 0.76); advanced models did not outperform classical ones; and the larger registers did not beat the survey. Simulating the stochastic biology of conception and pregnancy, we estimated a predictive ceiling (survey F1 ~ 0.86-0.94, register 0.88-0.96). Observed performance falls short of this ceiling, implicating imperfect data, methods, and unmodelled chance, while the ceiling itself shows that chance in reproduction alone sets a non-trivial limit on predicting individual lives.

cs.LG↗

arXiv2026

Embeddings of Nation-Level Social Networks

Full nation-scale social networks are now emerging from countries such as the Netherlands and Denmark, but these networks present challenging technical issues in working with large, multiplex, time-dependent networks. We report on our experiences in producing dynamic node embeddings of the population network of the Netherlands. We present (a) a layer-sensitive random walk strategy which improves on traditional flattening methods for multiplex networks, (b) a temporal alignment strategy that brings annual networks into the same embedding space, without leaking information to future years, and (c) the use of Fibonacci spirals and embedding whitening techniques for more balanced and effective partitioning. We demonstrate the effectiveness of these techniques in building embedding-based models for 13 downstream tasks.

cs.SI↗

arXiv2024

The marginal majority effect: when social influence produces lock-in

People are influenced by the choices of others, a phenomenon observed across contexts in the social and behavioral sciences. Social influence can lock in an initial popularity advantage of an option over a higher quality alternative. Yet several experiments designed to enable social influence have found that social systems self-correct rather than lock-in. Here we identify a behavioral phenomenon that makes inferior lock-in possible, which we call the 'marginal majority effect': A discontinuous increase in the choice probability of an option as its popularity exceeds that of a competing option. We demonstrate the existence of marginal majority effects in several recent experiments and show that lock-in always occurs when the effect is large enough to offset the quality effect on choice, but rarely otherwise. Our results reconcile conflicting past empirical evidence and connect a behavioral phenomenon to the possibility of social lock-in.

physics.soc-ph↗

arXiv2023

Inferring cumulative advantage from longitudinal records

Inequality in human success may emerge through endogenous success-breeds-success dynamics but may also originate in pre-existing differences in talent. It is widely recognized that the skew in static frequency distributions of success implied by a cumulative advantage model is also consistent with a talent model. Studies have turned to longitudinal records of success, seeking to exploit the time dimension for adjudication. Here we show that success histories suffer from a similar identification problem as static distributional evidence. We prove that for any talent model there exists an analogous path dependent model that generates the same longitudinal predictions, and vice versa. We formally identify such twins for prominent models in the literature, in both directions. These results imply that longitudinal data previously interpreted to support a talent model equally well fits a model of cumulative advantage and vice versa.

math.PR↗

arXiv2020

Halting SARS-CoV-2 by Targeting High-Contact Individuals

Network scientists have proposed that infectious diseases involving person-to-person transmission may be effectively halted by targeting interventions at a minority of highly connected individuals. Can this strategy be effective in combating a virus partly transmitted in close-range contact, as many believe SARS-CoV-2 to be? Effectiveness critically depends on high between-person variability in the number of close-range contacts. We analyze population survey data showing that indeed the distribution of close-range contacts across individuals is characterized by a small fraction of individuals reporting very high frequencies. Strikingly, we find that the average duration of contact is mostly invariant in the number of contacts, reinforcing the criticality of hubs. We simulate a population embedded in a network with empirically observed contact frequencies. Simulations show that targeting hubs robustly improves containment.

cs.SI↗

arXiv2020

"Stay Nearby or Get Checked": A Covid-19 Lockdown Exit Strategy

This paper repurposes the classic insight from network theory that long-distance connections drive disease propagation into a strategy for controlling a second wave of Covid-19. We simulate a scenario in which a lockdown is first imposed on a population and then partly lifted while long-range transmission is kept at a minimum. Simulated spreading patterns resemble contemporary distributions of Covid-19 across nations, regions, and provinces, providing some model validation. Results suggest that the proposed strategy may significantly flatten a second wave. We also find that post-lockdown flare-ups remain local longer, aiding geographical containment. Public policy may target long ties by heavily focusing medical testing and mobility tracking efforts on traffic and transport. This policy can be communicated to the general public as a simple and reasonable principle: Stay nearby or get checked.

cs.SI↗