Problem Solving and Decision Making Study Pack

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Last updated May 21, 2026

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Problem Solving and Decision Making Study Guide

Break down the full arc of problem solving and decision making, from recognizing a problem and choosing between algorithms and heuristics to the cognitive traps that derail judgment. Covers functional fixedness, mental set, confirmation bias, framing effects, and the representativeness, availability, and anchoring heuristics. Dual-process theory (System 1 vs. System 2) ties it all together.

Key Takeaways

  • •Problem solving moves through distinct phases: recognizing that a problem exists, constructing a mental representation of it, selecting a strategy, and evaluating the outcome.
  • •Algorithms guarantee a correct solution by exhaustively testing possibilities, while heuristics are mental shortcuts that save time but introduce the risk of systematic error.
  • •Common problem-solving obstacles include functional fixedness (inability to see objects beyond their typical use) and mental set (defaulting to previously successful strategies even when they no longer apply).
  • •Judgment under uncertainty relies heavily on cognitive heuristics such as representativeness, availability, and anchoring-and-adjustment, each of which can produce predictable biases.
  • •Dual-process theory distinguishes fast, automatic, intuitive thinking (System 1) from slow, deliberate, analytical thinking (System 2), and most everyday decisions draw on both.
  • •Confirmation bias leads decision-makers to seek and weight information that supports existing beliefs, distorting both problem diagnosis and solution evaluation.
  • •Framing effects demonstrate that logically equivalent information presented differently — as a gain versus a loss — can systematically shift preferences and choices.

The Structure of Problem Solving

Problem solving is the cognitive process of moving from a current state that is unsatisfactory to a goal state, and understanding its structure helps explain both successful strategies and common failures.

Defining the Problem Space

  • •A problem space consists of the initial state (current situation), the goal state (desired outcome), and all the possible intermediate states and operations that connect them.
  • •Problems can be well-defined, where both the starting point and goal are clear (e.g., solving an equation), or ill-defined, where the goal itself must be clarified before a solution path can be mapped (e.g., improving team morale).

Stages of the Problem-Solving Process

  • •Problem identification requires noticing that a discrepancy exists between the current state and an expected or desired state — a step that is often overlooked when problems develop gradually.
  • •Problem representation involves constructing an internal mental model of the problem; choosing the wrong representation (e.g., treating a spatial problem as a verbal one) can block progress entirely.
  • •Strategy selection involves choosing how to search the problem space, and evaluation involves testing whether the chosen solution actually closes the gap between the initial and goal states.

Insight and Restructuring

  • •Insight problems are solved not through incremental steps but through sudden restructuring of the problem representation, often after an impasse — a moment when all obvious approaches seem exhausted.
  • •Incubation, the period of stepping away from a problem, is associated with insight because it allows fixation on unproductive representations to dissipate.

Strategies: Algorithms and Heuristics

The strategies people use to navigate a problem space differ dramatically in their reliability, speed, and cognitive cost, ranging from exhaustive systematic procedures to efficient but fallible shortcuts.

Algorithmic Approaches

  • •An algorithm is a step-by-step procedure that, if followed correctly, guarantees a solution — for example, long division or a chess program that evaluates every possible move sequence.
  • •Algorithms are appropriate when the problem space is finite and well-defined, but they become impractical when the number of possible states is extremely large, as in complex real-world decisions.

Heuristic Approaches

  • •A heuristic is a mental shortcut or rule of thumb that reduces the cognitive effort of searching a problem space by focusing attention on the most promising paths rather than all paths.
  • •Means-end analysis is a widely used heuristic in which the solver compares the current state to the goal state, identifies the largest difference, and selects an operator to reduce that difference — repeating until the goal is reached.
  • •Working backward is a heuristic that starts from the goal state and traces the steps required to arrive there, which is especially useful in mathematics and planning problems where the endpoint is clearer than the starting actions.
  • •Analogical reasoning applies the solution structure from a previously solved problem to a new, structurally similar one — useful but risky when surface features mask structural differences.

Trade-offs Between Strategies

  • •Algorithms sacrifice speed for certainty; heuristics sacrifice certainty for speed.
  • •In most real-world problem solving, people default to heuristics and only shift to algorithmic thinking when a heuristic fails or when the stakes are high enough to justify the extra cognitive effort.

About this Study Pack

Created by Kibin to help students review key concepts, prepare for exams, and study more effectively. This Study Pack was checked for accuracy and curriculum alignment using authoritative educational sources. See sources below.

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