In this case, we’ll examine whether it’s better to build a new road or repair existing ones. We’ll go step by step and use a comparison table to make the process easy to understand.
Step 1: Identify the decision alternatives
In our example, we have two alternatives:
- Build a new road
- Repair existing roads
Step 2: Determine the evaluation criteria
We need to identify the factors that are important for the community when making this decision. Here are some possible criteria:
- Traffic congestion relief
- Environmental impact
- Safety improvements
- Time to complete the project
Step 3: Assign weights to the criteria
We’ll assign weights to each criterion, which will represent its importance relative to the other criteria. The sum of the weights should equal 1 (or 100%). In this case, we’ve used hypothetical weights, but you could gather input from community members to assign weights based on their preferences.
|Traffic congestion relief||0.25|
|Time to complete the project||0.10|
Step 4: Evaluate each alternative based on the criteria
We’ll rate each alternative for each criterion on a scale of 0 to 10, where 0 means that the alternative is poor with respect to the criterion, and 10 means that it’s excellent. We can use available data or expert opinions to determine these ratings.
|Criteria||Weight||Build New Road (Rating)||Repair Existing Roads (Rating)|
|Traffic congestion relief||0.25||7||6|
|Time to complete the project||0.10||6||5|
Step 5: Calculate the weighted scores
For each alternative, multiply the rating by the weight for each criterion, and then sum up the results.
Build New Road weighted score = (4 * 0.30) + (7 * 0.25) + (3 * 0.20) + (5 * 0.15) + (6 * 0.10) = 4.85
Repair Existing Roads weighted score = (8 * 0.30) + (6 * 0.25) + (7 * 0.20) + (6 * 0.15) + (5 * 0.10) = 6.25
Step 6: Make the decision Based on the weighted scores, repairing existing roads (6.25) has a higher score than building a new road (4.85). Therefore, the data-driven decision would be to repair existing roads.
Remember, this is just a hypothetical example, and the actual data and weights will vary depending on the specific community and its preferences. This framework can be easily adapted to other community decisions, such as choosing between different public transportation options or evaluating park improvement projects.