L. Chen Ocean University of China C. R. Jung Federal University of Rio Grande do Sul S. R. Musse* Pontifical Catholic University of Rio Grande do Sul M. Moneimne C. Wang University of Pennsylvania R. Fruchter V. Bazjanac Stanford University G. Chen Ocean University of China N. I. Badler University of Pennsylvania Presence, Vol. 26, No. 4, Fall 2017, 436–452 doi:10.1162/PRES_a_00308 © 2018 by the Massachusetts Institute of Technology Crowd Simulation Incorporating Thermal Environments and Responsive Behaviors Abstract Crowd simulation addresses algorithmic approaches to steering, navigation, per- ception, and behavioral models. Significant progress has been achieved in modeling interactions between agents and the environment to avoid collisions, exploit empir- ical local decision data, and plan efficient paths to goals. We address a relatively unexplored dimension of virtual human behavior: thermal perception, comfort, and appropriate behavioral responses. Thermal comfort is associated with the ambi- ent environment, agent density factors, and interpersonal thermal feedback. A key feature of our approach is the temporal integration of both thermal exposure and occupant density to directly influence agent movements and behaviors (e.g., cloth- ing changes) to increase thermal comfort. Empirical thermal comfort models are incorporated as a validation basis. Simple heat transfer models are used to model environment, agent, and interpersonal heat exchange. Our model’s generality makes it applicable to any existing crowd steering algorithm as it adds additional integrative terms to any cost function. Examples illustrate distinctive emergent behaviors such as balancing agent density with thermal comfort, hysteresis in responding to local- ized or brief thermal events, and discomfort and likely injury produced by extreme packing densities. 1 Introduction We are all familiar with the role environment temperature has on our personal human comfort. Recently virtual reality accessories have begun to appear that provide the user with thermal sensations (Ranasinghe, Jain, Karwita, Tolley, & Do, 2017). Comfort considerations often precipitate adaptive behaviors in real people. Conversely, human body heat can influ- ence nearby air temperature. Harvesting body heat to warm a building has already been used in Stockholm’s Central Station (Casey, 2011). Energy management and efficiency are beneficial societal goals. The simulation of virtual agents has been used in many applications, such as games (Silver- man, Johns, Cornwell, & O’Brien, 2006b; Silverman, Bharathy, O’Brien, & Cornwell, 2006a), virtual environments (Pandzic et al., 2001), and evac- uation scenarios (Helbing, Farkas, & Vicsek, 2000). In realistic scenarios, virtual agent behaviors should reflect analogous human responses to thermal *Correspondence to soraia.musse@pucrs.br. 436 PRESENCE: VOLUME 26, NUMBER 4