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104 P.-O. Siebers and F. Klügl

• Habits for energy-saving awareness category:
– Energy-saving awareness
– Likelihood of switching off unused electric appliances
– Likelihood of promoting greenness
To get the information we needed to fully define the stereotypes, we con-
ducted a survey amongst our school’s academics, researchers and PhD students,
anonymously asking them questions about their habits towards work time and
energy-saving awareness. We then analysed the data through cluster analysis to
come up with the stereotype groups, assigned some speaking name and populated
the stereotype tables with the “habit” information. The stereotype definitions we
ended up with can be found in Tables 6.3 and 6.4.

Defining Agent and Object Templates

For each of the relevant actor types we have identified in our scope table, we have
to develop an agent template containing all information for a prototypical agent.
These templates will act as a blueprint when we later create the actor population for
each simulation run. When it comes to modelling the environment, we need similar
templates for everything relevant we have identified in the scope table that lends
itself to be represented as an object (e.g. the appliances). For other things (e.g. the
weather), we need to consider other modelling methods. From a technical point of
view, there is no big difference between agents and objects. Thus we can use the
same types of diagrams to document their design. We will therefore use the term

Table 6.3 User stereotypes defining work time habits
Stereotype Working days Arrival time Leave time
Early bird Mon–Fri 5am-9am 4pm-7pm
Time table complier Mon–Fri 9am-10 am 5pm-6pm
Flexible worker Mon–Fri 10 am-1 pm 5 pm-11 pm
Hardcore worker Mon–Fri C Sat 8am-10 am 5 pm-11 pm

Table 6.4 User stereotypes defining energy-saving habits
Probability of Probability of sending
Energy saving switching off emails about energy
Stereotype awareness [0–100] unnecessary appliances issues to others
Environmental 95–100 0.95 0.9
champion
Energy saver 70–94 0.7 0.6
Regular user 30–69 0.4 0.2
Big user 0–29 0.2 0.05