arXiv subjects

Jaume Ojer

Publications and source records attributed to Jaume Ojer.

6 recordsLinked to original sources

arXiv2026

Modeling financial transactions via random walks on temporal networks

We model financial transactions as random walks on activity-driven temporal networks. By enforcing fund conservation, our framework analytically derives heavy-tailed distributions for the stationary balances and transaction sizes. Crucially, the latter is driven by variance in the spending propensity of individuals. Calibrated with empirical data from a closed, digital currency community, the model also reproduces observed correlations between inflows and outflows. Our findings provide a path for understanding emergent properties of the circulation of money.

physics.soc-ph↗

arXiv2025

Modeling individual attention dynamics on online social media

In the attention economy, understanding how individuals manage limited attention is critical. We introduce a simple model describing the decay of a user's engagement when facing multiple inputs. We analytically show that individual attention decay is determined by the overall duration of interactions, not their number or user activity. Our model is validated using data from Reddit's Change My View subreddit, where the user's attention dynamics is explicitly traceable. Despite its simplicity, our model offers a crucial microscopic perspective complementing macroscopic studies.

physics.soc-ph↗

arXiv2023

Charting multidimensional ideological polarization across demographic groups in the United States

Has ideological polarization actually increased in the last decades, or have voters simply sorted themselves into parties matching their ideology more closely? We present a novel methodology to quantify multidimensional ideological polarization, by embedding the respondents to a wide variety of political, social, and economic topics from the American National Election Studies (ANES) into a two-dimensional ideological space. By identifying several demographic attributes of the ANES respondents, we chart how political and socio-economic groups move through the ideological space in time. We observe that income and especially racial groups align into parties, but their ideological distance has not increased over time. Instead, Democrats and Republicans have become ideologically more distant in the last 30 years: Both parties moved away from the center, at different rates. Furthermore, Democratic voters have become ideologically more heterogeneous after 2010, indicating that partisan sorting has declined in the last decade.

physics.soc-ph↗

arXiv2023

Social Network Heterogeneity Promotes Depolarization of Multidimensional Correlated Opinions

We propose the multidimensional social compass model, based on two competing key ingredients: DeGroot learning, driven by the social influence exerted across multiple topics, and the preference of individuals to maintain their initial opinions. The interplay between these two mechanisms triggers a phase transition from polarization to consensus, determined by a threshold value of social influence. We analytically study the nature of the depolarization transition and its threshold depending on the number of topics discussed, the possible correlations between initial opinions, the topology of the underlying social networks, and the correlations between the initial opinion distribution and the network's structure. Theoretical predictions are validated by running numerical simulations on both synthetic and real social networks. We rely on several simplifying assumptions to explore different scenarios, such as a mean-field approximation for high dimension, or orthogonal initial orientations. We uncover an upper critical dimension (D_c = 5 topics) for uncorrelated initial opinions, distinguishing between discontinuous and continuous phase transitions. For the simplest D = 2 case and correlated initial opinions, we found that the depolarization threshold can vanish if the underlying connectivity is heterogeneous, as predicted by perturbation theory. Such an effect is due to the presence of hubs, which promote consensus in the population. We test this hypothesis by designing a rewiring algorithm that increases the structural heterogeneity of the underlying network, showing that the depolarization threshold decreases. Finally, we demonstrate that if hubs share the same initial opinion, the depolarization dynamics is significantly hindered.

physics.soc-ph↗

arXiv2022

Modeling Explosive Opinion Depolarization in Interdependent Topics

Understanding the dynamics of opinion depolarization is pivotal to reducing the political divide in our society. We propose an opinion dynamics model, which we name the social compass model, for interdependent topics represented in a polar space, where zealots holding extreme opinions are less prone to change their minds. We analytically show that the phase transition from polarization to consensus, as a function of increasing social influence, is explosive if topics are not correlated. We validate our theoretical framework through extensive numerical simulations and recover explosive depolarization also by using initial opinions from the American National Election Studies, including polarized and interdependent topics.

physics.soc-ph↗

arXiv2022

Flocking dynamics mediated by weighted social networks

We study the effects of animal social networks with a weighted pattern of interactions on the flocking transition exhibited by models of self-organized collective motion. Considering a model representing dynamics on a one-dimensional substrate, application of a heterogeneous mean-field theory provides a phase diagram as function of the heterogeneity of the network connections and the correlations between weights and degree. In this diagram we observe two phases, one corresponding to the presence of a transition and other to a transition suppressed in an always ordered system, already observed in the non-weighted case. Interestingly, a third phase, with no transition in an always disordered state, is also obtained. These predictions, numerically recovered in computer simulations, are also fulfilled for the more realistic Vicsek model, with movement in a two-dimensional space. Additionally, we observe at finite network sizes the presence of a maximum threshold for particular weight configurations, indicating that it is possible to tune weights to achieve a maximum resilience to noise effects. Simulations in real weighted animal social networks show that, in general, the presence of weights diminishes the value of the flocking threshold, thus increasing the fragility of the flocking state. The shift in the threshold is observed to depend on the heterogeneity of the weight pattern.

physics.soc-ph↗