Consequences: The Greatest Good and Its Problems
One family of moral theories says the right act is the one with the best results, counting everyone equally. It is used in every public health decision, and it also produces conclusions most people refuse to accept.
What a learner can do afterwards
- State the core claim of consequentialism and apply it to a public spending choice
- Give a case where it recommends something that feels clearly wrong, and say what the objection is
- Explain one repair consequentialists offer, such as judging rules rather than single acts
1 · Read
Consequentialism judges acts by their results only. Right means producing the best total outcome, classically the greatest happiness for the greatest number. Grade every theory on one scorecard. It should give clear verdicts, roughly fit our firmest convictions, and stay usable for real people with limited time and facts.
Give a council a fixed budget and the verdict is simple. Fund whatever saves the most healthy years per pound, even if it helps strangers over neighbours. Count everyone equally and pick the biggest total. That is the whole method: add the good, subtract the suffering, choose the largest sum.
The famous trouble comes fast. If framing one innocent person would calm a riot and save twenty lives, the maths seems to say do it. Most of us feel that verdict is clearly wrong. The theory also ignores intentions and duties, treating people as mere totals in the sum.
Its friends offer one repair. Judge rules instead of single acts. A rule like never punish the innocent earns trust and safety across thousands of cases, even when breaking it once looks tempting. Critics reply that a rule with exceptions is just case by case thinking in disguise.
Pick the act with the best total outcome, then face the hard case where the maths points at harming one innocent person.
2 · Watch
Take it off screen
Where it sits
8 questions wait behind this lesson, each with its answer explained. Every answer feeds the sky: stars light as they are learned, and dim when it is time to come back.