Decision Analysis — Oceanview Property Purchase

A master's-level walkthrough of the Oceanview Development property-purchase case (Camm et al., 4th ed., p. 780), in 34 slides. It builds the decision tree and folds it back to the no-research decision, which is to bid, at an expected value of $50,000. It then revises the referendum odds with Bayes' theorem from the survey's reliability and derives the conditional strategy: bid if approval is predicted, for an expected value of $229,268, and don't bid if rejection is predicted, where the expected value is -$74,576. Finally it values the $15,000 study, with an EVSI of $44,000 against an EVPI of $70,000, an efficiency of 62.9%. Five traps and four checks are each tied to a real decision-analysis misconception, the decision tree is drawn in SVG, and every number is reproduced from the R script and the answer key.

Subject: Business Analytics · 71 slides · applied lesson

Open the interactive version of this deck · Homework for this lesson

What this lesson covers

The lesson, slide by slide

1. Oceanview: Should Glenn Bid $5 Million?

Title

Business Analytics · Decision Analysis Case

A sealed-bid auction, a zoning referendum nobody can predict, and a $15,000 survey for sale. Today we build the decision tree, fold it back to a recommendation, revise the odds with Bayes, and prove whether that survey is worth buying.

2. What you will be able to do

Objectives

This case is the full decision-analysis toolkit on one problem. By the end you can:

3. What survived from Monte Carlo Simulation — Four Corners Case?

Warm-up

Discussion prompt

Before we open Decision Analysis — Oceanview Property Purchase: without looking back, what was the main idea of Monte Carlo Simulation — Four Corners Case, and what could you do by the end of it that you could not do before?

Hint: One sentence for the idea, one for the skill. If the second one is blank, that is the part to revisit.

Answer:

Master's-level walkthrough of the Four Corners financial-planning case in R (43 slides). Builds the deterministic compounding engine, Goal Seek as root-finding, the two input distributions (Uniform salary growth, Normal portfolio growth), the Monte Carlo engine, and the risk read-out (P(success), expected shortfall, VaR/CVaR), then the 25-year horizon and the reusable template.

4. The situation Glenn faces

Concept

Glenn Foreman runs Oceanview Development. A county tax foreclosure will sell a property by sealed-bid auction. His plan: bid $5,000,000, and if he wins, build and sell luxury condos.

The catch: the land is zoned for single-family homes. Condos need a zoning referendum to pass in November — and Glenn can't control that vote. So winning the bid is only half the gamble.

DateEvent
June 1Today — decide whether to bid
Aug 1Optional survey results arrive
Aug 15Sealed bids due (with 10% deposit)
Sep 1Winning bid announced
NovemberZoning referendum voted on

5. Fill in: Event for The situation Glenn faces

Comparison

Comparison matrix

From The situation Glenn faces: refill the Event column from what you know. The rest of the table is as it appeared.

DateEvent
June 1Today — decide whether to bid
Aug 1Optional survey results arrive
Aug 15Sealed bids due (with 10% deposit)
Sep 1Winning bid announced
NovemberZoning referendum voted on

6. Every number the case gives you

Concept

Decision problems become mechanical once the givens are laid out. List them first — every later slide traces back to this table.

QuantitySymbolValue
Bid amount—$5,000,000
P(this bid is highest)P(win)0.20
Deposit (10% of bid)—$500,000 (refunded if bid loses)
Prior P(referendum approved)P(s₁)0.30
Prior P(referendum rejected)P(s₂)0.70
Revenue if condos built—$15,000,000
Property + construction cost—$5M + $8M
Survey cost—$15,000
Survey reliabilityMeaningValue
P(A | s₁)predicts approval when it WILL pass0.90
P(N | s₁)predicts rejection when it will pass0.10
P(A | s₂)predicts approval when it will fail0.20
P(N | s₂)predicts rejection when it WILL fail0.80

7. What each one costs: Every number the case gives you

Trade off

Comparison matrix

From Every number the case gives you: every row here is a choice with a cost. Fill the Symbol column, then say which row you would actually pick and what you give up for it.

QuantitySymbolValue
Bid amount—$5,000,000
P(this bid is highest)P(win)0.20
Deposit (10% of bid)—$500,000 (refunded if bid loses)
Prior P(referendum approved)P(s₁)0.30
Prior P(referendum rejected)P(s₂)0.70
Revenue if condos built—$15,000,000
Property + construction cost—$5M + $8M
Survey cost—$15,000

8. Four questions, four analytical moves

Concept

The managerial report asks four things. Each maps to one move we'll build in order — nothing here is optional decoration.

Draw the tree
The logical sequence of decisions and chances.
Decide without research
Fold back the tree → bid or not.
Strategy with research
Bayes-revise, then a conditional plan.
Value the survey
EVSI vs. its $15,000 price; EVPI.

9. Which is which: Four questions, four analytical moves

Matching

Match the pairs

From Four questions, four analytical moves — match each one to what it actually does. The descriptions have been shuffled.

  • c1. Draw the tree
  • c2. Decide without research
  • c3. Strategy with research
  • c4. Value the survey
  • b1. The logical sequence of decisions and chances.
  • b2. Fold back the tree → bid or not.
  • b3. Bayes-revise, then a conditional plan.
  • b4. EVSI vs. its $15,000 price; EVPI.

Why: Draw the tree, Decide without research, Strategy with research, Value the survey are easy to tell apart while they are sitting next to their descriptions and much harder afterwards, which is what this checks.

10. Part 1 — Payoffs at Every Endpoint

Section

The dollars at each tree tip

11. Only three things can happen

Concept

Once a bid is submitted, exactly three endpoints are possible. Work out the profit for each explicitly — these are the numbers that sit at the tips of the tree.

  1. Win the bid AND referendum passes → build the condos.
  2. Win the bid AND referendum fails → walk away (don't complete the purchase).
  3. Lose the bid → deposit refunded, nothing gained or lost.

12. Break it if you can: Only three things can happen

Counterexample

Discussion prompt

Once a bid is submitted, exactly three endpoints are possible. Work out the profit for each explicitly — these are the numbers that sit at the tips of the tree.

That is stated as though it always holds. Do one of two things: produce a case where it fails, or say precisely what rules such a case out. "It just does" is not on the menu.

Hint: Hunt at the extremes first — zero, one, negative, empty, equal. If every extreme survives, the reason they survive is the proof.

13. What has to happen first: The three payoffs, in dollars

Ranking

Put in order

Put the moves of The three payoffs, in dollars into the order they have to happen.

  1. Build: Revenue − Property − Construction = $15M − $5M − $8M = +$2,000,000
  2. Walk away: forfeit only the 10% deposit = −0.10 × $5,000,000 = −$500,000
  3. Lose the bid: $0. Not bidding at all: $0.

Why: These are the moves of the worked example in the order it makes them, and each one is set up by the one before it. Won the bid and zoning approved, so all three cash flows land: sell for $15M, pay $5M for the land and $8M to build.

14. The three payoffs, in dollars

Worked example

Build: Revenue − Property − Construction = $15M − $5M − $8M = +$2,000,000

Why: Won the bid and zoning approved, so all three cash flows land: sell for $15M, pay $5M for the land and $8M to build.

Walk away: forfeit only the 10% deposit = −0.10 × $5,000,000 = −$500,000

Why: Zoning failed. Glenn's stated best option is NOT to complete the purchase — so Oceanview loses just the deposit, not the property.

Lose the bid: $0. Not bidding at all: $0.

Why: If the bid isn't highest the deposit is refunded — no gain, no loss. The whole decision is measured against this $0 baseline.

EndpointCash flowsPayoff
Win + approved (build)15M − 5M − 8M+$2,000,000
Win + rejected (walk)−10% of 5M−$500,000
Lose the biddeposit refunded$0

15. Draw the shape of it: The three payoffs, in dollars

Blank canvas

Draw it

Draw what The three payoffs, in dollars just did — the shape of it, not the line-by-line working. One picture, labels only where you need them. Then check it against the steps: anything you could not draw is a step you followed rather than understood.

16. Something is wrong here: losing the whole property when zoning fails

Anomaly

Predict first

A student writes this, and it looks reasonable:

Assume that if zoning fails, Oceanview is stuck with the land it bid on.

It is wrong. Say what breaks — and say it before you turn the page.

Correct: Treats the purchase as completed — as if the $5M were already spent and unrecoverable.

Read the case: if zoning is rejected, Glenn simply does not complete the purchase.

Why: Treats the purchase as completed — as if the $5M were already spent and unrecoverable.

17. Trap: losing the whole property when zoning fails

Trap

The trap

Assume that if zoning fails, Oceanview is stuck with the land it bid on.

Walk-away payoff = −$5,000,000 (we're out the property)

Why: Treats the purchase as completed — as if the $5M were already spent and unrecoverable.

Bidding now looks far too risky to ever consider

Why: A phantom −$5M loss poisons the whole tree and flips the recommendation.

The fix

Read the case: if zoning is rejected, Glenn simply does not complete the purchase.

Walk-away payoff = −$500,000 (deposit only)

Why: The bid required a 10% certified check; abandoning the purchase forfeits that $500,000 and nothing more.

The downside is capped at the deposit

Why: This bounded loss is exactly what makes bidding a live option — carry −$500,000, not −$5M.

18. Part 2 — The Tree & the No-Research Call

Section

Deliverables 1 and 2

19. Picture it first: The decision tree

Picture it

Figure (svg): Decision tree: a square decision node branches to Bid and Don't bid (payoff $0). Bid leads to a chance node Win 0.2 or Lose 0.8 (payoff $0). Win leads to a referendum chance node Approved 0.3 (payoff +$2.0M, build) or Rejected 0.7 (payoff −$0.5M, walk away).

Two chance nodes in series: first the bid (win 0.2), then the referendum (approve 0.3).

Discussion prompt

Read the picture before the words. What is this showing, and what is the one thing it is built to make obvious? Commit to an answer, then read on.

Hint: Name the parts, then say what changes between them — and if nothing changes, say what is being held still.

Answer:

This tree IS the first deliverable. Read it left to right: a square is a choice Oceanview controls; a circle is a chance event it does not.

20. The decision tree

Concept

This tree IS the first deliverable. Read it left to right: a square is a choice Oceanview controls; a circle is a chance event it does not.

Figure (svg): Decision tree: a square decision node branches to Bid and Don't bid (payoff $0). Bid leads to a chance node Win 0.2 or Lose 0.8 (payoff $0). Win leads to a referendum chance node Approved 0.3 (payoff +$2.0M, build) or Rejected 0.7 (payoff −$0.5M, walk away).

Two chance nodes in series: first the bid (win 0.2), then the referendum (approve 0.3).

The payoffs from Part 1 sit at the tips: +$2.0M, −$0.5M, and two $0 ends. Everything else is just weighting them by probability.

21. By analogy: The decision tree

Analogy

Discussion prompt

Explain The decision tree by analogy to something with no Business Analytics in it at all — a queue, a recipe, a map, a bank balance, whatever fits. Then say where your analogy breaks.

Hint: An analogy that never breaks is not an analogy, it is the same idea wearing a hat. Find the seam — that is the part that is actually new.

Answer:

The payoffs from Part 1 sit at the tips: +$2.0M, −$0.5M, and two $0 ends. Everything else is just weighting them by probability.

22. What "folding back" means

Intuition

You can't evaluate the tree left to right — you don't know the payoff until you reach a tip. So you work right to left, collapsing one node at a time.

At a chance node you can't choose, so you take the average: each payoff weighted by its probability. At a decision node you can choose, so you keep the best branch. Replace the node with that single number and move left.

The value that finally reaches the root is what the whole decision is worth today — and the branches you kept along the way ARE the recommended plan.

23. Teach it back: What "folding back" means

Explain it

Discussion prompt

Explain What "folding back" means to a student a year behind you. No notation, no jargon they have not met — and it still has to be true.

Hint: If your explanation needs a symbol they have never seen, you are describing the notation rather than the idea.

Answer:

You can't evaluate the tree left to right — you don't know the payoff until you reach a tip. So you work right to left, collapsing one node at a time.

24. Plan first: Fold it back with no survey

Step zero

Discussion prompt

Fold it back with no survey — before any calculation: what is the plan? Name the moves in order, in plain English, without doing the arithmetic.

Hint: It starts with: Referendum node: EV = 0.30(+$2,000,000) + 0.70(−$500,000) = 600,000 −…

Answer:

  1. Referendum node: EV = 0.30(+$2,000,000) + 0.70(−$500,000) = 600,000 − 350,000 = $250,000
  2. Bid node: EV = 0.20($250,000) + 0.80($0) = $50,000
  3. Decision node: compare EV(bid) = $50,000 vs. EV(don't bid) = $0 → BID

25. Fold it back with no survey

Worked example

Start at the rightmost chance node (the referendum), collapse it, then the bid node, then choose.

Referendum node: EV = 0.30(+$2,000,000) + 0.70(−$500,000) = 600,000 − 350,000 = $250,000

Why: Reached only if the bid wins. Average the two zoning outcomes with the prior 0.30 / 0.70.

Bid node: EV = 0.20($250,000) + 0.80($0) = $50,000

Why: The bid wins with probability 0.20 (reaching the $250,000 node) and loses with 0.80 (payoff $0, deposit refunded).

Decision node: compare EV(bid) = $50,000 vs. EV(don't bid) = $0 → BID

Why: Bidding's expected value clears the $0 of walking away, so the tree says submit the bid.

NodeComputationValue
Referendum0.3(2,000,000) + 0.7(−500,000)$250,000
Bid win/lose0.2(250,000) + 0.8(0)$50,000
Decisionmax($50,000, $0)Bid → $50,000

26. Work backwards from the answer: Fold it back with no survey

Reverse engineer

Discussion prompt

Work backwards. The example finished here:

Decision node: compare EV(bid) = $50,000 vs. EV(don't bid) = $0 → BID

What was it asked to do, and what must it have been given? Reconstruct the problem from its answer.

Hint: Every quantity in the result had to enter somewhere. Account for each one.

Answer:

Start at the rightmost chance node (the referendum), collapse it, then the bid node, then choose.

27. Something is wrong here: stopping at the referendum node

Anomaly

Predict first

A student writes this, and it looks reasonable:

See the referendum node's $250,000 and call that the value of bidding.

It is wrong. Say what breaks — and say it before you turn the page.

Correct: Silently assumes the bid is certain to win — it skips the 0.2 win-probability chance node entirely.

Push the $250,000 through the win/lose node before comparing anything.

Why: Silently assumes the bid is certain to win — it skips the 0.2 win-probability chance node entirely.

28. Trap: stopping at the referendum node

Trap

The trap

See the referendum node's $250,000 and call that the value of bidding.

"Bidding is worth $250,000."

Why: Silently assumes the bid is certain to win — it skips the 0.2 win-probability chance node entirely.

Overstates the decision's value 5×

Why: $250,000 is conditional on winning; the true, unconditional value is far lower. This error also inflates EVSI later.

The fix

Push the $250,000 through the win/lose node before comparing anything.

EV(bid) = 0.20 × $250,000 + 0.80 × $0 = $50,000

Why: Only 20% of the time do you reach that $250,000; 80% of the time the bid loses and pays $0.

Recommendation: bid — worth $50,000, not $250,000

Why: Same decision, honest number. Fold back through EVERY node between the tip and the root.

29. Without one step: The fold-back recipe

Constraint

Discussion prompt

Run The fold-back recipe with this step confiscated:

Work right to left, one node at a time.

Is it still possible? If it is, say what takes its place and what it costs you. If it is not, say exactly what that step was providing that nothing else does.

Hint: A step you can drop for free was never load-bearing. If you cannot drop it, name the thing that goes wrong the moment it is gone.

Answer:

  1. Draw left→right: squares for choices you control, circles for chance events.
  2. Put the payoff at each tip.
  3. Work right to left, one node at a time.
  4. At a chance node: EV = Σ (probability × payoff).
  5. At a decision node: keep the highest-EV branch. The value at the root is the decision's worth; the kept branches are the strategy.

30. The fold-back recipe

Pattern

Every decision tree in this course collapses by the same five moves:

  1. Draw left→right: squares for choices you control, circles for chance events.
  2. Put the payoff at each tip.
  3. Work right to left, one node at a time.
  4. At a chance node: EV = Σ (probability × payoff).
  5. At a decision node: keep the highest-EV branch. The value at the root is the decision's worth; the kept branches are the strategy.

31. Where does it stop working: The fold-back recipe

Edge cases

Discussion prompt

The fold-back recipe works on the cases you have just seen. Push it to the edge: what is the most degenerate input it still handles — empty, zero, one item, everything equal — and what is the first case where it stops being true? Name the case, not just "it breaks".

Hint: Try the smallest legal input, then the largest, then the one where two things collide. Methods are specified at their edges; the middle takes care of itself.

Answer:

Every decision tree in this course collapses by the same five moves:

32. Rule out three: Check: the no-research decision

Elimination

Eliminate the wrong options

What is EV(bid) with no survey, and what should Oceanview do?

3 of these 4 are wrong. Strike them one at a time, and say what rules each one out before you strike the next. The survivor is the answer.

  • A. $50,000 — submit the bid
  • B. $250,000 — submit the bid
  • C. $50,000 — do not bid
  • D. $75,000 — submit the bid

Survives elimination: A

Why: EV(bid) = 0.20 × $250,000 + 0.80 × $0 = $50,000. Since $50,000 beats the $0 of not bidding, submit the bid. This $50,000 is the no-information benchmark every later value is measured against.

33. Check: the no-research decision

Check

The referendum node folds to $250,000 and the bid wins with probability 0.20 (else $0). Work the bid node on paper before you tap.

Check your understanding

What is EV(bid) with no survey, and what should Oceanview do?

  • A. $50,000 — submit the bid (correct)
  • B. $250,000 — submit the bid
  • C. $50,000 — do not bid
  • D. $75,000 — submit the bid

Answer: A

Why: EV(bid) = 0.20 × $250,000 + 0.80 × $0 = $50,000. Since $50,000 beats the $0 of not bidding, submit the bid. This $50,000 is the no-information benchmark every later value is measured against.

Why B tempts people
Reported the referendum node's $250,000 as the bid's value — skipping the 0.20 win-probability node. That number assumes the bid is certain to win.
Why C tempts people
Right EV, wrong call: any expected value above the $0 of not bidding favors bidding. A small positive EV still beats zero.
Why D tempts people
Used the referendum probabilities (0.30 / 0.70) at the win/lose node instead of 0.20 / 0.80: 0.30 × 250,000 = $75,000. The two chance nodes have different probabilities — don't swap them.

34. Part 3 — Revising the Odds with Bayes

Section

Turning a survey into new probabilities

35. The survey and its track record

Concept

Glenn can buy a voter survey for $15,000. It returns one of two predictions: A (approval) or N (rejection). It isn't perfect — the case gives its historical accuracy.

If the truth is…P(predicts A)P(predicts N)
s₁ — will be approved0.900.10
s₂ — will be rejected0.200.80

These are P(prediction | state) — how the survey behaves when we already know the outcome. But on August 1 Glenn faces the reverse: he'll see a prediction and need P(state | prediction).

36. Fill in: P(predicts N) for The survey and its track record

Comparison

Comparison matrix

From The survey and its track record: refill the P(predicts N) column from what you know. The rest of the table is as it appeared.

If the truth is…P(predicts A)P(predicts N)
s₁ — will be approved0.900.10
s₂ — will be rejected0.200.80

37. Why we must flip the probabilities

Intuition

The survey tells us how reliable it is given the truth. Glenn needs the opposite: given the survey's word, how likely is approval now? Those two are not the same number.

Bayes' theorem flips them — and it does NOT ignore what we already knew. The prior (only 0.30 chance of approval) still pulls on the answer. A 90%-accurate 'approve' doesn't make approval 90% likely; it updates 0.30 upward, not all the way.

38. By analogy: Why we must flip the probabilities

Analogy

Discussion prompt

Explain Why we must flip the probabilities by analogy to something with no Business Analytics in it at all — a queue, a recipe, a map, a bank balance, whatever fits. Then say where your analogy breaks.

Hint: An analogy that never breaks is not an analogy, it is the same idea wearing a hat. Find the seam — that is the part that is actually new.

Answer:

The survey tells us how reliable it is given the truth. Glenn needs the opposite: given the survey's word, how likely is approval now? Those two are not the same number.

39. Guess the shape of the answer: Bayes: joint table → marginals → posteriors

Estimation

Predict first

Three mechanical steps. First, multiply each prior by each conditional to get joint probabilities P(prediction and state).

Commit before you compute: what does Bayes: joint table → marginals → posteriors come out to? A rough magnitude and the right form is enough — the point is to have something concrete to be wrong about.

Correct: Posteriors: divide each joint by its column total

Why: A prediction you can defend turns the computation into a check rather than a leap of faith — and an answer that contradicts it is caught on the spot. P(s₁|A)=0.27/0.41=0.6585, P(s₂|A)=0.3415, P(s₁|N)=0.03/0.59=0.0508, P(s₂|N)=0.9492.

40. Bayes: joint table → marginals → posteriors

Worked example

Three mechanical steps. First, multiply each prior by each conditional to get joint probabilities P(prediction and state).

StatePrior P(s)Joint with AJoint with N
s₁ approved0.300.30 × 0.9 = 0.270.30 × 0.1 = 0.03
s₂ rejected0.700.70 × 0.2 = 0.140.70 × 0.8 = 0.56
Column total1.00P(A) = 0.41P(N) = 0.59

Marginals: P(A) = 0.27 + 0.14 = 0.41, P(N) = 0.03 + 0.56 = 0.59

Why: Sum each column — these are the probabilities the survey says 'approve' or 'reject' at all, the branch probabilities in the tree.

\[ P(s_1 \mid A) = \dfrac{P(A \mid s_1)\,P(s_1)}{P(A)} = \dfrac{0.9 \times 0.30}{0.41} = 0.6585 \]

Posteriors: divide each joint by its column total

Why: P(s₁|A)=0.27/0.41=0.6585, P(s₂|A)=0.3415, P(s₁|N)=0.03/0.59=0.0508, P(s₂|N)=0.9492.

PredictionP(approve | pred)P(reject | pred)
A (approve)0.65850.3415
N (reject)0.05080.9492
Prior (no survey)0.300.70

A favorable survey more than doubles approval odds (0.30 → 0.66); an unfavorable one nearly erases them (0.30 → 0.05). That gap is what makes the survey valuable.

41. Decode the notation: Bayes: joint table → marginals → posteriors

Notation

Annotate

From Bayes: joint table → marginals → posteriors — read this one piece at a time. What is each part doing?

On: \( P(s_1 \mid A) = \dfrac{P(A \mid s_1)\,P(s_1)}{P(A)} = \dfrac{0.9 \times 0.30}{0.41} = 0.6585 \)

  • Sum each column — these are the probabilities the survey says 'approve' or 'reject' at all, the branch probabilities in the tree.
  • P(s₁|A)=0.27/0.41=0.6585, P(s₂|A)=0.3415, P(s₁|N)=0.03/0.59=0.0508, P(s₂|N)=0.9492.

42. Something is wrong here: reading the survey's accuracy as the answer

Anomaly

Predict first

A student writes this, and it looks reasonable:

The survey says 'approve' and it's 90% accurate when approval will happen — so approval is now 90% likely.

It is wrong. Say what breaks — and say it before you turn the page.

Correct: Grabs P(A | s₁) = 0.9 and uses it as P(s₁ | A).

Flip it with Bayes — divide the joint probability by the marginal.

Why: Grabs P(A | s₁) = 0.9 and uses it as P(s₁ | A). This is the classic inversion — confusing P(prediction | state) with P(state | prediction).

43. Trap: reading the survey's accuracy as the answer

Trap

The trap

The survey says 'approve' and it's 90% accurate when approval will happen — so approval is now 90% likely.

P(approve | A) = 0.90

Why: Grabs P(A | s₁) = 0.9 and uses it as P(s₁ | A). This is the classic inversion — confusing P(prediction | state) with P(state | prediction).

Ignores that approval was only 30% likely to begin with

Why: Throws away the prior entirely, so an unreliable-for-rare-events signal looks far stronger than it is.

The fix

Flip it with Bayes — divide the joint probability by the marginal.

P(approve | A) = 0.27 / 0.41 = 0.6585

Why: The joint 0.27 shares the 'approve' branch with the 0.14 false-alarm from s₂; dividing by P(A)=0.41 accounts for both.

Approval rises to 66%, not 90%

Why: The low 0.30 prior holds it back. Bayes updates the prior — it never just replaces it with the reliability.

44. Break it on purpose: reading the survey's accuracy as the answer

Break the constraint

Discussion prompt

The rule this trap just fixed:

The joint 0.27 shares the 'approve' branch with the 0.14 false-alarm from s₂; dividing by P(A)=0.41 accounts for both.

Now break it on purpose. Build a case that violates it and follow the consequences until something visibly fails. Where does the failure first show up — and would you have noticed it if you had not been looking?

Hint: The dangerous rules are the ones whose violation still produces an answer. If yours fails loudly, try to find one that fails quietly.

Answer:

Grabs P(A | s₁) = 0.9 and uses it as P(s₁ | A). This is the classic inversion — confusing P(prediction | state) with P(state | prediction).

45. Answer it before you see the options: Check: the posterior after a rejection…

Prediction

Predict first

After the survey predicts rejection, what is P(approved | N) = P(s₁ | N)?

Answer it in your own words, now, with nothing to choose from. The options are on the next slide — and picking the right one off a list is an easier skill than producing it.

Correct: ≈ 0.05

Why: P(s₁ | N) = joint / marginal = 0.03 / 0.59 ≈ 0.0508. A rejection prediction drives approval odds from 0.30 down to about 5% — the survey nearly rules approval out.

46. Check: the posterior after a rejection prediction

Check

The survey predicts rejection (N). Joint P(N and s₁) = 0.03 and P(N) = 0.59. What is the chance the referendum still passes? Compute before tapping.

Check your understanding

After the survey predicts rejection, what is P(approved | N) = P(s₁ | N)?

  • A. ≈ 0.05 (correct)
  • B. 0.10
  • C. 0.30
  • D. 0.03

Answer: A

Why: P(s₁ | N) = joint / marginal = 0.03 / 0.59 ≈ 0.0508. A rejection prediction drives approval odds from 0.30 down to about 5% — the survey nearly rules approval out.

Why B tempts people
Used P(N | s₁) = 0.10 directly — the inversion again. That's how the survey behaves when approval is true, not the chance of approval given the prediction.
Why C tempts people
Kept the prior 0.30 and ignored the survey. The whole point of paying for the survey is to move off the prior.
Why D tempts people
Stopped at the joint probability 0.03 without dividing by P(N) = 0.59. A joint is not a conditional until you normalize by the marginal.

47. Part 4 — The Strategy With Research

Section

Deliverable 3

48. Now the decision can wait for the survey

Concept

Because results arrive August 1, before the August 15 deadline, Oceanview can let its bid depend on the prediction. The tree grows a survey-result node with two branches — A and N — each its own bid-or-not subtree.

One thing does NOT change: P(win) stays 0.20. The survey polls voters about zoning; it says nothing about rival bidders. Only the referendum probabilities get revised — to the posteriors from Part 3.

49. What has to happen first: Fold back each survey branch

Ranking

Put in order

Put the moves of Fold back each survey branch into the order they have to happen.

  1. Survey says A → referendum node: 0.6585($2M) + 0.3415(−$500k) = 1,317,073 − 170,732 = $1,146,341
  2. EV(bid | A) = 0.20 × $1,146,341 = $229,268 > 0 → BID
  3. Survey says N → referendum node: 0.0508($2M) + 0.9492(−$500k) = 101,695 − 474,576 = −$372,881
  4. EV(bid | N) = 0.20 × (−$372,881) = −$74,576 < 0 → DON'T BID

Why: These are the moves of the worked example in the order it makes them, and each one is set up by the one before it. Use the favorable posteriors 0.6585 / 0.3415 in place of the 0.30 / 0.70 priors.

50. Fold back each survey branch

Worked example

Repeat the Part-2 roll-back twice, swapping in the posterior probabilities for each prediction.

Survey says A → referendum node: 0.6585($2M) + 0.3415(−$500k) = 1,317,073 − 170,732 = $1,146,341

Why: Use the favorable posteriors 0.6585 / 0.3415 in place of the 0.30 / 0.70 priors.

EV(bid | A) = 0.20 × $1,146,341 = $229,268 > 0 → BID

Why: Push through the unchanged 0.20 win node. Positive, so bid when the survey predicts approval.

Survey says N → referendum node: 0.0508($2M) + 0.9492(−$500k) = 101,695 − 474,576 = −$372,881

Why: Now the unfavorable posteriors 0.0508 / 0.9492 apply — approval is nearly ruled out.

EV(bid | N) = 0.20 × (−$372,881) = −$74,576 < 0 → DON'T BID

Why: Negative expected value; walking away ($0) beats bidding. Do not bid when the survey predicts rejection.

PredictionEV(bid | prediction)Action
A — approval+$229,268Bid
N — rejection−$74,576Do not bid

51. What each one costs: Fold back each survey branch

Trade off

Comparison matrix

From Fold back each survey branch: every row here is a choice with a cost. Fill the Action column, then say which row you would actually pick and what you give up for it.

PredictionEV(bid | prediction)Action
A — approval+$229,268Bid
N — rejection−$74,576Do not bid

52. Something is wrong here: bidding anyway after a rejection prediction

Anomaly

Predict first

A student writes this, and it looks reasonable:

"Bidding was worth $50,000 before, and we already paid for the survey — so bid regardless."

It is wrong. Say what breaks — and say it before you turn the page.

Correct: Ignores that the survey rewrote the odds — approval just fell from 0.30 to 0.05.

Act on the revised odds — that's the entire reason you bought them.

Why: Ignores that the survey rewrote the odds — approval just fell from 0.30 to 0.05. The old number no longer applies.

53. Trap: bidding anyway after a rejection prediction

Trap

The trap

"Bidding was worth $50,000 before, and we already paid for the survey — so bid regardless."

Reuse the prior-based +$50,000 after the survey says N

Why: Ignores that the survey rewrote the odds — approval just fell from 0.30 to 0.05. The old number no longer applies.

Bids into a −$74,576 expected loss

Why: Also treats the sunk $15,000 as a reason to act — a sunk cost, which should never drive the decision.

The fix

Act on the revised odds — that's the entire reason you bought them.

Recompute with the posteriors: EV(bid | N) = −$74,576

Why: The N branch uses 0.0508 / 0.9492, giving a negative referendum node and a negative bid value.

Don't bid on N; bid only on A

Why: A conditional strategy — different action per prediction — is what makes the information worth anything.

54. Answer it before you see the options: Check: the value of an approval…

Prediction

Predict first

What is EV(bid | approval predicted), and what should Oceanview do?

Answer it in your own words, now, with nothing to choose from. The options are on the next slide — and picking the right one off a list is an easier skill than producing it.

Correct: $229,268 — submit the bid

Why: EV(bid | A) = 0.20 × $1,146,341 = $229,268. It's positive, so bid. The favorable survey roughly quadruples the bid's value versus the $50,000 no-survey case.

55. Check: the value of an approval prediction

Check

Survey predicts approval → referendum node folds to $1,146,341, and the bid still wins with probability 0.20. Find EV(bid | A) and the action.

Check your understanding

What is EV(bid | approval predicted), and what should Oceanview do?

  • A. $229,268 — submit the bid (correct)
  • B. $1,146,341 — submit the bid
  • C. $50,000 — submit the bid
  • D. $229,268 — do not bid

Answer: A

Why: EV(bid | A) = 0.20 × $1,146,341 = $229,268. It's positive, so bid. The favorable survey roughly quadruples the bid's value versus the $50,000 no-survey case.

Why B tempts people
Reported the referendum node ($1,146,341) as the bid's value — the same skip-the-0.20-win-node error from Part 2, now inside the A branch.
Why C tempts people
Used the original priors (0.30 / 0.70) instead of the revised posteriors (0.6585 / 0.3415). That throws away the survey information you paid $15,000 for.
Why D tempts people
Correct EV, wrong call: +$229,268 clears the $0 of walking away, so bidding is the right action.

56. Part 5 — Is the $15,000 Study Worth It?

Section

Deliverable 4 — EVSI & EVPI

57. What EVSI measures

Concept

The survey is worth buying only if it raises expected profit by more than its price. EVSI — expected value of sample information — is how much better you do with the (imperfect) survey than without it, before subtracting the fee.

\[ \text{EVSI} = EV_{\text{with info}} - EV_{\text{without info}} \]

We already have the without-info value: $50,000 (Part 2). We need the with-info value: fold back the survey tree, letting Oceanview take the best action under each prediction.

58. Break it if you can: What EVSI measures

Counterexample

Discussion prompt

We already have the without-info value: $50,000 (Part 2). We need the with-info value: fold back the survey tree, letting Oceanview take the best action under each prediction.

That is stated as though it always holds. Do one of two things: produce a case where it fails, or say precisely what rules such a case out. "It just does" is not on the menu.

Hint: Hunt at the extremes first — zero, one, negative, empty, equal. If every extreme survives, the reason they survive is the proof.

59. What has to happen first: EVSI = $44,000 → hire the firm

Ranking

Put in order

Put the moves of EVSI = $44,000 → hire the firm into the order they have to happen.

  1. EV(with info) = P(A)·EV(bid | A) + P(N)·EV(no bid | N) = 0.41($229,268) + 0.59($0) = $94,000
  2. EVSI = $94,000 − $50,000 = $44,000
  3. Compare to the price: $44,000 worth vs. $15,000 cost → net gain $29,000

Why: These are the moves of the worked example in the order it makes them, and each one is set up by the one before it. Weight each prediction by how often it occurs (marginals 0.41 / 0.59) times the value of the best action then — bid on A, walk on N ($0).

60. EVSI = $44,000 → hire the firm

Worked example

EV(with info) = P(A)·EV(bid | A) + P(N)·EV(no bid | N) = 0.41($229,268) + 0.59($0) = $94,000

Why: Weight each prediction by how often it occurs (marginals 0.41 / 0.59) times the value of the best action then — bid on A, walk on N ($0).

EVSI = $94,000 − $50,000 = $44,000

Why: Subtract the best you could do WITHOUT the survey. That difference is the survey's gross worth.

Compare to the price: $44,000 worth vs. $15,000 cost → net gain $29,000

Why: The information is worth almost 3× its price, so buy it. Equivalently, the study branch is worth $94,000 − $15,000 = $79,000 vs. $50,000 without.

PathExpected value
No survey (just bid)$50,000
Buy survey, act on it (gross)$94,000
Buy survey, net of $15,000 fee$79,000

61. Fill in: Expected value for EVSI = $44,000 → hire the firm

Comparison

Comparison matrix

From EVSI = $44,000 → hire the firm: refill the Expected value column from what you know. The rest of the table is as it appeared.

PathExpected value
No survey (just bid)$50,000
Buy survey, act on it (gross)$94,000
Buy survey, net of $15,000 fee$79,000

62. Guess the shape of the answer: EVPI and efficiency: how good is this survey?

Estimation

Predict first

EVPI — expected value of perfect information — is the ceiling: what a flawless crystal ball on the referendum would be worth. It bounds what any survey could ever earn.

Commit before you compute: what does EVPI and efficiency: how good is this survey? come out to? A rough magnitude and the right form is enough — the point is to have something concrete to be wrong about.

Correct: EVPI = $120,000 − $50,000 = $70,000

Why: A prediction you can defend turns the computation into a check rather than a leap of faith — and an answer that contradicts it is caught on the spot. Perfect foresight beats the no-info $50,000 by $70,000 — the most any information about the referendum could be worth.

63. EVPI and efficiency: how good is this survey?

Worked example

EVPI — expected value of perfect information — is the ceiling: what a flawless crystal ball on the referendum would be worth. It bounds what any survey could ever earn.

With perfect info, bid only when it will pass: EV = 0.30 × (0.20 × $2,000,000) + 0.70 × $0 = 0.30 × $400,000 = $120,000

Why: You'd know the outcome before bidding — bid only in the 30% of cases zoning passes (still subject to the 0.20 win node), never otherwise.

EVPI = $120,000 − $50,000 = $70,000

Why: Perfect foresight beats the no-info $50,000 by $70,000 — the most any information about the referendum could be worth.

\[ \text{Efficiency} = \dfrac{\text{EVSI}}{\text{EVPI}} = \dfrac{44{,}000}{70{,}000} \approx 0.629 \]

The $15,000 survey captures about 63% of the value of perfect foresight — strong for an imperfect, cheap study. Buy it.

64. Decode the notation: EVPI and efficiency: how good is this survey?

Notation

Annotate

From EVPI and efficiency: how good is this survey? — read this one piece at a time. What is each part doing?

On: \( \text{Efficiency} = \dfrac{\text{EVSI}}{\text{EVPI}} = \dfrac{44{,}000}{70{,}000} \approx 0.629 \)

  • You'd know the outcome before bidding — bid only in the 30% of cases zoning passes (still subject to the 0.20 win node), never otherwise.
  • Perfect foresight beats the no-info $50,000 by $70,000 — the most any information about the referendum could be worth.

65. Something is wrong here: subtracting the fee instead of the baseline

Anomaly

Predict first

A student writes this, and it looks reasonable:

"EVSI is the with-survey value minus what the survey costs."

It is wrong. Say what breaks — and say it before you turn the page.

Correct: Subtracts the $15,000 price. But that mixes the value question with the cost question — and ignores the $50,000 you could already make without any survey.

EVSI compares information to NO information — the fee is a separate, later comparison.

Why: Subtracts the $15,000 price. But that mixes the value question with the cost question — and ignores the $50,000 you could already make without any survey.

66. Trap: subtracting the fee instead of the baseline

Trap

The trap

"EVSI is the with-survey value minus what the survey costs."

EVSI = $94,000 − $15,000 = $79,000

Why: Subtracts the $15,000 price. But that mixes the value question with the cost question — and ignores the $50,000 you could already make without any survey.

Overstates the information's worth

Why: $79,000 is the net study-branch payoff, not EVSI. Compared against the $15,000 fee it double-counts the fee.

The fix

EVSI compares information to NO information — the fee is a separate, later comparison.

EVSI = $94,000 − $50,000 = $44,000

Why: With-info value minus the best without-info value. This is a pure value, computed before any mention of price.

THEN weigh $44,000 against the $15,000 fee → net +$29,000

Why: Value first, cost second. Because $44,000 > $15,000, the survey clears its price.

67. Which of these survive contact with Decision Analysis — Oceanview Property…?

Two truths and a lie

Sort into buckets

Some of these hold up and some are the exact mistakes this lesson is built to prevent. Sort them.

Holds up
Once a bid is submitted, exactly three endpoints are possible. Work out the profit for each explicitly — these are the numbers that sit at the tips of the tree.; The payoffs from Part 1 sit at the tips: +$2.0M, −$0.5M, and two $0 ends. Everything else is just weighting them by probability.; You can't evaluate the tree left to right — you don't know the payoff until you reach a tip. So you work right to left, collapsing one node at a time.
Breaks
Assume that if zoning fails, Oceanview is stuck with the land it bid on.; See the referendum node's $250,000 and call that the value of bidding.
sound
These are stated as this lesson states them — each one survives the edge cases Decision Analysis — Oceanview Property Purchase puts it through.
flawed
Each of these is lifted from a trap in this deck: reasonable-sounding, and wrong in a way that only shows up once you rely on it.

68. Rule out three: Check: EVSI and the hire decision

Elimination

Eliminate the wrong options

What is the EVSI, and should Oceanview buy the $15,000 survey?

3 of these 4 are wrong. Strike them one at a time, and say what rules each one out before you strike the next. The survivor is the answer.

  • A. $44,000 — yes, buy it
  • B. $79,000 — yes, buy it
  • C. $94,000 — yes, buy it
  • D. $44,000 — no, don't buy it

Survives elimination: A

Why: EVSI = $94,000 − $50,000 = $44,000 — the gain over the best no-survey action. Since $44,000 far exceeds the $15,000 price (net +$29,000), buy the survey.

69. Check: EVSI and the hire decision

Check

Gross EV with the survey is $94,000; the best without it is $50,000; the survey costs $15,000. Find the EVSI and the call.

Check your understanding

What is the EVSI, and should Oceanview buy the $15,000 survey?

  • A. $44,000 — yes, buy it (correct)
  • B. $79,000 — yes, buy it
  • C. $94,000 — yes, buy it
  • D. $44,000 — no, don't buy it

Answer: A

Why: EVSI = $94,000 − $50,000 = $44,000 — the gain over the best no-survey action. Since $44,000 far exceeds the $15,000 price (net +$29,000), buy the survey.

Why B tempts people
Subtracted the $15,000 fee from the $94,000 instead of the $50,000 no-info baseline. $79,000 is the net study-branch value, not the EVSI.
Why C tempts people
Used the gross with-info value ($94,000) as the EVSI, forgetting that $50,000 was already attainable with no survey at all.
Why D tempts people
Right EVSI, wrong decision: $44,000 of value against a $15,000 cost is clearly worth buying — a net gain of $29,000.

70. Connect it up: Decision Analysis — Oceanview Property Purchase

Connect it up

Draw it

One page, no notation unless you need it: draw how these connect — Part 1 — Payoffs at Every Endpoint · Part 2 — The Tree & the No-Research Call · Part 3 — Revising the Odds with Bayes · Part 4 — The Strategy With Research · Part 5 — Is the $15,000 Study Worth It?. Put an arrow wherever one of them is what makes another possible, and label the arrow with why.

71. What you can do now

Recap

IdeaThe one-line takeaway
Fold-backAverage at chance nodes, choose at decision nodes, right to left
Walk-away payoffZoning fails → forfeit only the $500k deposit, not the land
Don't stop earlyPush every node through the 0.20 win probability
Bayes flipP(state | prediction) ≠ P(prediction | state); the prior still counts
Act on the surveyA rejection prediction turns a +$50k bid into a −$75k loss
EVSI vs. costValue = with-info − without-info; compare to the fee separately
EfficiencyEVSI / EVPI = 63% of a perfect crystal ball

Bring this to a session and we'll rebuild the whole tree in R live — reproduce the answer key line by line, then flex the inputs (what if P(win) = 0.30? a pricier, sharper survey?) and turn it into a clean managerial report.

Sources

  1. Case Problem: Property Purchase Strategy — Oceanview Development Corporation, managerial-report tasks — Camm, Cochran, Fry, Ohlmann et al., 4th ed., p. 780.
  2. Oceanview_Answer_Key.pdf — step-by-step solution (payoffs, fold-back, Bayes revision, EVSI, EVPI/efficiency) — Course answer key; every value reproduced by the author.
  3. oceanview_analysis.R — payoffs, EV fold-back, Bayes table, EVSI/EVPI computed in R — R script accompanying the assignment; outputs verified against the answer key to the dollar.
  4. Anderson, Sweeney & Williams — Decision Analysis: expected value of sample information (EVSI) and perfect information (EVPI) — Statistics / Quantitative Methods for Business — Bayesian revision and value-of-information definitions.

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