196
J. Maaß
10.8 How to Play the eLAN Serious Game
To implement the serious game, a series of moderated workshops was held in which
key stakeholders from the case study area were asked to react to scenarios of rising
energy prices. They were asked to act according to their best intentions and knowledge
as they would in reality, and thus to decide on measures and strategies to counter
the negative effects of rising energy prices identified for their municipality or their
respective sphere of action.
Several serious game sessions were held during the eLAN project between 2012
and 2013. Decision-makers from different administrative levels reacted to a scenario
of constantly high energy prices, which would stress the public purse through costs
for providing transport, social services and energy for heating, street lights, etc. If
energy prices consistently rose and were not a peak event that ceased after a few days
or weeks (like in the 1970s or in 2008), stakeholders would be more likely to be forced
to take action to counter the noticeable effects of increased fuel and heating costs. One
possible option was to directly subsidise fuel costs for consumers. Another option
would be a systemic option for hardship cases to improve their financial situation,
strained by rising energy prices. Those were two possible strategies decision-makers
might decide on. These and other possible answers were anticipated by the project
as possible outcomes of the serious game.
For the project’s starting scenario, “2015”, a price of USD 200 per barrel of crude
oil (Brent) was set. This was calculated to result in a pump price of EUR 2.20 per litre.
The average additional cost per household paid for energy (heating, transport and
electricity) in the scenario was subsequently calculated to be EUR 130 per month
compared to prices in 2010. For the later scenario, “2025”, the prices were set at
USD 400 per barrel of crude oil (Brent), which amounted to an additional EUR 340
compared to 2010 prices.
To formulate decisions, measures, etc., decision-makers were provided with LuT
model results translated into figures, tables and maps, as well as newspaper articles.
These materials consisted of an easily understandable multifaceted set of information,
which gave tangible meaning to abstract model numbers. The aim behind providing
and distributing information in this way was to simulate the real conditions in which
policymaking occurs as closely as possible.
Suggested policies by decision-makers were constrained by their current powers
within the German federal system as well as by current municipal budgetary restrictions. The restrictions were defined to be as realistic as possible to prevent participants from pursuing “unrealistic” responses, e.g. in the sense of spending more on
measures than they would be able to in reality. The project also attempted to prevent
the inclusion of more measures or ideas in their policy portfolio than they would if
the scenario was true.
The outcomes of the sessions were then quantified, translated into parameters for
the LuT model and fed back into it. For a more detailed explanation of the serious
game design, see Gertz et al. (2015).
J. Maaß
10.8 How to Play the eLAN Serious Game
To implement the serious game, a series of moderated workshops was held in which
key stakeholders from the case study area were asked to react to scenarios of rising
energy prices. They were asked to act according to their best intentions and knowledge
as they would in reality, and thus to decide on measures and strategies to counter
the negative effects of rising energy prices identified for their municipality or their
respective sphere of action.
Several serious game sessions were held during the eLAN project between 2012
and 2013. Decision-makers from different administrative levels reacted to a scenario
of constantly high energy prices, which would stress the public purse through costs
for providing transport, social services and energy for heating, street lights, etc. If
energy prices consistently rose and were not a peak event that ceased after a few days
or weeks (like in the 1970s or in 2008), stakeholders would be more likely to be forced
to take action to counter the noticeable effects of increased fuel and heating costs. One
possible option was to directly subsidise fuel costs for consumers. Another option
would be a systemic option for hardship cases to improve their financial situation,
strained by rising energy prices. Those were two possible strategies decision-makers
might decide on. These and other possible answers were anticipated by the project
as possible outcomes of the serious game.
For the project’s starting scenario, “2015”, a price of USD 200 per barrel of crude
oil (Brent) was set. This was calculated to result in a pump price of EUR 2.20 per litre.
The average additional cost per household paid for energy (heating, transport and
electricity) in the scenario was subsequently calculated to be EUR 130 per month
compared to prices in 2010. For the later scenario, “2025”, the prices were set at
USD 400 per barrel of crude oil (Brent), which amounted to an additional EUR 340
compared to 2010 prices.
To formulate decisions, measures, etc., decision-makers were provided with LuT
model results translated into figures, tables and maps, as well as newspaper articles.
These materials consisted of an easily understandable multifaceted set of information,
which gave tangible meaning to abstract model numbers. The aim behind providing
and distributing information in this way was to simulate the real conditions in which
policymaking occurs as closely as possible.
Suggested policies by decision-makers were constrained by their current powers
within the German federal system as well as by current municipal budgetary restrictions. The restrictions were defined to be as realistic as possible to prevent participants from pursuing “unrealistic” responses, e.g. in the sense of spending more on
measures than they would be able to in reality. The project also attempted to prevent
the inclusion of more measures or ideas in their policy portfolio than they would if
the scenario was true.
The outcomes of the sessions were then quantified, translated into parameters for
the LuT model and fed back into it. For a more detailed explanation of the serious
game design, see Gertz et al. (2015).