How would you make decisions that impact 5,000 people? What about 50 million? What about an entire country’s population? I certainly don’t envy the leaders and politicians trying to do just that. Choices on that scale will always have inadvertent consequences. The Veil of Ignorance theory, however, is a tool to minimize those consequences.
The term was popularized in economics and has primarily been used to drive decisions that have a more beneficial outcome. The Veil of Ignorance is a way to force us to think through a decision from multiple perspectives and remove our own bias. Specifically, when making decisions that impact a large group of people, one should make the decision with the assumption that they don’t know where in society they will be. If you were a different race, gender, or socioeconomic status, would the same policy have a good outcome for you?
Healthcare is a perfect topic to use as an example. Let’s say your water bottle magically turned into a genie, granting you the ability to design a health care system for the whole country. Your task is to help the most people receive the best care. To test your thinking, try answering the following questions while imagining yourself in different groups. E.g. Imagine being a woman, a man, gay, straight, Black, Asian, Mexican, rich, or poor. Combine and add options as needed.
- Who should pay for healthcare? What’s the right cost for emergency operations?
- What procedures should be offered? What medications should be covered?
- Should one be able to switch doctors whenever they want?
- Who should get vaccines first? Should they be mandatory?
- Can religious beliefs be incorporated into healthcare delivery?
- Who should get time off when a baby is born?
- How long should one have to wait for a surgery? (e.g. ACL repair)
The goal is to make choices that do the most good. Our own biases cloud our judgement and using the Veil of Ignorance as a tool can alleviate them. We certainly don’t want ignorant leaders, but someone that knows how to use ignorance to suspend assumptions is certainly preferable.