Abstract
This essay provides a critical overview of the mathematical kinetic theory of active particles, which is used to model and study collective systems consisting of interacting living entities, such as those involved in behavior and evolution. The main objective is to study the interactions of large systems of living entities mathematically. More specifically, the study relates to the complex features of living systems and the mathematical tools inspired by statistical physics. The focus is on the mathematical description of these interactions and their role in deriving differential systems that describe the aforementioned dynamics. The paper demonstrates that studying these interactions naturally yields new mathematical insights into systems in the natural sciences and behavioral economics.

Abstract
We investigate the effects of wariness (defined as individuals' concern for their minimum utility over time) on poverty traps and equilibrium multiplicity in an overlapping generations (OLG) model. We explore conditions under which (i) wariness amplifies or mitigates the likelihood of poverty traps in the economy and (ii) it gives rise to multiple intertemporal equilibria. Furthermore, we conduct comparative statics to characterize these effects and to examine how the interplay between wariness, productivity, and factor substitutability influences the dynamics of the economy.

Abstract
Social and behavioral scientists increasingly aim to study how humans interact, collaborate, and make decisions alongside artificial intelligence. However, the experimental infrastructure for such work remains underdeveloped: (1) few platforms support real-time, multi-party studies at scale; (2) most deployments require bespoke engineering, limiting replicability and accessibility, and (3) existing tools do not treat AI agents as first-class participants. We present Deliberate Lab, an open-source platform for large-scale, real-time behavioral experiments that supports both human participants and large language model (LLM)-based agents. We report on a 12-month public deployment of the platform (N=88 experimenters, N=9195 experiment participants), analyzing usage patterns and workflows. Case studies and usage scenarios are aggregated from platform users, complemented by in-depth interviews with select experimenters. By lowering technical barriers and standardizing support for hybrid human-AI experimentation, Deliberate Lab expands the methodological repertoire for studying collective decision-making and human-centered AI.

Abstract
Previous work has shown that when multiple selfish Autonomous Vehicles (AVs) are introduced to future cities and start learning optimal routing strategies using Multi-Agent Reinforcement Learning (MARL), they may destabilize traffic systems, as they would require a significant amount of time to converge to the optimal solution, equivalent to years of real-world commuting. We demonstrate that moving beyond the selfish component in the reward significantly relieves this issue. If each AV, apart from minimizing its own travel time, aims to reduce its impact on the system, this will be beneficial not only for the system-wide performance but also for each individual player in this routing game. By introducing an intrinsic reward signal based on the marginal cost matrix, we significantly reduce training time and achieve convergence more reliably. Marginal cost quantifies the impact of each individual action (route-choice) on the system (total travel time). Including it as one of the components of the reward can reduce the degree of non-stationarity by aligning agents' objectives. Notably, the proposed counterfactual formulation preserves the system's equilibria and avoids oscillations. Our experiments show that training MARL algorithms with our novel reward formulation enables the agents to converge to the optimal solution, whereas the baseline algorithms fail to do so. We show these effects in both a toy network and the real-world network of Saint-Arnoult. Our results optimistically indicate that social awareness (i.e., including marginal costs in routing decisions) improves both the system-wide and individual performance of future urban systems with AVs.

AI Insights

• The study blends user‑equilibrium and system‑optimal routing concepts to guide autonomous vehicles toward globally efficient paths.
• Deep Q‑learning and policy‑gradient agents jointly learn counterfactual advantages, dramatically cutting training epochs.
• PettingZoo and RouteChoiceEnv provide ready‑made environments for replicating the multi‑agent traffic experiments.
• Scalability remains a challenge; the method’s performance degrades in highly complex urban grids.
• Assuming perfect real‑time traffic visibility limits practical deployment in noisy, sensor‑impaired cities.
• Sheffi’s equilibrium analysis and Tan’s cooperative‑vs‑independent RL reviews offer foundational context for this work.

Abstract
We prove uniqueness results and Harnack inequality for Bessel operators \begin{align*} %\label{def L transf alpha} D_t-\Delta_{x} -2a\cdot\nabla_xD_y- D_{yy}- \frac cy D_y % \nonumber \\[1ex]&=y^{\alpha}\sum_{i,j=1}^{N+1}a_{ij}D_{ij}+y^{\alpha-1}\left(v,\nabla\right)-by^{\alpha-2}. \end{align*} in the strip $[0,T]\times \mathbb{R}^{N+1}_+=\{0 \leq t \leq T, x \in \mathbb{R}^N, y>0\}$ under Neumann boundary conditions at $y=0$.

Abstract
We report on two months of ethnographic fieldwork in a women's centre in Pattaya, and interviews with 76 participants. Our findings, as they relate to digital security, show how (i) women in Pattaya, often working in the sex and massage industries, perceived relationships with farang men as their best, and sometimes only, option to achieve security; (ii) the strategies used by the women to appeal to a farang involved presenting themselves online, mirroring how they were being advertised by bar owners to attract customers; (iii) appealing to what they considered `Western ideals', the women sought out `Western technologies' and appropriated them for their benefit; (iv) the women navigated a series of online security risks, such as scams and abuse, which shaped their search for a farang; (v) the women developed collective security through knowledge-sharing to protect themselves and each other in their search for a farang. We situate our work in emerging digital security scholarship within marginalised contexts.

Abstract
Although more women than men enter social science disciplines, they are underrepresented at senior levels. To investigate this leaky pipeline, this study analyzed the career trajectories of 78,216 psychology researchers using large-scale bibliometric data. Despite overall constituting over 60\% of these researchers, women experienced consistently higher attrition rates than men, particularly in the early years following their first publication. Academic performance, particularly first-authored publications, was strongly associated with early-career retention -- more so than collaboration networks or institutional environment. After controlling for gender differences in publication-, collaboration-, and institution-level factors, women remained more likely to leave academia, especially in early-career stages, pointing to persistent barriers that hinder women's academic careers. These findings suggest that in psychology and potentially other social science disciplines, the core challenge lies in retention rather than recruitment, underscoring the need for targeted, early-career interventions to promote long-term gender equity.

AI Insights

• The authors use a time‑varying effect model to track how gender gaps in publication output shift over career stages.
• Scopus’ intensive longitudinal data enable mapping of individual researchers’ publication trajectories across decades.
• Women’s productivity climbs over time, yet promotion rates lag behind men’s, revealing a hidden promotion gap.
• The study champions inclusive hiring and early‑career interventions as key to improving retention.
• Suggested readings—“Keeping Women in the Science Pipeline” and “Problems in the Pipeline”—frame these findings within systemic barriers.
• A caveat: Scopus coverage may miss some disciplines or regions, limiting generalizability.