PSY 390 Week 5 Problem Solving and Decision Making Example

Stuck on a Staffing Puzzle: Problem Solving, Heuristics and a Clinic Manager's Big Decision

[Student Name]

University of Phoenix

PSY/390: Learning and Cognition

Week 5 Assignment

[Instructor Name]

[Date]

The manager, clinic and figures are composites written for a model paper; research findings come from the sources listed.

Problem solving and decision making draw on everything the mind does: learning from experience, remembering past cases, using language and concepts and taking shortcuts. This closing paper examines how people solve problems and make choices, where their strategies succeed and where they fail, through one manager facing one problem and one decision.

The Manager

Hanh Nguyen, forty-one, manages an urgent care clinic in Phoenix, Arizona. For six months, she has been unable to fill evening shifts reliably. Each month, she tries the same remedies: overtime for current staff and agency nurses at a high hourly rate. Burnout is rising and costs are climbing. Meanwhile, the clinic's owner wants her advice on opening a second location in a growing suburb, and she must present a recommendation to the board.

Solving the Staffing Problem

Problem solving moves through stages: defining the problem, generating possible solutions, choosing and carrying out one and evaluating the result. Strategies range from algorithms, step-by-step procedures that guarantee a solution if one exists, to heuristics, shortcuts such as working backward from the goal or breaking the problem into parts.

Hanh has defined the problem as "not enough nurses," and every solution she generates involves adding nurse hours. She has fallen into a mental set, repeating approaches that worked before, even though they are no longer working.

Fixedness and Insight

Duncker (1945) gave participants a candle, a box of thumbtacks and matches and asked them to attach the candle to a wall so it could burn without dripping on the table. Many failed to see that the empty box could be tacked to the wall as a shelf. When the tacks were presented outside the box, people solved the problem more readily. Seeing the box as a container blocked seeing it as a support, a barrier known as functional fixedness.

Hanh's resources are fixed in her mind in their usual roles. Her medical assistants are seen only as assistants; her telehealth system is used only for follow-ups. When she redefined the problem as "evening patients waiting too long" rather than "not enough nurses," new solutions appeared: training medical assistants for expanded intake tasks within their scope, routing low-acuity evening visits to telehealth and adjusting the posted evening hours to match demand data. Reframing the problem produced the insight.

The Expansion Decision

For the second clinic, Hanh must estimate demand, costs and risk. Tversky and Kahneman (1974) found that when outcomes are uncertain, people lean on a small set of mental shortcuts that save effort yet go wrong in patterned ways. Under the availability heuristic, events that spring to mind readily are judged to be frequent. Representativeness judges by similarity to a prototype, often ignoring base rates. Anchoring adjusts estimates from an initial value, usually too little.

All three appear in Hanh's thinking. A friend's clinic in a nearby suburb thrived, a vivid example that makes success seem likely, though many new clinics struggle. The suburb looks like the area where her current clinic succeeded, so she judges it similar without checking demand data. And the owner opened the discussion by mentioning annual revenue of $2.4 million for the new site; Hanh's own projections cluster near that figure, though she has no independent basis for it.

The owner's first number, $2.4 million, quietly became the starting point for every estimate in the room.

How the Question Is Framed

Tversky and Kahneman (1981) showed that choices change when the same outcomes are described differently. In their best-known problem, people chose the certain option when outcomes were described as lives saved but the risky option when the same outcomes were described as lives lost. People tend to avoid risk when facing gains and seek risk when facing losses.

At the board meeting, one member describes the expansion as a way to "protect 40 jobs" if the current clinic's market shrinks; another describes it as "putting 60 jobs at risk" if it fails. The facts are the same, but the gain frame encourages caution about the alternative, while the loss frame can push the board toward a gamble. Hanh plans to present both frames side by side.

Improving the Decision

Research suggests concrete steps. First, take an outside view: instead of relying on the friend's story, Hanh gathers data on how comparable clinics in similar suburbs performed in their first three years. Second, set her own estimates before hearing others' figures, to limit anchoring. Third, run a premortem: imagine the new clinic has failed in two years and list reasons why, which surfaces risks such as staffing, the very problem she has not solved. Fourth, present the decision in both gain and loss frames.

Her analysis leads to a recommendation: delay expansion one year until the evening staffing model is stable, while securing an option on the suburban site.

Confirmation Bias in the Room

Once leaning toward expansion, Hanh noticed she was collecting articles about booming suburbs and skimming past reports of clinic closures. Seeking evidence that fits a favored option, and discounting the rest, is confirmation bias. Asking a colleague to argue the case against expansion gave the opposing evidence a fair hearing.

When Heuristics Help

Heuristics are not simply errors. In familiar situations with good feedback, experienced managers' quick judgments are often accurate. Hanh's intuition about patient flow, built over years, is reliable. The danger lies in unfamiliar decisions, like opening a new site, where feedback is slow and vivid examples mislead.

Connecting the Course

This final topic draws on the earlier weeks. Hanh's mental set is a product of reinforcement: overtime once worked. Her vivid memory of the friend's clinic reflects how memory favors striking cases. Her categories of staff roles show how concepts can constrain thinking. Learning, memory and language all feed the shortcuts people use when they solve problems and decide.

Conclusion

Hanh's staffing problem yielded once she broke a mental set and overcame functional fixedness by redefining the problem. Her expansion decision was shaped by availability, representativeness, anchoring and framing, and structured techniques such as an outside view and a premortem improved it. Problem solving and decision making show cognition at its most powerful and most fallible.

References

Duncker, K. (1945). On problem-solving (L. S. Lees, Trans.). Psychological Monographs, 58(5), i-113. https://doi.org/10.1037/h0093599

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131. https://doi.org/10.1126/science.185.4157.1124

Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453-458. https://doi.org/10.1126/science.7455683