Informally,it'shardtotellwhetheryour friends have similar outcomes because they were influenced by your treatment, or whether it's due to some common trait that caused you to be friends in the firstplace.

Employee turnover is a critical challenge in financial markets, yet little is known about the role of professional networks in shaping career moves. Using the Hong Kong Securities and Futures Commission (SFC) public register (2007-2024), we construct temporal networks of 121,883 professionals and 4,979 firms to analyze and predict employee departures. We introduce a graph-based feature propagation framework that captures peer influence and organizational stability. Our analysis shows a contagion effect: professionals are 23% more likely to leave when over 30% of their peers depart within six months. Embedding these network signals into machine learning models improves turnover prediction by 30% over baselines. These results highlight the predictive power of temporal network effects in workforce dynamics, and demonstrate how network-based analytics can inform regulatory monitoring, talent management, and systemic risk assessment.

We address the problem of using observational data to estimate peer contagion effects, the influence of treatments applied to individuals in a network on the outcomes of their neighbors. A main challenge to such estimation is that homophily - the tendency of connected units to share similar latent traits - acts as an unobserved confounder for contagion effects. Informally, it's hard to tell whether your friends have similar outcomes because they were influenced by your treatment, or whether it's due to some common trait that caused you to be friends in the first place. Because these common causes are not usually directly observed, they cannot be simply adjusted for. We describe an approach to perform the required adjustment using node embeddings learned from the network itself. The main aim is to perform this adjustment nonparametrically, without functional form assumptions on either the process that generated the network or the treatment assignment and outcome processes. The key contributions are to nonparametrically formalize the causal effect in a way that accounts for homophily, and to show how embedding methods can be used to identify and estimate this effect. Code is available at https://github.com/IrinaCristali/Peer-Contagion-on-Networks.

Why do we sometimes decide to take risks and other times choose to play it safe? The work is described in the March 21 online early edition of the Proceedings of the National Academy of Sciences. In the study led by John O'Doherty, professor of psychology and director of the Caltech Brain Imaging Center, 24 volunteers repeatedly participated in three types of trials: a "Self" trial, in which the participants were asked to choose between taking a guaranteed 10 or making a risky gamble with a potentially higher payoff; an "Observe" trial, in which the participants observed the risk-taking behavior of a peer (in the trial, this meant a computer algorithm trained to behave like a peer), allowing the participants to learn how often the peer takes a risk; and a "Predict" trial, in which the participants were asked to predict the risk-taking tendencies of an observed peer, earning a cash prize for a correct prediction. Notably in these trials the participants did not observe gamble outcomes, preventing them from further learning about gambles. O'Doherty and his colleagues found that the participants were much more likely to make the gamble for more money in the "Self" trial when they had previously observed a risk-taking peer in the "Observe" trial.