Problem

Alternative A pays $+\$40$ with probability $0.9$ and $-\$200$ with probability $0.1$; B pays $+\$10$ for certain. Maximize EV only among alternatives with maximum loss at most $\$100$. Compute EVs, test eligibility, and recommend.

Big Picture

What this problem is really about

A constrained decision has two stages, and reversing them changes the problem. First compute the downside measure named in the rule and remove every alternative that crosses the hard limit. Expected value becomes the objective only inside the remaining feasible set, so a higher average cannot rescue an alternative that failed the eligibility test.

Answer choices

Choose your path.

Preview only

A is ineligible and B eligible, but claim B has the larger EV $16 and choose it for the wrong optimization reason.

A has EV $16 but worst loss $200, so it is ineligible. B has EV $10 and worst loss $0, so choose B.

A has EV $16 and worst loss $200; call it eligible because variance is absent, then choose A over eligible B.

A has EV $16 and B EV $10, so choose A before checking that A's $200 worst loss exceeds the $100 eligibility limit.

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Alternative A pays \(+\$40\) with probability \(0.9\) and \(-\$200\) with probability \(0.1\); B pays \(+\$10\) for certain. Maximize EV only among alternatives with maximum loss at most \(\$100\). Compute EVs, test eligibility, and recommend.

What this problem is really about

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