arXiv subjects

Radu Tanase

Publications and source records attributed to Radu Tanase.

4 recordsLinked to original sources

arXiv2025

What shifts threshold distributions in social contagions?

Individual thresholds in social contagions capture what fraction of others must adopt a new product or behavior before an individual adopts it. Yet in a given choice context and population, we rarely know the empirical threshold distribution or what explains its heterogeneity. This limits the ability to predict diffusion dynamics and design effective network interventions. We address this by measuring thresholds with an incentivized online experiment in which participants make adoption decisions for products that vary in attractiveness and payoff uncertainty. The results show that product characteristics shift threshold distributions: lower attractiveness and higher uncertainty increase thresholds. Individual characteristics, however, remain essential for explaining threshold heterogeneity. We then use data-driven simulations to show that both product characteristics and individual characteristics moderate the effect of long ties in social contagions. In sum, the threshold distribution is the behavioral microfoundation of social contagion models and identifying empirical correlates of thresholds is crucial for understanding how network structure affects social contagions.

physics.soc-ph↗

arXiv2025

The amplifier effect of artificial agents in social contagion

Recent advances in artificial intelligence have led to the proliferation of artificial agents in social contexts, ranging from education to online social media and financial markets, among many others. The increasing rate at which artificial and human agents interact makes it urgent to understand the consequences of human-machine interactions for the propagation of new ideas, products, and behaviors in society. Across two distinct empirical contexts, we find here that artificial agents lead to significantly faster and wider social contagion. To this end, we replicate a choice experiment previously conducted with human subjects by using artificial agents powered by large language models (LLMs). We use the experiment's results to measure the adoption thresholds of artificial agents and their impact on the spread of social contagion. We find that artificial agents tend to exhibit lower adoption thresholds than humans, which leads to wider network-based social contagions. Our findings suggest that the increased presence of artificial agents in real-world networks may accelerate behavioral shifts, potentially in unforeseen ways.

cs.SI↗

arXiv2024

Integrating behavioral experimental findings into dynamical models to inform social change interventions

Addressing global challenges often involves stimulating the large-scale adoption of new products or behaviors. Research traditions that focus on individual decision making suggest that achieving this objective requires identifying the drivers of individual discrete adoption choices. On the other hand, computational approaches rooted in complexity science focus on maximizing the propagation of a given product or behavior throughout social networks of interconnected adopters. Here, by integrating discrete choice modeling into the complex contagion theory, we propose a method to estimate individual-level thresholds to adoption. We validate the predictive power of this approach in two choice experiments. By integrating the estimated thresholds into computational simulations, we show that state-of-the-art seeding policies for initiating large-scale behavioral change might be suboptimal if they neglect individual-level behavioral drivers, which can be corrected through the proposed experimental method.

physics.soc-ph↗

arXiv2017

Controlling complex policy problems: a multimethodological approach using system dynamics and network controllability

Notwithstanding the usefulness of system dynamics in analyzing complex policy problems, policy design is far from straightforward and in many instances trial-and-error driven. To address this challenge, we propose to combine system dynamics with network controllability, an emerging field in network science, to facilitate the detection of effective leverage points in system dynamics models and thus to support the design of influential policies. We illustrate our approach by analyzing a classic system dynamics model: the World Dynamics model. We show that it is enough to control only 53% of the variables to steer the entire system to an arbitrary final state. We further rank all variables according to their importance in controlling the system and we validate our approach by showing that high ranked variables have a significantly larger impact on the system behavior compared to low ranked variables.

physics.soc-ph↗