Problem-Solving Strategies

We encounter problems every day—from deciding what to wear, to fixing a glitchy phone, to tackling a complex exam question. Effective problem-solving begins with accurately identifying the problem, followed by choosing a strategy to reach a solution. Psychologists refer to these approaches as problem-solving strategies: structured ways of thinking that help us move from a challenge toward an answer.

Trial and Error

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them. For example, a well-known strategy is trial and error.

trial and error

Trial and error involves trying different solutions until one finally works.

Trial and error is simple and often effective—especially when there are limited options—but not always efficient. For example, your printer isn’t working. You might:

• Check the ink cartridge
• Look for a paper jam
• Reconnect the printer to your laptop
• Restart everything

You keep trying solutions until you discover one that fixes the issue. This strategy is common in everyday life, even if it isn’t the fastest.

Algorithms

Another type of strategy is an algorithm.

algorithm

An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed.

At its simplest, an algorithm is: Input → Steps (rules) → Output.

You already use algorithms constantly, even if you don’t call them that.

Examples of algorithms you may use include:

• Morning commute algorithm:

1. Check the weather.
2. If it’s raining → take the bus.
3. If it’s not raining → walk to campus.

• Gas tank algorithm: If the gas gauge is below ¼ → stop for fuel on the way home. Otherwise → keep driving.

AI Examples of Algorithms

Algorithms are used constantly in the digital world:

• Google uses algorithms to sort and rank search results
• Instagram, TikTok, and YouTube use algorithms to decide what appears in your feed
• Your GPS uses algorithms to calculate the fastest route

Algorithms guarantee accuracy, but they can be slow or impractical for complex real-life decisions.

Artificial intelligence also uses algorithms, but instead of being hand-written by a person, many modern AI systems use machine learning algorithms, which learn rules from data.

For example, a streaming service recommendation algorithm:

• Analyzes what you’ve watched, skipped, or liked.
• Compares your pattern to millions of other users.
• Learns which shows people with similar viewing habits click next.
• Predicts what you are most likely to watch.
• Recommends that show to you.

Unlike human algorithms, these rules weren’t explicitly programmed—they emerged from patterns in enormous datasets.

Machine learning researchers describe this as systems that “extract patterns from data to make predictions” (Mühlhoff, 2024). Instead of giving the computer the rules, programmers give it many examples and let it discover the rules on its own.

Human and AI algorithms share something important: both rely on explicit or learned rules to make decisions.

• Humans use algorithms based on experience (“If the first fix doesn’t work, try the next step”).
• AI uses algorithms learned from large-scale data (“Users who clicked X also clicked Y—recommend Y”).

Algorithms are precise and dependable—but they can be slow and rigid. That’s why people often use heuristics, or mental shortcuts, when fast decisions are needed.

Heuristics

A heuristic is another type of problem-solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974).

heuristic

A heuristic is a general problem-solving “rule of thumb.” Heuristics save time by simplifying decisions, but they do not guarantee a correct solution (Tversky & Kahneman, 1974).

Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Common Heuristics

• Working backwards-start with the desired outcome and reason backward.
  • For example, you need to arrive at a 4:00 PM wedding in Philadelphia. You know you want to arrive by 3:30 PM, and without traffic it takes 2.5 hours to drive there from D.C., so you plan backward to determine when to leave—adjusting for traffic as needed.
• Breaking a large task into smaller steps-this strategy reduces overwhelm and makes big goals manageable.
  • For example, when writing a research paper, you follow steps:
    • Brainstorm topic
    • Draft thesis
    • Conduct research
    • Create outline
    • Write draft
    • Revise
    • Edit and finalize

Problem-solving strategies

Table 1. Problem-Solving Strategies

Method	Description	Example
Trial and error	Continue trying different solutions until problem is solved	Restarting your phone, toggling Wi-Fi/Bluetooth to fix a connection issue
Algorithm	Step-by-step problem-solving formula	Using an instruction manual to install software
Heuristic	General problem-solving framework	Working backward, simplifying steps, “rule of thumb”