Explore Lean Thinking and Practice / Problem-Solving
Explore the process that’s foundational to assuring every individual becomes engaged by arming them with methods they can use to overcome obstacles and improve their work process.
Overcoming obstacles to achieve or elevate a standard
In a lean management system, everyone is engaged in ongoing problem-solving that is guided by two characteristics:
- Everything described or claimed should be based on verifiable facts, not assumptions and interpretations.
- Problem-solving is never-ending; that is, it begins rather than ends when an improvement plan is implemented. The implementation process is a learning opportunity to discover how to make progress toward the target condition.
Lean thinkers & practitioners understand that the problem-solving process is impeded if you make the common mistake of mechanically reaching for a familiar or favorite problem-solving methodology or, worse, jump quickly to a solution.
Leaders and teams avoid this trap by recognizing that most business problems fall into four categories, each requiring different thought processes, improvement methods, and management cadences.
The Four Types of Problems
Type 1: Troubleshooting: reactive problem-solving that hinges upon rapidly returning abnormal conditions to known standards. It provides some immediate relief but does not address the root cause.
Type 2: Gap from Standard: structured problem-solving that focuses on defining the problem, setting goals, analyzing the root cause, and establishing countermeasures, checks, standards, and follow-up activities. The aim is to prevent the problem from recurring by eliminating its underlying causes.
Type 3: Target Condition: continuous improvement ( kaizen ) that goes beyond existing standards of performance. It may utilize existing methods in new, creative ways to deliver superior value or performance toward a new target state of improvement.
Type 4: Open-ended: innovative problem-solving based on creativity, synthesis, and recognition of opportunity. It establishes new norms that often entail unexpected products, processes, systems, or value for the customer well beyond current levels.
By helping everyone in the organization to understand the importance of taking ownership of seeing and solving all types of problems, lean thinking & practice:
- Engenders a sense of empowerment and autonomy in all workers, which in turn promotes engagement in and ownership of the work process
- Enables organizations to overcome obstacles at their source, so they do not become more significant problems upstream
Ultimately, building a problem-solving culture creates a competitive advantage that is difficult for competitors to match.
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Encyclopedia of the Sciences of Learning pp 2680–2683 Cite as
- David H. Jonassen 2 &
- Woei Hung 3
- Reference work entry
Cognition ; Problem typology ; Problem-based learning ; Problems ; Reasoning
Problem solving is the process of constructing and applying mental representations of problems to finding solutions to those problems that are encountered in nearly every context.
Problem solving is the process of articulating solutions to problems. Problems have two critical attributes. First, a problem is an unknown in some context. That is, there is a situation in which there is something that is unknown (the difference between a goal state and a current state). Those situations vary from algorithmic math problems to vexing and complex social problems, such as violence in society (see Problem Typology ). Second, finding or solving for the unknown must have some social, cultural, or intellectual value. That is, someone believes that it is worth finding the unknown. If no one perceives an unknown or a need to determine an unknown, there is no perceived problem. Finding...
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Bransford, J., & Stein, B. S. (1984). The IDEAL problem solver: A guide for improving thinking, learning, and creativity . New York: WH Freeman.
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Authors and affiliations.
School of Information Science and Learning Technologies, University of Missouri, 221C Townsend Hall, 65211, Columbia, MO, USA
Dr. David H. Jonassen
College of Education and Human Development, University of North Dakota, 231 Centennial Drive, Stop 7189, 58202, Grand Forks, ND, USA
Dr. Woei Hung
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Correspondence to David H. Jonassen .
Editors and affiliations.
Faculty of Economics and Behavioral Sciences, Department of Education, University of Freiburg, 79085, Freiburg, Germany
Prof. Dr. Norbert M. Seel
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Jonassen, D.H., Hung, W. (2012). Problem Solving. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_208
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48 Problem Solving
Department of Psychological and Brain Sciences, University of California, Santa Barbara
- Published: 03 June 2013
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Problem solving refers to cognitive processing directed at achieving a goal when the problem solver does not initially know a solution method. A problem exists when someone has a goal but does not know how to achieve it. Problems can be classified as routine or nonroutine, and as well defined or ill defined. The major cognitive processes in problem solving are representing, planning, executing, and monitoring. The major kinds of knowledge required for problem solving are facts, concepts, procedures, strategies, and beliefs. Classic theoretical approaches to the study of problem solving are associationism, Gestalt, and information processing. Current issues and suggested future issues include decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific thinking, everyday thinking, and the cognitive neuroscience of problem solving. Common themes concern the domain specificity of problem solving and a focus on problem solving in authentic contexts.
The study of problem solving begins with defining problem solving, problem, and problem types. This introduction to problem solving is rounded out with an examination of cognitive processes in problem solving, the role of knowledge in problem solving, and historical approaches to the study of problem solving.
Definition of Problem Solving
Problem solving refers to cognitive processing directed at achieving a goal for which the problem solver does not initially know a solution method. This definition consists of four major elements (Mayer, 1992 ; Mayer & Wittrock, 2006 ):
Cognitive —Problem solving occurs within the problem solver’s cognitive system and can only be inferred indirectly from the problem solver’s behavior (including biological changes, introspections, and actions during problem solving). Process —Problem solving involves mental computations in which some operation is applied to a mental representation, sometimes resulting in the creation of a new mental representation. Directed —Problem solving is aimed at achieving a goal. Personal —Problem solving depends on the existing knowledge of the problem solver so that what is a problem for one problem solver may not be a problem for someone who already knows a solution method.
The definition is broad enough to include a wide array of cognitive activities such as deciding which apartment to rent, figuring out how to use a cell phone interface, playing a game of chess, making a medical diagnosis, finding the answer to an arithmetic word problem, or writing a chapter for a handbook. Problem solving is pervasive in human life and is crucial for human survival. Although this chapter focuses on problem solving in humans, problem solving also occurs in nonhuman animals and in intelligent machines.
How is problem solving related to other forms of high-level cognition processing, such as thinking and reasoning? Thinking refers to cognitive processing in individuals but includes both directed thinking (which corresponds to the definition of problem solving) and undirected thinking such as daydreaming (which does not correspond to the definition of problem solving). Thus, problem solving is a type of thinking (i.e., directed thinking).
Reasoning refers to problem solving within specific classes of problems, such as deductive reasoning or inductive reasoning. In deductive reasoning, the reasoner is given premises and must derive a conclusion by applying the rules of logic. For example, given that “A is greater than B” and “B is greater than C,” a reasoner can conclude that “A is greater than C.” In inductive reasoning, the reasoner is given (or has experienced) a collection of examples or instances and must infer a rule. For example, given that X, C, and V are in the “yes” group and x, c, and v are in the “no” group, the reasoning may conclude that B is in “yes” group because it is in uppercase format. Thus, reasoning is a type of problem solving.
Definition of Problem
A problem occurs when someone has a goal but does not know to achieve it. This definition is consistent with how the Gestalt psychologist Karl Duncker ( 1945 , p. 1) defined a problem in his classic monograph, On Problem Solving : “A problem arises when a living creature has a goal but does not know how this goal is to be reached.” However, today researchers recognize that the definition should be extended to include problem solving by intelligent machines. This definition can be clarified using an information processing approach by noting that a problem occurs when a situation is in the given state, the problem solver wants the situation to be in the goal state, and there is no obvious way to move from the given state to the goal state (Newell & Simon, 1972 ). Accordingly, the three main elements in describing a problem are the given state (i.e., the current state of the situation), the goal state (i.e., the desired state of the situation), and the set of allowable operators (i.e., the actions the problem solver is allowed to take). The definition of “problem” is broad enough to include the situation confronting a physician who wishes to make a diagnosis on the basis of preliminary tests and a patient examination, as well as a beginning physics student trying to solve a complex physics problem.
Types of Problems
It is customary in the problem-solving literature to make a distinction between routine and nonroutine problems. Routine problems are problems that are so familiar to the problem solver that the problem solver knows a solution method. For example, for most adults, “What is 365 divided by 12?” is a routine problem because they already know the procedure for long division. Nonroutine problems are so unfamiliar to the problem solver that the problem solver does not know a solution method. For example, figuring out the best way to set up a funding campaign for a nonprofit charity is a nonroutine problem for most volunteers. Technically, routine problems do not meet the definition of problem because the problem solver has a goal but knows how to achieve it. Much research on problem solving has focused on routine problems, although most interesting problems in life are nonroutine.
Another customary distinction is between well-defined and ill-defined problems. Well-defined problems have a clearly specified given state, goal state, and legal operators. Examples include arithmetic computation problems or games such as checkers or tic-tac-toe. Ill-defined problems have a poorly specified given state, goal state, or legal operators, or a combination of poorly defined features. Examples include solving the problem of global warming or finding a life partner. Although, ill-defined problems are more challenging, much research in problem solving has focused on well-defined problems.
Cognitive Processes in Problem Solving
The process of problem solving can be broken down into two main phases: problem representation , in which the problem solver builds a mental representation of the problem situation, and problem solution , in which the problem solver works to produce a solution. The major subprocess in problem representation is representing , which involves building a situation model —that is, a mental representation of the situation described in the problem. The major subprocesses in problem solution are planning , which involves devising a plan for how to solve the problem; executing , which involves carrying out the plan; and monitoring , which involves evaluating and adjusting one’s problem solving.
For example, given an arithmetic word problem such as “Alice has three marbles. Sarah has two more marbles than Alice. How many marbles does Sarah have?” the process of representing involves building a situation model in which Alice has a set of marbles, there is set of marbles for the difference between the two girls, and Sarah has a set of marbles that consists of Alice’s marbles and the difference set. In the planning process, the problem solver sets a goal of adding 3 and 2. In the executing process, the problem solver carries out the computation, yielding an answer of 5. In the monitoring process, the problem solver looks over what was done and concludes that 5 is a reasonable answer. In most complex problem-solving episodes, the four cognitive processes may not occur in linear order, but rather may interact with one another. Although some research focuses mainly on the execution process, problem solvers may tend to have more difficulty with the processes of representing, planning, and monitoring.
Knowledge for Problem Solving
An important theme in problem-solving research is that problem-solving proficiency on any task depends on the learner’s knowledge (Anderson et al., 2001 ; Mayer, 1992 ). Five kinds of knowledge are as follows:
Facts —factual knowledge about the characteristics of elements in the world, such as “Sacramento is the capital of California” Concepts —conceptual knowledge, including categories, schemas, or models, such as knowing the difference between plants and animals or knowing how a battery works Procedures —procedural knowledge of step-by-step processes, such as how to carry out long-division computations Strategies —strategic knowledge of general methods such as breaking a problem into parts or thinking of a related problem Beliefs —attitudinal knowledge about how one’s cognitive processing works such as thinking, “I’m good at this”
Although some research focuses mainly on the role of facts and procedures in problem solving, complex problem solving also depends on the problem solver’s concepts, strategies, and beliefs (Mayer, 1992 ).
Historical Approaches to Problem Solving
Psychological research on problem solving began in the early 1900s, as an outgrowth of mental philosophy (Humphrey, 1963 ; Mandler & Mandler, 1964 ). Throughout the 20th century four theoretical approaches developed: early conceptions, associationism, Gestalt psychology, and information processing.
The start of psychology as a science can be set at 1879—the year Wilhelm Wundt opened the first world’s psychology laboratory in Leipzig, Germany, and sought to train the world’s first cohort of experimental psychologists. Instead of relying solely on philosophical speculations about how the human mind works, Wundt sought to apply the methods of experimental science to issues addressed in mental philosophy. His theoretical approach became structuralism —the analysis of consciousness into its basic elements.
Wundt’s main contribution to the study of problem solving, however, was to call for its banishment. According to Wundt, complex cognitive processing was too complicated to be studied by experimental methods, so “nothing can be discovered in such experiments” (Wundt, 1911/1973 ). Despite his admonishments, however, a group of his former students began studying thinking mainly in Wurzburg, Germany. Using the method of introspection, subjects were asked to describe their thought process as they solved word association problems, such as finding the superordinate of “newspaper” (e.g., an answer is “publication”). Although the Wurzburg group—as they came to be called—did not produce a new theoretical approach, they found empirical evidence that challenged some of the key assumptions of mental philosophy. For example, Aristotle had proclaimed that all thinking involves mental imagery, but the Wurzburg group was able to find empirical evidence for imageless thought .
The first major theoretical approach to take hold in the scientific study of problem solving was associationism —the idea that the cognitive representations in the mind consist of ideas and links between them and that cognitive processing in the mind involves following a chain of associations from one idea to the next (Mandler & Mandler, 1964 ; Mayer, 1992 ). For example, in a classic study, E. L. Thorndike ( 1911 ) placed a hungry cat in what he called a puzzle box—a wooden crate in which pulling a loop of string that hung from overhead would open a trap door to allow the cat to escape to a bowl of food outside the crate. Thorndike placed the cat in the puzzle box once a day for several weeks. On the first day, the cat engaged in many extraneous behaviors such as pouncing against the wall, pushing its paws through the slats, and meowing, but on successive days the number of extraneous behaviors tended to decrease. Overall, the time required to get out of the puzzle box decreased over the course of the experiment, indicating the cat was learning how to escape.
Thorndike’s explanation for how the cat learned to solve the puzzle box problem is based on an associationist view: The cat begins with a habit family hierarchy —a set of potential responses (e.g., pouncing, thrusting, meowing, etc.) all associated with the same stimulus (i.e., being hungry and confined) and ordered in terms of strength of association. When placed in the puzzle box, the cat executes its strongest response (e.g., perhaps pouncing against the wall), but when it fails, the strength of the association is weakened, and so on for each unsuccessful action. Eventually, the cat gets down to what was initially a weak response—waving its paw in the air—but when that response leads to accidentally pulling the string and getting out, it is strengthened. Over the course of many trials, the ineffective responses become weak and the successful response becomes strong. Thorndike refers to this process as the law of effect : Responses that lead to dissatisfaction become less associated with the situation and responses that lead to satisfaction become more associated with the situation. According to Thorndike’s associationist view, solving a problem is simply a matter of trial and error and accidental success. A major challenge to assocationist theory concerns the nature of transfer—that is, where does a problem solver find a creative solution that has never been performed before? Associationist conceptions of cognition can be seen in current research, including neural networks, connectionist models, and parallel distributed processing models (Rogers & McClelland, 2004 ).
The Gestalt approach to problem solving developed in the 1930s and 1940s as a counterbalance to the associationist approach. According to the Gestalt approach, cognitive representations consist of coherent structures (rather than individual associations) and the cognitive process of problem solving involves building a coherent structure (rather than strengthening and weakening of associations). For example, in a classic study, Kohler ( 1925 ) placed a hungry ape in a play yard that contained several empty shipping crates and a banana attached overhead but out of reach. Based on observing the ape in this situation, Kohler noted that the ape did not randomly try responses until one worked—as suggested by Thorndike’s associationist view. Instead, the ape stood under the banana, looked up at it, looked at the crates, and then in a flash of insight stacked the crates under the bananas as a ladder, and walked up the steps in order to reach the banana.
According to Kohler, the ape experienced a sudden visual reorganization in which the elements in the situation fit together in a way to solve the problem; that is, the crates could become a ladder that reduces the distance to the banana. Kohler referred to the underlying mechanism as insight —literally seeing into the structure of the situation. A major challenge of Gestalt theory is its lack of precision; for example, naming a process (i.e., insight) is not the same as explaining how it works. Gestalt conceptions can be seen in modern research on mental models and schemas (Gentner & Stevens, 1983 ).
The information processing approach to problem solving developed in the 1960s and 1970s and was based on the influence of the computer metaphor—the idea that humans are processors of information (Mayer, 2009 ). According to the information processing approach, problem solving involves a series of mental computations—each of which consists of applying a process to a mental representation (such as comparing two elements to determine whether they differ).
In their classic book, Human Problem Solving , Newell and Simon ( 1972 ) proposed that problem solving involved a problem space and search heuristics . A problem space is a mental representation of the initial state of the problem, the goal state of the problem, and all possible intervening states (based on applying allowable operators). Search heuristics are strategies for moving through the problem space from the given to the goal state. Newell and Simon focused on means-ends analysis , in which the problem solver continually sets goals and finds moves to accomplish goals.
Newell and Simon used computer simulation as a research method to test their conception of human problem solving. First, they asked human problem solvers to think aloud as they solved various problems such as logic problems, chess, and cryptarithmetic problems. Then, based on an information processing analysis, Newell and Simon created computer programs that solved these problems. In comparing the solution behavior of humans and computers, they found high similarity, suggesting that the computer programs were solving problems using the same thought processes as humans.
An important advantage of the information processing approach is that problem solving can be described with great clarity—as a computer program. An important limitation of the information processing approach is that it is most useful for describing problem solving for well-defined problems rather than ill-defined problems. The information processing conception of cognition lives on as a keystone of today’s cognitive science (Mayer, 2009 ).
Classic Issues in Problem Solving
Three classic issues in research on problem solving concern the nature of transfer (suggested by the associationist approach), the nature of insight (suggested by the Gestalt approach), and the role of problem-solving heuristics (suggested by the information processing approach).
Transfer refers to the effects of prior learning on new learning (or new problem solving). Positive transfer occurs when learning A helps someone learn B. Negative transfer occurs when learning A hinders someone from learning B. Neutral transfer occurs when learning A has no effect on learning B. Positive transfer is a central goal of education, but research shows that people often do not transfer what they learned to solving problems in new contexts (Mayer, 1992 ; Singley & Anderson, 1989 ).
Three conceptions of the mechanisms underlying transfer are specific transfer , general transfer , and specific transfer of general principles . Specific transfer refers to the idea that learning A will help someone learn B only if A and B have specific elements in common. For example, learning Spanish may help someone learn Latin because some of the vocabulary words are similar and the verb conjugation rules are similar. General transfer refers to the idea that learning A can help someone learn B even they have nothing specifically in common but A helps improve the learner’s mind in general. For example, learning Latin may help people learn “proper habits of mind” so they are better able to learn completely unrelated subjects as well. Specific transfer of general principles is the idea that learning A will help someone learn B if the same general principle or solution method is required for both even if the specific elements are different.
In a classic study, Thorndike and Woodworth ( 1901 ) found that students who learned Latin did not subsequently learn bookkeeping any better than students who had not learned Latin. They interpreted this finding as evidence for specific transfer—learning A did not transfer to learning B because A and B did not have specific elements in common. Modern research on problem-solving transfer continues to show that people often do not demonstrate general transfer (Mayer, 1992 ). However, it is possible to teach people a general strategy for solving a problem, so that when they see a new problem in a different context they are able to apply the strategy to the new problem (Judd, 1908 ; Mayer, 2008 )—so there is also research support for the idea of specific transfer of general principles.
Insight refers to a change in a problem solver’s mind from not knowing how to solve a problem to knowing how to solve it (Mayer, 1995 ; Metcalfe & Wiebe, 1987 ). In short, where does the idea for a creative solution come from? A central goal of problem-solving research is to determine the mechanisms underlying insight.
The search for insight has led to five major (but not mutually exclusive) explanatory mechanisms—insight as completing a schema, insight as suddenly reorganizing visual information, insight as reformulation of a problem, insight as removing mental blocks, and insight as finding a problem analog (Mayer, 1995 ). Completing a schema is exemplified in a study by Selz (Fridja & de Groot, 1982 ), in which people were asked to think aloud as they solved word association problems such as “What is the superordinate for newspaper?” To solve the problem, people sometimes thought of a coordinate, such as “magazine,” and then searched for a superordinate category that subsumed both terms, such as “publication.” According to Selz, finding a solution involved building a schema that consisted of a superordinate and two subordinate categories.
Reorganizing visual information is reflected in Kohler’s ( 1925 ) study described in a previous section in which a hungry ape figured out how to stack boxes as a ladder to reach a banana hanging above. According to Kohler, the ape looked around the yard and found the solution in a flash of insight by mentally seeing how the parts could be rearranged to accomplish the goal.
Reformulating a problem is reflected in a classic study by Duncker ( 1945 ) in which people are asked to think aloud as they solve the tumor problem—how can you destroy a tumor in a patient without destroying surrounding healthy tissue by using rays that at sufficient intensity will destroy any tissue in their path? In analyzing the thinking-aloud protocols—that is, transcripts of what the problem solvers said—Duncker concluded that people reformulated the goal in various ways (e.g., avoid contact with healthy tissue, immunize healthy tissue, have ray be weak in healthy tissue) until they hit upon a productive formulation that led to the solution (i.e., concentrating many weak rays on the tumor).
Removing mental blocks is reflected in classic studies by Duncker ( 1945 ) in which solving a problem involved thinking of a novel use for an object, and by Luchins ( 1942 ) in which solving a problem involved not using a procedure that had worked well on previous problems. Finding a problem analog is reflected in classic research by Wertheimer ( 1959 ) in which learning to find the area of a parallelogram is supported by the insight that one could cut off the triangle on one side and place it on the other side to form a rectangle—so a parallelogram is really a rectangle in disguise. The search for insight along each of these five lines continues in current problem-solving research.
Heuristics are problem-solving strategies, that is, general approaches to how to solve problems. Newell and Simon ( 1972 ) suggested three general problem-solving heuristics for moving from a given state to a goal state: random trial and error , hill climbing , and means-ends analysis . Random trial and error involves randomly selecting a legal move and applying it to create a new problem state, and repeating that process until the goal state is reached. Random trial and error may work for simple problems but is not efficient for complex ones. Hill climbing involves selecting the legal move that moves the problem solver closer to the goal state. Hill climbing will not work for problems in which the problem solver must take a move that temporarily moves away from the goal as is required in many problems.
Means-ends analysis involves creating goals and seeking moves that can accomplish the goal. If a goal cannot be directly accomplished, a subgoal is created to remove one or more obstacles. Newell and Simon ( 1972 ) successfully used means-ends analysis as the search heuristic in a computer program aimed at general problem solving, that is, solving a diverse collection of problems. However, people may also use specific heuristics that are designed to work for specific problem-solving situations (Gigerenzer, Todd, & ABC Research Group, 1999 ; Kahneman & Tversky, 1984 ).
Current and Future Issues in Problem Solving
Eight current issues in problem solving involve decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific problem solving, everyday thinking, and the cognitive neuroscience of problem solving.
Decision making refers to the cognitive processing involved in choosing between two or more alternatives (Baron, 2000 ; Markman & Medin, 2002 ). For example, a decision-making task may involve choosing between getting $240 for sure or having a 25% change of getting $1000. According to economic theories such as expected value theory, people should chose the second option, which is worth $250 (i.e., .25 x $1000) rather than the first option, which is worth $240 (1.00 x $240), but psychological research shows that most people prefer the first option (Kahneman & Tversky, 1984 ).
Research on decision making has generated three classes of theories (Markman & Medin, 2002 ): descriptive theories, such as prospect theory (Kahneman & Tversky), which are based on the ideas that people prefer to overweight the cost of a loss and tend to overestimate small probabilities; heuristic theories, which are based on the idea that people use a collection of short-cut strategies such as the availability heuristic (Gigerenzer et al., 1999 ; Kahneman & Tversky, 2000 ); and constructive theories, such as mental accounting (Kahneman & Tversky, 2000 ), in which people build a narrative to justify their choices to themselves. Future research is needed to examine decision making in more realistic settings.
Intelligence and Creativity
Although researchers do not have complete consensus on the definition of intelligence (Sternberg, 1990 ), it is reasonable to view intelligence as the ability to learn or adapt to new situations. Fluid intelligence refers to the potential to solve problems without any relevant knowledge, whereas crystallized intelligence refers to the potential to solve problems based on relevant prior knowledge (Sternberg & Gregorenko, 2003 ). As people gain more experience in a field, their problem-solving performance depends more on crystallized intelligence (i.e., domain knowledge) than on fluid intelligence (i.e., general ability) (Sternberg & Gregorenko, 2003 ). The ability to monitor and manage one’s cognitive processing during problem solving—which can be called metacognition —is an important aspect of intelligence (Sternberg, 1990 ). Research is needed to pinpoint the knowledge that is needed to support intelligent performance on problem-solving tasks.
Creativity refers to the ability to generate ideas that are original (i.e., other people do not think of the same idea) and functional (i.e., the idea works; Sternberg, 1999 ). Creativity is often measured using tests of divergent thinking —that is, generating as many solutions as possible for a problem (Guilford, 1967 ). For example, the uses test asks people to list as many uses as they can think of for a brick. Creativity is different from intelligence, and it is at the heart of creative problem solving—generating a novel solution to a problem that the problem solver has never seen before. An important research question concerns whether creative problem solving depends on specific knowledge or creativity ability in general.
Teaching of Thinking Skills
How can people learn to be better problem solvers? Mayer ( 2008 ) proposes four questions concerning teaching of thinking skills:
What to teach —Successful programs attempt to teach small component skills (such as how to generate and evaluate hypotheses) rather than improve the mind as a single monolithic skill (Covington, Crutchfield, Davies, & Olton, 1974 ). How to teach —Successful programs focus on modeling the process of problem solving rather than solely reinforcing the product of problem solving (Bloom & Broder, 1950 ). Where to teach —Successful programs teach problem-solving skills within the specific context they will be used rather than within a general course on how to solve problems (Nickerson, 1999 ). When to teach —Successful programs teaching higher order skills early rather than waiting until lower order skills are completely mastered (Tharp & Gallimore, 1988 ).
Overall, research on teaching of thinking skills points to the domain specificity of problem solving; that is, successful problem solving depends on the problem solver having domain knowledge that is relevant to the problem-solving task.
Expert Problem Solving
Research on expertise is concerned with differences between how experts and novices solve problems (Ericsson, Feltovich, & Hoffman, 2006 ). Expertise can be defined in terms of time (e.g., 10 years of concentrated experience in a field), performance (e.g., earning a perfect score on an assessment), or recognition (e.g., receiving a Nobel Prize or becoming Grand Master in chess). For example, in classic research conducted in the 1940s, de Groot ( 1965 ) found that chess experts did not have better general memory than chess novices, but they did have better domain-specific memory for the arrangement of chess pieces on the board. Chase and Simon ( 1973 ) replicated this result in a better controlled experiment. An explanation is that experts have developed schemas that allow them to chunk collections of pieces into a single configuration.
In another landmark study, Larkin et al. ( 1980 ) compared how experts (e.g., physics professors) and novices (e.g., first-year physics students) solved textbook physics problems about motion. Experts tended to work forward from the given information to the goal, whereas novices tended to work backward from the goal to the givens using a means-ends analysis strategy. Experts tended to store their knowledge in an integrated way, whereas novices tended to store their knowledge in isolated fragments. In another study, Chi, Feltovich, and Glaser ( 1981 ) found that experts tended to focus on the underlying physics concepts (such as conservation of energy), whereas novices tended to focus on the surface features of the problem (such as inclined planes or springs). Overall, research on expertise is useful in pinpointing what experts know that is different from what novices know. An important theme is that experts rely on domain-specific knowledge rather than solely general cognitive ability.
Analogical reasoning occurs when people solve one problem by using their knowledge about another problem (Holyoak, 2005 ). For example, suppose a problem solver learns how to solve a problem in one context using one solution method and then is given a problem in another context that requires the same solution method. In this case, the problem solver must recognize that the new problem has structural similarity to the old problem (i.e., it may be solved by the same method), even though they do not have surface similarity (i.e., the cover stories are different). Three steps in analogical reasoning are recognizing —seeing that a new problem is similar to a previously solved problem; abstracting —finding the general method used to solve the old problem; and mapping —using that general method to solve the new problem.
Research on analogical reasoning shows that people often do not recognize that a new problem can be solved by the same method as a previously solved problem (Holyoak, 2005 ). However, research also shows that successful analogical transfer to a new problem is more likely when the problem solver has experience with two old problems that have the same underlying structural features (i.e., they are solved by the same principle) but different surface features (i.e., they have different cover stories) (Holyoak, 2005 ). This finding is consistent with the idea of specific transfer of general principles as described in the section on “Transfer.”
Mathematical and Scientific Problem Solving
Research on mathematical problem solving suggests that five kinds of knowledge are needed to solve arithmetic word problems (Mayer, 2008 ):
Factual knowledge —knowledge about the characteristics of problem elements, such as knowing that there are 100 cents in a dollar Schematic knowledge —knowledge of problem types, such as being able to recognize time-rate-distance problems Strategic knowledge —knowledge of general methods, such as how to break a problem into parts Procedural knowledge —knowledge of processes, such as how to carry our arithmetic operations Attitudinal knowledge —beliefs about one’s mathematical problem-solving ability, such as thinking, “I am good at this”
People generally possess adequate procedural knowledge but may have difficulty in solving mathematics problems because they lack factual, schematic, strategic, or attitudinal knowledge (Mayer, 2008 ). Research is needed to pinpoint the role of domain knowledge in mathematical problem solving.
Research on scientific problem solving shows that people harbor misconceptions, such as believing that a force is needed to keep an object in motion (McCloskey, 1983 ). Learning to solve science problems involves conceptual change, in which the problem solver comes to recognize that previous conceptions are wrong (Mayer, 2008 ). Students can be taught to engage in scientific reasoning such as hypothesis testing through direct instruction in how to control for variables (Chen & Klahr, 1999 ). A central theme of research on scientific problem solving concerns the role of domain knowledge.
Everyday thinking refers to problem solving in the context of one’s life outside of school. For example, children who are street vendors tend to use different procedures for solving arithmetic problems when they are working on the streets than when they are in school (Nunes, Schlieman, & Carraher, 1993 ). This line of research highlights the role of situated cognition —the idea that thinking always is shaped by the physical and social context in which it occurs (Robbins & Aydede, 2009 ). Research is needed to determine how people solve problems in authentic contexts.
Cognitive Neuroscience of Problem Solving
The cognitive neuroscience of problem solving is concerned with the brain activity that occurs during problem solving. For example, using fMRI brain imaging methodology, Goel ( 2005 ) found that people used the language areas of the brain to solve logical reasoning problems presented in sentences (e.g., “All dogs are pets…”) and used the spatial areas of the brain to solve logical reasoning problems presented in abstract letters (e.g., “All D are P…”). Cognitive neuroscience holds the potential to make unique contributions to the study of problem solving.
Problem solving has always been a topic at the fringe of cognitive psychology—too complicated to study intensively but too important to completely ignore. Problem solving—especially in realistic environments—is messy in comparison to studying elementary processes in cognition. The field remains fragmented in the sense that topics such as decision making, reasoning, intelligence, expertise, mathematical problem solving, everyday thinking, and the like are considered to be separate topics, each with its own separate literature. Yet some recurring themes are the role of domain-specific knowledge in problem solving and the advantages of studying problem solving in authentic contexts.
Some important issues for future research include the three classic issues examined in this chapter—the nature of problem-solving transfer (i.e., How are people able to use what they know about previous problem solving to help them in new problem solving?), the nature of insight (e.g., What is the mechanism by which a creative solution is constructed?), and heuristics (e.g., What are some teachable strategies for problem solving?). In addition, future research in problem solving should continue to pinpoint the role of domain-specific knowledge in problem solving, the nature of cognitive ability in problem solving, how to help people develop proficiency in solving problems, and how to provide aids for problem solving.
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Definition of problem-solving
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How to master the seven-step problem-solving process
In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.
Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.
Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].
Charles and Hugo, welcome to the podcast. Thank you for being here.
Hugo Sarrazin: Our pleasure.
Charles Conn: It’s terrific to be here.
Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?
Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”
You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”
I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.
I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.
Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.
Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.
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Simon London: So this is a concise problem statement.
Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.
Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.
How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.
Hugo Sarrazin: Yeah.
Charles Conn: And in the wrong direction.
Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?
Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.
What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.
Simon London: What’s a good example of a logic tree on a sort of ratable problem?
Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.
If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.
When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.
Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.
Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.
People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.
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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?
Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.
Simon London: Not going to have a lot of depth to it.
Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.
Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.
Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.
Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.
Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.
Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.
Simon London: Right. Right.
Hugo Sarrazin: So it’s the same thing in problem solving.
Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.
Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?
Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.
Simon London: Would you agree with that?
Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.
You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.
Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?
Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.
Simon London: Step six. You’ve done your analysis.
Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”
Simon London: But, again, these final steps are about motivating people to action, right?
Charles Conn: Yeah.
Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.
Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.
Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.
Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.
Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?
Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.
You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.
Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.
Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”
Hugo Sarrazin: Every step of the process.
Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?
Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.
Simon London: Problem definition, but out in the world.
Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.
Simon London: So, Charles, are these complements or are these alternatives?
Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.
Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?
Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.
The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.
Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.
Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.
Hugo Sarrazin: Absolutely.
Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.
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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.
Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.
Charles Conn: It was a pleasure to be here, Simon.
Hugo Sarrazin: It was a pleasure. Thank you.
Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.
Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.
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Five routes to more innovative problem solving
What Is Problem Solving?
You will often see beach clean-up drives being publicized in coastal cities. There are already dustbins available on the beaches,…
You will often see beach clean-up drives being publicized in coastal cities. There are already dustbins available on the beaches, so why do people need to organize these drives? It’s evident that despite advertising and posting anti-littering messages, some of us don’t follow the rules.
Temporary food stalls and shops make it even more difficult to keep the beaches clean. Since people can’t ask the shopkeepers to relocate or prevent every single person from littering, the clean-up drive is needed. This is an ideal example of problem-solving psychology in humans. ( 230-fifth.com ) So, what is problem-solving? Let’s find out.
What Is Problem-Solving?
At its simplest, the meaning of problem-solving is the process of defining a problem, determining its cause, and implementing a solution. The definition of problem-solving is rooted in the fact that as humans, we exert control over our environment through solutions. We move forward in life when we solve problems and make decisions.
We can better define the problem-solving process through a series of important steps.
Identify The Problem:
This step isn’t as simple as it sounds. Most times, we mistakenly identify the consequences of a problem rather than the problem itself. It’s important that we’re careful to identify the actual problem and not just its symptoms.
Define The Problem:
Once the problem has been identified correctly, you should define it. This step can help clarify what needs to be addressed and for what purpose.
Form A Strategy:
Develop a strategy to solve your problem. Defining an approach will provide direction and clarity on the next steps.
Organize The Information:
Organizing information systematically will help you determine whether something is missing. The more information you have, the easier it’ll become for you to arrive at a solution.
We may not always be armed with the necessary resources to solve a problem. Before you commit to implementing a solution for a problem, you should determine the availability of different resources—money, time and other costs.
The true meaning of problem-solving is to work towards an objective. If you measure your progress, you can evaluate whether you’re on track. You could revise your strategies if you don’t notice the desired level of progress.
Evaluate The Results:
After you spot a solution, evaluate the results to determine whether it’s the best possible solution. For example, you can evaluate the success of a fitness routine after several weeks of exercise.
Meaning Of Problem-Solving Skill
Now that we’ve established the definition of problem-solving psychology in humans, let’s look at how we utilize our problem-solving skills. These skills help you determine the source of a problem and how to effectively determine the solution. Problem-solving skills aren’t innate and can be mastered over time. Here are some important skills that are beneficial for finding solutions.
Communication is a critical skill when you have to work in teams. If you and your colleagues have to work on a project together, you’ll have to collaborate with each other. In case of differences of opinion, you should be able to listen attentively and respond respectfully in order to successfully arrive at a solution.
As a problem-solver, you need to be able to research and identify underlying causes. You should never treat a problem lightly. In-depth study is imperative because often people identify only the symptoms and not the actual problem.
Once you have researched and identified the factors causing a problem, start working towards developing solutions. Your analytical skills can help you differentiate between effective and ineffective solutions.
You’ll have to make a decision after you’ve identified the source and methods of solving a problem. If you’ve done your research and applied your analytical skills effectively, it’ll become easier for you to take a call or a decision.
Organizations really value decisive problem-solvers. Harappa Education’s Defining Problems course will guide you on the path to developing a problem-solving mindset. Learn how to identify the different types of problems using the Types of Problems framework. Additionally, the SMART framework, which is a five-point tool, will teach you to create specific and actionable objectives to address problem statements and arrive at solutions.
Explore topics & skills such as Problem Solving Skills , PICK Chart , How to Solve Problems & Barriers to Problem Solving from our Harappa Diaries blog section and develop your skills.
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Soft Systems Methodology (SSM)
Understanding very complex issues.
By the Mind Tools Content Team
Some problem solving tools can oversimplify the world when, in reality, it can be complex and messy.
In cases where many different factors contribute to an issue, and there are lots of different perspectives to consider, it can be difficult to tell where the root of the problem really lies. All this confusion can make finding a solution seem impossible. What you need is a problem solving approach that gives you a clear view of what's involved, so that you can focus on what you can do to improve the situation. In situations like this, Soft Systems Methodology (SSM) might be just what you need.
How SSM Was Developed
Soft Systems Methodology grew out of general systems theory, which views everything in the world as part of an open, dynamic, and interconnected system. The various parts of this system interact with one another, often in a nonlinear way, to produce a result.
According to general systems theory, organizations consist of complex, dynamic, goal-oriented processes – and all of these work together, in a coordinated way, to produce a particular result. For example, if a company's strategy is to maximize profits by bringing new products to market quickly, then the systems within the company must all work together to achieve this goal.
When something goes wrong within the system, or any of its subsystems, you must analyze the individual parts to discover a solution. In hard sciences, you can do this in a very controlled, analytical way. However, when you add human or "soft" elements – like social interaction, corporate politics, and individual perspectives – it's a much more difficult process.
That's why Peter Checkland, a management scientist and systems professor, applied the science of systems to the process of solving messy and confusing management problems. The result was Soft Systems Methodology – a way to explore complex situations with different stakeholders; numerous goals; different viewpoints and assumptions; and complicated interactions and relationships.
SSM helps you compare the "real world" with a model of how the world could be. Through this modeling process, you can go beyond the individual perspectives that might limit your thinking – and you can recognize what's causing the problems within the system.
Because SSM deals with real-world situations, it needs to reflect real-world problems, which often have nonlinear relationships that are not well defined. As a result, SSM activities are also nonlinear and not perfectly defined. Many other problem-solving tools can be shown as flow charts with a series of clearly defined steps. But diagrams used in SSM are more like mind maps – they show relationships between activities, but they don't show a linear route through them.
Checkland warns against thinking of SSM as a step-by-step process. However, if SSM is to be useful, you need to know where to begin. In this article, therefore, we'll give you a series of steps to help you get started (you can abandon this stepwise approach when you're more familiar with the methodology). To learn more about using the SSM approach, read " Learning for Action ," by Peter Checkland and John Poulter.
"Learning for Action" by Peter Checkland and John Poulter, © John Wiley & Sons, Inc. Terms reproduced with permission.
Although it's easy to think of Soft Systems Methodology as a "problem-solving approach," Checkland encourages SSM users to avoid thinking of a "problem" that can be "solved" by a "solution." These words imply that something is well defined and straightforward. Instead, he prefers the terms problematical situation and improvements .
Here's an example: "My car won't start" is a problem that might be solved by the solution "Charge the battery." However, consider "People don't enjoy driving this new model of car." This is a problematical situation for the manufacturer, which needs to look for actions that might improve the driving experience for customers.
Step 1: Explore the Problematical Situation
Create what Checkland calls a "rich picture" of what's happening. This is, in effect, a mind map . It shows the main individuals, groups, organizations, relationships, cultures, politics, and processes involved in the situation. Also, try to identify the different perspectives, or "worldviews," that different groups have of the situation.
Then, among these individuals or groups, identify the "client" who wants an improvement in the situation, the "practitioner" who is carrying out the SSM-based investigation, and the stakeholders who would be affected by an improvement in the situation.
Your goal, here, is to include as much relevant information as possible on a large sheet of paper.
Step 2: Create Purposeful Activity Models
Identify the "purposeful activities" being carried out by people involved in the situation. These are things that they're doing, as well as the actions they're taking to improve the problematical situation. Make note of which activities belong to which worldview.
Then, create a "root definition" of each activity. This is a more sophisticated description of the basic idea, and it contains enough detail to stimulate an in-depth discussion later on.
Checkland proposes two tools for developing the root definition. The first is called PQR:
- P stands for "What?"
- Q stands for "How?"
- R stands for "Why?"
If you answer the above questions, you can complete this formula: "Do P, by doing Q, to help achieve R."
The other tool is CATWOE . Use this to further improve the root definition by thinking about the following:
- Customers – Who receives the system's output?
- Actors – Who performs the work within, or implements changes to, the system?
- Transformation – What is affected by the system, and what does it do? This is often considered the most important part of CATWOE.
- Worldview – What is the big picture?
- Owner – Who owns the process?
- Environment – What are the restrictions and limits on any solution? What else is happening around it?
Finally, develop these into purposeful activity models. Ideally, you'll have 5–7 steps to cover all of these descriptions for each purposeful activity model, although you can break down individual steps into their own root definitions and activity models.
Checkland recommends reviewing these in the light of "three E's":
- Efficacy – Ways to monitor if the transformation is, in fact, creating the intended outcome.
- Efficiency – Ways to monitor if the benefits of the transformation are greater than the cost (in time, effort, and money) of creating them.
- Effectiveness – Ways to identify if the individual transformation also contributes to higher-level or longer-term goals.
Step 3: Discuss the Problematic Situation
Discuss in detail each purposeful activity model. Your goal is to find ways to improve the problematic situation. Some of the following questions may help:
- Does each part of the model truly represent what happens in reality?
- Do the dependencies and relationships between activities in the model also exist in reality?
- Is each activity efficacious, efficient, and effective?
- Who performs each activity? Who else could do it?
- How is each activity done? How else could it be done?
- When and where is each activity done? When or where else could it be done?
Having created a list of possible improvements, you may want to create purposeful activity models for each one. Following the process for doing so helps ensure that you've considered all of the various worldviews involved, which is necessary for the improvement to have a realistic chance of being implemented.
Step 4: Define "Actions to Improve"
The group doing the SSM-based analysis of the problematical situation now has to agree on which actions it thinks will improve the situation, and the group must define those actions in enough detail to create an implementation plan.
Remember, because people have different worldviews, there won't necessarily be agreement on which actions to take to improve the situation. However, everyone involved should reach what Checkland describes as an "accommodation" or compromise, so that they agree on practical options that meet the three E's – efficacy, efficiency, and effectiveness.
Change generally involves people, processes , and things . New "things" are usually the easiest to change: you can simply buy new equipment or systems. New processes need a lot of definition, but they can also be reasonably clear and straightforward to implement. Changes to people – involving culture or attitudes – are typically much more difficult. For more on this, see our article on Change Management .
We've presented Soft Systems Methodology here as a set of steps, but experienced SSM practitioners usually perform its activities in a repeated and ongoing manner – and they're flexible with SSM ideas, rather than following a strict process.
Checkland, P. and Poulter, J. (2007). ‘ Learning for Action: A Short Definitive Account of Soft Systems Methodology, and Its Use for Practitioners, Teachers and Students,’ Hoboken: Wiley.
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How Do You Identify a Systems Problem?
Some of the policy issues public health professionals face can be difficult to understand and challenging to resolve. Youth violence, teen pregnancy, and obesity—to name just a few—are complicated and multi-factorial problems that can feel very “messy” at times. These are called systems problems, and they might benefit from new ways of thinking about both the issues and the potential solutions.
What is the ‘system’ in systems thinking?
A system refers to the elements that work together to generate the results you want or desire to change. The system is the interplay between: policies and procedures, infrastructure, spending decisions, human actions, and intangible drivers of behavior (e.g., trust, goodwill, etc.).
Chances are that the public health issues of concern to you share characteristics of “systems problems.” Systems problems share four fundamental characteristics:
- They are dynamic in nature, meaning they change over time
- They include multiple organizations/people with diverse interests
- They are interconnected, meaning that dependencies between individuals, organizations, regions, etc. exist and are important
- They can be hard to describe
Recognizing systems problems can be a valuable process for better understanding the causes of the problem, deciding on various policy options, and engaging with partners and decision makers—which can lead to identifying higher impact solutions.
It’s possible to use Thinking in Systems (TiS) to effectively frame systems problems and to think through the design of policy initiatives that generate intended effects while minimizing unintended consequences. TiS enables individuals and groups to come together to a clearer understanding of what the problem looks like, how the underlying system works, and where there might be potential for policy leverage.
How Can You Use TiS with the CDC Policy Process?
The CDC Policy Process includes five domains:
- Problem Identification
- Policy Analysis
- Strategy and Policy Development
- Policy Enactment
Evaluation and Stakeholder Engagement and education take place during every domain in the process.
Because many of the issues or problems faced in public health are “systems problems,” understanding some key sets of systems thinking skills can help at various phases of the CDC Policy Process, including: identifying policy problems, analyzing solutions, better informing decision-making, and improving stakeholder engagement.
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What Is Problem-Solving Therapy?
Arlin Cuncic, MA, is the author of "Therapy in Focus: What to Expect from CBT for Social Anxiety Disorder" and "7 Weeks to Reduce Anxiety." She has a Master's degree in psychology.
Daniel B. Block, MD, is an award-winning, board-certified psychiatrist who operates a private practice in Pennsylvania.
Verywell / Madelyn Goodnight
Things to consider, how to get started.
Problem-solving therapy is a form of therapy that provides patients with tools to identify and solve problems that arise from life stressors, both big and small. Its aim is to improve your overall quality of life and reduce the negative impact of psychological and physical illness.
Problem-solving therapy can be used to treat depression , among other conditions. It can be administered by a doctor or mental health professional and may be combined with other treatment approaches.
Problem-solving therapy is based on a model that takes into account the importance of real-life problem-solving. In other words, the key to managing the impact of stressful life events is to know how to address issues as they arise. Problem-solving therapy is very practical in its approach and is only concerned with the present, rather than delving into your past.
This form of therapy can take place one-on-one or in a group format and may be offered in person or online via telehealth . Sessions can be anywhere from 30 minutes to two hours long.
There are two major components that make up the problem-solving therapy framework:
- Applying a positive problem-solving orientation to your life
- Using problem-solving skills
A positive problem-solving orientation means viewing things in an optimistic light, embracing self-efficacy , and accepting the idea that problems are a normal part of life. Problem-solving skills are behaviors that you can rely on to help you navigate conflict, even during times of stress. This includes skills like:
- Knowing how to identify a problem
- Defining the problem in a helpful way
- Trying to understand the problem more deeply
- Setting goals related to the problem
- Generating alternative, creative solutions to the problem
- Choosing the best course of action
- Implementing the choice you have made
- Evaluating the outcome to determine next steps
Problem-solving therapy is all about training you to become adaptive in your life so that you will start to see problems as challenges to be solved instead of insurmountable obstacles. It also means that you will recognize the action that is required to engage in effective problem-solving techniques.
One problem-solving technique, called planful problem-solving, involves following a series of steps to fix issues in a healthy, constructive way:
- Problem definition and formulation : This step involves identifying the real-life problem that needs to be solved and formulating it in a way that allows you to generate potential solutions.
- Generation of alternative solutions : This stage involves coming up with various potential solutions to the problem at hand. The goal in this step is to brainstorm options to creatively address the life stressor in ways that you may not have previously considered.
- Decision-making strategies : This stage involves discussing different strategies for making decisions as well as identifying obstacles that may get in the way of solving the problem at hand.
- Solution implementation and verification : This stage involves implementing a chosen solution and then verifying whether it was effective in addressing the problem.
Other techniques your therapist may go over include:
- Problem-solving multitasking , which helps you learn to think clearly and solve problems effectively even during times of stress
- Stop, slow down, think, and act (SSTA) , which is meant to encourage you to become more emotionally mindful when faced with conflict
- Healthy thinking and imagery , which teaches you how to embrace more positive self-talk while problem-solving
What Problem-Solving Therapy Can Help With
Problem-solving therapy addresses issues related to life stress and is focused on helping you find solutions to concrete issues. This approach can be applied to problems associated with a variety of psychological and physiological symptoms.
Problem-solving therapy may help address mental health issues, like:
- Chronic stress due to accumulating minor issues
- Complications associated with traumatic brain injury (TBI)
- Emotional distress
- Post-traumatic stress disorder (PTSD)
- Problems associated with a chronic disease like cancer, heart disease, or diabetes
- Self-harm and feelings of hopelessness
- Substance use
- Suicidal ideation
This form of therapy is also helpful for dealing with specific life problems, such as:
- Death of a loved one
- Dissatisfaction at work
- Everyday life stressors
- Family problems
- Financial difficulties
- Relationship conflicts
Your doctor or mental healthcare professional will be able to advise whether problem-solving therapy could be helpful for your particular issue. In general, if you are struggling with specific, concrete problems that you are having trouble finding solutions for, problem-solving therapy could be helpful for you.
Benefits of Problem-Solving Therapy
The skills learned in problem-solving therapy can be helpful for managing all areas of your life. These can include:
- Being able to identify which stressors trigger your negative emotions (e.g., sadness, anger)
- Confidence that you can handle problems that you face
- Having a systematic approach on how to deal with life's problems
- Having a toolbox of strategies to solve the problems you face
- Increased confidence to find creative solutions
- Knowing how to identify which barriers will impede your progress
- Knowing how to manage emotions when they arise
- Reduced avoidance and increased action-taking
- The ability to accept life problems that can't be solved
- The ability to make effective decisions
- The development of patience (realizing that not all problems have a "quick fix")
This form of therapy was initially developed to help people combat stress through effective problem-solving, and it was later adapted to specifically address clinical depression. Today, much of the research on problem-solving therapy deals with its effectiveness in treating depression.
Problem-solving therapy has been shown to help depression in:
- Older adults
- People coping with serious illnesses like breast cancer
Problem-solving therapy also appears to be effective as a brief treatment for depression, offering benefits in as little as six to eight sessions with a therapist or another healthcare professional. This may make it a good option for someone who is unable to commit to a lengthier treatment for depression.
Problem-solving therapy is not a good fit for everyone. It may not be effective at addressing issues that don't have clear solutions, like seeking meaning or purpose in life. Problem-solving therapy is also intended to treat specific problems, not general habits or thought patterns .
In general, it's also important to remember that problem-solving therapy is not a primary treatment for mental disorders. If you are living with the symptoms of a serious mental illness such as bipolar disorder or schizophrenia , you may need additional treatment with evidence-based approaches for your particular concern.
Problem-solving therapy is best aimed at someone who has a mental or physical issue that is being treated separately, but who also has life issues that go along with that problem that has yet to be addressed.
For example, it could help if you can't clean your house or pay your bills because of your depression, or if a cancer diagnosis is interfering with your quality of life.
Your doctor may be able to recommend therapists in your area who utilize this approach, or they may offer it themselves as part of their practice. You can also search for a problem-solving therapist with help from the American Psychological Association’s (APA) Society of Clinical Psychology .
If receiving problem-solving therapy from a doctor or mental healthcare professional is not an option for you, you could also consider implementing it as a self-help strategy using a workbook designed to help you learn problem-solving skills on your own.
During your first session, your therapist may spend some time explaining their process and approach. They may ask you to identify the problem you’re currently facing, and they’ll likely discuss your goals for therapy.
Problem-solving therapy may be a short-term intervention that's focused on solving a specific issue in your life. If you need further help with something more pervasive, it can also become a longer-term treatment option.
Pierce D. Problem solving therapy - Use and effectiveness in general practice . Aust Fam Physician . 2012;41(9):676-679.
Cuijpers P, Wit L de, Kleiboer A, Karyotaki E, Ebert DD. Problem-solving therapy for adult depression: An updated meta-analysis . Eur Psychiatry . 2018;48(1):27-37. doi:10.1016/j.eurpsy.2017.11.006
Nezu AM, Nezu CM, D'Zurilla TJ. Problem-Solving Therapy: A Treatment Manual . New York; 2013. doi:10.1891/9780826109415.0001
Hatcher S, Sharon C, Parag V, Collins N. Problem-solving therapy for people who present to hospital with self-harm: Zelen randomised controlled trial . Br J Psychiatry . 2011;199(4):310-316. doi:10.1192/bjp.bp.110.090126
Sorsdahl K, Stein DJ, Corrigall J, et al. The efficacy of a blended motivational interviewing and problem solving therapy intervention to reduce substance use among patients presenting for emergency services in South Africa: A randomized controlled trial . Subst Abuse Treat Prev Policy . 2015;10(1):46. doi:doi.org/10.1186/s13011-015-0042-1
Kirkham JG, Choi N, Seitz DP. Meta-analysis of problem solving therapy for the treatment of major depressive disorder in older adults . Int J Geriatr Psychiatry . 2016;31(5):526-535. doi:10.1002/gps.4358
Garand L, Rinaldo DE, Alberth MM, et al. Effects of problem solving therapy on mental health outcomes in family caregivers of persons with a new diagnosis of mild cognitive impairment or early dementia: A randomized controlled trial . Am J Geriatr Psychiatry . 2014;22(8):771-781. doi:10.1016/j.jagp.2013.07.007
Hopko DR, Armento MEA, Robertson SMC, et al. Brief behavioral activation and problem-solving therapy for depressed breast cancer patients: Randomized trial . J Consult Clin Psychol . 2011;79(6):834-849. doi:10.1037/a0025450
Nieuwsma JA, Trivedi RB, McDuffie J, Kronish I, Benjamin D, Williams JW. Brief psychotherapy for depression: A systematic review and meta-analysis . Int J Psychiatry Med . 2012;43(2):129-151. doi:10.2190/PM.43.2.c
By Arlin Cuncic, MA Arlin Cuncic, MA, is the author of "Therapy in Focus: What to Expect from CBT for Social Anxiety Disorder" and "7 Weeks to Reduce Anxiety." She has a Master's degree in psychology.
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Systems Thinking explained
Systems thinking: This article explains systems thinking in a practical way. You can read about the definition of this term, its origin and various examples. You will also read about important elements and you will find practical tips to get started with it yourself. Enjoy reading!
What is Systems Thinking? The basics explained
The definition of systems thinking.
Systems thinking is a scientific approach and diagnostic tool for overseeing the whole in solving a problem, rather than focusing on individual elements. The advantage of this is that it is possible to properly consider what role certain parts of a system play in the larger whole.
The behavior of a system is viewed as an interplay of interacting building blocks, rather than a simple chain of cause and effect relationships. Feedback plays an important role in this. You will read more about the concept of feedback or feedback loops later.
Systems thinking as a discipline has developed since the 1950s and continues to be expanded with theories (systems theory) and methods. It is therefore not a stand-alone theory or technique, but rather a way of understanding complex systems. It is mainly aimed at discovering connections, patterns and relationships between different elements, in order to understand how a system works.
It has become an important part of many subject areas. It originally arose from a concern of specialists about the increasing complexity of systems and organizations. But also in biology and psychology, for example, as noted by Ludwig von Bertalanffy.
- Systems thinking looks at connected wholes instead of separate parts; the big picture
- Systems thinkers are curious and have an open mind
- A systems thinker is constantly trying to expand the range of methods and options available to solve a problem
When is Systems Thinking used?
Systems thinking can be used to solve a complex problem at work, school or even at home. The key is to apply a systems thinking perspective when problems have many interrelated parts.
According to the systems thinker, that could be a problem that meets the following four criteria:
- The matter is important
- The problem is recurring
- The problem is known and has a known history
- People have previously unsuccessfully tried to solve the problem
What is a System?
According to Talcott Parsons, a system is a complex set of interdependencies between parts, components, and processes that relate to distinct regularities of a relationship, as to similar interdependencies between such a complex whole and its environment.
Gestalt psychology is an example of this in psychology. Wolfgang Köhler and Kurt Lewin were the founders of this. They argued that certain functionally related units that are inextricably linked also contain human characteristics.
Systems thinking: feedback and synthesis
Words like interconnectedness, feedback loops, and synthesis can sound overwhelming to some people. They are important characteristics of this problem solving approach. A few main themes are therefore discussed below.
Instead of linear, systems thinking should be thought in a circular way. This change of mentality is necessary because in a system everything is connected. This concept is also very present in biology.
Everything that lives needs something else to survive. People need oxygen, food and water. For example, trees need carbon dioxide and sunlight. Many things need other things to survive. If one fails, it has an effect on many other living parts.
Synthesis refers to combining two or more things to create something new. Synthesis, unlike analysis, is the primary goal of systems thinking. Analysis fits more into a reductionist worldview, where the world or systems are divided into parts. Synthesis is the ability to see interconnectedness.
Because everything in a system is interconnected, there are constant feedback loops and flows of information between the elements of a system. It is important to understand these feedback loops, especially the dynamics that drive them.
To properly understand feedback loops, there must be an understanding of causality. Causality deals with the question: How does one result in the other? In a dynamic and constantly evolving system, this is a major challenge. Cause and effect are common concepts in life. Parents also try to explain these things to their children. Certain actions lead to consequences.
Causality as a concept in systems thinking is about anything that helps to decipher how things interact. A good understanding of this leads to a deeper perspective on feedback loops, connections and relationships. All fundamental parts of mapping systems.
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Now it is your turn
What do you think? Do you recognize the explanation of systems thinking? Do you have experience with systems thinking? Or are your colleagues working on this discipline? In which other fields do you think is this approach important? Do you want more information about a topic that is linked to this? Let us know in the comments below.
Share your experience and knowledge in the comments box below.
- Assaraf, O. B. Z., & Orion, N. (2010). System thinking skills at the elementary school level . Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 47(5), 540-563.
- Checkland, P. (1999). Systems thinking . Rethinking management information systems, 45-56.
- Zexian, Y., & Xuhui, Y. (2010). A revolution in the field of systems thinking—a review of Checkland’s system thinking . Systems Research and Behavioral Science: The Official Journal of the International Federation for Systems Research, 27(2), 140-155.
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Published on: 03/28/2023 | Last update: 01/11/2023
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5 Whys Root Cause Analysis (Toyoda)
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- Problem Solving
A managerial problem can be described as the gap between a given current state of affairs and a future desired state. Problem solving may then be thought of as the process of analyzing the situation and developing a solution to bridge the gap. While it is widely recognized that different diagnostic techniques are appropriate in different situations, problem solving as a formal analytical framework applies to all but the simplest managerial problems. The framework is analogous to the scientific method used in chemistry, astronomy, and the other physical sciences. In both cases, the purpose underlying the analytic process is to minimize the influence of the investigator's personal biases, maximize the likelihood of an accurate result, and facilitate communication among affected parties.
Problem solving was popularized by W. Edwards Deming and the expansion of the total quality management movement in the 1980s. While Deming described what he called the Shewhart cycle, the technique is more commonly known as the Deming Wheel or simply as the PDCA cycle. Regardless of the name, a problem solver is urged to follow a step-by-step approach to problem solving-plan, do, check, act (hence the PDCA acronym).
In the planning stage, a manager develops a working hypothesis about why a given problem exists and then develops a proposed solution to the problem. The second step is to implement, or do, the proposed remedy. Next, the manager studies or checks the result of the action taken. The focus of this review is to determine whether the proposed solution achieved the desired result-was the problem solved? The fourth step then depends upon the interpretation of the check on results. If the problem was solved, the manager acts to institutionalize the proposed solution. This might mean establishing controls or changing policy manuals to ensure that the new way of doing business continues. However, if the check indicates that the problem was not solved or was only partially corrected, the manager acts by initiating a new cycle. Indeed, the technique is represented as a cycle based on the belief that many problems are never fully solved. For example, suppose that the problem in a given manufacturing facility is determined to be that labor productivity is too low. A change in processing methods may be found to successfully increase labor productivity. However, this does not preclude additional increases in labor productivity. Therefore, the PDCA cycle suggests that managers should pursue a course of continuous improvement activity.
The problem-solving framework can be used in a wide variety of business situations, including both large-scale management-change initiatives and routine improvement or corrective activity. Indeed, management consultants may be thought of as professional problem solvers. By relying on the proven problem-solving framework, external consultants are often able to overcome their lack of specific industry experience or knowledge of an organization's internal dynamics to provide meaningful analysis and suggestions for improvement. To more fully explore the issues presented by problem solving, the four-step PDCA cycle is expanded to a nine-step framework in the next section.
Perhaps the only generalizable caveat regarding problem solving is to guard against overuse of the framework. For example, Florida Power & Light became well known for their problem-solving ability in the late 1980s. One of their most successful initiatives was to institute an aggressive tree-trimming program to anticipate and prevent power failure due to downed limbs falling on electrical lines during storms. They were so successful that they integrated the problem-solving framework into their day-to-day managerial decision making and organizational culture. While this resulted in well reasoned decisions, it also meant that implementing even simple changes like moving a filing cabinet closer to the people using it required an overly bureaucratic approval process. This phenomenon is commonly referred to as paralysis of analysis. Therefore, managers should remain aware of the costs in both time and resources associated with the problem-solving framework. Accordingly, the nine-step framework described below is offered as a suggested guide to problem solving. Managers should feel free to simplify the framework as appropriate given their particular situation.
THE PROBLEM-SOLVING FRAMEWORK
Although business problems in the form of a broken piece of machinery or an irate customer are readily apparent, many problems present themselves in a more subtle fashion. For example, if a firm's overall sales are increasing, but its percentage of market share is declining, there is no attention-grabbing incident to indicate that a problem exists. However, the problem-solving framework is still helpful in analyzing the current state of affairs and developing a management intervention to guide the firm toward the future desired state. Therefore, a solid approach to problem solving begins with a solid approach to problem identification. Whatever techniques are used, a firm's approach to problem identification should address three common identification shortfalls. First and most obviously, the firm wants to avoid being blindsided. Many problems develop over time; however, unless the firm is paying attention, warning signals may go unheeded until it is too late to effectively respond. A second common error of problem identification is not appropriating properly. This means that although a firm recognizes that an issue exists, they do not recognize the significance of the problem and fail to dedicate sufficient resources to its solution. It can be argued that not prioritizing properly has kept many traditional retail firms from responding effectively to emerging internet-based competitors. Finally, a third common error in problem identification is overreaction-the Chicken Little syndrome. Just as every falling acorn does not indicate that the sky is falling, neither does every customer complaint indicates that a crisis exists. Therefore, a firm's problem identification methods should strive to present an accurate assessment of the problems and opportunities facing the firm.
While no specific problem-identification technique will be appropriate for every situation, there are several techniques that are widely applicable. Two of the most useful techniques are statistical process control (SPC) and benchmarking. SPC is commonly used in the repetitive manufacturing industries, but can also prove useful in any stable production or service-delivery setting. A well formulated SPC program serves to inform managers when their operational processes are performing as expected and when something unexpected is introducing variation in process outputs. A simplified version of SPC is to examine performance outliers-those instances when performance was unusually poor or unusually good. It is believed that determining what went wrong, or conversely what went right, may inspire process or product modifications. Competitive benchmarking allows managers to keep tabs on their competition and thereby gauge their customers' evolving expectations. For instance, benchmarking might involve reverse engineering-disassembling a competitor's product-to study its design features and estimate the competitor's manufacturing costs. Texas Nameplate Company, Inc., a 1998 Malcolm Baldrige National Quality Award winner, uses competitive benchmarking by periodically ordering products from their competitors to compare their delivery-time performance.
Additional listening and problem identification techniques include the time-tested management-by-walking-around, revamped with a Japanese influence as going to gemba. The technique suggests that managers go to where the action is-to the production floor, point of delivery, or even to the customer's facilities to directly observe how things are done and how the product is used. Other methods include active solicitation of customer complaints and feedback. Bennigan's Restaurants offer a five-dollar credit toward future purchases to randomly selected customers who respond to telephone surveys on their satisfaction with their most recent restaurant visit. Granite Rock Company, a 1992 Baldrige Award winner, goes even farther by allowing customers to choose not to pay for any item that fails to meet their expectations. All that Granite Rock asks in return is an explanation of why the product was unsatisfactory.
The amount of resources that should be dedicated to verification will vary greatly depending upon how the problem itself is manifested. If the problem is straightforward and well-defined, only a cursory level of verification may be appropriate. However, many business problems are complex and ill defined. These situations may be similar to the case of a physician who is confronted with a patient that has self-diagnosed his medical condition. While considering the patient's claim, the doctor will conduct her own analysis to verify the diagnosis. Similarly, the need for verification is especially important when a manager is asked to step in and solve a problem that has been identified by someone else. The introduction of the manager's fresh perspective and the possibility of a hidden agenda on the part of the individual who initially identified the issue under consideration suggests that a "trust, but verify" approach may be prudent. Otherwise, the manager may eventually discover she has expended a great deal of time and effort pursuing a solution to the wrong problem.
In the case of particularly ambiguous problems, McKinsey & Company, a management-consulting group, uses a technique they call Forces at Work. In this analysis, McKinsey's consultants review the external pressures on the client firm arising from suppliers, customers, competitors, regulators, technology shifts, and substitute products. They then attempt to document the direction and magnitude of any changes in the various pressures on the firm. In addition, they review any internal changes, such as shifts in labor relations or changes in production technology. Finally, they look at how the various factors are impacting the way the firm designs, manufactures, distributes, sells, and services its products. Essentially, McKinsey attempts to create comprehensive before-and-after snapshots of their client's business environment. Focusing on the differences between the two, they hope to identify and clarify the nature of the challenges facing the firm.
The next step in problem solving is to formally define the problem to be addressed. This is a negotiation between the individuals tasked with solving the problem and the individuals who over-see their work. Essentially, the parties need to come to an agreement on what a solution to the problem will look like. Are the overseers anticipating an implementation plan, a fully operational production line, a recommendation for capital investment, or a new product design? What metrics are considered important-cycle time, material costs, market share, scrap rates, or warranty costs? Complex problems may be broken down into mutually exclusive and collectively exhaustive components, allowing each piece to be addressed separately. The negotiation should recognize that the scope of the problem that is defined will drive the resource requirements of the problem solvers. The more focused the problem definition, the fewer resources necessary to generate a solution. Finally, the time frame for problem analysis should also be established. Many business problems require an expedited or emergency response. This may mean that the problem solvers need to generate a temporary or interim solution to the problem before they can fully explore the underlying causes of the problem. Ensuring that the overseers recognize the limitations inherent in an interim solution serves to preserve the credibility of the problem solvers.
Now that the problem has been formally defined, the next step is for the problem solvers to attempt to identify the causes of the problem. The ultimate goal is to uncover the root cause or causes of the problem. The root cause is defined as that condition or event that, if corrected or eliminated, would prevent the problem from occurring. However, the problem solver should focus on potential root causes they are within the realm of potential control. For example, finding that a particular weight of motor oil is insufficient to protect an engine from overheating readily leads to an actionable plan for improvement. Finding that the root cause of a problem is gravity does not.
A common technique for generating potential root causes is the cause-and-effect diagram (also known as the fishbone or Ishikawa diagram). Using the diagram as a brainstorming tool, problem solvers traditionally review how the characteristics or operation of raw materials, labor inputs, equipment, physical environment, and management policies might cause the identified problem. Each branch of the diagram then becomes a statement of a causal hypothesis. For example, one branch of the diagram might suggest that low salaries are leading to high employee turnover, which in turn results in inexperienced operators running the machinery, which leads to a high scrap rate and ultimately higher material costs. This analysis suggests that to address the problem of high material costs, the firm may have to address the root cause of insufficient salaries.
Collection and examination of data may also lead the problem solver toward causal hypotheses. Check sheets, scatter plots, Pareto diagrams, data stratification, and a number of other graphical and statistical tools can aid problem solvers as they look for relationships between the problems identified and various input variables. Patterns in the data, changes in a variable over time, or comparisons to similar systems may all be useful in developing working theories about why something is happening. The problem solver should also consider the possibility of multiple causes or interaction effects. Perhaps the problem manifests only when a specific event occurs and certain conditions are met-the temperature is above 85 degrees or the ambient humidity is abnormally low.
Once the problem solver has identified the likely root causes of the problem, an examination of the available evidence should be used to confirm or disconfirm which potential causes actually are present and impacting the performance under consideration. This might entail developing an experiment where the candidate cause is controlled to determine whether its manipulation influences the presence of the problem. At this stage of the analysis, the problem solver should remain open to disconfirming evidence. Many elegant theories fail to achieve the necessary confirmation when put to the test. At this stage of the analysis it is also common for the problem solver to discover simple, easily implemented actions that will solve all or part of the problem. If this occurs, then clearly the problem solver should grasp the opportunity to "pick the low hanging fruit." Even if only a small component of the problem is solved, these interim wins serve to build momentum and add credibility to the problem-solving process.
Once the root causes of the problem have been identified, the problem solver can concentrate on developing approaches to prevent, eliminate, or control them. This is a creative process. The problem solver should feel free to challenge assumptions about how business was conducted in the past. At times, an effective approach is to generalize the relationship between the cause and the problem. Then the problem solver can look for similar relationships between other cause and effects that might provide insight on how to address the issues at hand. In general, it is useful to attempt to generate multiple candidate solutions. By keeping the creative process going, even after a viable solution is proposed, the problem solver retains the possibility of identifying a more effective or less expensive solution to the problem.
EVALUATION OF ALTERNATIVES.
Assuming that the problem was well defined, evaluation of the effectiveness of alternative solutions should be relatively straightforward. The issue is simply to what extent each alternative alleviates the problem. Using the metrics previously identified as important for judging success, the various alternatives can generally be directly compared. However, in addition to simply measuring the end result, the problem solvers may also want to consider the resources necessary to implement each solution. Organizations are made up of real people, with real strengths and weaknesses. A given solution may require competencies or access to finite resources that simply do not exist in the organization. In addition, there may be political considerations within the organization that influence the desirability of one alternative over another. Therefore, the problem solver may want to consider both the tangible and intangible benefits and costs of each alternative.
A very common problem-solving failure is for firms to stop once the plan of action is developed. Regardless of how good the plan is, it is useless unless it is implemented. Therefore, once a specific course of action has been approved, it should continue to receive the necessary attention and support to achieve success. The work should be broken down into tasks that can be assigned and managed. Specific mile-stones with target dates for completion should be established. Traditional project management techniques, such as the critical path method (CPM) or the program evaluation and review technique (PERT) are very useful to oversee implementation efforts.
Another common failure is for firms to simply move on after a solution has been implemented. At a minimum, a post-implementation evaluation of whether or not the problem has been solved should be conducted. If appropriate and using the metrics that were established earlier, this process should again be relatively straightforward-were the expected results achieved? The review can also determine whether additional improvement activities are justified. As the PDCA cycle suggests, some problems are never solved, they are only diminished. If the issue at hand is of that nature, then initiating a new cycle of problem-solving activity may be appropriate.
A secondary consideration for the post-implementation review is a debriefing of the problem solvers themselves. By its very nature, problem solving often presents managers with novel situations. As a consequence, the problem-solving environment is generally rich in learning opportunities. To the extent that such learning can be captured and shared throughout the organization, the management capital of the firm can be enhanced. In addition, a debriefing may also provide valuable insights into the firm's problem-solving process itself. Given the firm's unique competitive environment, knowing what worked and what did not may help focus future problem-solving initiatives.
INSTITUTIONALIZATION AND CONTROL.
The final step in problem solving is to institutionalize the results of the initiative. It is natural for any system to degrade over time. Therefore, any changes made as a result of the problem-solving effort should be locked in before they are lost. This might entail amending policy manuals, establishing new control metrics, or even rewriting job descriptions. In addition, the firm should also consider whether the problem addressed in the initiative at hand is an isolated incident or whether the solution can be leveraged throughout the organization. Frequently, similar problems are present in other departments or other geographic locations. If this is the case, institutionalization might involve transferring the newly developed practices to these new settings.
SEE ALSO: Project Management
Daniel R. Heiser
Revised by Badie N. Farah
Deming, W. Edwards. Out of the Crisis. Cambridge: Massachusetts Institute of Technology, Center for Advanced Engineering Study, 1992.
Ketola, Jeanne and Kathy Roberts. Correct! Prevent! Improve!: Driving Improvement Through Problem Solving and Corrective and Preventive Action. Milwaukee: ASQ Quality Press, 2003.
National Institute of Standards and Technology. "Award Recipients." Malcolm Baldrige National Quality Award Program, 1999. Available from http://www.quality.nist.gov.
Rasiel, Ethan M. The McKinsey Mind: Using the Techniques of the World's Top Strategic Consultants to Help You and Your Business. New York: McGraw-Hill, 2001.
——. The McKinsey Way—Understanding and Implementing the Problem-Solving Tools and Management Techniques of the World's Top Strategic Consulting Firm. New York: McGraw-Hill, 1999.
Smith, Gerald F. Quality Problem Solving. Milwaukee: ASQ Quality Press, 1998.
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Reviving the american dream.
The American political system helped create today’s problems, and only the American political system can solve them.
By David Leonhardt
Many Americans have come to see the political system as rigged. They worry that grass-roots political movements are powerless to overcome entrenched interests, whether those interests are self-serving politicians, large employers or dominant social media platforms. And I understand why this cynicism exists.
For most Americans, progress has slowed to a crawl in recent decades. Income and wealth inequality have both soared. The top 1 percent have pulled away from everyone else, while working-class Americans often struggle to afford the best health care and homes in good school districts.
The clearest sign of our problems is this statistic: In 1980, the U.S. had a typical life expectancy for an affluent country. Today, we have the lowest such life expectancy, worse than those of Britain, France, Germany, Canada, Japan or South Korea, as well as some less rich countries, like China or Chile. The main reason is the stagnation of life expectancy for working-class people.
Life Expectancy in Select High-Income Countries
For nearly a half-century, our economy has failed to deliver on the basic promise of the American dream — that living standards meaningfully improve over time for most citizens.
These themes will probably sound familiar to regular readers of this newsletter. The Morning often covers them because I believe that they shape so many parts of American life, including our polarized politics and angry national dialogue. I have just written a book — my first, called “ Ours Was the Shining Future: The Story of the American Dream ” — that tries to explain how we got here.
(For the New York Times Audio app, I read part of the introduction , including my own family’s story.)
In today’s newsletter, I want to tell you why I nonetheless emerged from writing the book with hope about the country’s future: In short, the American political system helped create today’s problems, and only the American political system can solve them.
When inequality fell
For all the cynicism about politics today, it is worth remembering how often grass-roots political movements in the U.S. have managed to succeed. In the 1920s and 1930s, the country had a highly unequal economy and a Supreme Court that threw out most policies to reduce inequality. But activists — like A. Philip Randolph, a preacher’s son from Jacksonville, Fla., who took on a powerful railroad company — didn’t respond by giving up on the system as hopelessly rigged.
They instead used the tools of democracy to create mass prosperity. They spent decades building a labor movement that, despite many short-term defeats, ultimately changed public opinion, won elections and remade federal policy to put workers and corporations on a more equal footing. The rise of the labor movement from the 1930s through the 1950s led to incomes rising even more rapidly for the poor and middle class than for the rich, and to the white-Black wage gap shrinking.
One big lesson I took from my research was the unparalleled role of labor unions in combating inequality (a role that more Americans seem to have recognized recently).
There are plenty of other examples of grass-roots movements remaking American life. The civil-rights and women’s movements of the 1960s also overcame long odds, as did the disability-rights movement of the 1970s and the marriage-equality movement of the 2000s.
Other examples come from the political right. In the 1950s and 1960s, a group of conservatives, including Milton Friedman and Robert Bork, began trying to sell the country on the virtues of a low-tax, light-regulation economy. For years, they struggled to do so and were frustrated by their failures. Friedman kept a list of newspapers and magazines that did not even review his first major book.
But the conservatives kept trying — and the oil crisis that began 50 years ago last week eventually helped them succeed. A politician who embraced their ideas, Ronald Reagan, won the presidency and moved the U.S. closer to the laissez-faire ideal than almost any other country.
The conservatives who sold this vision promised it would lead to a new prosperity for all. They were wrong about that, of course. Since 1980, the U.S. has become a grim outlier on many indicators of human well-being. But the conservatives were right that overhauling the country’s economic policy was possible.
This history does not suggest that the political system is hopelessly broken. It instead suggests that the U.S. doesn’t have a broadly prosperous economy largely because the country has no mass movement organized around the goal of lifting living standards for the middle class and the poor. If such a movement existed, it might well succeed. It has before.
The central lesson I took from immersing myself in the past century of the American economy is that it can change, sometimes much more quickly than people expect. When it has changed in a major way, it often has been because Americans have used the political system to change it. The future can be different from the past.
(You can read more about the book here .)
The latest in israel.
Hamas released two more hostages, Israeli women aged 79 and 85 whose husbands remain captive. The older of the two said she was held in a tunnel but given medical care and hygiene products, the BBC reports.
Emmanuel Macron, France’s president, arrived in Israel . He is expected to push for the release of more hostages and aid to Gaza.
Israeli officials showed raw footage from Hamas’s Oct. 7 attack to a group of reporters, including images of bloodied corpses in a bedroom, brutalized young women and soldiers without heads.
The Latest in Gaza
While Israel delays its ground invasion, it continues to attack from the sky . Israeli officials said the military had hit more than 400 targets in the past 24 hours.
The Hamas-run health ministry in Gaza says more than 5,000 people have been killed there since the start of the war. The Times could not verify the total.
The aid convoys entering southern Gaza include emergency delivery kits for pregnant women who will likely give birth on their own as hospitals shut down.
U.S. officials are concerned that Israel has not sufficiently prepared for a ground war in Gaza, where Hamas maintains intricate tunnel networks under densely populated areas.
House Republicans heard privately from eight candidates for speaker and a ninth dropped out. They plan to pick a nominee today .
All but two of the candidates — Tom Emmer and Austin Scott — objected to certifying President Biden’s 2020 win.
Jim Jordan’s bid for speaker failed, but anti-establishment outsiders like him appear close to becoming the dominant Republican faction in the House , Nate Cohn explains.
Senator Robert Menendez, a New Jersey Democrat, pleaded not guilty to a charge that he plotted to act as an agent of Egypt.
María Machado, a center-right candidate, is leading in Venezuela’s opposition presidential primary , highlighting voters’ dissatisfaction with years of authoritarian rule.
After fleeing Venezuela and attempting to cross the Darién Gap, a mother and daughter followed Biden’s rules and applied for legal asylum. A year later, they’re still waiting .
An armed group killed 13 law enforcement officers in the Mexican state of Guerrero, where cartels fight turf wars.
War in Ukraine
The war has disrupted the coming of age of many young Ukrainians, exposing them to death, depression and displacement.
The battlefield has a cruel math: Kill or be killed .
“It’s like our country exploded”: The scale of Canada’s wildfires this year was without precedent.
After a drought, Maine tried to restrict bottled water companies that draw from local sources. The maker of Poland Spring stepped in to kill the bill .
Gov. Gavin Newsom of California is touting his climate policies — including an effort end oil drilling — as he considers a future run for president .
Other Big Stories
The United Automobile Workers union expanded its strike to a Ram pickup truck plant in Michigan, owned by Stellantis.
An off-duty pilot accused of trying to cut fuel to the engines during an Alaska Airlines flight was charged with attempted murder .
A “super fog” hit New Orleans, causing traffic pileups that killed at least seven people .
Teachers want to give their students high letter grades out of kindness. But tougher grading helps them improve in the long run, Tim Donahue argues.
Here are columns by Nicholas Kristof on Gazan children and Michelle Goldberg on Palestinian authors .
Health: It’s the season of pumpkin flavor everything. But is pumpkin actually good for you ?
Call that crazy? She made a TikTok video about pesto. It inspired people to spill their secrets .
Walkout: Women in Iceland are taking the day off to protest gender inequality .
Lives Lived: The historian Natalie Zemon Davis wrote about peasants, unsung women and Martin Guerre, a 16th-century village impostor recalled in a 1982 movie. She died at 94 .
World Series bound: The Texas Rangers crushed their in-state rivals , the Houston Astros, 11-4, to reach their first World Series in over a decade.
One more game: The Arizona Diamondbacks staved off elimination in the N.L.C.S., beating the Philadelphia Phillies, 5-1. Game 7 is tonight in Philadelphia.
Monday Night Football: The Minnesota Vikings beat the San Francisco 49ers .
N.B.A.: The superstar forward Giannis Antetokounmpo and the Milwaukee Bucks agreed to a three-year , $186 million max extension. The league’s season tips off tonight.
Pink wave: Lionel Messi has made Inter Miami’s eye-catching jersey the hottest piece of sports merchandise on the planet.
ARTS AND IDEAS
Sondheim’s final note: Shortly before Stephen Sondheim’s death two years ago, he gave a team of collaborators permission to complete his last, unfinished musical. The show, “Here We Are,” premiered this week in New York, and it’s a worthy send-off for Sondheim’s career , the Times critic Jesse Green writes. The surrealist story, inspired by two Luis Buñuel films, finds a group of obnoxious high-society types searching for an elusive meal. “It is never less than a pleasure to watch as it confidently polishes and embraces its illogic,” Jesse writes.
More on culture
Caster Semenya, the Olympic runner, explains how she has coped with being banned from the sport in an interview with The Cut.
Late night hosts joked about the lack of diversity in the House speaker race .
THE MORNING RECOMMENDS …
Make sopa de fideo, a Mexican staple great for a chilly night .
Shower as often as you need — which might not be every day .
Buy a piano on a budget .
Give these great presents under $25.
Here is today’s Spelling Bee . Yesterday’s pangram was hologram .
And here are today’s Mini Crossword , Wordle , Sudoku and Connections .
Thanks for spending part of your morning with The Times. See you tomorrow. — David
P.S. New York Magazine named Carolyn Ryan, a Times managing editor, and Mara Gay, a Times Opinion writer, on its list of powerful New Yorkers .
Sign up here to get this newsletter in your inbox . Reach our team at [email protected] .
David Leonhardt writes The Morning, The Times’s flagship daily newsletter. He has previously been an Op-Ed columnist, Washington bureau chief, co-host of “The Argument” podcast, founding editor of The Upshot section and a staff writer for The Times Magazine. In 2011, he received the Pulitzer Prize for commentary. More about David Leonhardt
More of the same won’t solve Cleveland’s lead-poisoning problem. New approaches are needed.
- Published: Nov. 03, 2023, 12:14 p.m.
- Other Voices
I read the Oct. 28 article about landlords not complying with the city’s lead-safe program and I wanted to cry (” Most landlords fail to comply on lead-safety law ”). I moved to Cleveland ten years ago, and poisoning from lead paint was a big problem then, and nothing has changed. In ten years! Back then, I wrote to the newspaper, my state representative, my state senator, my U.S. senator and representative, anyone I could think of, and nothing has changed.
I even volunteered to learn to be a lead inspector for free , if it would help. Nothing.
Everyone knows the definition of insanity -- doing the same thing over and over again and expecting different results. I think this fits the definition. Please, Mayor Justin Bibb, city officials, do something different. This too important. And if you don’t fix this and children continue to be impacted, shame on you.