complex problem solving and decision making

CONCEPTUAL ANALYSIS article

Complex problem solving: what it is and what it is not.

• 1 Department of Psychology, University of Bamberg, Bamberg, Germany
• 2 Department of Psychology, Heidelberg University, Heidelberg, Germany

Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems. Psychometric issues such as reliable assessments and addressing correlations with other instruments have been in the foreground of these discussions and have left the content validity of complex problem solving in the background. In this paper, we return the focus to content issues and address the important features that define complex problems.

Succeeding in the 21st century requires many competencies, including creativity, life-long learning, and collaboration skills (e.g., National Research Council, 2011 ; Griffin and Care, 2015 ), to name only a few. One competence that seems to be of central importance is the ability to solve complex problems ( Mainzer, 2009 ). Mainzer quotes the Nobel prize winner Simon (1957) who wrote as early as 1957:

The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problem whose solution is required for objectively rational behavior in the real world or even for a reasonable approximation to such objective rationality. (p. 198)

The shift from well-defined to ill-defined problems came about as a result of a disillusion with the “general problem solver” ( Newell et al., 1959 ): The general problem solver was a computer software intended to solve all kind of problems that can be expressed through well-formed formulas. However, it soon became clear that this procedure was in fact a “special problem solver” that could only solve well-defined problems in a closed space. But real-world problems feature open boundaries and have no well-determined solution. In fact, the world is full of wicked problems and clumsy solutions ( Verweij and Thompson, 2006 ). As a result, solving well-defined problems and solving ill-defined problems requires different cognitive processes ( Schraw et al., 1995 ; but see Funke, 2010 ).

Well-defined problems have a clear set of means for reaching a precisely described goal state. For example: in a match-stick arithmetic problem, a person receives a false arithmetic expression constructed out of matchsticks (e.g., IV = III + III). According to the instructions, moving one of the matchsticks will make the equations true. Here, both the problem (find the appropriate stick to move) and the goal state (true arithmetic expression; solution is: VI = III + III) are defined clearly.

Ill-defined problems have no clear problem definition, their goal state is not defined clearly, and the means of moving towards the (diffusely described) goal state are not clear. For example: The goal state for solving the political conflict in the near-east conflict between Israel and Palestine is not clearly defined (living in peaceful harmony with each other?) and even if the conflict parties would agree on a two-state solution, this goal again leaves many issues unresolved. This type of problem is called a “complex problem” and is of central importance to this paper. All psychological processes that occur within individual persons and deal with the handling of such ill-defined complex problems will be subsumed under the umbrella term “complex problem solving” (CPS).

Systematic research on CPS started in the 1970s with observations of the behavior of participants who were confronted with computer simulated microworlds. For example, in one of those microworlds participants assumed the role of executives who were tasked to manage a company over a certain period of time (see Brehmer and Dörner, 1993 , for a discussion of this methodology). Today, CPS is an established concept and has even influenced large-scale assessments such as PISA (“Programme for International Student Assessment”), organized by the Organization for Economic Cooperation and Development ( OECD, 2014 ). According to the World Economic Forum, CPS is one of the most important competencies required in the future ( World Economic Forum, 2015 ). Numerous articles on the subject have been published in recent years, documenting the increasing research activity relating to this field. In the following collection of papers we list only those published in 2010 and later: theoretical papers ( Blech and Funke, 2010 ; Funke, 2010 ; Knauff and Wolf, 2010 ; Leutner et al., 2012 ; Selten et al., 2012 ; Wüstenberg et al., 2012 ; Greiff et al., 2013b ; Fischer and Neubert, 2015 ; Schoppek and Fischer, 2015 ), papers about measurement issues ( Danner et al., 2011a ; Greiff et al., 2012 , 2015a ; Alison et al., 2013 ; Gobert et al., 2015 ; Greiff and Fischer, 2013 ; Herde et al., 2016 ; Stadler et al., 2016 ), papers about applications ( Fischer and Neubert, 2015 ; Ederer et al., 2016 ; Tremblay et al., 2017 ), papers about differential effects ( Barth and Funke, 2010 ; Danner et al., 2011b ; Beckmann and Goode, 2014 ; Greiff and Neubert, 2014 ; Scherer et al., 2015 ; Meißner et al., 2016 ; Wüstenberg et al., 2016 ), one paper about developmental effects ( Frischkorn et al., 2014 ), one paper with a neuroscience background ( Osman, 2012 ) 1 , papers about cultural differences ( Güss and Dörner, 2011 ; Sonnleitner et al., 2014 ; Güss et al., 2015 ), papers about validity issues ( Goode and Beckmann, 2010 ; Greiff et al., 2013c ; Schweizer et al., 2013 ; Mainert et al., 2015 ; Funke et al., 2017 ; Greiff et al., 2017 , 2015b ; Kretzschmar et al., 2016 ; Kretzschmar, 2017 ), review papers and meta-analyses ( Osman, 2010 ; Stadler et al., 2015 ), and finally books ( Qudrat-Ullah, 2015 ; Csapó and Funke, 2017b ) and book chapters ( Funke, 2012 ; Hotaling et al., 2015 ; Funke and Greiff, 2017 ; Greiff and Funke, 2017 ; Csapó and Funke, 2017a ; Fischer et al., 2017 ; Molnàr et al., 2017 ; Tobinski and Fritz, 2017 ; Viehrig et al., 2017 ). In addition, a new “Journal of Dynamic Decision Making” (JDDM) has been launched ( Fischer et al., 2015 , 2016 ) to give the field an open-access outlet for research and discussion.

This paper aims to clarify aspects of validity: what should be meant by the term CPS and what not? This clarification seems necessary because misunderstandings in recent publications provide – from our point of view – a potentially misleading picture of the construct. We start this article with a historical review before attempting to systematize different positions. We conclude with a working definition.

Historical Review

The concept behind CPS goes back to the German phrase “komplexes Problemlösen” (CPS; the term “komplexes Problemlösen” was used as a book title by Funke, 1986 ). The concept was introduced in Germany by Dörner and colleagues in the mid-1970s (see Dörner et al., 1975 ; Dörner, 1975 ) for the first time. The German phrase was later translated to CPS in the titles of two edited volumes by Sternberg and Frensch (1991) and Frensch and Funke (1995a) that collected papers from different research traditions. Even though it looks as though the term was coined in the 1970s, Edwards (1962) used the term “dynamic decision making” to describe decisions that come in a sequence. He compared static with dynamic decision making, writing:

In dynamic situations, a new complication not found in the static situations arises. The environment in which the decision is set may be changing, either as a function of the sequence of decisions, or independently of them, or both. It is this possibility of an environment which changes while you collect information about it which makes the task of dynamic decision theory so difficult and so much fun. (p. 60)

The ability to solve complex problems is typically measured via dynamic systems that contain several interrelated variables that participants need to alter. Early work (see, e.g., Dörner, 1980 ) used a simulation scenario called “Lohhausen” that contained more than 2000 variables that represented the activities of a small town: Participants had to take over the role of a mayor for a simulated period of 10 years. The simulation condensed these ten years to ten hours in real time. Later, researchers used smaller dynamic systems as scenarios either based on linear equations (see, e.g., Funke, 1993 ) or on finite state automata (see, e.g., Buchner and Funke, 1993 ). In these contexts, CPS consisted of the identification and control of dynamic task environments that were previously unknown to the participants. Different task environments came along with different degrees of fidelity ( Gray, 2002 ).

According to Funke (2012) , the typical attributes of complex systems are (a) complexity of the problem situation which is usually represented by the sheer number of involved variables; (b) connectivity and mutual dependencies between involved variables; (c) dynamics of the situation, which reflects the role of time and developments within a system; (d) intransparency (in part or full) about the involved variables and their current values; and (e) polytely (greek term for “many goals”), representing goal conflicts on different levels of analysis. This mixture of features is similar to what is called VUCA (volatility, uncertainty, complexity, ambiguity) in modern approaches to management (e.g., Mack et al., 2016 ).

In his evaluation of the CPS movement, Sternberg (1995) compared (young) European approaches to CPS with (older) American research on expertise. His analysis of the differences between the European and American traditions shows advantages but also potential drawbacks for each side. He states (p. 301): “I believe that although there are problems with the European approach, it deals with some fundamental questions that American research scarcely addresses.” So, even though the echo of the European approach did not enjoy strong resonance in the US at that time, it was valued by scholars like Sternberg and others. Before attending to validity issues, we will first present a short review of different streams.

Different Approaches to CPS

In the short history of CPS research, different approaches can be identified ( Buchner, 1995 ; Fischer et al., 2017 ). To systematize, we differentiate between the following five lines of research:

(a) The search for individual differences comprises studies identifying interindividual differences that affect the ability to solve complex problems. This line of research is reflected, for example, in the early work by Dörner et al. (1983) and their “Lohhausen” study. Here, naïve student participants took over the role of the mayor of a small simulated town named Lohhausen for a simulation period of ten years. According to the results of the authors, it is not intelligence (as measured by conventional IQ tests) that predicts performance, but it is the ability to stay calm in the face of a challenging situation and the ability to switch easily between an analytic mode of processing and a more holistic one.

(b) The search for cognitive processes deals with the processes behind understanding complex dynamic systems. Representative of this line of research is, for example, Berry and Broadbent’s (1984) work on implicit and explicit learning processes when people interact with a dynamic system called “Sugar Production”. They found that those who perform best in controlling a dynamic system can do so implicitly, without explicit knowledge of details regarding the systems’ relations.

(c) The search for system factors seeks to identify the aspects of dynamic systems that determine the difficulty of complex problems and make some problems harder than others. Representative of this line of research is, for example, work by Funke (1985) , who systematically varied the number of causal effects within a dynamic system or the presence/absence of eigendynamics. He found, for example, that solution quality decreases as the number of systems relations increases.

(d) The psychometric approach develops measurement instruments that can be used as an alternative to classical IQ tests, as something that goes “beyond IQ”. The MicroDYN approach ( Wüstenberg et al., 2012 ) is representative for this line of research that presents an alternative to reasoning tests (like Raven matrices). These authors demonstrated that a small improvement in predicting school grade point average beyond reasoning is possible with MicroDYN tests.

(e) The experimental approach explores CPS under different experimental conditions. This approach uses CPS assessment instruments to test hypotheses derived from psychological theories and is sometimes used in research about cognitive processes (see above). Exemplary for this line of research is the work by Rohe et al. (2016) , who test the usefulness of “motto goals” in the context of complex problems compared to more traditional learning and performance goals. Motto goals differ from pure performance goals by activating positive affect and should lead to better goal attainment especially in complex situations (the mentioned study found no effect).

To be clear: these five approaches are not mutually exclusive and do overlap. But the differentiation helps to identify different research communities and different traditions. These communities had different opinions about scaling complexity.

The Race for Complexity: Use of More and More Complex Systems

In the early years of CPS research, microworlds started with systems containing about 20 variables (“Tailorshop”), soon reached 60 variables (“Moro”), and culminated in systems with about 2000 variables (“Lohhausen”). This race for complexity ended with the introduction of the concept of “minimal complex systems” (MCS; Greiff and Funke, 2009 ; Funke and Greiff, 2017 ), which ushered in a search for the lower bound of complexity instead of the higher bound, which could not be defined as easily. The idea behind this concept was that whereas the upper limits of complexity are unbound, the lower limits might be identifiable. Imagine starting with a simple system containing two variables with a simple linear connection between them; then, step by step, increase the number of variables and/or the type of connections. One soon reaches a point where the system can no longer be considered simple and has become a “complex system”. This point represents a minimal complex system. Despite some research having been conducted in this direction, the point of transition from simple to complex has not been identified clearly as of yet.

Some years later, the original “minimal complex systems” approach ( Greiff and Funke, 2009 ) shifted to the “multiple complex systems” approach ( Greiff et al., 2013a ). This shift is more than a slight change in wording: it is important because it taps into the issue of validity directly. Minimal complex systems have been introduced in the context of challenges from large-scale assessments like PISA 2012 that measure new aspects of problem solving, namely interactive problems besides static problem solving ( Greiff and Funke, 2017 ). PISA 2012 required test developers to remain within testing time constraints (given by the school class schedule). Also, test developers needed a large item pool for the construction of a broad class of problem solving items. It was clear from the beginning that MCS deal with simple dynamic situations that require controlled interaction: the exploration and control of simple ticket machines, simple mobile phones, or simple MP3 players (all of these example domains were developed within PISA 2012) – rather than really complex situations like managerial or political decision making.

As a consequence of this subtle but important shift in interpreting the letters MCS, the definition of CPS became a subject of debate recently ( Funke, 2014a ; Greiff and Martin, 2014 ; Funke et al., 2017 ). In the words of Funke (2014b , p. 495):

It is funny that problems that nowadays come under the term ‘CPS’, are less complex (in terms of the previously described attributes of complex situations) than at the beginning of this new research tradition. The emphasis on psychometric qualities has led to a loss of variety. Systems thinking requires more than analyzing models with two or three linear equations – nonlinearity, cyclicity, rebound effects, etc. are inherent features of complex problems and should show up at least in some of the problems used for research and assessment purposes. Minimal complex systems run the danger of becoming minimal valid systems.

Searching for minimal complex systems is not the same as gaining insight into the way how humans deal with complexity and uncertainty. For psychometric purposes, it is appropriate to reduce complexity to a minimum; for understanding problem solving under conditions of overload, intransparency, and dynamics, it is necessary to realize those attributes with reasonable strength. This aspect is illustrated in the next section.

Importance of the Validity Issue

The most important reason for discussing the question of what complex problem solving is and what it is not stems from its phenomenology: if we lose sight of our phenomena, we are no longer doing good psychology. The relevant phenomena in the context of complex problems encompass many important aspects. In this section, we discuss four phenomena that are specific to complex problems. We consider these phenomena as critical for theory development and for the construction of assessment instruments (i.e., microworlds). These phenomena require theories for explaining them and they require assessment instruments eliciting them in a reliable way.

The first phenomenon is the emergency reaction of the intellectual system ( Dörner, 1980 ): When dealing with complex systems, actors tend to (a) reduce their intellectual level by decreasing self-reflections, by decreasing their intentions, by stereotyping, and by reducing their realization of intentions, (b) they show a tendency for fast action with increased readiness for risk, with increased violations of rules, and with increased tendency to escape the situation, and (c) they degenerate their hypotheses formation by construction of more global hypotheses and reduced tests of hypotheses, by increasing entrenchment, and by decontextualizing their goals. This phenomenon illustrates the strong connection between cognition, emotion, and motivation that has been emphasized by Dörner (see, e.g., Dörner and Güss, 2013 ) from the beginning of his research tradition; the emergency reaction reveals a shift in the mode of information processing under the pressure of complexity.

The second phenomenon comprises cross-cultural differences with respect to strategy use ( Strohschneider and Güss, 1999 ; Güss and Wiley, 2007 ; Güss et al., 2015 ). Results from complex task environments illustrate the strong influence of context and background knowledge to an extent that cannot be found for knowledge-poor problems. For example, in a comparison between Brazilian and German participants, it turned out that Brazilians accept the given problem descriptions and are more optimistic about the results of their efforts, whereas Germans tend to inquire more about the background of the problems and take a more active approach but are less optimistic (according to Strohschneider and Güss, 1998 , p. 695).

The third phenomenon relates to failures that occur during the planning and acting stages ( Jansson, 1994 ; Ramnarayan et al., 1997 ), illustrating that rational procedures seem to be unlikely to be used in complex situations. The potential for failures ( Dörner, 1996 ) rises with the complexity of the problem. Jansson (1994) presents seven major areas for failures with complex situations: acting directly on current feedback; insufficient systematization; insufficient control of hypotheses and strategies; lack of self-reflection; selective information gathering; selective decision making; and thematic vagabonding.

The fourth phenomenon describes (a lack of) training and transfer effects ( Kretzschmar and Süß, 2015 ), which again illustrates the context dependency of strategies and knowledge (i.e., there is no strategy that is so universal that it can be used in many different problem situations). In their own experiment, the authors could show training effects only for knowledge acquisition, not for knowledge application. Only with specific feedback, performance in complex environments can be increased ( Engelhart et al., 2017 ).

These four phenomena illustrate why the type of complexity (or degree of simplicity) used in research really matters. Furthermore, they demonstrate effects that are specific for complex problems, but not for toy problems. These phenomena direct the attention to the important question: does the stimulus material used (i.e., the computer-simulated microworld) tap and elicit the manifold of phenomena described above?

Dealing with partly unknown complex systems requires courage, wisdom, knowledge, grit, and creativity. In creativity research, “little c” and “BIG C” are used to differentiate between everyday creativity and eminent creativity ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). Everyday creativity is important for solving everyday problems (e.g., finding a clever fix for a broken spoke on my bicycle), eminent creativity changes the world (e.g., inventing solar cells for energy production). Maybe problem solving research should use a similar differentiation between “little p” and “BIG P” to mark toy problems on the one side and big societal challenges on the other. The question then remains: what can we learn about BIG P by studying little p? What phenomena are present in both types, and what phenomena are unique to each of the two extremes?

Discussing research on CPS requires reflecting on the field’s research methods. Even if the experimental approach has been successful for testing hypotheses (for an overview of older work, see Funke, 1995 ), other methods might provide additional and novel insights. Complex phenomena require complex approaches to understand them. The complex nature of complex systems imposes limitations on psychological experiments: The more complex the environments, the more difficult is it to keep conditions under experimental control. And if experiments have to be run in labs one should bring enough complexity into the lab to establish the phenomena mentioned, at least in part.

There are interesting options to be explored (again): think-aloud protocols , which have been discredited for many years ( Nisbett and Wilson, 1977 ) and yet are a valuable source for theory testing ( Ericsson and Simon, 1983 ); introspection ( Jäkel and Schreiber, 2013 ), which seems to be banned from psychological methods but nevertheless offers insights into thought processes; the use of life-streaming ( Wendt, 2017 ), a medium in which streamers generate a video stream of think-aloud data in computer-gaming; political decision-making ( Dhami et al., 2015 ) that demonstrates error-proneness in groups; historical case studies ( Dörner and Güss, 2011 ) that give insights into the thinking styles of political leaders; the use of the critical incident technique ( Reuschenbach, 2008 ) to construct complex scenarios; and simulations with different degrees of fidelity ( Gray, 2002 ).

The methods tool box is full of instruments that have to be explored more carefully before any individual instrument receives a ban or research narrows its focus to only one paradigm for data collection. Brehmer and Dörner (1993) discussed the tensions between “research in the laboratory and research in the field”, optimistically concluding “that the new methodology of computer-simulated microworlds will provide us with the means to bridge the gap between the laboratory and the field” (p. 183). The idea behind this optimism was that computer-simulated scenarios would bring more complexity from the outside world into the controlled lab environment. But this is not true for all simulated scenarios. In his paper on simulated environments, Gray (2002) differentiated computer-simulated environments with respect to three dimensions: (1) tractability (“the more training subjects require before they can use a simulated task environment, the less tractable it is”, p. 211), correspondence (“High correspondence simulated task environments simulate many aspects of one task environment. Low correspondence simulated task environments simulate one aspect of many task environments”, p. 214), and engagement (“A simulated task environment is engaging to the degree to which it involves and occupies the participants; that is, the degree to which they agree to take it seriously”, p. 217). But the mere fact that a task is called a “computer-simulated task environment” does not mean anything specific in terms of these three dimensions. This is one of several reasons why we should differentiate between those studies that do not address the core features of CPS and those that do.

What is not CPS?

Even though a growing number of references claiming to deal with complex problems exist (e.g., Greiff and Wüstenberg, 2015 ; Greiff et al., 2016 ), it would be better to label the requirements within these tasks “dynamic problem solving,” as it has been done adequately in earlier work ( Greiff et al., 2012 ). The dynamics behind on-off-switches ( Thimbleby, 2007 ) are remarkable but not really complex. Small nonlinear systems that exhibit stunningly complex and unstable behavior do exist – but they are not used in psychometric assessments of so-called CPS. There are other small systems (like MicroDYN scenarios: Greiff and Wüstenberg, 2014 ) that exhibit simple forms of system behavior that are completely predictable and stable. This type of simple systems is used frequently. It is even offered commercially as a complex problem-solving test called COMPRO ( Greiff and Wüstenberg, 2015 ) for business applications. But a closer look reveals that the label is not used correctly; within COMPRO, the used linear equations are far from being complex and the system can be handled properly by using only one strategy (see for more details Funke et al., 2017 ).

Why do simple linear systems not fall within CPS? At the surface, nonlinear and linear systems might appear similar because both only include 3–5 variables. But the difference is in terms of systems behavior as well as strategies and learning. If the behavior is simple (as in linear systems where more input is related to more output and vice versa), the system can be easily understood (participants in the MicroDYN world have 3 minutes to explore a complex system). If the behavior is complex (as in systems that contain strange attractors or negative feedback loops), things become more complicated and much more observation is needed to identify the hidden structure of the unknown system ( Berry and Broadbent, 1984 ; Hundertmark et al., 2015 ).

Another issue is learning. If tasks can be solved using a single (and not so complicated) strategy, steep learning curves are to be expected. The shift from problem solving to learned routine behavior occurs rapidly, as was demonstrated by Luchins (1942) . In his water jar experiments, participants quickly acquired a specific strategy (a mental set) for solving certain measurement problems that they later continued applying to problems that would have allowed for easier approaches. In the case of complex systems, learning can occur only on very general, abstract levels because it is difficult for human observers to make specific predictions. Routines dealing with complex systems are quite different from routines relating to linear systems.

What should not be studied under the label of CPS are pure learning effects, multiple-cue probability learning, or tasks that can be solved using a single strategy. This last issue is a problem for MicroDYN tasks that rely strongly on the VOTAT strategy (“vary one thing at a time”; see Tschirgi, 1980 ). In real-life, it is hard to imagine a business manager trying to solve her or his problems by means of VOTAT.

What is CPS?

In the early days of CPS research, planet Earth’s dynamics and complexities gained attention through such books as “The limits to growth” ( Meadows et al., 1972 ) and “Beyond the limits” ( Meadows et al., 1992 ). In the current decade, for example, the World Economic Forum (2016) attempts to identify the complexities and risks of our modern world. In order to understand the meaning of complexity and uncertainty, taking a look at the worlds’ most pressing issues is helpful. Searching for strategies to cope with these problems is a difficult task: surely there is no place for the simple principle of “vary-one-thing-at-a-time” (VOTAT) when it comes to global problems. The VOTAT strategy is helpful in the context of simple problems ( Wüstenberg et al., 2014 ); therefore, whether or not VOTAT is helpful in a given problem situation helps us distinguish simple from complex problems.

Because there exist no clear-cut strategies for complex problems, typical failures occur when dealing with uncertainty ( Dörner, 1996 ; Güss et al., 2015 ). Ramnarayan et al. (1997) put together a list of generic errors (e.g., not developing adequate action plans; lack of background control; learning from experience blocked by stereotype knowledge; reactive instead of proactive action) that are typical of knowledge-rich complex systems but cannot be found in simple problems.

Complex problem solving is not a one-dimensional, low-level construct. On the contrary, CPS is a multi-dimensional bundle of competencies existing at a high level of abstraction, similar to intelligence (but going beyond IQ). As Funke et al. (2018) state: “Assessment of transversal (in educational contexts: cross-curricular) competencies cannot be done with one or two types of assessment. The plurality of skills and competencies requires a plurality of assessment instruments.”

There are at least three different aspects of complex systems that are part of our understanding of a complex system: (1) a complex system can be described at different levels of abstraction; (2) a complex system develops over time, has a history, a current state, and a (potentially unpredictable) future; (3) a complex system is knowledge-rich and activates a large semantic network, together with a broad list of potential strategies (domain-specific as well as domain-general).

Complex problem solving is not only a cognitive process but is also an emotional one ( Spering et al., 2005 ; Barth and Funke, 2010 ) and strongly dependent on motivation (low-stakes versus high-stakes testing; see Hermes and Stelling, 2016 ).

Furthermore, CPS is a dynamic process unfolding over time, with different phases and with more differentiation than simply knowledge acquisition and knowledge application. Ideally, the process should entail identifying problems (see Dillon, 1982 ; Lee and Cho, 2007 ), even if in experimental settings, problems are provided to participants a priori . The more complex and open a given situation, the more options can be generated (T. S. Schweizer et al., 2016 ). In closed problems, these processes do not occur in the same way.

In analogy to the difference between formative (process-oriented) and summative (result-oriented) assessment ( Wiliam and Black, 1996 ; Bennett, 2011 ), CPS should not be reduced to the mere outcome of a solution process. The process leading up to the solution, including detours and errors made along the way, might provide a more differentiated impression of a person’s problem-solving abilities and competencies than the final result of such a process. This is one of the reasons why CPS environments are not, in fact, complex intelligence tests: research on CPS is not only about the outcome of the decision process, but it is also about the problem-solving process itself.

Complex problem solving is part of our daily life: finding the right person to share one’s life with, choosing a career that not only makes money, but that also makes us happy. Of course, CPS is not restricted to personal problems – life on Earth gives us many hard nuts to crack: climate change, population growth, the threat of war, the use and distribution of natural resources. In sum, many societal challenges can be seen as complex problems. To reduce that complexity to a one-hour lab activity on a random Friday afternoon puts it out of context and does not address CPS issues.

Theories about CPS should specify which populations they apply to. Across populations, one thing to consider is prior knowledge. CPS research with experts (e.g., Dew et al., 2009 ) is quite different from problem solving research using tasks that intentionally do not require any specific prior knowledge (see, e.g., Beckmann and Goode, 2014 ).

More than 20 years ago, Frensch and Funke (1995b) defined CPS as follows:

CPS occurs to overcome barriers between a given state and a desired goal state by means of behavioral and/or cognitive, multi-step activities. The given state, goal state, and barriers between given state and goal state are complex, change dynamically during problem solving, and are intransparent. The exact properties of the given state, goal state, and barriers are unknown to the solver at the outset. CPS implies the efficient interaction between a solver and the situational requirements of the task, and involves a solver’s cognitive, emotional, personal, and social abilities and knowledge. (p. 18)

The above definition is rather formal and does not account for content or relations between the simulation and the real world. In a sense, we need a new definition of CPS that addresses these issues. Based on our previous arguments, we propose the following working definition:

Complex problem solving is a collection of self-regulated psychological processes and activities necessary in dynamic environments to achieve ill-defined goals that cannot be reached by routine actions. Creative combinations of knowledge and a broad set of strategies are needed. Solutions are often more bricolage than perfect or optimal. The problem-solving process combines cognitive, emotional, and motivational aspects, particularly in high-stakes situations. Complex problems usually involve knowledge-rich requirements and collaboration among different persons.

The main differences to the older definition lie in the emphasis on (a) the self-regulation of processes, (b) creativity (as opposed to routine behavior), (c) the bricolage type of solution, and (d) the role of high-stakes challenges. Our new definition incorporates some aspects that have been discussed in this review but were not reflected in the 1995 definition, which focused on attributes of complex problems like dynamics or intransparency.

This leads us to the final reflection about the role of CPS for dealing with uncertainty and complexity in real life. We will distinguish thinking from reasoning and introduce the sense of possibility as an important aspect of validity.

CPS as Combining Reasoning and Thinking in an Uncertain Reality

Leading up to the Battle of Borodino in Leo Tolstoy’s novel “War and Peace”, Prince Andrei Bolkonsky explains the concept of war to his friend Pierre. Pierre expects war to resemble a game of chess: You position the troops and attempt to defeat your opponent by moving them in different directions.

“Far from it!”, Andrei responds. “In chess, you know the knight and his moves, you know the pawn and his combat strength. While in war, a battalion is sometimes stronger than a division and sometimes weaker than a company; it all depends on circumstances that can never be known. In war, you do not know the position of your enemy; some things you might be able to observe, some things you have to divine (but that depends on your ability to do so!) and many things cannot even be guessed at. In chess, you can see all of your opponent’s possible moves. In war, that is impossible. If you decide to attack, you cannot know whether the necessary conditions are met for you to succeed. Many a time, you cannot even know whether your troops will follow your orders…”

In essence, war is characterized by a high degree of uncertainty. A good commander (or politician) can add to that what he or she sees, tentatively fill in the blanks – and not just by means of logical deduction but also by intelligently bridging missing links. A bad commander extrapolates from what he sees and thus arrives at improper conclusions.

Many languages differentiate between two modes of mentalizing; for instance, the English language distinguishes between ‘thinking’ and ‘reasoning’. Reasoning denotes acute and exact mentalizing involving logical deductions. Such deductions are usually based on evidence and counterevidence. Thinking, however, is what is required to write novels. It is the construction of an initially unknown reality. But it is not a pipe dream, an unfounded process of fabrication. Rather, thinking asks us to imagine reality (“Wirklichkeitsfantasie”). In other words, a novelist has to possess a “sense of possibility” (“Möglichkeitssinn”, Robert Musil; in German, sense of possibility is often used synonymously with imagination even though imagination is not the same as sense of possibility, for imagination also encapsulates the impossible). This sense of possibility entails knowing the whole (or several wholes) or being able to construe an unknown whole that could accommodate a known part. The whole has to align with sociological and geographical givens, with the mentality of certain peoples or groups, and with the laws of physics and chemistry. Otherwise, the entire venture is ill-founded. A sense of possibility does not aim for the moon but imagines something that might be possible but has not been considered possible or even potentially possible so far.

Thinking is a means to eliminate uncertainty. This process requires both of the modes of thinking we have discussed thus far. Economic, political, or ecological decisions require us to first consider the situation at hand. Though certain situational aspects can be known, but many cannot. In fact, von Clausewitz (1832) posits that only about 25% of the necessary information is available when a military decision needs to be made. Even then, there is no way to guarantee that whatever information is available is also correct: Even if a piece of information was completely accurate yesterday, it might no longer apply today.

Once our sense of possibility has helped grasping a situation, problem solvers need to call on their reasoning skills. Not every situation requires the same action, and we may want to act this way or another to reach this or that goal. This appears logical, but it is a logic based on constantly shifting grounds: We cannot know whether necessary conditions are met, sometimes the assumptions we have made later turn out to be incorrect, and sometimes we have to revise our assumptions or make completely new ones. It is necessary to constantly switch between our sense of possibility and our sense of reality, that is, to switch between thinking and reasoning. It is an arduous process, and some people handle it well, while others do not.

If we are to believe Tuchman’s (1984) book, “The March of Folly”, most politicians and commanders are fools. According to Tuchman, not much has changed in the 3300 years that have elapsed since the misguided Trojans decided to welcome the left-behind wooden horse into their city that would end up dismantling Troy’s defensive walls. The Trojans, too, had been warned, but decided not to heed the warning. Although Laocoön had revealed the horse’s true nature to them by attacking it with a spear, making the weapons inside the horse ring, the Trojans refused to see the forest for the trees. They did not want to listen, they wanted the war to be over, and this desire ended up shaping their perception.

The objective of psychology is to predict and explain human actions and behavior as accurately as possible. However, thinking cannot be investigated by limiting its study to neatly confined fractions of reality such as the realms of propositional logic, chess, Go tasks, the Tower of Hanoi, and so forth. Within these systems, there is little need for a sense of possibility. But a sense of possibility – the ability to divine and construe an unknown reality – is at least as important as logical reasoning skills. Not researching the sense of possibility limits the validity of psychological research. All economic and political decision making draws upon this sense of possibility. By not exploring it, psychological research dedicated to the study of thinking cannot further the understanding of politicians’ competence and the reasons that underlie political mistakes. Christopher Clark identifies European diplomats’, politicians’, and commanders’ inability to form an accurate representation of reality as a reason for the outbreak of World War I. According to Clark’s (2012) book, “The Sleepwalkers”, the politicians of the time lived in their own make-believe world, wrongfully assuming that it was the same world everyone else inhabited. If CPS research wants to make significant contributions to the world, it has to acknowledge complexity and uncertainty as important aspects of it.

For more than 40 years, CPS has been a new subject of psychological research. During this time period, the initial emphasis on analyzing how humans deal with complex, dynamic, and uncertain situations has been lost. What is subsumed under the heading of CPS in modern research has lost the original complexities of real-life problems. From our point of view, the challenges of the 21st century require a return to the origins of this research tradition. We would encourage researchers in the field of problem solving to come back to the original ideas. There is enough complexity and uncertainty in the world to be studied. Improving our understanding of how humans deal with these global and pressing problems would be a worthwhile enterprise.

Author Contributions

JF drafted a first version of the manuscript, DD added further text and commented on the draft. JF finalized the manuscript.

Authors Note

After more than 40 years of controversial discussions between both authors, this is the first joint paper. We are happy to have done this now! We have found common ground!

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank the Deutsche Forschungsgemeinschaft (DFG) for the continuous support of their research over many years. Thanks to Daniel Holt for his comments on validity issues, thanks to Julia Nolte who helped us by translating German text excerpts into readable English and helped us, together with Keri Hartman, to improve our style and grammar – thanks for that! We also thank the two reviewers for their helpful critical comments on earlier versions of this manuscript. Finally, we acknowledge financial support by Deutsche Forschungsgemeinschaft and Ruprecht-Karls-Universität Heidelberg within their funding programme Open Access Publishing .

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Keywords : complex problem solving, validity, assessment, definition, MicroDYN

Citation: Dörner D and Funke J (2017) Complex Problem Solving: What It Is and What It Is Not. Front. Psychol. 8:1153. doi: 10.3389/fpsyg.2017.01153

Received: 14 March 2017; Accepted: 23 June 2017; Published: 11 July 2017.

Reviewed by:

Copyright © 2017 Dörner and Funke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Joachim Funke, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Solving Complex Decision Problems

A Heuristic Process

• © 2017
• Latest edition
• Rudolf Grünig 0 ,
• Richard Kühn 1

Chair of Management, University of Fribourg, Fribourg, Switzerland

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Belfaux, Switzerland

• Presents a general heuristic decision-making procedure for complex problems
• Focuses on analyzing the problem and on developing and assessing potential solution
• Includes a case study which illustrates how the proposed decision-making procedure is applied in practice
• Offers executives an approach to solving complex problems systematically and successfully

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About this book

Making decisions is certainly the most important task managers are faced with, and it is often a very difficult one. This book offers a procedure for solving complex decision problems step by step. Unlike other texts, the book focuses on problem analysis, on developing potential solutions, and on establishing a decision-making matrix.

In this fourth edition of the book, published under a new title, the authors present simplified, actionable guidelines that can be easily applied to the individual steps in the heuristic process.

The book is intended for decision-makers at companies, non-profit organizations and in public administration whose work involves complex problems. It will also benefit students and participants in executive courses.

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Decision Theory and Rules of Thumb

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Heuristic Logic. A Kernel

• Complex problems
• Decision-making
• Problem solving
• Heuristic decision-making
• Rational decision-making

Table of contents (15 chapters)

Front matter, introduction.

• Rudolf Grünig, Richard Kühn

Decision Problems and Decision-Making Procedures

Decision problems, goal and problem-finding systems as requirements for the discovery of decision problems, rational decision-making, decision-making procedures, a general heuristic decision-making procedure, overview of the decision-making procedure, problem verification and analysis, developing and evaluating solution options, decision maxims for establishing the overall consequences of the options, overall evaluation of the options and decision, a case study illustrating the application of the procedure, special problems and approaches to solve them, decision sequences, information procurement decisions, collective decisions, final remarks, back matter, authors and affiliations.

Rudolf Grünig

Richard Kühn

About the authors

Prof. Dr. Rudolf Grünig is Professor of Business Administration at the University of Fribourg and Lecturer in Strategic Management in various postgraduate programs in Switzerland and abroad. He is also member of the Board or strategy consultant for different Swiss companies.

Prof. Dr. Richard Kühn is the founder and former director of the “Institute for Marketing and Strategic Management” at the University of Bern. He works as member of the Board and business consultant for important Swiss companies. He is the author of numerous publications in marketing and strategic management.

Bibliographic Information

Book Title : Solving Complex Decision Problems

Book Subtitle : A Heuristic Process

Authors : Rudolf Grünig, Richard Kühn

Translated by : Claire O'Dea, Anthony Clark, Maude Montani

DOI : https://doi.org/10.1007/978-3-662-53814-2

Publisher : Springer Berlin, Heidelberg

eBook Packages : Business and Management , Business and Management (R0)

Copyright Information : Springer International Publishing AG 2017

Hardcover ISBN : 978-3-662-53813-5 Published: 22 June 2017

Softcover ISBN : 978-3-662-57163-7 Published: 15 August 2018

eBook ISBN : 978-3-662-53814-2 Published: 13 June 2017

Edition Number : 4

Number of Pages : XVII, 193

Number of Illustrations : 122 b/w illustrations

Additional Information : Originally published with the title "Successful Decision-Making - A Systematic Approach to Complex Problems"

Topics : Business Strategy/Leadership , Operations Research/Decision Theory

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Problem Solving And Decision Making: 10 Hacks That Managers Love

Understanding problem solving & decision making, why are problem solving and decision making skills essential in the workplace, five techniques for effective problem solving, five techniques for effective decision making, frequently asked questions.

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• Improved efficiency and productivity: Employees with strong problem solving and decision making skills are better equipped to identify and solve issues that may arise in their work. This leads to improved efficiency and productivity as they can complete their work more timely and effectively.
• Improved customer satisfaction: Problem solving and decision making skills also help employees address any concerns or issues customers may have. This leads to enhanced customer satisfaction as customers feel their needs are being addressed and their problems are resolved.
• Effective teamwork: When working in teams, problem solving and decision making skills are essential for effective collaboration . Groups that can effectively identify and solve problems together are more likely to successfully achieve their goals.
• Innovation: Effective problem-solving and decision-making skills are also crucial for driving innovation in the workplace. Employees who think creatively and develop new solutions to problems are more likely to develop innovative ideas to move the business forward.
• Risk management: Problem solving and decision making skills are also crucial for managing risk in the workplace. By identifying potential risks and developing strategies to mitigate them, employees can help minimize the negative impact of risks on the business.

• Brainstorming: Brainstorming is a technique for generating creative ideas and solutions to problems. In a brainstorming session, a group of people share their thoughts and build on each other’s suggestions. The goal is to generate a large number of ideas in a short amount of time. For example, a team of engineers could use brainstorming to develop new ideas for improving the efficiency of a manufacturing process.
• Root Cause Analysis: Root cause analysis is a technique for identifying the underlying cause of a problem. It involves asking “why” questions to uncover the root cause of the problem. Once the root cause is identified, steps can be taken to address it. For example, a hospital could use root cause analysis to investigate why patient falls occur and identify the root cause, such as inadequate staffing or poor lighting.
• SWOT Analysis: SWOT analysis is a technique for evaluating the strengths, weaknesses, opportunities, and threats related to a problem or situation. It involves assessing internal and external factors that could impact the problem and identifying ways to leverage strengths and opportunities while minimizing weaknesses and threats. For example, a small business could use SWOT analysis to evaluate its market position and identify opportunities to expand its product line or improve its marketing.
• Pareto Analysis: Pareto analysis is a technique for identifying the most critical problems to address. It involves ranking problems by impact and frequency and first focusing on the most significant issues. For example, a software development team could use Pareto analysis to prioritize bugs and issues to fix based on their impact on the user experience.
• Decision Matrix Analysis: Decision matrix analysis evaluates alternatives and selects the best course of action. It involves creating a matrix to compare options based on criteria and weighting factors and selecting the option with the highest score. For example, a manager could use decision matrix analysis to evaluate different software vendors based on criteria such as price, features, and support and select the vendor with the best overall score.

• Cost-Benefit Analysis: Cost-benefit analysis is a technique for evaluating the costs and benefits of different options. It involves comparing each option’s expected costs and benefits and selecting the one with the highest net benefit. For example, a company could use cost-benefit analysis to evaluate a new product line’s potential return on investment.
• Decision Trees: Decision trees are a visual representation of the decision-making process. They involve mapping out different options and their potential outcomes and probabilities. This helps to identify the best course of action based on the likelihood of different outcomes. For example, a farmer could use a decision tree to choose crops to plant based on the expected weather patterns.
• SWOT Analysis: SWOT analysis can also be used for decision making. By identifying the strengths, weaknesses, opportunities, and threats of different options, a decision maker can evaluate each option’s potential risks and benefits. For example, a business owner could use SWOT analysis to assess the potential risks and benefits of expanding into a new market.
• Pros and Cons Analysis: Pros and cons analysis lists the advantages and disadvantages of different options. It involves weighing the pros and cons of each option to determine the best course of action. For example, an individual could use a pros and cons analysis to decide whether to take a job offer.
• Six Thinking Hats: The six thinking hats technique is a way to think about a problem from different perspectives. It involves using six different “hats” to consider various aspects of the decision. The hats include white (facts and figures), red (emotions and feelings), black (risks and drawbacks), yellow (benefits and opportunities), green (creativity and new ideas), and blue (overview and control). For example, a team could use the six thinking hats technique to evaluate different options for a marketing campaign.

Aastha Bensla

Aastha, a passionate industrial psychologist, writer, and counselor, brings her unique expertise to Risely. With specialized knowledge in industrial psychology, Aastha offers a fresh perspective on personal and professional development. Her broad experience as an industrial psychologist enables her to accurately understand and solve problems for managers and leaders with an empathetic approach.

How strong are your decision making skills?

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How are problem solving and decision making related?

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10 Signs You’re Struggling with Analysis Paralysis at Work

Evidence based decision making: 4 proven hacks for managers, 6 best books on decision making for managers, best decision coaches to guide you toward great choices.

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8 Steps in the Decision-Making Process

• 04 Feb 2020

Strong decision-making skills are essential for newly appointed and seasoned managers alike. The ability to navigate complex challenges and develop a plan can not only lead to more effective team management but drive key organizational change initiatives and objectives.

Despite decision-making’s importance in business, a recent survey by McKinsey shows that just 20 percent of professionals believe their organizations excel at it. Survey respondents noted that, on average, they spend 37 percent of their time making decisions, but more than half of it’s used ineffectively.

For managers, it’s critical to ensure effective decisions are made for their organizations’ success. Every managerial decision must be accompanied by research and data , collaboration, and alternative solutions.

Few managers, however, reap the benefits of making more thoughtful choices due to undeveloped decision-making models.

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Why Is Making Decisions Important?

According to Harvard Business School Professor Leonard Schlesinger, who’s featured in the online course Management Essentials , most managers view decision-making as a single event, rather than a process. This can lead to managers overestimating their abilities to influence outcomes and closing themselves off from alternative perspectives and diverse ways of thinking.

“The reality is, it’s very rare to find a single point in time where ‘a decision of significance’ is made and things go forward from there,” Schlesinger says. “Embedded in this work is the notion that what we’re really talking about is a process. The role of the manager in managing that process is actually quite straightforward, yet, at the same time, extraordinarily complex.”

If you want to further your business knowledge and be more effective in your role, it’s critical to become a strong decision-maker. Here are eight steps in the decision-making process you can employ to become a better manager and have greater influence in your organization.

Steps in the Decision-Making Process

1. frame the decision.

Pinpointing the issue is the first step to initiating the decision-making process. Ensure the problem is carefully analyzed, clearly defined, and everyone involved in the outcome agrees on what needs to be solved. This process will give your team peace of mind that each key decision is based on extensive research and collaboration.

Schlesinger says this initial action can be challenging for managers because an ill-formed question can result in a process that produces the wrong decision.

“The real issue for a manager at the start is to make sure they are actively working to shape the question they’re trying to address and the decision they’re trying to have made,” Schlesinger says. “That’s not a trivial task.”

2. Structure Your Team

Managers must assemble the right people to navigate the decision-making process.

“The issue of who’s going to be involved in helping you to make that decision is one of the most central issues you face,” Schlesinger says. “The primary issue being the membership of the collection of individuals or group that you’re bringing together to make that decision.”

As you build your team, Schlesinger advises mapping the technical, political, and cultural underpinnings of the decision that needs to be made and gathering colleagues with an array of skills and experience levels to help you make an informed decision. .

“You want some newcomers who are going to provide a different point of view and perspective on the issue you’re dealing with,” he says. “At the same time, you want people who have profound knowledge and deep experience with the problem.”

It’s key to assign decision tasks to colleagues and invite perspectives that uncover blindspots or roadblocks. Schlesinger notes that attempting to arrive at the “right answer” without a team that will ultimately support and execute it is a “recipe for failure.”

3. Consider the Timeframe

This act of mapping the issue’s intricacies should involve taking the decision’s urgency into account. Business problems with significant implications sometimes allow for lengthier decision-making processes, whereas other challenges call for more accelerated timelines.

“As a manager, you need to shape the decision-making process in terms of both of those dimensions: The criticality of what it is you’re trying to decide and, more importantly, how quickly it needs to get decided given the urgency,” Schlesinger says. “The final question is, how much time you’re going to provide yourself and the group to invest in both problem diagnosis and decisions.”

4. Establish Your Approach

In the early stages of the decision-making process, it’s critical to set ground rules and assign roles to team members. Doing so can help ensure everyone understands how they contribute to problem-solving and agrees on how a solution will be reached.

“It’s really important to get clarity upfront around the roles people are going to play and the ways in which decisions are going to get made,” Schlesinger says. “Often, managers leave that to chance, so people self-assign themselves to roles in ways that you don’t necessarily want, and the decision-making process defers to consensus, which is likely to lead to a lower evaluation of the problem and a less creative solution.”

5. Encourage Discussion and Debate

One of the issues of leading a group that defaults to consensus is that it can shut out contrarian points of view and deter inventive problem-solving. Because of this potential pitfall, Schlesinger notes, you should designate roles that focus on poking holes in arguments and fostering debate.

“What we’re talking about is establishing a process of devil’s advocacy, either in an individual or a subgroup role,” he says. “That’s much more likely to lead to a deeper critical evaluation and generate a substantial number of alternatives.”

Schlesinger adds that this action can take time and potentially disrupt group harmony, so it’s vital for managers to guide the inner workings of the process from the outset to ensure effective collaboration and guarantee more quality decisions will be made.

“What we need to do is establish norms in the group that enable us to be open to a broader array of data and decision-making processes,” he says. “If that doesn’t happen upfront, but in the process without a conversation, it’s generally a source of consternation and some measure of frustration.”

Related: 3 Group Decision-Making Techniques for Success

6. Navigate Group Dynamics

In addition to creating a dynamic in which candor and debate are encouraged, there are other challenges you need to navigate as you manage your team throughout the decision-making process.

One is ensuring the size of the group is appropriate for the problem and allows for an efficient workflow.

“In getting all the people together that have relevant data and represent various political and cultural constituencies, each incremental member adds to the complexity of the decision-making process and the amount of time it takes to get a decision made and implemented,” Schlesinger says.

Another task, he notes, is identifying which parts of the process can be completed without face-to-face interaction.

“There’s no question that pieces of the decision-making process can be deferred to paper, email, or some app,” Schlesinger says. “But, at the end of the day, given that so much of decision-making requires high-quality human interaction, you need to defer some part of the process for ill-structured and difficult tasks to a face-to-face meeting.”

7. Ensure the Pieces Are in Place for Implementation

Throughout your team’s efforts to arrive at a decision, you must ensure you facilitate a process that encompasses:

• Shared goals that were presented upfront
• Alternative options that have been given rigorous thought and fair consideration
• Sound methods for exploring decisions’ consequences

According to Schlesinger, these components profoundly influence the quality of the solution that’s ultimately identified and the types of decisions that’ll be made in the future.

“In the general manager’s job, the quality of the decision is only one part of the equation,” he says. “All of this is oriented toward trying to make sure that once a decision is made, we have the right groupings and the right support to implement.”

8. Achieve Closure and Alignment

Achieving closure in the decision-making process requires arriving at a solution that sufficiently aligns members of your group and garners enough support to implement it.

As with the other phases of decision-making, clear communication ensures your team understands and commits to the plan.

In a video interview for the online course Management Essentials , Harvard Business School Dean Nitin Nohria says it’s essential to explain the rationale behind the decision to your employees.

“If it’s a decision that you have to make, say, ‘I know there were some of you who thought differently, but let me tell you why we went this way,’” Nohria says. “This is so the people on the other side feel heard and recognize the concerns they raised are things you’ve tried to incorporate into the decision and, as implementation proceeds, if those concerns become real, then they’ll be attended to.”

How to Improve Your Decision-Making

An in-depth understanding of the decision-making process is vital for all managers. Whether you’re an aspiring manager aiming to move up at your organization or a seasoned executive who wants to boost your job performance, honing your approach to decision-making can improve your managerial skills and equip you with the tools to advance your career.

Do you want to become a more effective decision-maker? Explore Management Essentials —one of our online leadership and management courses —to learn how you can influence the context and environment in which decisions get made.

This article was update on July 15, 2022. It was originally published on February 4, 2020.

About the Author

• INTERPERSONAL SKILLS
• Decision-Making and Problem Solving

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Interpersonal Skills:

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Decision-Making and Problem-Solving

• Effective Decision Making
• Decision-Making Framework
• Introduction to Problem Solving

Identifying and Structuring Problems

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Implementing a Solution and Feedback

• Creative Problem-Solving

Social Problem-Solving

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The SkillsYouNeed Guide to Interpersonal Skills

Making decisions and solving problems are two key areas in life, whether you are at home or at work. Whatever you’re doing, and wherever you are, you are faced with countless decisions and problems, both small and large, every day.

Many decisions and problems are so small that we may not even notice them. Even small decisions, however, can be overwhelming to some people. They may come to a halt as they consider their dilemma and try to decide what to do.

Small and Large Decisions

In your day-to-day life you're likely to encounter numerous 'small decisions', including, for example:

Tea or coffee?

What shall I have in my sandwich? Or should I have a salad instead today?

What shall I wear today?

Larger decisions may occur less frequently but may include:

Should we repaint the kitchen? If so, what colour?

Should we relocate?

Should I propose to my partner? Do I really want to spend the rest of my life with him/her?

These decisions, and others like them, may take considerable time and effort to make.

The relationship between decision-making and problem-solving is complex. Decision-making is perhaps best thought of as a key part of problem-solving: one part of the overall process.

Our approach at Skills You Need is to set out a framework to help guide you through the decision-making process. You won’t always need to use the whole framework, or even use it at all, but you may find it useful if you are a bit ‘stuck’ and need something to help you make a difficult decision.

Decision Making

Effective Decision-Making

This page provides information about ways of making a decision, including basing it on logic or emotion (‘gut feeling’). It also explains what can stop you making an effective decision, including too much or too little information, and not really caring about the outcome.

A Decision-Making Framework

This page sets out one possible framework for decision-making.

The framework described is quite extensive, and may seem quite formal. But it is also a helpful process to run through in a briefer form, for smaller problems, as it will help you to make sure that you really do have all the information that you need.

Problem Solving

Introduction to Problem-Solving

This page provides a general introduction to the idea of problem-solving. It explores the idea of goals (things that you want to achieve) and barriers (things that may prevent you from achieving your goals), and explains the problem-solving process at a broad level.

The first stage in solving any problem is to identify it, and then break it down into its component parts. Even the biggest, most intractable-seeming problems, can become much more manageable if they are broken down into smaller parts. This page provides some advice about techniques you can use to do so.

Sometimes, the possible options to address your problem are obvious. At other times, you may need to involve others, or think more laterally to find alternatives. This page explains some principles, and some tools and techniques to help you do so.

Having generated solutions, you need to decide which one to take, which is where decision-making meets problem-solving. But once decided, there is another step: to deliver on your decision, and then see if your chosen solution works. This page helps you through this process.

‘Social’ problems are those that we encounter in everyday life, including money trouble, problems with other people, health problems and crime. These problems, like any others, are best solved using a framework to identify the problem, work out the options for addressing it, and then deciding which option to use.

This page provides more information about the key skills needed for practical problem-solving in real life.

Further Reading from Skills You Need

The Skills You Need Guide to Interpersonal Skills eBooks.

Develop your interpersonal skills with our series of eBooks. Learn about and improve your communication skills, tackle conflict resolution, mediate in difficult situations, and develop your emotional intelligence.

Guiding you through the key skills needed in life

As always at Skills You Need, our approach to these key skills is to provide practical ways to manage the process, and to develop your skills.

Neither problem-solving nor decision-making is an intrinsically difficult process and we hope you will find our pages useful in developing your skills.

Start with: Decision Making Problem Solving

See also: Improving Communication Interpersonal Communication Skills Building Confidence

How to Make Great Decisions, Quickly

by Martin G. Moore

Summary .   

• Great decisions are shaped by consideration of many different viewpoints. This doesn’t mean you should seek out everyone’s opinion. The right people with the relevant expertise need to clearly articulate their views to help you broaden your perspective and make the best choice.
• Great decisions are made as close as possible to the action. Remember that the most powerful people at your company are rarely on the ground doing the hands-on work. Seek input and guidance from team members who are closest to the action.
• Great decisions address the root cause, not just the symptoms. Although you may need to urgently address the symptoms, once this is done you should always develop a plan to fix the root cause, or else the problem is likely to repeat itself.
• Great decisions balance short-term and long-term value. Finding the right balance between short-term and long-term risks and considerations is key to unlocking true value.
• Great decisions are timely. If you consider all of the elements listed above, then it’s simply a matter of addressing each one with a heightened sense of urgency.

Like many young leaders, early in my career, I thought a great decision was one that attracted widespread approval. When my colleagues smiled and nodded their collective heads, it reinforced (in my mind, at least) that I was an excellent decision maker.

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Convergent Thinking Explained: How to Sharpen Your Problem-Solving Skills

In today's fast-paced world, employers consistently seek and promote individuals who solve problems efficiently, while universities make problem-solving skills a core requirement in their admissions. One of the most effective ways to approach problem-solving is through convergent thinking , a method that allows individuals to focus on finding the single best solution to a problem. In contrast to divergent thinking , which emphasizes generating multiple possible solutions, convergent thinking is about honing in on the most logical and practical answer. When employed effectively, this method enhances critical thinking, analytical reasoning, and logical problem-solving—skills that are increasingly valued in both academic and professional settings.

Whether you're a student, educator, or professional, understanding convergent thinking leads to faster solutions when analyzing complex problems and making decisions. This article will explore the definition, importance, and techniques for developing convergent thinking, as well as practical applications in various real-life scenarios.

What is Convergent Thinking?

Convergent thinking refers to the cognitive process of focusing on finding a single, well-established solution to a problem. Like a detective following clues to reach one conclusion, it guides you through step-by-step evaluation of evidence to arrive at the most effective answer. When solving a math equation or diagnosing a medical condition, you methodically eliminate incorrect possibilities until reaching the best choice, making it essential for tasks that require clarity and precision.

Unlike divergent thinking , which encourages creativity and generating multiple possibilities, convergent thinking systematically eliminates options until reaching one outcome. For instance, in a math problem where there’s one correct answer, convergent thinking is key to reaching that conclusion.

Why is Convergent Thinking Important?

The importance of convergent thinking lies in its role in problem-solving and decision-making. Here's why it's a critical skill in both personal and professional life:

• Logical Problem-Solving: Convergent thinking is invaluable in situations where there’s a need for a clear, definitive solution. This might be solving complex technical issues, making important financial decisions, or addressing logistical challenges in business operations.
• Efficiency: By narrowing down multiple possibilities, convergent thinking helps save time and resources, leading to faster, more focused decisions.
• Informed Decision-Making: It relies on existing knowledge and facts, ensuring that decisions are based on sound reasoning and reliable information.

Convergent vs. Divergent Thinking: Understanding the Key Differences

While both convergent and divergent thinking are important for comprehensive problem-solving, they serve different purposes.

• Divergent Thinking: This type of thinking involves generating a variety of potential solutions to a problem. It fosters creativity and innovation, making it useful in brainstorming sessions, research, or any situation that requires exploring new ideas.
• Convergent Thinking: In contrast, convergent thinking focuses on evaluating and narrowing down those ideas to arrive at the best solution. It's highly structured and methodical, often used in situations where precision and accuracy are paramount.

A simple analogy is comparing divergent thinking to casting a wide net to gather possibilities, while convergent thinking is like filtering that catch to select the best one.

Techniques to Develop Convergent Thinking Skills

Improving your convergent thinking abilities requires practice, discipline, and the use of specific techniques. Here are a few effective strategies:

• Focus on Facts and Data: Start by collecting relevant data and information before making decisions. By basing your thought process on facts, you can reduce the chances of error and avoid baseless assumptions.
• Practice Analytical Reasoning: Analytical reasoning involves breaking down complex problems into smaller, more manageable parts. Practice solving puzzles, engaging in strategic games like chess, or working on complex problems to strengthen this skill.
• Use Logical Problem-Solving Models: Employ logical models such as the Five Whys or Root Cause Analysis to get to the heart of a problem. These models force you to follow a step-by-step process, helping to focus on logical solutions.
• Decision-Making Exercises: Engage in exercises that force you to make decisions based on available data. For example, hypothetical business cases or decision-making scenarios in real life can help sharpen convergent thinking skills.
• Structured Problem-Solving: Use structured frameworks such as the Scientific Method or PEST Analysis to guide your thought process. These tools provide a systematic approach to solving problems, ensuring that your conclusions are based on logic and evidence.

Practical Applications of Convergent Thinking

Convergent thinking can be applied across various domains, helping individuals and organizations to streamline their decision-making and problem-solving processes.

• In Education: Convergent thinking is essential in academic environments, particularly in subjects like mathematics, science, and engineering, where students must arrive at a single, correct solution. It also plays a crucial role in exams and standardized tests that require focused thinking to identify the best answer.
• In Business: Professionals often use convergent thinking to solve logistical, operational, or strategic problems. For instance, selecting the best vendor for a project or identifying the most cost-effective marketing strategy requires a convergent approach. Business leaders utilize this method to analyze data, assess risks, and make informed decisions that impact their organization's success.
• In Medicine: Doctors often use convergent thinking when diagnosing a patient. They systematically eliminate possibilities based on symptoms, test results, and medical history to arrive at the correct diagnosis. This type of logical problem-solving is critical for ensuring that patients receive the most effective treatment.
• In Everyday Life: From choosing the right insurance policy to deciding on the best route to avoid traffic, convergent thinking helps simplify decision-making and ensures the best outcome based on available data. It is particularly useful when dealing with practical, day-to-day decisions that require focused thinking to evaluate options and make the best choice.

Convergent Thinking and Analytical Reasoning

Analytical reasoning is closely tied to convergent thinking. Analytical reasoning involves breaking down complex information into smaller parts to understand relationships and derive meaningful conclusions. This is a key component of convergent thinking, as it enables individuals to systematically evaluate data and evidence to arrive at the most logical solution.

For example, when solving a problem involving multiple variables, analytical reasoning allows you to isolate each variable, examine its impact, and determine how it contributes to the overall solution. This type of logical problem-solving is crucial for making informed decisions, particularly in areas such as finance, engineering, and healthcare, where precision is paramount.

To develop analytical reasoning skills, individuals can engage in activities such as:

• Solving Logic Puzzles: Logic puzzles challenge you to use deductive reasoning to arrive at a solution. This helps enhance your ability to think analytically and focus on relevant details.
• Engaging in Strategic Games: Games like chess require players to think several steps ahead, anticipate their opponent's moves, and devise a strategy to achieve a specific outcome. This type of strategic thinking reinforces the principles of convergent thinking and analytical reasoning.
• Analyzing Case Studies: Reviewing case studies, particularly in business or law, allows individuals to practice breaking down complex scenarios, identifying key issues, and determining the best course of action based on available evidence.

How Convergent Thinking Enhances Decision-Making Processes

Decision-making processes are often complex and require careful consideration of multiple factors. Convergent thinking plays a vital role in enhancing these processes by providing a structured approach to evaluating options and making informed choices.

In decision-making, convergent thinking involves:

• Gathering Relevant Information: The first step in making a decision is to collect all relevant information. This may involve gathering data, consulting experts, or reviewing past experiences. By focusing on factual information, convergent thinking ensures that decisions are based on a solid foundation.
• Evaluating Options: Once all relevant information is gathered, convergent thinking involves evaluating each option to determine its viability. This step requires critical thinking to assess the pros and cons of each choice and eliminate those that are not feasible.
• Selecting the Best Solution: After evaluating all options, convergent thinking guides individuals to select the best solution based on logic and evidence. This ensures that the final decision is not only practical but also backed by sound reasoning.

For example, a business leader faced with a strategic decision, such as entering a new market, would use convergent thinking to analyze market data, assess potential risks, and determine the most viable approach. This type of focused thinking helps ensure that the decision is aligned with the organization's goals and objectives.

How Convergent Thinking Complements Divergent Thinking

While convergent thinking narrows down solutions, it works best when paired with divergent thinking, which allows for creativity and innovation. For example, in product development, teams may use divergent thinking to brainstorm new features or solutions. Later, they apply convergent thinking to filter through those ideas and choose the most viable one for implementation.

This balance between divergent and convergent thinking ensures a holistic approach to problem-solving—one that encourages creativity while still maintaining a clear focus on practical solutions. Divergent thinking allows individuals to explore a wide range of possibilities, while convergent thinking ensures that the final solution is both effective and implementable.

Consider the example of a marketing campaign. During the brainstorming phase, a team may use divergent thinking to generate a list of potential campaign ideas. Once all ideas are on the table, convergent thinking is used to evaluate each idea based on criteria such as budget, target audience, and expected outcomes. This process ensures that the final campaign is both creative and feasible, maximizing its chances of success.

Techniques for Balancing Divergent and Convergent Thinking

To effectively balance divergent and convergent thinking, individuals and teams can use the following techniques:

• Mind Mapping: Mind mapping is a visual tool that helps individuals organize their thoughts and ideas. It can be used during the divergent phase to generate ideas and during the convergent phase to categorize and evaluate those ideas.
• Brainstorm and Evaluate: Set aside dedicated time for brainstorming without judgment or evaluation. Once all ideas have been generated, switch to convergent thinking to assess each idea based on specific criteria.
• SWOT Analysis: Use SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis to evaluate ideas generated during the divergent phase. This structured approach helps ensure that the final solution is well-rounded and considers all relevant factors.

Convergent thinking is a powerful cognitive skill that enhances your ability to solve problems efficiently and make informed decisions. By focusing on logic, facts, and data, this method of thinking helps to streamline processes, making it essential for both personal and professional success. Convergent thinking, when combined with critical thinking and analytical reasoning, provides a reliable framework for logical problem-solving and effective decision-making.

Now that you understand the value of convergent thinking, it’s time to apply these techniques in your daily life. Whether you're solving complex problems at work, making decisions in your personal life, or simply looking to enhance your analytical skills, convergent thinking offers a reliable framework for arriving at the best solutions. Practice focusing on data, employing logical models, and balancing divergent and convergent thinking to improve your problem-solving abilities and make more informed decisions.

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Decision Making vs. Problem Solving

What's the difference.

Decision making and problem solving are two closely related concepts that are essential in both personal and professional settings. While decision making refers to the process of selecting the best course of action among various alternatives, problem solving involves identifying and resolving issues or obstacles that hinder progress towards a desired outcome. Decision making often involves evaluating different options based on their potential outcomes and consequences, while problem solving requires analyzing the root causes of a problem and developing effective strategies to overcome it. Both skills require critical thinking, creativity, and the ability to weigh pros and cons. Ultimately, decision making and problem solving are interconnected and complementary processes that enable individuals to navigate complex situations and achieve desired goals.

Further Detail

Introduction.

Decision making and problem solving are two essential cognitive processes that individuals and organizations engage in to navigate through various challenges and achieve desired outcomes. While they are distinct processes, decision making and problem solving share several attributes and are often interconnected. In this article, we will explore the similarities and differences between decision making and problem solving, highlighting their key attributes and how they contribute to effective problem-solving and decision-making processes.

Definition and Purpose

Decision making involves selecting a course of action from multiple alternatives based on available information, preferences, and goals. It is a cognitive process that individuals use to make choices and reach conclusions. On the other hand, problem solving refers to the process of finding solutions to specific issues or challenges. It involves identifying, analyzing, and resolving problems to achieve desired outcomes.

Both decision making and problem solving share the purpose of achieving a desired outcome or resolving a particular situation. They require individuals to think critically, evaluate options, and consider potential consequences. While decision making focuses on choosing the best course of action, problem solving emphasizes finding effective solutions to specific problems or challenges.

Attributes of Decision Making

Decision making involves several key attributes that contribute to its effectiveness:

• Rationality: Decision making is often based on rational thinking, where individuals evaluate available information, weigh pros and cons, and make logical choices.
• Subjectivity: Decision making is influenced by personal preferences, values, and biases. Individuals may prioritize certain factors or options based on their subjective judgment.
• Uncertainty: Many decisions are made under conditions of uncertainty, where individuals lack complete information or face unpredictable outcomes. Decision makers must assess risks and make informed judgments.
• Time Constraints: Decision making often occurs within time constraints, requiring individuals to make choices efficiently and effectively.
• Trade-offs: Decision making involves considering trade-offs between different options, as individuals must prioritize certain factors or outcomes over others.

Attributes of Problem Solving

Problem solving also encompasses several key attributes that contribute to its effectiveness:

• Analytical Thinking: Problem solving requires individuals to analyze and break down complex problems into smaller components, facilitating a deeper understanding of the issue at hand.
• Creativity: Effective problem solving often involves thinking outside the box and generating innovative solutions. It requires individuals to explore alternative perspectives and consider unconventional approaches.
• Collaboration: Problem solving can benefit from collaboration and teamwork, as diverse perspectives and expertise can contribute to more comprehensive and effective solutions.
• Iterative Process: Problem solving is often an iterative process, where individuals continuously evaluate and refine their solutions based on feedback and new information.
• Implementation: Problem solving is not complete without implementing the chosen solution. Individuals must take action and monitor the outcomes to ensure the problem is effectively resolved.

Interconnection and Overlap

While decision making and problem solving are distinct processes, they are interconnected and often overlap. Decision making is frequently a part of the problem-solving process, as individuals must make choices and select the most appropriate solution to address a specific problem. Similarly, problem solving is inherent in decision making, as individuals must identify and analyze problems or challenges before making informed choices.

Moreover, both decision making and problem solving require critical thinking skills, the ability to evaluate information, and the consideration of potential consequences. They both involve a systematic approach to gather and analyze relevant data, explore alternatives, and assess the potential risks and benefits of different options.

Decision making and problem solving are fundamental cognitive processes that individuals and organizations engage in to navigate through challenges and achieve desired outcomes. While decision making focuses on selecting the best course of action, problem solving emphasizes finding effective solutions to specific problems or challenges. Both processes share attributes such as rationality, subjectivity, uncertainty, time constraints, and trade-offs (in decision making), as well as analytical thinking, creativity, collaboration, iterative process, and implementation (in problem solving).

Understanding the similarities and differences between decision making and problem solving can enhance our ability to approach complex situations effectively. By leveraging the attributes of both processes, individuals and organizations can make informed choices, address challenges, and achieve desired outcomes.

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