Language-driven generative agents have enabled large-scale social simulations with transformative uses, from interpersonal training to aiding global policy-making.However, recent studies indicate that generative agent behaviors often deviate from expert expectations and real-world data-a phenomenon we term the Behavior-Realism Gap.To address this, we introduce a theoretical framework called Persona-Environment Behavioral Alignment (PEBA), formulated as a distribution matching problem grounded in Lewin's behavior equation stating that behavior is a function of the person and their environment.Leveraging PEBA, we propose PersonaEvolve (PEvo), an LLM-based optimization algorithm that iteratively refines agent personas, implicitly aligning their collective behaviors with realistic expert benchmarks within a specified environmental context.We validate PEvo in an active shooter incident simulation we developed, achieving an 84% average reduction in distributional divergence compared to no steering and a 34% improvement over explicit instruction baselines.Results also show PEvo-refined personas generalize to novel, related simulation scenarios.Our method greatly enhances behavioral realism and reliability in high-stakes social simulations.More broadly, the PEBA-PEvo framework provides a principled approach to developing trustworthy LLM-driven social simulations.1

Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do not recognize stress appraisal and presume stress is a negative mental state. Yet, stress can be classified according to its influence on individuals; the way people perceive a stressor determines whether the stress reaction is considered as eustress (positive stress) or distress (negative stress). Thus, this study aims to assess the potential of using an ML approach to determine stress appraisal and identify eustress and distress instances using physiological and behavioral features. The results indicate that distress leads to higher perceived stress arousal compared to eustress. An XGBoost model that combined physiological and behavioral features using a 30 second time window had 83.38% and 78.79% F1-scores for predicting eustress and distress, respectively. Gender-based models resulted in an average increase of 2-4% in eustress and distress prediction accuracy. Finally, a model to predict the simultaneous assessment of eustress and distress, distinguishing between pure eustress, pure distress, eustress-distress coexistence, and the absence of stress achieved a moderate F1-score of 65.12%. The results of this study lay the foundation for work management interventions to maximize eustress and minimize distress in the workplace.

Heating, ventilation, and air conditioning (HVAC) systems account for 43% of building energy consumption, yet only 38% of commercial building occupants are satisfied with the thermal environment. The primary reasons for low occupant satisfaction are that HVAC operations do not integrate occupant comfort requirements nor control the thermal environment at the individual level. Personal comfort systems (PCSs) enable local control of the thermal environment around each occupant. However, full manual control of PCS can be inefficient, and fully automated PCS reduces an occupant's perceived control over the environment, which can then lead to lower satisfaction. A better solution might lie somewhere between fully manual and fully automated environmental control. In this article, we describe the development and implementation of an Internet-of-Things (IoT)-based intelligent agent that learns individual occupant comfort requirements and controls the thermal environment using PCS (i.e., a local fan and a heater). We tested different levels of automation where control is shared between an intelligent agent and the end user. Our results show that PCS use improves occupant satisfaction and including some level of automation can improve occupant satisfaction further than what is possible with manually operated PCS. Among the levels of automation investigated, inquisitive automation, where the user approves/declines the control actions of the intelligent agent before execution, led to highest occupant satisfaction with the thermal environment.

Behavioral intervention strategies have yet to become successful in the development of initiatives to foster pro-environmental behaviors in buildings. In this paper, we explored the potentials of increasing the effectiveness of requests aiming to promote pro-environmental behaviors by engaging users in a social dialog, given the effects of two possible personas that are more related to the buildings (i.e., building vs. building manager). We tested our hypotheses and evaluated our findings in virtual and physical environments and found similar effects in both environments. Our results showed that social dialog involvement persuaded respondents to perform more pro-environmental actions. However, these effects were significant when the requests were delivered by an agent representing the building. In addition, these strategies were not equally effective across all types of people and their effects varied for people with different characteristics. Our findings provide useful design choices for persuasive technologies aiming to promote pro-environmental behaviors.