| Course | DBA-8000 Critical Thought and Informed Action Models of Decision Making and Organization Achievement |
|---|---|
| Module | Module 1 |
| Paper type | Module 1 written assignment: theoretical frameworks for decision making |
| Length | About 697 words, 5 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | Doctor of Business Administration |
| Updated | October 2026 |
Free sample paper for DBA 8000 Module 1
Four Rules Say Build, One Says Wait: Decision Theory Applied to a Core Laboratory Investment
Student Name
Doctor of Business Administration, Thomas Edison State University
DBA-8000: Critical Thought and Informed Action: Models of Decision Making and Organization Achievement
Mentor Name
Month Day, Year
Four Rules Say Build, One Says Wait: Decision Theory Applied to a Core Laboratory Investment
The Decision
Our network runs nine hospital and outpatient laboratories processing about 6 million tests a year. Volumes are rising, staffing is short and our analyzers are aging. Three alternatives are on the table: build an automated core laboratory that consolidates routine testing from all sites; upgrade analyzers in the existing laboratories; or keep the current structure and outsource specialized tests to a reference laboratory. The outcome depends mainly on future test volume, which in turn depends on hospital affiliations and payer contracts we do not control.
Framing the Decision as a Matrix
Peterson (2017) describes a decision problem as a set of acts, a set of states of the world and the outcomes that result from each act in each state, represented in a decision matrix. The states here are three levels of test volume growth over seven years. Outcomes are estimated net present values in millions of dollars.
A good matrix requires states that are mutually exclusive and exhaustive and outcomes that capture what matters. Our outcomes are financial; quality and turnaround time are treated separately below.
| Act | High growth | Moderate growth | Low growth |
|---|---|---|---|
| Build automated core lab | 18.0 | 7.0 | negative 6.0 |
| Upgrade existing labs | 9.0 | 5.0 | 1.0 |
| Outsource specialized tests | 3.0 | 2.0 | 1.5 |
Decisions Under Ignorance
If we cannot assign probabilities to the states, Peterson presents several rules.
The minimax regret rule, which Savage (1951) introduced, compares each outcome with the best available in that state. The regret table explains its result here: building has a maximum regret of 7.5, if growth is low and outsourcing would have been best; upgrading has a maximum regret of 9.0 and outsourcing 15.0, both if growth is high.
| Rule | How it works | Choice |
|---|---|---|
| Maximin | Choose the act with the best worst outcome | Outsource (worst case 1.5) |
| Maximax | Choose the act with the best best outcome | Build (best case 18.0) |
| Minimax regret | Choose the act whose largest regret is smallest | Build (largest regret 7.5) |
| Insufficient reason | Treat states as equally likely and maximize the average | Build (average 6.33) |
Decisions Under Risk
Our planning team, using payer contract trends and affiliation talks, estimates probabilities of 0.3 for high growth, 0.5 for moderate and 0.2 for low. The principle of maximizing expected value then gives:
Building has the highest expected value, by $2.3 million over upgrading.
| Act | Expected value |
|---|---|
| Build automated core lab | 0.3 x 18 + 0.5 x 7 + 0.2 x negative 6 = 7.7 |
| Upgrade existing labs | 0.3 x 9 + 0.5 x 5 + 0.2 x 1 = 5.4 |
| Outsource specialized tests | 0.3 x 3 + 0.5 x 2 + 0.2 x 1.5 = 2.2 |
The Value of Information
If we knew the future state before deciding, we would build under high or moderate growth and outsource under low growth, for an expected value of 0.3 x 18 + 0.5 x 7 + 0.2 x 1.5 = 9.2. Perfect information would therefore be worth at most $1.5 million, the gap between 9.2 and 7.7. That figure caps what any market study or affiliation negotiation is worth before deciding. Raiffa (1968) developed this logic of valuing information within decision analysis, treating further study as a decision with its own costs and expected benefits.
Which Framework Fits
Four of five rules favor building; only maximin favors outsourcing. Maximin suits a decision maker who cannot survive the worst case. A loss of $6 million would hurt but not threaten our network, whose annual revenue is about $420 million, so maximin's extreme caution is not warranted. Because our probability estimates rest on real evidence, expected value is the most defensible rule. Still, Peterson notes that expected value ignores risk attitude; a board averse to the low-growth loss might reasonably prefer upgrading, which gives up $2.3 million of expected value to avoid any losing state.
Conclusion
Decision theory clarifies the choice: building the core lab maximizes expected value and wins under most rules, while its downside is survivable. Spending up to $1.5 million to learn more about future volume would be rational, but a far smaller study of affiliation prospects would capture most of that value.
References
Peterson, M. (2017). An introduction to decision theory (2nd ed.). Cambridge University Press.
Raiffa, H. (1968). Decision analysis: Introductory lectures on choices under uncertainty. Addison-Wesley.
Savage, L. J. (1951). The theory of statistical decision. Journal of the American Statistical Association, 46(253), 55-67. https://doi.org/10.1080/01621459.1951.10500768
Reading the DBA 8000 Module 1 assignment instructions
Grades in DBA-8000 come from four discussions, a pair of brief reflections, four written assignments that share 40 percent, one reflection paper and a final project. The texts are Peterson's An Introduction to Decision Theory and The Lean Six Sigma Pocket Toolbook, with APA style throughout. This first written assignment covers theoretical frameworks for decision making. Expect to frame an organizational decision as acts, states and outcomes, apply decision rules under ignorance and under risk, and argue which framework fits the decision and why. Choose a decision with real stakes in your organization and estimate outcomes as carefully as you can, stating where each figure comes from. Doctoral work should engage the decision theory literature directly, including classic sources on the rules you apply.
How the DBA 8000 Module 1 example is put together
The paper frames a laboratory investment as three acts and three states in a decision matrix. A table applies four rules for decisions under ignorance, with a regret explanation. A second table computes expected values under estimated probabilities. A section computes the value of perfect information and interprets it as a budget limit. A section argues which framework fits, weighing risk attitude, and the conclusion recommends a course of action. Research on the origins of minimax regret and on valuing information grounds the rules in their sources, and the conclusion turns the value of information into practical advice about how much further study is worth. A table lays out each rule, how it works and its choice.
Reading the DBA 8000 Module 1 grading rubric
Mentors grade written assignments at the doctoral level on accurate use of decision theory, rigorous application, critical judgment about which framework fits and scholarly writing in APA style. Papers that apply several rules and explain why they disagree show command of the theory. Computing and interpreting the value of information demonstrates analytical depth. Weighing the decision maker's capacity to absorb a loss shows the judgment that separates scholarship from calculation. Grounding each rule in its original literature, and explaining when it fits, shows doctoral-level command. Interpreting the value of information as a practical budget for further study shows how theory guides action. Tables make each rule's logic easy to follow.
DBA 8000 Module 1 help: mistakes that cost marks
Students lose points by building matrices with overlapping states, by applying rules mechanically without asking which fits or by ignoring risk attitude. Others present expected value as the only rational rule. Define acts, states and outcomes carefully. Apply several rules. Explain disagreements. Compute what information is worth. Argue for the framework that fits the decision maker. State where each estimate comes from, and explain which rule fits the decision maker's capacity to absorb the worst outcome. When the rules disagree, say so openly and explain the split. A free first sample on your own DBA-8000 written assignment is available on request. If two decision rules point to different choices, say which rule suits the lab's attitude to risk and why.
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DBA 8000 Module 1 questions, answered
Where can I find a free DBA 8000 Module 1 sample paper?
Decision rules applied to a clinical laboratory's core lab investment are in the assignment above.
What is a decision under ignorance?
A decision in which the decision maker cannot assign probabilities to the possible states of the world.
What is the minimax regret rule?
Choose the act whose largest regret, the gap from the best outcome in each state, is smallest.
What is the expected value of perfect information?
The difference between the expected value with perfect knowledge of the state and the best expected value without it.
What textbooks does DBA-8000 use?
Peterson's An Introduction to Decision Theory and The Lean Six Sigma Pocket Toolbook.