3. Prescriptive Analytics

Decision-Making under Uncertainty

Compare actions through payoffs, probabilities, regret and robustness

Decision-Making under Uncertainty

Separate action from state

HarborMart must decide evening picker capacity before demand is known.

Contribution after staffing costNormal demand, 60%High demand, 40%
current staffing£4,000£2,000
add one picker£3,700£3,600
add two pickers£3,200£4,200

Expected values are:

EV(0)=0.6(4,000)+0.4(2,000)=£3,200,EV(0)=0.6(4{,}000)+0.4(2{,}000)=£3{,}200,EV(1)=£3,660,EV(2)=£3,600.EV(1)=£3{,}660,\qquad EV(2)=£3{,}600.

With these probabilities and a risk-neutral objective, one extra picker wins by £60 over two. That small margin should trigger sensitivity analysis, not a confident slogan.

Expected value of perfect information

With advance knowledge, HarborMart would use current staffing under normal demand and two extra pickers under high demand:

EVPI=0.6(4,000)+0.4(4,200)=£4,080.EV_{PI}=0.6(4{,}000)+0.4(4{,}200)=£4{,}080.

Therefore

EVPI=4,0803,660=£420.EVPI=4{,}080-3{,}660=£420.

£420 is an upper bound on what perfect demand information is worth for this decision. A forecast costing £1,000 per evening cannot be justified by this simplified payoff table.

Regret reveals fragility

Regret is the gap from the best action in each state:

RegretNormalHighMaximum
current staffing£0£2,200£2,200
add one£300£600£600
add two£800£0£800

The minimax-regret action is also one extra picker. Agreement with expected value strengthens the case here; it is not guaranteed in general.

Risk, ambiguity and model error

  • risk: probabilities are treated as known;
  • parameter uncertainty: probabilities/payoffs are estimated;
  • model uncertainty: the listed states omit plausible mechanisms;
  • ambiguity: no single probability distribution is credible.

Use scenarios, intervals, robust optimisation or explicit judgement according to the source of uncertainty. Adding decimal places does not remove ambiguity.

Recent decision theory explicitly studies models as approximations; see Cerreia-Vioglio et al., Making Decisions Under Model Misspecification. Its formal treatment is a postgraduate extension, not a reason to abandon transparent classroom scenarios.

Quick check

High-demand probability rises from 40% to 45%. Which action now has highest expected value?

Answer
Current: £3,100; one extra: £3,655; two extra: £3,650. One extra still wins, now by only £5. The recommendation is extremely sensitive and implementation flexibility may dominate the nominal optimum.

Next: Linear Optimisation

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