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
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.
Objectives
This case is the full decision-analysis toolkit on one problem. By the end you can:
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.
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.
| Date | Event |
|---|---|
| June 1 | Today — decide whether to bid |
| Aug 1 | Optional survey results arrive |
| Aug 15 | Sealed bids due (with 10% deposit) |
| Sep 1 | Winning bid announced |
| November | Zoning referendum voted on |
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.
| Date | Event |
|---|---|
| June 1 | Today — decide whether to bid |
| Aug 1 | Optional survey results arrive |
| Aug 15 | Sealed bids due (with 10% deposit) |
| Sep 1 | Winning bid announced |
| November | Zoning referendum voted on |
Concept
Decision problems become mechanical once the givens are laid out. List them first — every later slide traces back to this table.
| Quantity | Symbol | Value |
|---|---|---|
| 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 reliability | Meaning | Value |
|---|---|---|
| P(A | s₁) | predicts approval when it WILL pass | 0.90 |
| P(N | s₁) | predicts rejection when it will pass | 0.10 |
| P(A | s₂) | predicts approval when it will fail | 0.20 |
| P(N | s₂) | predicts rejection when it WILL fail | 0.80 |
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.
| Quantity | Symbol | Value |
|---|---|---|
| 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 |
Concept
The managerial report asks four things. Each maps to one move we'll build in order — nothing here is optional decoration.
Matching
Match the pairs
From Four questions, four analytical moves — match each one to what it actually does. The descriptions have been shuffled.
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.
Section
The dollars at each tree tip
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.
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.
Ranking
Put in order
Put the moves of The three payoffs, in dollars into the order they have to happen.
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.
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.
| Endpoint | Cash flows | Payoff |
|---|---|---|
| Win + approved (build) | 15M − 5M − 8M | +$2,000,000 |
| Win + rejected (walk) | −10% of 5M | −$500,000 |
| Lose the bid | deposit refunded | $0 |
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.
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.
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.
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.
Section
Deliverables 1 and 2
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).
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.
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).
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.
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.
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.
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.
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:
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.
| Node | Computation | Value |
|---|---|---|
| Referendum | 0.3(2,000,000) + 0.7(−500,000) | $250,000 |
| Bid win/lose | 0.2(250,000) + 0.8(0) | $50,000 |
| Decision | max($50,000, $0) | Bid → $50,000 |
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.
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.
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.
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.
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:
Pattern
Every decision tree in this course collapses by the same five moves:
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:
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.
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.
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?
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.
Section
Turning a survey into new probabilities
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 approved | 0.90 | 0.10 |
| s₂ — will be rejected | 0.20 | 0.80 |
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).
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 approved | 0.90 | 0.10 |
| s₂ — will be rejected | 0.20 | 0.80 |
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.
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.
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.
Worked example
Three mechanical steps. First, multiply each prior by each conditional to get joint probabilities P(prediction and state).
| State | Prior P(s) | Joint with A | Joint with N |
|---|---|---|---|
| s₁ approved | 0.30 | 0.30 × 0.9 = 0.27 | 0.30 × 0.1 = 0.03 |
| s₂ rejected | 0.70 | 0.70 × 0.2 = 0.14 | 0.70 × 0.8 = 0.56 |
| Column total | 1.00 | P(A) = 0.41 | P(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.
| Prediction | P(approve | pred) | P(reject | pred) |
|---|---|---|
| A (approve) | 0.6585 | 0.3415 |
| N (reject) | 0.0508 | 0.9492 |
| Prior (no survey) | 0.30 | 0.70 |
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.
Notation
Annotate
From Bayes: joint table → marginals → posteriors — read this one piece at a time. What is each part doing?
On: \( P(s_1 \mid A) = \dfrac{P(A \mid s_1)\,P(s_1)}{P(A)} = \dfrac{0.9 \times 0.30}{0.41} = 0.6585 \)
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).
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.
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.
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).
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.
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)?
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.
Section
Deliverable 3
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.
Ranking
Put in order
Put the moves of Fold back each survey branch into the order they have to happen.
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.
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.
| Prediction | EV(bid | prediction) | Action |
|---|---|---|
| A — approval | +$229,268 | Bid |
| N — rejection | −$74,576 | Do not bid |
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.
| Prediction | EV(bid | prediction) | Action |
|---|---|---|
| A — approval | +$229,268 | Bid |
| N — rejection | −$74,576 | Do not bid |
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.
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.
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.
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.
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?
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.
Section
Deliverable 4 — EVSI & EVPI
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.
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.
Ranking
Put in order
Put the moves of EVSI = $44,000 → hire the firm into the order they have to happen.
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).
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.
| Path | Expected value |
|---|---|
| No survey (just bid) | $50,000 |
| Buy survey, act on it (gross) | $94,000 |
| Buy survey, net of $15,000 fee | $79,000 |
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.
| Path | Expected value |
|---|---|
| No survey (just bid) | $50,000 |
| Buy survey, act on it (gross) | $94,000 |
| Buy survey, net of $15,000 fee | $79,000 |
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.
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.
Notation
Annotate
From EVPI and efficiency: how good is this survey? — read this one piece at a time. What is each part doing?
On: \( \text{Efficiency} = \dfrac{\text{EVSI}}{\text{EVPI}} = \dfrac{44{,}000}{70{,}000} \approx 0.629 \)
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.
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.
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.
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.
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.
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.
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?
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.
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.
Recap
| Idea | The one-line takeaway |
|---|---|
| Fold-back | Average at chance nodes, choose at decision nodes, right to left |
| Walk-away payoff | Zoning fails → forfeit only the $500k deposit, not the land |
| Don't stop early | Push every node through the 0.20 win probability |
| Bayes flip | P(state | prediction) ≠ P(prediction | state); the prior still counts |
| Act on the survey | A rejection prediction turns a +$50k bid into a −$75k loss |
| EVSI vs. cost | Value = with-info − without-info; compare to the fee separately |
| Efficiency | EVSI / 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.
Want this taught 1-on-1? Alexander tutors Business Analytics — $55/session, free consultation.