Energies 2015,8 11003 behaviors.Such studies would also improve if they could find which types of networks are most common in commercial buildings.In addition,they could find whether there is any relationship between the building type and network type. Azar and Menassa [12,87]used ABM to model occupancy-related behaviors in social sub-networks to show how occupants'interactions impact the energy-use of buildings.They tested various numbers of sub-networks in a typical United States'commercial building,and concluded that traditional modeling techniques (such as single-network modeling and bounded confidence models)are not applicable to simulate social networks and sub-networks in commercial buildings.However,in their studies,they did not considered the four main social network types studied by Anderson et al.[78].In fact,they only considered the small-world and scale-free network.Studying all social network types could be more effective to show the limitations of traditional modeling techniques. 3.2.Multi Agent Systems Compared to ABM,Multi Agent Systems(MAS)provide the opportunity for agents(i.e.,occupants) to communicate more with each other as well as with their built environment.MAS divides a complex problem into sub-problems solved by representative agents [63];for this reason,this approach is employed to model complex problems with multiple cyber agents.ABM is related to,but clearly distinct from,the MAS concept [92].A MAS can contain combined ABM,and in cases where the problem of energy saving is a multi-dimensional problem,MAS is an appropriate application [92,93].MAS may balance between occupants'preferences and energy saving;ABM fails to achieve this aim.In fact, concerning the commercial sector,MAS typically helps make tradeoffs between both building demands and occupant comfort [94,95]. Qiao et al.[96]introduced some prospects to indicate how MAS can simulate occupant behaviors to adjust device control in commercial buildings.Dounis and Caraiscos [93]presented MAS architecture for energy efficiency and comfort in built environments.They indicated that various advanced techniques(e.g.,Fuzzy Logic,Markov Chain Model,and Neural Networks)are implementing methods used in order to develop a MAS tool for improving the efficiency of building control systems.In addition, their simulation results from implementing MAS on a building showed that this model can manage occupants'preferences for thermal and luminance comfort,indoor air quality,and energy conservation. However,they did not clearly respond to the balance between thermal comfort and energy conversation. In some cases,achieving a level of thermal comfort could lead to an increase in energy consumption. They proposed MAS architecture for managing both energy efficiency and occupant comfort,and conducted a tradeoff between these two parties is needed.Klein et al.[63]proposed a MAS tool to model the management and control of appliances and occupants in a building.Their model could simulate and predict how changes to the building,occupant behavior(i.e.,preferences and schedule),and operational policies affect energy use and occupant comfort.In fact,their model simulated occupancy behavior as well as building operational policies.Based on their results from employing the model on a case study of a three-story university building,an improvement in occupants'comfort level and a reduction in energy consumption were realized.For this model,some data needed to be manually input.However, since such models need a large group of input data to simulate and predict energy use and occupantEnergies 2015, 8 11003 behaviors. Such studies would also improve if they could find which types of networks are most common in commercial buildings. In addition, they could find whether there is any relationship between the building type and network type. Azar and Menassa [12,87] used ABM to model occupancy-related behaviors in social sub-networks to show how occupants’ interactions impact the energy-use of buildings. They tested various numbers of sub-networks in a typical United States’ commercial building, and concluded that traditional modeling techniques (such as single-network modeling and bounded confidence models) are not applicable to simulate social networks and sub-networks in commercial buildings. However, in their studies, they did not considered the four main social network types studied by Anderson et al. [78]. In fact, they only considered the small-world and scale-free network. Studying all social network types could be more effective to show the limitations of traditional modeling techniques. 3.2. Multi Agent Systems Compared to ABM, Multi Agent Systems (MAS) provide the opportunity for agents (i.e., occupants) to communicate more with each other as well as with their built environment. MAS divides a complex problem into sub-problems solved by representative agents [63]; for this reason, this approach is employed to model complex problems with multiple cyber agents. ABM is related to, but clearly distinct from, the MAS concept [92]. A MAS can contain combined ABM, and in cases where the problem of energy saving is a multi-dimensional problem, MAS is an appropriate application [92,93]. MAS may balance between occupants’ preferences and energy saving; ABM fails to achieve this aim. In fact, concerning the commercial sector, MAS typically helps make tradeoffs between both building demands and occupant comfort [94,95]. Qiao et al. [96] introduced some prospects to indicate how MAS can simulate occupant behaviors to adjust device control in commercial buildings. Dounis and Caraiscos [93] presented MAS architecture for energy efficiency and comfort in built environments. They indicated that various advanced techniques (e.g., Fuzzy Logic, Markov Chain Model, and Neural Networks) are implementing methods used in order to develop a MAS tool for improving the efficiency of building control systems. In addition, their simulation results from implementing MAS on a building showed that this model can manage occupants’ preferences for thermal and luminance comfort, indoor air quality, and energy conservation. However, they did not clearly respond to the balance between thermal comfort and energy conversation. In some cases, achieving a level of thermal comfort could lead to an increase in energy consumption. They proposed MAS architecture for managing both energy efficiency and occupant comfort, and conducted a tradeoff between these two parties is needed. Klein et al. [63] proposed a MAS tool to model the management and control of appliances and occupants in a building. Their model could simulate and predict how changes to the building, occupant behavior (i.e., preferences and schedule), and operational policies affect energy use and occupant comfort. In fact, their model simulated occupancy behavior as well as building operational policies. Based on their results from employing the model on a case study of a three-story university building, an improvement in occupants’ comfort level and a reduction in energy consumption were realized. For this model, some data needed to be manually input. However, since such models need a large group of input data to simulate and predict energy use and occupant