A city in Ohio is considering replacing its fleet of gasoline-powered automobiles with electric cars. The manufacturer of the electric cars claims that this municipality will experience significant cost savings over the life of the fleet if it chooses to pursue the conversion. If the manufacturer is correct, the city will save about $1.5 million dollars. If the new technology employed within the electric cars is faulty, as some critics suggest, the conversion to electric cars will cost the city $675,000. A third possibility is that less seri￾ous problems will arise and the city will break even with the conversion. A consultant hired by the city estimates that the probabilities of these three outcomes are 0.30, 0.30, and 0.40, respectively. The city has an opportunity to implement a pilot program that would indicate the potential cost or savings resulting from a switch to electric cars. The pilot program involves renting a small number of electric cars for three months and running them under typical conditions. This program would cost the city $75,000. The city’s consultant believes that the results of the pilot program would be significant but not conclusive; she submits the values in the file P06_59.xlsx, a compilation of probabilities based on the experience of other cities, to support her contention. For example, the first row of her table indicates that if a conversion to electric cars will actually result in a savings of $1.5 million, the pilot program will indicate that the city saves money, loses money, and breaks even with probabilities 0.6, 0.1, and 0.3, respectively. What actions should the city take to maximize its expected savings? When should it run the pilot program, if ever?

A city in Ohio is considering replacing its fleet of
gasoline-powered automobiles with electric cars.
The manufacturer of the electric cars claims that this
municipality will experience significant cost savings
over the life of the fleet if it chooses to pursue the
conversion. If the manufacturer is correct, the city will
save about $1.5 million dollars. If the new technology
employed within the electric cars is faulty, as some
critics suggest, the conversion to electric cars will cost

the city $675,000. A third possibility is that less seri￾ous problems will arise and the city will break even
with the conversion. A consultant hired by the city
estimates that the probabilities of these three outcomes
are 0.30, 0.30, and 0.40, respectively. The city has an
opportunity to implement a pilot program that would
indicate the potential cost or savings resulting from
a switch to electric cars. The pilot program involves
renting a small number of electric cars for three
months and running them under typical conditions.
This program would cost the city $75,000. The city’s
consultant believes that the results of the pilot program
would be significant but not conclusive; she submits
the values in the file P06_59.xlsx, a compilation of
probabilities based on the experience of other cities, to
support her contention. For example, the first row of
her table indicates that if a conversion to electric cars
will actually result in a savings of $1.5 million, the
pilot program will indicate that the city saves money,
loses money, and breaks even with probabilities 0.6,
0.1, and 0.3, respectively. What actions should the city
take to maximize its expected savings? When should it
run the pilot program, if ever?