Answer: (iv) A good decision applies quantitative approaches — though in ML/decision theory all (i)-(iii) also contribute, expected exam answer is (iv).

Explanation:

In Statistical Decision Theory / ML:

• Good decisions are not just based on logic (i) alone — logic without data is insufficient.
• Considering all data (ii) and all alternatives (iii) are part of rational decision making, but alone not distinctive.
• Quantitative approaches (iv) — using probability, expected loss/risk $R(a) = \int L(a,y) p(y|x) dy$, utility, optimization — differentiate good from bad decisions objectively. This aligns with Bayesian decision theory, where you choose action $a^* = \arg\min_a E[L]$.

Textbook often phrases: "A good decision is based on logic, considers all available data and alternatives, and applies quantitative approaches." If single best is asked, (iv) captures the statistical/ML perspective (quantitative risk minimization) that the other options miss. If multiple correct, (ii)+(iii)+(iv) would be joint.