Traffic Congestion Solutions
Challenging the Assumptions
As the mayor faces the problem of traffic congestion, it would be typical to assume that the best way to reduce congestion would be to find ways to make commuting easier. For instance, the mayor may expand the number of road lanes available - however this may actually encourage TTC users to drive to work. Alternatively, the mayor may expand public transportation. However, if such a plan were successful, commuters would quickly notice that roads are less congested and start driving again.
The assumptions discussed above would fall under the 85% areas of context and if I allow these ideas to dominate I will not be able to think outside the box. The assumption that making it easier to commute to Toronto will reduce traffic is an example of the single-loop-learning method. Measures based on these assumptions may ultimately result in the same traffic congestions coming back to surface in the long run and therefore do not create a sustainable solution. I should instead invoke the double-loop-learning method to break out of assumption based thinking and to challenge fundamental norms.
15% Recommendation
In the language of the 15% leverage theory, the assumption based solutions discussed above would require the city to put in 85% of the work to create a 15% result. The mayor should instead think of ways that the city could make it more difficult to drive downtown. The city could implement a tax incentive based permit system whereby drivers would be required to hold a permit that would only allow them to drive downtown four out of five business days of the week. In addition the city could reduce parking spaces and increase parking prices. By doing this, the city discourages downtown driving in a manner that would actually increase the use of public transportation and car-pooling. By implementing this idea, the city could use small initiatives to administratively structure a plan that would have a much larger impact.
Framing and
As the mayor faces the problem of traffic congestion, it would be typical to assume that the best way to reduce congestion would be to find ways to make commuting easier. For instance, the mayor may expand the number of road lanes available - however this may actually encourage TTC users to drive to work. Alternatively, the mayor may expand public transportation. However, if such a plan were successful, commuters would quickly notice that roads are less congested and start driving again.
The assumptions discussed above would fall under the 85% areas of context and if I allow these ideas to dominate I will not be able to think outside the box. The assumption that making it easier to commute to Toronto will reduce traffic is an example of the single-loop-learning method. Measures based on these assumptions may ultimately result in the same traffic congestions coming back to surface in the long run and therefore do not create a sustainable solution. I should instead invoke the double-loop-learning method to break out of assumption based thinking and to challenge fundamental norms.
15% Recommendation
In the language of the 15% leverage theory, the assumption based solutions discussed above would require the city to put in 85% of the work to create a 15% result. The mayor should instead think of ways that the city could make it more difficult to drive downtown. The city could implement a tax incentive based permit system whereby drivers would be required to hold a permit that would only allow them to drive downtown four out of five business days of the week. In addition the city could reduce parking spaces and increase parking prices. By doing this, the city discourages downtown driving in a manner that would actually increase the use of public transportation and car-pooling. By implementing this idea, the city could use small initiatives to administratively structure a plan that would have a much larger impact.
Framing and