Model Answer
0 min readIntroduction
Problem-solving is a fundamental cognitive process, and humans employ various strategies to navigate challenges. Two prominent approaches are heuristics and algorithms. While algorithms guarantee a solution if applied correctly, they can be time-consuming and resource-intensive. Heuristics, on the other hand, are mental shortcuts that offer quick, though not always accurate, solutions. Furthermore, our past experiences and learned approaches can sometimes hinder effective problem-solving, a phenomenon known as ‘mental set’. This answer will delineate the differences between heuristics and algorithms and illustrate the role of mental set in reasoning, supported by experimental evidence.
Heuristics vs. Algorithms: A Comparative Analysis
Both heuristics and algorithms are problem-solving strategies, but they differ significantly in their approach and outcomes.
| Feature | Heuristic | Algorithm |
|---|---|---|
| Definition | A mental shortcut or rule of thumb used to simplify problem-solving. | A step-by-step procedure that guarantees a solution if followed correctly. |
| Accuracy | Not always accurate; can lead to biases and errors. | Guaranteed accuracy if implemented properly. |
| Efficiency | Fast and efficient; requires less cognitive effort. | Can be slow and computationally expensive, especially for complex problems. |
| Applicability | Useful for ill-defined problems with incomplete information. | Best suited for well-defined problems with clear rules and goals. |
| Examples | Availability heuristic, representativeness heuristic, anchoring and adjustment. | Mathematical formulas, computer programs, recipes. |
For instance, the availability heuristic leads us to overestimate the likelihood of events that are easily recalled (e.g., plane crashes seem more frequent than car accidents due to media coverage). Conversely, an algorithm would be used to calculate the precise trajectory of a rocket launch, ensuring accuracy.
Mental Set in Reasoning
Mental set refers to the tendency to approach a problem in a particular way based on previous experience, even if that approach is not the most efficient or effective for the current problem. It’s a cognitive bias that can hinder flexible thinking and problem-solving.
Luchins’ Water Jar Problem (1942)
Abraham Luchins conducted a series of experiments using the water jar problem. Participants were presented with three jars of different capacities (A, B, and C) and a target amount of water. They had to use only the jars to measure out the target amount. Initially, participants were given a series of problems that could be solved using a specific sequence of steps (e.g., fill A, pour into B, empty B into C). After several such problems, Luchins introduced a problem that could be solved more efficiently with a single step (e.g., fill jar B directly). However, participants continued to use the previously learned, more complex sequence, demonstrating a strong mental set. This showed that prior success with a particular method can inhibit the discovery of simpler solutions.
Duncker’s Radiating Lines Problem (1945)
Karl Duncker’s radiating lines problem involved participants being presented with six points arranged in a circle and asked to connect them with four straight lines without lifting their pen from the paper. Most participants initially struggled, as they were fixated on the constraint of staying within the circle. However, when Duncker subtly hinted at the need to extend the lines beyond the circle, participants were able to solve the problem easily. This demonstrated how a pre-existing mental framework (staying within the circle) could prevent participants from seeing a simpler solution. The experiment highlighted the importance of functional fixedness, a related concept where people tend to see objects as having only their typical use, hindering creative problem-solving.
The impact of mental set isn’t limited to laboratory settings. In real-world scenarios, engineers might continue using a familiar design approach even when a newer, more efficient technology becomes available, or doctors might rely on a standard diagnostic procedure even when presented with atypical symptoms.
Conclusion
In conclusion, heuristics and algorithms represent distinct problem-solving strategies, differing in accuracy, efficiency, and applicability. While algorithms offer guaranteed solutions, heuristics provide quick, albeit potentially flawed, shortcuts. Furthermore, the phenomenon of mental set demonstrates how past experiences can create cognitive rigidity, hindering flexible thinking and effective problem-solving. Understanding these cognitive processes is crucial for improving decision-making and fostering innovation in various domains. Recognizing and overcoming mental sets requires conscious effort to challenge assumptions and explore alternative approaches.
Answer Length
This is a comprehensive model answer for learning purposes and may exceed the word limit. In the exam, always adhere to the prescribed word count.