{"id":448,"date":"2023-03-02T20:16:56","date_gmt":"2023-03-02T20:16:56","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introductiontopsychology\/chapter\/problem-solving\/"},"modified":"2025-11-14T15:32:23","modified_gmt":"2025-11-14T15:32:23","slug":"problem-solving","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introductiontopsychology\/chapter\/problem-solving\/","title":{"raw":"Thinking and Problem Solving: Learn It 4\u2014Solving Problems","rendered":"Thinking and Problem Solving: Learn It 4\u2014Solving Problems"},"content":{"raw":"

Problem-Solving Strategies<\/span><\/h2>\r\n

We encounter problems every day\u2014from deciding what to wear, to fixing a glitchy phone, to tackling a complex exam question. Effective problem-solving begins with accurately identifying the problem<\/strong>, followed by choosing a strategy to reach a solution. Psychologists refer to these approaches as problem-solving strategies<\/strong>: structured ways of thinking that help us move from a challenge toward an answer.<\/p>\r\n

Trial and Error<\/h3>\r\n

A <\/span>problem-solving strategy<\/strong> 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 <\/span>trial and error<\/strong>.<\/span><\/p>\r\n

\r\n

trial and error<\/h3>\r\n

Trial and error<\/strong> involves trying different solutions until one finally works.<\/p>\r\n<\/section>\r\n

Trial and error is simple and often effective\u2014especially when there are limited options\u2014but not always efficient. For example, your printer isn\u2019t working. You might:<\/p>\r\n

    \r\n\t
  • Check the ink cartridge<\/li>\r\n\t
  • Look for a paper jam<\/li>\r\n\t
  • Reconnect the printer to your laptop<\/li>\r\n\t
  • Restart everything<\/li>\r\n<\/ul>\r\n

    You keep trying solutions until you discover one that fixes the issue. This strategy is common in everyday life, even if it isn\u2019t the fastest.<\/p>\r\n

    Algorithms<\/h3>\r\n

    Another type of strategy is an algorithm.<\/p>\r\n

    \r\n

    algorithm<\/h3>\r\n

    An algorithm<\/strong> 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.<\/p>\r\n<\/section>\r\n

    At its simplest, an algorithm is: Input \u2192 Steps (rules) \u2192 Output.<\/strong><\/p>\r\n

    You already use algorithms constantly, even if you don\u2019t call them that.<\/p>\r\n

    \r\n

    Examples of algorithms you may use include:<\/p>\r\n

      \r\n\t
    • \r\n

      Morning commute algorithm:<\/strong><\/p>\r\n<\/li>\r\n<\/ul>\r\n

        \r\n\t
      1. Check the weather.<\/li>\r\n\t
      2. If it\u2019s raining \u2192 take the bus.<\/li>\r\n\t
      3. If it\u2019s not raining \u2192 walk to campus.<\/li>\r\n<\/ol>\r\n
          \r\n\t
        • \r\n

          Gas tank algorithm: <\/strong>If the gas gauge is below \u00bc \u2192 stop for fuel on the way home. Otherwise \u2192 keep driving.<\/p>\r\n<\/li>\r\n<\/ul>\r\n<\/section>\r\n

          \r\n

          AI Examples of Algorithms<\/strong><\/h3>\r\n

          Algorithms are used constantly in the digital world:<\/p>\r\n

            \r\n\t
          • Google uses algorithms to sort and rank search results<\/li>\r\n\t
          • Instagram, TikTok, and YouTube use algorithms to decide what appears in your feed<\/li>\r\n\t
          • Your GPS uses algorithms to calculate the fastest route<\/li>\r\n<\/ul>\r\n

            Algorithms guarantee accuracy, but they can be slow or impractical for complex real-life decisions.<\/p>\r\n

            Artificial intelligence also uses algorithms, but instead of being hand-written by a person, many modern AI systems use machine learning algorithms<\/strong>, which learn<\/em> rules from data.<\/p>\r\n

            For example, a\u00a0streaming service recommendation algorithm:<\/strong><\/p>\r\n

              \r\n\t
            • Analyzes what you\u2019ve watched, skipped, or liked.<\/li>\r\n\t
            • Compares your pattern to millions of other users.<\/li>\r\n\t
            • Learns which shows people with similar viewing habits click next.<\/li>\r\n\t
            • Predicts what you are most likely to watch.<\/li>\r\n\t
            • Recommends that show to you.<\/li>\r\n<\/ul>\r\n

              Unlike human algorithms, these rules weren\u2019t explicitly programmed\u2014they emerged from patterns in enormous datasets.<\/p>\r\n

              Machine learning researchers describe this as systems that \u201cextract patterns from data to make predictions\u201d (M\u00fchlhoff, 2024). Instead of giving the computer the rules, programmers give it many examples and let it discover<\/em> the rules on its own.<\/p>\r\n

              Human and AI algorithms share something important: both rely on explicit or learned rules to make decisions.<\/p>\r\n

                \r\n\t
              • Humans use algorithms based on experience (\u201cIf the first fix doesn\u2019t work, try the next step\u201d).<\/li>\r\n\t
              • AI uses algorithms learned from large-scale data (\u201cUsers who clicked X also clicked Y\u2014recommend Y\u201d).<\/li>\r\n<\/ul>\r\n

                Algorithms are precise and dependable\u2014but they can be slow and rigid. That\u2019s why people often use heuristics, or mental shortcuts, when fast decisions are needed.<\/p>\r\n<\/section>\r\n

                Heuristics<\/h3>\r\n

                A heuristic<\/strong> 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).<\/p>\r\n

                \r\n

                heuristic<\/h3>\r\n

                A heuristic<\/strong> is a general problem-solving \u201crule of thumb.\u201d Heuristics save time by simplifying decisions, but they do not<\/strong> guarantee a correct solution (Tversky & Kahneman, 1974).<\/p>\r\n<\/section>\r\n

                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):<\/p>\r\n

                  \r\n\t
                • When one is faced with too much information<\/li>\r\n\t
                • When the time to make a decision is limited<\/li>\r\n\t
                • When the decision to be made is unimportant<\/li>\r\n\t
                • When there is access to very little information to use in making the decision<\/li>\r\n\t
                • When an appropriate heuristic happens to come to mind in the same moment<\/li>\r\n<\/ul>\r\n

                  Common Heuristics<\/strong><\/h4>\r\n
                    \r\n\t
                  • Working backwards<\/strong>-start with the desired outcome and reason backward.\r\n\r\n
                      \r\n\t
                    • 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\u2014adjusting for traffic as needed.<\/li>\r\n<\/ul>\r\n<\/li>\r\n\t
                    • Breaking a large task into smaller steps<\/strong>-this strategy reduces overwhelm and makes big goals manageable.\r\n\r\n
                        \r\n\t
                      • For example, when writing a research paper, you follow steps:\r\n\r\n
                          \r\n\t
                        • Brainstorm topic<\/li>\r\n\t
                        • Draft thesis<\/li>\r\n\t
                        • Conduct research<\/li>\r\n\t
                        • Create outline<\/li>\r\n\t
                        • Write draft<\/li>\r\n\t
                        • Revise<\/li>\r\n\t
                        • Edit and finalize<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n
                          \r\n

                          Problem-solving strategies<\/h3>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
                          Table 1. Problem-Solving Strategies<\/caption>\r\n
                          Method<\/th>\r\nDescription<\/th>\r\nExample<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
                          Trial and error<\/td>\r\nContinue trying different solutions until problem is solved<\/td>\r\nRestarting your phone, toggling Wi-Fi\/Bluetooth to fix a connection issue<\/td>\r\n<\/tr>\r\n
                          Algorithm<\/td>\r\nStep-by-step problem-solving formula<\/td>\r\nUsing an instruction manual to install software<\/td>\r\n<\/tr>\r\n
                          Heuristic<\/td>\r\nGeneral problem-solving framework<\/td>\r\nWorking backward, simplifying steps, \u201crule of thumb\"<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/section>\r\n
                          [ohm2_question height=\"400\"]4089[\/ohm2_question]<\/section>","rendered":"

                          Problem-Solving Strategies<\/span><\/h2>\n

                          We encounter problems every day\u2014from deciding what to wear, to fixing a glitchy phone, to tackling a complex exam question. Effective problem-solving begins with accurately identifying the problem<\/strong>, followed by choosing a strategy to reach a solution. Psychologists refer to these approaches as problem-solving strategies<\/strong>: structured ways of thinking that help us move from a challenge toward an answer.<\/p>\n

                          Trial and Error<\/h3>\n

                          A <\/span>problem-solving strategy<\/strong> 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 <\/span>trial and error<\/strong>.<\/span><\/p>\n

                          \n

                          trial and error<\/h3>\n

                          Trial and error<\/strong> involves trying different solutions until one finally works.<\/p>\n<\/section>\n

                          Trial and error is simple and often effective\u2014especially when there are limited options\u2014but not always efficient. For example, your printer isn\u2019t working. You might:<\/p>\n

                            \n
                          • Check the ink cartridge<\/li>\n
                          • Look for a paper jam<\/li>\n
                          • Reconnect the printer to your laptop<\/li>\n
                          • Restart everything<\/li>\n<\/ul>\n

                            You keep trying solutions until you discover one that fixes the issue. This strategy is common in everyday life, even if it isn\u2019t the fastest.<\/p>\n

                            Algorithms<\/h3>\n

                            Another type of strategy is an algorithm.<\/p>\n

                            \n

                            algorithm<\/h3>\n

                            An algorithm<\/strong> 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.<\/p>\n<\/section>\n

                            At its simplest, an algorithm is: Input \u2192 Steps (rules) \u2192 Output.<\/strong><\/p>\n

                            You already use algorithms constantly, even if you don\u2019t call them that.<\/p>\n

                            \n

                            Examples of algorithms you may use include:<\/p>\n

                              \n
                            • \n

                              Morning commute algorithm:<\/strong><\/p>\n<\/li>\n<\/ul>\n

                                \n
                              1. Check the weather.<\/li>\n
                              2. If it\u2019s raining \u2192 take the bus.<\/li>\n
                              3. If it\u2019s not raining \u2192 walk to campus.<\/li>\n<\/ol>\n
                                  \n
                                • \n

                                  Gas tank algorithm: <\/strong>If the gas gauge is below \u00bc \u2192 stop for fuel on the way home. Otherwise \u2192 keep driving.<\/p>\n<\/li>\n<\/ul>\n<\/section>\n

                                  \n

                                  AI Examples of Algorithms<\/strong><\/h3>\n

                                  Algorithms are used constantly in the digital world:<\/p>\n

                                    \n
                                  • Google uses algorithms to sort and rank search results<\/li>\n
                                  • Instagram, TikTok, and YouTube use algorithms to decide what appears in your feed<\/li>\n
                                  • Your GPS uses algorithms to calculate the fastest route<\/li>\n<\/ul>\n

                                    Algorithms guarantee accuracy, but they can be slow or impractical for complex real-life decisions.<\/p>\n

                                    Artificial intelligence also uses algorithms, but instead of being hand-written by a person, many modern AI systems use machine learning algorithms<\/strong>, which learn<\/em> rules from data.<\/p>\n

                                    For example, a\u00a0streaming service recommendation algorithm:<\/strong><\/p>\n

                                      \n
                                    • Analyzes what you\u2019ve watched, skipped, or liked.<\/li>\n
                                    • Compares your pattern to millions of other users.<\/li>\n
                                    • Learns which shows people with similar viewing habits click next.<\/li>\n
                                    • Predicts what you are most likely to watch.<\/li>\n
                                    • Recommends that show to you.<\/li>\n<\/ul>\n

                                      Unlike human algorithms, these rules weren\u2019t explicitly programmed\u2014they emerged from patterns in enormous datasets.<\/p>\n

                                      Machine learning researchers describe this as systems that \u201cextract patterns from data to make predictions\u201d (M\u00fchlhoff, 2024). Instead of giving the computer the rules, programmers give it many examples and let it discover<\/em> the rules on its own.<\/p>\n

                                      Human and AI algorithms share something important: both rely on explicit or learned rules to make decisions.<\/p>\n

                                        \n
                                      • Humans use algorithms based on experience (\u201cIf the first fix doesn\u2019t work, try the next step\u201d).<\/li>\n
                                      • AI uses algorithms learned from large-scale data (\u201cUsers who clicked X also clicked Y\u2014recommend Y\u201d).<\/li>\n<\/ul>\n

                                        Algorithms are precise and dependable\u2014but they can be slow and rigid. That\u2019s why people often use heuristics, or mental shortcuts, when fast decisions are needed.<\/p>\n<\/section>\n

                                        Heuristics<\/h3>\n

                                        A heuristic<\/strong> 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).<\/p>\n

                                        \n

                                        heuristic<\/h3>\n

                                        A heuristic<\/strong> is a general problem-solving \u201crule of thumb.\u201d Heuristics save time by simplifying decisions, but they do not<\/strong> guarantee a correct solution (Tversky & Kahneman, 1974).<\/p>\n<\/section>\n

                                        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):<\/p>\n

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

                                          Common Heuristics<\/strong><\/h4>\n
                                            \n
                                          • Working backwards<\/strong>-start with the desired outcome and reason backward.\n
                                              \n
                                            • 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\u2014adjusting for traffic as needed.<\/li>\n<\/ul>\n<\/li>\n
                                            • Breaking a large task into smaller steps<\/strong>-this strategy reduces overwhelm and makes big goals manageable.\n
                                                \n
                                              • For example, when writing a research paper, you follow steps:\n
                                                  \n
                                                • Brainstorm topic<\/li>\n
                                                • Draft thesis<\/li>\n
                                                • Conduct research<\/li>\n
                                                • Create outline<\/li>\n
                                                • Write draft<\/li>\n
                                                • Revise<\/li>\n
                                                • Edit and finalize<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
                                                  \n

                                                  Problem-solving strategies<\/h3>\n\n\n\n\n\n\n\n
                                                  Table 1. Problem-Solving Strategies<\/caption>\n
                                                  Method<\/th>\nDescription<\/th>\nExample<\/th>\n<\/tr>\n<\/thead>\n
                                                  Trial and error<\/td>\nContinue trying different solutions until problem is solved<\/td>\nRestarting your phone, toggling Wi-Fi\/Bluetooth to fix a connection issue<\/td>\n<\/tr>\n
                                                  Algorithm<\/td>\nStep-by-step problem-solving formula<\/td>\nUsing an instruction manual to install software<\/td>\n<\/tr>\n
                                                  Heuristic<\/td>\nGeneral problem-solving framework<\/td>\nWorking backward, simplifying steps, \u201crule of thumb”<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/section>\n