Theory application of single loop learning

Using the single loop learning and double loop learning apply in the two cases.

Case 1
Nowadays, the government policy claims to increase tobacco duty for public health.

Now, I try to use single loop learning to apply this case.

1. Governing variable: The government always set different policy to decrease number of smokers. It is because they believe smoking that is a bad habits for people.

2. Action strategies: They think many ways to decrease the desire of smoking for people. First, the government decides more places to be a non-smoking area which can decrease the number of smoker in particular places. Then, it can decrease the effect of the second hand smoking.

3. Consequences: Many people decide to go to smoking place to smoke which can decline the second hand effect in non-smoking area.

For the loop, the government thinks that policy is not enough to make the smokers to give up this habit. It means that they believe that they need to set up more rules or policy to achieve their goal. So, they go to step 2(Action strategies).

New Action strategies: Now, the economy is not ideal for people. The government increases the tobacco duty by 50 per cent with immediate effect. It is because the price of its is one of the main factor to make decision for smokers.

New Consequences: Although they have recieved many commons about the smoke to abstain smoking which is the price to decline their desire of smoking, many smokers buy a different types of product to be a substitutes such as e-cigar(電子煙) , smuggle tobacco(走私煙).

So, I think this action can make a good effect in short run. For the long run, the government should do more research about how to solve this problem in long run.

To sum up, this case is like to single loop learning. The reason is that the government is not believed their governing variable which has problem occurred.