In some instances, an occupant comfort mean function may be used. In such a case, an occupant comfort mean function aggregates the comfort states of all occupants. An occupant comfort mean function, is attained by any of the following techniques: averaging methods, such as arithmetic mean, geometric mean, harmonic mean, tri-mean, median, mode, mid-range, quadratic mean (RMS), cubic mean, generalized mean, weighted mean; machine learning and statistical techniques, such as linear regression, logistic regression, polynomial regression, k-means clustering, k-nearest neighbors, decision trees, perceptron, multi-layer perceptron, kernel methods, support vector machines, ensemble methods, boosting, bagging, na?ve Bayes, expectation maximization, Gaussian mixture models, Gaussian processes, principal component analysis, singular value decomposition, reinforcement learning, Voronoi decomposition; and social theory voting techniques and concepts, such as social welfare functions, social choice functions, single transferrable vote, Bucklin's rule, social decision schemes, collective utility functions, and/or Condorcet method and extensions such as Copeland's rule, maximin, Dodgson's rule, Young's rule, and/or ranked pairs.