Research efforts have demonstrated the potentials of improving the performance of Heating, Ventilation, and Air-Conditioning (HVAC) systems by leveraging personalized thermal comfort preferences and profiles. However, there are remaining challenges for effective control in collective conditioning in multi-occupancy scenarios. In this study, we have investigated the impact of personal thermal comfort sensitivities – distinct individual reactions to temperature variations– on collective conditioning. To this end, we have explored whether taking the thermal comfort sensitivity into account influences the selection of temperature setpoints and the overall probability of achieving comfort. We have also examined the impact of different thermostat temperature resolutions (0.1, 0.5, and 1.0 °C) on these factors with a hypothesis that finer resolutions could aid in achieving improved overall thermal comfort. In doing so, we have proposed an agent-based control mechanism to simulate the multi-occupancy space, controlled by an HVAC agent to provide air conditioning for multiple human agents using three operational strategies to compare conventional strategies with our proposed approach. The first strategy relies on majority thermal votes, the second one relies on the gap between thermal preferences (i.e., preferred temperature) and ambient temperature, and the third strategy uses thermal comfort sensitivity in addition to preferences. The investigations were conducted by using stochastically modeled comfort profiles (six actual comfort profiles and 15 mathematically synthesized profiles from actual data). These profiles were used to model the behavior of human agents in diverse multi-occupancy scenarios, modeling two to ten occupants in a space for different thermostat temperature resolutions. Our investigations demonstrated that thermal comfort sensitivity plays a statistically significant role in collective conditioning as it resulted in changes of temperature setpoint in 86% of cases and a higher probability of achieving collective comfort. •The impact of individual thermal sensitivity has been evaluated in multi-occupancy scenarios.•The impact of individual thermal sensitivity on HVAC operational strategy is evaluated.•Agent-based modeling was adopted in evaluating the diverse multi-occupancy scenarios.•The influence of temperature setpoint resolution on collective comfort were evaluated.•Selected setpoint temperature and probability of collective thermal comfort were used as metrics.