Hubbry Logo
Decision-makingDecision-makingMain
Open search
Decision-making
Community hub
Decision-making
logo
8 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Decision-making
Decision-making
from Wikipedia
Lamp doesn't work. Under a boolean reads: Lap pluged in? If not; Plug in lamp. If yes continue to next boolean; Boalb burnt out? If yes, replace bulb. If not contunie to last innstruction; replace lamp.
Sample flowchart representing a decision process when confronted with a lamp that fails to light

In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several possible alternative options. It could be either rational or irrational. The decision-making process is a reasoning process based on assumptions of values, preferences and beliefs of the decision-maker.[1] Every decision-making process produces a final choice, which may or may not prompt action.[2]

Research about decision-making is also published under the label problem solving, particularly in European psychological research.[3]

Overview

[edit]

Decision-making can be regarded as a problem-solving activity yielding a solution deemed to be optimal, or at least satisfactory. It is therefore a process which can be more or less rational or irrational and can be based on explicit or tacit knowledge and beliefs. Tacit knowledge is often used to fill the gaps in complex decision-making processes.[4] Usually, both of these types of knowledge, tacit and explicit, are used together in the decision-making process.

Human performance has been the subject of active research from several perspectives:

  • Psychological: examining individual decisions in the context of a set of needs, preferences and values the individual has or seeks.
  • Cognitive: the decision-making process is regarded as a continuous process integrated in the interaction with the environment.
  • Normative: the analysis of individual decisions concerned with the logic of decision-making, or communicative rationality, and the invariant choice it leads to.[5]

A major part of decision-making involves the analysis of a finite set of alternatives described in terms of evaluative criteria. Then the task might be to rank these alternatives in terms of how attractive they are to the decision-maker(s) when all the criteria are considered simultaneously. Another task might be to find the best alternative or to determine the relative total priority of each alternative (for instance, if alternatives represent projects competing for funds) when all the criteria are considered simultaneously. Solving such problems is the focus of multiple-criteria decision analysis (MCDA). This area of decision-making, although long established, has attracted the interest of many researchers and practitioners and is still highly debated as there are many MCDA methods which may yield very different results when they are applied to exactly the same data.[6] This leads to the formulation of a decision-making paradox. Logical decision-making is an important part of all science-based professions, where specialists apply their knowledge in a given area to make informed decisions. For example, medical decision-making often involves a diagnosis and the selection of appropriate treatment. But naturalistic decision-making research shows that in situations with higher time pressure, higher stakes, or increased ambiguities, experts may use intuitive decision-making rather than structured approaches. They may follow a recognition-primed decision that fits their experience, and arrive at a course of action without weighing alternatives.[7]

The decision-maker's environment can play a part in the decision-making process. For example, environmental complexity is a factor that influences cognitive function.[8] A complex environment is an environment with a large number of different possible states which come and go over time.[9] Studies done at the University of Colorado have shown that more complex environments correlate with higher cognitive function, which means that a decision can be influenced by the location. One experiment measured complexity in a room by the number of small objects and appliances present; a simple room had less of those things. Cognitive function was greatly affected by the higher measure of environmental complexity, making it easier to think about the situation and make a better decision.[8]

Problem solving vs. decision making

[edit]

It is important to differentiate between problem solving, or problem analysis, and decision-making. Problem solving is the process of investigating the given information and finding all possible solutions through invention or discovery. Traditionally, it is argued that problem solving is a step towards decision making, so that the information gathered in that process may be used towards decision-making.[10][page needed]

Characteristics of problem-solving
  • Problems are merely deviations from performance standards.
  • Problems must be precisely identified and described
  • Problems are caused by a change from a distinctive feature
  • Something can always be used to distinguish between what has and has not been affected by a cause
  • Causes of problems can be deduced from relevant changes found in analyzing the problem
  • The most likely cause of a problem is the one that exactly explains all the facts while having the fewest (or weakest) assumptions (Occam's razor).
Characteristics of decision-making
  • Objectives must first be established
  • Objectives must be classified and placed in order of importance
  • Alternative actions must be developed
  • The alternatives must be evaluated against all the objectives
  • The alternative that is able to achieve all the objectives is the tentative decision
  • The tentative decision is evaluated for more possible consequences
  • Decisive actions are taken, and additional actions are taken to prevent any adverse consequences from becoming problems and starting both systems (problem analysis and decision-making) all over again
  • There are steps that are generally followed that result in a decision model that can be used to determine an optimal production plan[11]
  • In a situation featuring conflict, role-playing may be helpful for predicting decisions to be made by involved parties[12]
  • When participants do not agree on what the future will look like, Decision-making Under Deep Uncertainty may play a role.[13]

Analysis paralysis

[edit]

When a group or individual is unable to make it through the problem-solving step on the way to making a decision, they could be experiencing analysis paralysis. Analysis paralysis is the state that a person enters where they are unable to make a decision, in effect paralyzing the outcome.[14][15] Some of the main causes for analysis paralysis is the overwhelming flood of incoming data or the tendency to overanalyze the situation at hand.[16] There are said to be three different types of analysis paralysis.[17]

  • The first is analysis process paralysis. This type of paralysis is often spoken of as a cyclical process. One is unable to make a decision because they get stuck going over the information again and again for fear of making the wrong decision.
  • The second is decision precision paralysis. This paralysis is cyclical, just like the first one, but instead of going over the same information, the decision-maker will find new questions and information from their analysis and that will lead them to explore into further possibilities rather than making a decision.
  • The third is risk uncertainty paralysis. This paralysis occurs when the decision-maker wants to eliminate any uncertainty but the examination of provided information is unable to get rid of all uncertainty.

Extinction by instinct

[edit]

On the opposite side of analysis paralysis is the phenomenon called extinction by instinct. Extinction by instinct is the state that a person is in when they make careless decisions without detailed planning or thorough systematic processes.[18] Extinction by instinct can possibly be fixed by implementing a structural system, like checks and balances into a group or one's life. Analysis paralysis is the exact opposite where a group's schedule could be saturated by too much of a structural checks and balance system.[18]

Groupthink is another occurrence that falls under the idea of extinction by instinct. Groupthink is when members in a group become more involved in the "value of the group (and their being part of it) higher than anything else"; thus, creating a habit of making decisions quickly and unanimously. In other words, a group stuck in groupthink is participating in the phenomenon of extinction by instinct.[19]

Information overload

[edit]

Information overload is "a gap between the volume of information and the tools we have to assimilate it".[20] Information used in decision-making is to reduce or eliminate the uncertainty.[21] Excessive information affects problem processing and tasking, which affects decision-making.[22] Psychologist George Armitage Miller suggests that humans' decision making becomes inhibited because human brains can only hold a limited amount of information.[23] Crystal C. Hall and colleagues described an "illusion of knowledge", which means that as individuals encounter too much knowledge, it can interfere with their ability to make rational decisions.[24] Other names for information overload are information anxiety, information explosion, infobesity, and infoxication.[25][26][27][28]

Decision fatigue

[edit]

Decision fatigue is when a sizable amount of decision-making leads to a decline in decision-making skills. People who make decisions in an extended period of time begin to lose mental energy needed to analyze all possible solutions. Impulsive decision-making and decision avoidance are two possible paths that extend from decision fatigue. Impulse decisions are made more often when a person is tired of analysis situations or solutions; the solution they make is to act and not think.[29] Decision avoidance is when a person evades the situation entirely by not ever making a decision. Decision avoidance is different from analysis paralysis because this sensation is about avoiding the situation entirely, while analysis paralysis is continually looking at the decisions to be made but still unable to make a choice.[30][self-published source]

Post-decision analysis

[edit]

Evaluation and analysis of past decisions are complementary to decision-making. See also mental accounting and Postmortem documentation.

Neuroscience

[edit]

Decision-making is a region of intense study in the fields of systems neuroscience, and cognitive neuroscience. Several brain structures, including the anterior cingulate cortex (ACC), orbitofrontal cortex, and the overlapping ventromedial prefrontal cortex are believed to be involved in decision-making processes. A neuroimaging study[31] found distinctive patterns of neural activation in these regions depending on whether decisions were made on the basis of perceived personal volition or following directions from someone else. Patients with damage to the ventromedial prefrontal cortex have difficulty making advantageous decisions.[32][page needed]

A common laboratory paradigm for studying neural decision-making is the two-alternative forced choice task (2AFC), in which a subject has to choose between two alternatives within a certain time. A study of a two-alternative forced choice task involving rhesus monkeys found that neurons in the parietal cortex not only represent the formation of a decision[33] but also signal the degree of certainty (or "confidence") associated with the decision.[34] A 2012 study found that rats and humans can optimally accumulate incoming sensory evidence, to make statistically optimal decisions.[35] Another study found that lesions to the ACC in the macaque resulted in impaired decision-making in the long run of reinforcement guided tasks suggesting that the ACC may be involved in evaluating past reinforcement information and guiding future action.[36] It has recently been argued that the development of formal frameworks will allow neuroscientists to study richer and more naturalistic paradigms than simple 2AFC decision tasks; in particular, such decisions may involve planning and information search across temporally extended environments.[37]

Emotions

[edit]

Emotion appears able to aid the decision-making process. Decision-making often occurs in the face of uncertainty about whether one's choices will lead to benefit or harm (see also Risk). The somatic marker hypothesis is a neurobiological theory of how decisions are made in the face of uncertain outcomes.[38] This theory holds that such decisions are aided by emotions, in the form of bodily states, that are elicited during the deliberation of future consequences and that mark different options for behavior as being advantageous or disadvantageous. This process involves an interplay between neural systems that elicit emotional/bodily states and neural systems that map these emotional/bodily states.[39] A recent lesion mapping study of 152 patients with focal brain lesions conducted by Aron K. Barbey and colleagues provided evidence to help discover the neural mechanisms of emotional intelligence.[40][41][42]

Decision-making techniques

[edit]

Decision-making techniques can be separated into two broad categories: group decision-making techniques and individual decision-making techniques. Individual decision-making techniques can also often be applied by a group.

Group

[edit]
  • Consensus decision-making tries to avoid "winners" and "losers". Consensus requires that a majority approve a given course of action, but that the minority agree to go along with the course of action. In other words, if the minority opposes the course of action, consensus requires that the course of action be modified to remove objectionable features.
  • Voting-based methods:
    • Majority requires support from more than 50% of the members of the group. Thus, the bar for action is lower than with consensus. See also Condorcet method.
    • Plurality, where the largest faction in a group decides, even if it falls short of a majority.
    • Score voting (or range voting) lets each member score one or more of the available options, specifying both preference and intensity of preference information. The option with the highest total or average is chosen. This method has experimentally been shown to produce the lowest Bayesian regret among common voting methods, even when voters are strategic.[43] It addresses issues of voting paradox and majority rule. See also approval voting.
    • Quadratic voting allows participants to cast their preference and intensity of preference for each decision (as opposed to a simple for or against decision). As in score voting, it addresses issues of voting paradox and majority rule.
  • Delphi method is a structured communication technique for groups, originally developed for collaborative forecasting but has also been used for policy making.[44]
  • Dotmocracy is a facilitation method that relies on the use of special forms called Dotmocracy. They are sheets that allows large groups to collectively brainstorm and recognize agreements on an unlimited number of ideas they have each written.[45]
  • Participative decision-making occurs when an authority opens up the decision-making process to a group of people for a collaborative effort.
  • Decision engineering uses a visual map of the decision-making process based on system dynamics and can be automated through a decision modeling tool, integrating big data, machine learning, and expert knowledge as appropriate.

Individual

[edit]

Steps

[edit]

A variety of researchers have formulated similar prescriptive steps aimed at improving decision-making.

GOFER

[edit]

In the 1980s, psychologist Leon Mann and colleagues developed a decision-making process called GOFER, which they taught to adolescents, as summarized in the book Teaching Decision Making To Adolescents.[48] The process was based on extensive earlier research conducted with psychologist Irving Janis.[49] GOFER is an acronym for five decision-making steps:[50]

  1. Goals clarification: Survey values and objectives.
  2. Options generation: Consider a wide range of alternative actions.
  3. Facts-finding: Search for information.
  4. Consideration of Effects: Weigh the positive and negative consequences of the options.
  5. Review and implementation: Plan how to review the options and implement them.

Other

[edit]

In 2007, Pam Brown of Singleton Hospital in Swansea, Wales, divided the decision-making process into seven steps:[51]

  1. Outline the goal and outcome.
  2. Gather data.
  3. Develop alternatives (i.e., brainstorming).
  4. List pros and cons of each alternative.
  5. Make the decision.
  6. Immediately take action to implement it.
  7. Learn from and reflect on the decision.

In 2008, Kristina Guo published the DECIDE model of decision-making, which has six parts:[52]

  1. Define the problem
  2. Establish or Enumerate all the criteria (constraints)
  3. Consider or Collect all the alternatives
  4. Identify the best alternative
  5. Develop and implement a plan of action
  6. Evaluate and monitor the solution and examine feedback when necessary

In 2009, professor John Pijanowski described how the Arkansas Program, an ethics curriculum at the University of Arkansas, used eight stages of moral decision-making based on the work of James Rest:[53]: 6 

  1. Establishing community: Create and nurture the relationships, norms, and procedures that will influence how problems are understood and communicated. This stage takes place prior to and during a moral dilemma.
  2. Perception: Recognize that a problem exists.
  3. Interpretation: Identify competing explanations for the problem, and evaluate the drivers behind those interpretations.
  4. Judgment: Sift through various possible actions or responses and determine which is more justifiable.
  5. Motivation: Examine the competing commitments which may distract from a more moral course of action and then prioritize and commit to moral values over other personal, institutional or social values.
  6. Action: Follow through with action that supports the more justified decision.
  7. Reflection in action.
  8. Reflection on action.

Group stages

[edit]

There are four stages or phases that should be involved in all group decision-making:[54]

  • Orientation. Members meet for the first time and start to get to know each other.
  • Conflict. Once group members become familiar with each other, disputes, little fights and arguments occur. Group members eventually work it out.
  • Emergence. The group begins to clear up vague opinions by talking about them.
  • Reinforcement. Members finally make a decision and provide justification for it.

It is said that establishing critical norms in a group improves the quality of decisions, while the majority of opinions (called consensus norms) do not.[55]

Conflicts in socialization are divided in to functional and dysfunctional types. Functional conflicts are mostly the questioning the managers assumptions in their decision making and dysfunctional conflicts are like personal attacks and every action which decrease team effectiveness. Functional conflicts are the better ones to gain higher quality decision-making caused by the increased team knowledge and shared understanding.[56]

Rational and irrational

[edit]

In economics, it is thought that if humans are rational and free to make their own decisions, then they would behave according to rational choice theory.[57]: 368–370  Rational choice theory says that a person consistently makes choices that lead to the best situation for themselves, taking into account all available considerations including costs and benefits; the rationality of these considerations is from the point of view of the person themselves, so a decision is not irrational just because someone else finds it questionable.

In reality, however, there are some factors that affect decision-making abilities and cause people to make irrational decisions – for example, to make contradictory choices when faced with the same problem framed in two different ways (see also Allais paradox).

Rational decision-making is a multi-step process for making choices between alternatives. The process of rational decision-making favors logic, objectivity, and analysis over subjectivity and insight. The irrational decision is more counter to logic. The decisions are made in haste and outcomes are not considered.[58]

One of the most prominent theories of decision-making is subjective expected utility (SEU) theory, which describes the rational behavior of the decision-maker.[59] The decision maker assesses different alternatives by their utilities and the subjective probability of occurrence.[59]

Rational decision-making is often grounded on experience and theories that are able to put this approach on solid mathematical grounds so that subjectivity is reduced to a minimum, see e.g. scenario optimization.

Rational decision is generally seen as the best or most likely decision to achieve the set goals or outcome.[60]

Children, adolescents, and adults

[edit]

Children

[edit]

It has been found that, unlike adults, children are less likely to have research strategy behaviors. One such behavior is adaptive decision-making, which is described as funneling and then analyzing the more promising information provided if the number of options to choose from increases. Adaptive decision-making behavior is somewhat present for children, ages 11–12 and older, but decreases in the presence the younger they are.[61] The reason children are not as fluid in their decision making is that they lack the ability to weigh the cost and effort needed to gather information in the decision-making process. Some possibilities that explain this inability are knowledge deficits and lack of utilization skills. Children lack the metacognitive knowledge necessary to know when to use any strategies they do possess to change their approach to decision-making.[61]

When it comes to the idea of fairness in decision-making, children and adults differ much less. Children are able to understand the concept of fairness in decision-making from an early age. Toddlers and infants, ranging from 9–21 months, understand basic principles of equality. The main difference found is that more complex principles of fairness in decision making such as contextual and intentional information do not come until children get older.[62]

Adolescents

[edit]

During their adolescent years, teens are known for their high-risk behaviors and rash decisions. Research[63] has shown that there are differences in cognitive processes between adolescents and adults during decision-making. Researchers have concluded that differences in decision-making are not due to a lack of logic or reasoning, but more due to the immaturity of psychosocial capacities that influence decision-making. Examples of their undeveloped capacities which influence decision-making would be impulse control, emotion regulation, delayed gratification and resistance to peer pressure. In the past, researchers have thought that adolescent behavior was simply due to incompetency regarding decision-making. Currently, researchers have concluded that adults and adolescents are both competent decision-makers, not just adults. However, adolescents' competent decision-making skills decrease when psychosocial capacities become present.

Research[64] has shown that risk-taking behaviors in adolescents may be the product of interactions between the socioemotional brain network and its cognitive-control network. The socioemotional part of the brain processes social and emotional stimuli and has been shown to be important in reward processing. The cognitive-control network assists in planning and self-regulation. Both of these sections of the brain change over the course of puberty. However, the socioemotional network changes quickly and abruptly, while the cognitive-control network changes more gradually. Because of this difference in change, the cognitive-control network, which usually regulates the socioemotional network, struggles to control the socioemotional network when psychosocial capacities are present.[clarification needed]

When adolescents are exposed to social and emotional stimuli, their socioemotional network is activated as well as areas of the brain involved in reward processing. Because teens often gain a sense of reward from risk-taking behaviors, their repetition becomes ever more probable due to the reward experienced. In this, the process mirrors addiction. Teens can become addicted to risky behavior because they are in a high state of arousal and are rewarded for it not only by their own internal functions but also by their peers around them. A recent study suggests that adolescents have difficulties adequately adjusting beliefs in response to bad news (such as reading that smoking poses a greater risk to health than they thought), but do not differ from adults in their ability to alter beliefs in response to good news.[65] This creates biased beliefs, which may lead to greater risk-taking.[66]

Adults

[edit]

Adults are generally better able to control their risk-taking because their cognitive-control system has matured enough to the point where it can control the socioemotional network, even in the context of high arousal or when psychosocial capacities are present. Also, adults are less likely to find themselves in situations that push them to do risky things. For example, teens are more likely to be around peers who peer pressure them into doing things, while adults are not as exposed to this sort of social setting.[67][68]

Cognitive and personal biases

[edit]

Biases usually affects decision-making processes. They appear more when decision task has time pressure, is done under high stress and/or are highly complex.[69]

Here is a list of commonly debated biases in judgment and decision-making:

  • Selective search for evidence (also known as confirmation bias): People tend to be willing to gather facts that support certain conclusions but disregard other facts that support different conclusions. Individuals who are highly defensive in this manner show significantly greater left prefrontal cortex activity as measured by EEG than do less defensive individuals.[70]
  • Premature termination of search for evidence: People tend to accept the first alternative that looks like it might work.
  • Cognitive inertia is the unwillingness to change existing thought patterns in the face of new circumstances.
  • Selective perception: People actively screen out information that they do not think is important (see also Prejudice). In one demonstration of this effect, the discounting of arguments with which one disagrees (by judging them as untrue or irrelevant) was decreased by selective activation of the right prefrontal cortex.[71]
  • Wishful thinking is a tendency to want to see things in a certain – usually positive – light, which can distort perception and thinking.[72]
  • Choice-supportive bias occurs when people distort their memories of chosen and rejected options to make the chosen options seem more attractive.
  • Recency: People tend to place more attention on more recent information and either ignore or forget more distant information (see Semantic priming). The opposite effect in the first set of data or other information is termed primacy effect.[73][page needed]
  • Repetition bias is a willingness to believe what one has been told most often and by the greatest number of different sources.
  • Anchoring and adjustment: Decisions are unduly influenced by initial information that shapes our view of subsequent information.
  • Groupthink is peer pressure to conform to the opinions held by the group.
  • Source credibility bias is a tendency to reject a person's statement on the basis of a bias against the person, organization, or group to which the person belongs. People preferentially accept statements by others that they like (see also Prejudice).
  • Incremental decision-making and escalating commitment: People look at a decision as a small step in a process, and this tends to perpetuate a series of similar decisions. This can be contrasted with zero-based decision-making (see Slippery slope).
  • Attribution asymmetry: People tend to attribute their own success to internal factors, including abilities and talents, but explain their failures in terms of external factors such as bad luck. The reverse bias is shown when people explain others' success or failure.
  • Role fulfillment is a tendency to conform to others' decision-making expectations.
  • Underestimating uncertainty and the illusion of control: People tend to underestimate future uncertainty because of a tendency to believe they have more control over events than they really do.
  • Framing bias: This is best avoided by increasing numeracy and presenting data in several formats (for example, using both absolute and relative scales).[74]
    • Sunk-cost fallacy is a specific type of framing effect that affects decision-making. It involves an individual making a decision about a current situation based on what they have previously invested in the situation.[57]: 372  An example of this would be an individual who is refraining from dropping a class that they are most likely to fail, due to the fact that they feel as though they have done so much work in the course thus far.
  • Prospect theory involves the idea that when faced with a decision-making event, an individual is more likely to take on a risk when evaluating potential losses, and is more likely to avoid risks when evaluating potential gains. This can influence one's decision-making depending if the situation entails a threat or opportunity.[57]: 373 
  • Optimism bias is a tendency to overestimate the likelihood of positive events occurring in the future and underestimate the likelihood of negative life events.[75] Such biased expectations are generated and maintained in the face of counter-evidence through a tendency to discount undesirable information.[76] An optimism bias can alter risk perception and decision-making in many domains, ranging from finance to health.
  • Reference class forecasting was developed to eliminate or reduce cognitive biases in decision-making.

Cognitive limitations in groups

[edit]

In groups, people generate decisions through active and complex processes. One method consists of three steps: initial preferences are expressed by members; the members of the group then gather and share information concerning those preferences; finally, the members combine their views and make a single choice about how to face the problem. Although these steps are relatively ordinary, judgements are often distorted by cognitive and motivational biases, including "sins of commission", "sins of omission", and "sins of imprecision".[77][page needed]

Cognitive styles

[edit]

Optimizing vs. satisficing

[edit]

Herbert A. Simon coined the phrase "bounded rationality" to express the idea that human decision-making is limited by available information, available time and the mind's information-processing ability. Further psychological research has identified individual differences between two cognitive styles: maximizers try to make an optimal decision, whereas satisficers simply try to find a solution that is "good enough". Maximizers tend to take longer to make decisions due to the need to maximize performance across all variables and make tradeoffs carefully; they also tend to more often regret their decisions (perhaps because they are more able than satisficers to recognize that a decision turned out to be sub-optimal).[78]

Intuitive vs. rational

[edit]

The psychologist Daniel Kahneman, adopting terms originally proposed by the psychologists Keith Stanovich and Richard West, has theorized that a person's decision-making is the result of an interplay between two kinds of cognitive processes: an automatic intuitive system (called "System 1") and an effortful rational system (called "System 2"). System 1 is a bottom-up, fast, and implicit system of decision-making, while system 2 is a top-down, slow, and explicit system of decision-making.[79] System 1 includes simple heuristics in judgment and decision-making such as the affect heuristic, the availability heuristic, the familiarity heuristic, and the representativeness heuristic.

Combinatorial vs. positional

[edit]

Styles and methods of decision-making were elaborated by Aron Katsenelinboigen, the founder of predispositioning theory. In his analysis of styles and methods, Katsenelinboigen referred to the game of chess, saying that "chess does disclose various methods of operation, notably the creation of predisposition methods which may be applicable to other, more complex systems."[80]: 5 

Katsenelinboigen states that apart from the methods (reactive and selective) and sub-methods randomization, predispositions, programming), there are two major styles: positional and combinational. Both styles are utilized in the game of chess. The two styles reflect two basic approaches to uncertainty: deterministic (combinational style) and indeterministic (positional style). Katsenelinboigen's definition of the two styles is the following.

The combinational style is characterized by:

  • a very narrow, clearly defined, primarily material goal; and
  • a program that links the initial position with the outcome.

In defining the combinational style in chess, Katsenelinboigen wrote: "The combinational style features a clearly formulated limited objective, namely the capture of material (the main constituent element of a chess position). The objective is implemented via a well-defined, and in some cases, unique sequence of moves aimed at reaching the set goal. As a rule, this sequence leaves no options for the opponent. Finding a combinational objective allows the player to focus all his energies on efficient execution, that is, the player's analysis may be limited to the pieces directly partaking in the combination. This approach is the crux of the combination and the combinational style of play.[80]: 57 

The positional style is distinguished by:

  • a positional goal; and
  • a formation of semi-complete linkages between the initial step and final outcome.

"Unlike the combinational player, the positional player is occupied, first and foremost, with the elaboration of the position that will allow him to develop in the unknown future. In playing the positional style, the player must evaluate relational and material parameters as independent variables. ... The positional style gives the player the opportunity to develop a position until it becomes pregnant with a combination. However, the combination is not the final goal of the positional player – it helps him to achieve the desirable, keeping in mind a predisposition for future development. The pyrrhic victory is the best example of one's inability to think positionally."[81]

The positional style serves to:

  • create a predisposition to the future development of the position;
  • induce the environment in a certain way;
  • absorb an unexpected outcome in one's favor; and
  • avoid the negative aspects of unexpected outcomes.

Influence of Myers–Briggs type

[edit]

According to Isabel Briggs Myers, a person's decision-making process depends to a significant degree on their cognitive style.[82][page needed] Myers developed a set of four bi-polar dimensions, called the Myers–Briggs Type Indicator (MBTI). The terminal points on these dimensions are: thinking and feeling; extroversion and introversion; judgment and perception; and sensing and intuition. She claimed that a person's decision-making style correlates well with how they score on these four dimensions. For example, someone who scored near the thinking, extroversion, sensing, and judgment ends of the dimensions would tend to have a logical, analytical, objective, critical, and empirical decision-making style. However, some psychologists say that the MBTI lacks reliability and validity and is poorly constructed.[83][84]

Other studies suggest that these national or cross-cultural differences in decision-making exist across entire societies. For example, Maris Martinsons has found that American, Japanese and Chinese business leaders each exhibit a distinctive national style of decision-making.[85]

The Myers–Briggs typology has been the subject of criticism regarding its poor psychometric properties.[86][87][88]

General decision-making style (GDMS)

[edit]

In the general decision-making style (GDMS) test developed by Suzanne Scott and Reginald Bruce, there are five decision-making styles: rational, intuitive, dependent, avoidant, and spontaneous.[89][90] These five different decision-making styles change depending on the context and situation, and one style is not necessarily better than any other. In the examples below, the individual is working for a company and is offered a job from a different company.

  • The rational style is an in-depth search for, and a strong consideration of, other options and/or information prior to making a decision. In this style, the individual would research the new job being offered, review their current job, and look at the pros and cons of taking the new job versus staying with their current company.
  • The intuitive style is confidence in one's initial feelings and gut reactions. In this style, if the individual initially prefers the new job because they have a feeling that the work environment is better suited for them, then they would decide to take the new job. The individual might not make this decision as soon as the job is offered.
  • The dependent style is asking for other people's input and instructions on what decision should be made. In this style, the individual could ask friends, family, coworkers, etc., but the individual might not ask all of these people.
  • The avoidant style is averting the responsibility of making a decision. In this style, the individual would not make a decision. Therefore, the individual would stick with their current job.
  • The spontaneous style is a need to make a decision as soon as possible rather than waiting to make a decision. In this style, the individual would either reject or accept the job as soon as it is offered.

See also

[edit]

References

[edit]

Further reading

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Decision-making is the cognitive process by which individuals or groups select a course of action from available alternatives to achieve desired outcomes, often under conditions of uncertainty or limited information. This process, fundamental to human behavior, encompasses identifying problems, evaluating options, and implementing choices that shape personal decisions, organizational strategies, and public policies. It involves rational and irrational approaches, influenced by biological and psychological factors such as neuroscience and emotions, alongside developmental aspects, cognitive biases and limitations, and diverse styles including intuitive, optimizing, and satisficing methods. Techniques and strategies, from individual tools to group methods, aim to improve decision quality across contexts like healthcare, finance, and policy-making.

Fundamentals

Definition and Overview

Decision-making is the cognitive process by which individuals or groups select a or course of action from among several alternative options, involving the evaluation of those options based on specific criteria. This process is fundamental to , encompassing both conscious and influences that guide choices in uncertain environments. The roots of decision-making theory trace back to , particularly Aristotle's concept of , or practical wisdom, which emphasized reasoned judgment in ethical and practical affairs to achieve virtuous outcomes. This idea evolved through centuries of philosophical inquiry into modern psychological frameworks, notably with Herbert Simon's introduction of in the 1950s, which recognized that human decisions are constrained by limited information, cognitive capacity, and time, leading to rather than optimal choices. Decision-making permeates all aspects of life, manifesting in personal choices such as selecting a meal based on preferences and availability, professional scenarios like strategizing investments to maximize returns, and societal contexts such as policymakers weighing options for environmental regulations to balance and sustainability. These examples illustrate its ubiquity, as individuals constantly navigate trade-offs to adapt to changing circumstances. At its core, decision-making involves identifying the decision context, which sets the problem's boundaries; generating alternatives, or possible options; establishing criteria, such as costs, benefits, or risks; and anticipating outcomes to inform the final selection. These components form the foundational structure, enabling systematic evaluation even in complex situations.

Relation to Problem Solving

Problem solving encompasses a broader cognitive process that involves identifying an issue, analyzing its causes, generating potential solutions, and implementing resolutions to restore a desired state or address deviations from expectations. In contrast, decision-making serves as a critical within this framework, primarily focusing on the evaluation and selection of alternatives from among those generated during . The two processes overlap significantly in their reliance on of options, assessment of risks, and of outcomes, yet typically precedes and contextualizes decision-making by first defining the problem space. For instance, in a context, might entail conducting to identify declining sales due to competitive pressures, while decision-making then involves choosing a specific , such as product diversification or adjustments, to address the identified issue. Several pitfalls arise at the intersection of these processes, potentially undermining effective outcomes. occurs when excessive deliberation over alternatives during the decision phase halts progress, leading to inaction despite thorough problem identification. Extinction by instinct refers to hasty selections based on outdated or impulsive habits, bypassing rigorous problem and risking in dynamic environments. can overwhelm individuals during evaluation, as an abundance of data from problem impairs the ability to discern relevant alternatives for decision-making. emerges from repeated choices within prolonged problem-solving cycles, diminishing cognitive resources and resulting in suboptimal selections. Finally, post-decision , while valuable for learning, can devolve into biased evaluation of outcomes, where distortions of facts reinforce prior choices irrationally rather than informing future problem solving.

The Decision-Making Process

Key Steps

The decision-making process generally follows a structured sequence of stages that guide individuals or groups from recognizing a need to evaluating outcomes, providing a foundational approach applicable across various contexts. This framework emphasizes systematic progression to enhance the quality and effectiveness of choices. The standard steps include:
  1. Identify the decision: Recognize the need or problem that requires a choice, clarifying the objectives and scope.
  2. Gather relevant : Collect pertinent data from internal knowledge and external sources to inform the process.
  3. Identify alternatives: Generate possible options or solutions that address the identified need.
  4. Weigh the evidence: Analyze the pros and cons of each alternative, considering risks, benefits, and alignment with goals.
  5. Choose among alternatives: Select the most suitable option based on the evaluation.
  6. Take action: Implement the chosen alternative through concrete steps.
  7. Review the decision and its consequences: Assess the results, learning from successes and shortcomings to refine future processes.
These steps are not always linear; the process often exhibits an iterative nature, where earlier stages may be revisited based on new insights or evolving circumstances, particularly in complex or uncertain scenarios. In group settings, the process incorporates additional considerations, such as fostering to build consensus among participants, ensuring collective buy-in without delving into specific facilitation techniques. For instance, in making a choice, an individual might first identify the need to transition from their current role due to limited growth opportunities, then gather information on industry trends and personal skills, generate alternatives like pursuing or switching sectors, weigh factors such as salary potential and work-life balance, select a path like applying for a new position, implement by updating their resume and networking, and finally review the outcome after six months to adjust if needed.

Common Models

Common models of decision-making provide structured frameworks that extend the general steps of the process by offering theoretical lenses for understanding how individuals select among alternatives. These models have evolved historically from classical economic assumptions of perfect , as formalized in expected theory by von Neumann and Morgenstern in 1944, which posits that decision-makers evaluate all possible outcomes probabilistically to maximize expected . In the mid-20th century, behavioral approaches emerged, incorporating psychological insights to address limitations of these idealizations, marking a shift toward more realistic depictions of human . The rational decision-making model represents the classical ideal, where individuals systematically identify the problem, generate all feasible options, gather complete information, evaluate alternatives based on objective criteria to maximize , and select the optimal choice. This model assumes unlimited cognitive capacity, availability, and logical consistency, often serving as a normative benchmark for evaluating real-world decisions. It underpins fields like and , where decisions aim to achieve the highest possible benefit relative to costs. In contrast, Herbert Simon's model, introduced in 1957, acknowledges cognitive and environmental constraints that prevent full rationality, leading individuals to "satisfice" by selecting the first acceptable option rather than optimizing. Simon argued that limited information processing, time, and foresight cause decision-makers to simplify problems through heuristics and approximations, a concept detailed in his seminal work Models of Man. This model has profoundly influenced and by highlighting how real decisions balance aspiration levels with feasible outcomes. The intuitive decision-making model, exemplified by Gary Klein's (RPD) framework from 1993, describes rapid, pattern-based choices drawn from experience rather than deliberate analysis. In the RPD model, experts recognize situational cues that trigger mental simulations of plausible actions, allowing quick evaluation without exhaustive option generation, particularly effective in dynamic environments like or . This approach relies on accumulated to achieve effective outcomes under , contrasting with analytical models by emphasizing subconscious . The GOFER model, developed by Leon Mann and colleagues in 1988, offers a practical, step-by-step framework grounded in conflict theory: set Goals (define clear objectives), generate Options (brainstorm alternatives), gather Facts (collect relevant information), evaluate Effects (assess consequences and risks), and Review (implement, monitor, and revise the decision). Designed for educational and applied settings, it promotes balanced with decision stress by integrating cognitive and motivational elements, as validated in high school interventions that improved self-reported decision skills. These models adapt to real-world constraints such as time pressure by shifting emphasis; for instance, under tight deadlines, rational processes often yield to intuitive or strategies to maintain functionality, as evidenced in studies showing reduced search and increased reliance on heuristics. explicitly accounts for such limits by prioritizing viable shortcuts, while GOFER's review phase allows iterative adjustments in constrained scenarios. This flexibility ensures models remain applicable across contexts, from to crisis response.

Rational and Irrational Approaches

Rational Decision-Making

Rational decision-making refers to a systematic, logic-based approach to choosing among alternatives, grounded in the assumption that decision-makers possess , evaluate options objectively, and select the one that maximizes overall or benefit. This normative framework emphasizes evidence-driven evaluation over or , aiming for outcomes that align with predefined goals such as or value optimization. A foundational principle of rational decision-making is expected utility theory, which posits that individuals should choose actions based on their expected , calculated as the weighted average of utilities across possible outcomes, where weights are the probabilities of those outcomes occurring. Formulated by and in their seminal work, the theory is expressed mathematically as: EU(a)=iP(oia)U(oi)EU(a) = \sum_{i} P(o_i \mid a) \cdot U(o_i) Here, EU(a)EU(a) is the expected utility of action aa, P(oia)P(o_i \mid a) is the probability of outcome oio_i given action aa, and U(oi)U(o_i) is the of outcome oio_i. This approach assumes transitivity of preferences, completeness of information, and independence of choices, enabling precise comparisons under uncertainty. The process of rational decision-making typically involves a structured sequence: defining the decision criteria, identifying and quantifying all feasible alternatives, assessing their costs and benefits systematically—often through cost-benefit analysis—and selecting the option with the highest net positive value. Cost-benefit analysis, a key evaluative tool, quantifies tangible and intangible factors by assigning monetary values to outcomes, such as comparing investment returns against risks, to ensure decisions align with resource constraints and objectives. This methodical evaluation promotes transparency and reproducibility in choices. In controlled environments, rational decision-making yields optimal results, particularly in financial investments where it facilitates by balancing expected returns against volatility, as seen in applications that prioritize maximization. For instance, investors using this approach can allocate assets to achieve diversified, high-utility outcomes under known probabilities. Despite its theoretical rigor, rational decision-making remains an idealized model, frequently impractical in real-world scenarios due to incomplete information, time pressures, and cognitive constraints that prevent full optimization. Herbert Simon's concept of underscores these limitations, arguing that decision-makers "satisfice" rather than maximize because they operate with partial knowledge and finite computational capacity. Detailed explorations of resulting deviations, such as cognitive biases, fall outside this framework's scope.

Irrational and Heuristic-Based Decision-Making

Irrational and heuristic-based decision-making refers to cognitive processes where individuals rely on mental shortcuts, or heuristics, rather than exhaustive logical , often leading to efficient but potentially biased outcomes. These approaches enable rapid judgments in complex or uncertain situations but can introduce systematic errors, diverging from the deliberate evaluation emphasized in rational models. Pioneering work by psychologists and identified key heuristics that underpin such intuitive decision-making. One prominent heuristic is , where people assess the likelihood of an event based on the ease with which examples come to mind, often overestimating vivid or recent occurrences. For instance, media coverage of plane crashes may inflate perceived flying risks despite statistical rarity. Another is representativeness, which involves judging probability by how closely an event resembles a typical , leading to stereotyping or ignoring base rates, such as assuming a shy person is more likely a than a salesperson despite occupational frequencies. Anchoring occurs when an initial piece of information influences subsequent judgments, even if arbitrary; for example, a high starting in negotiations can pull final offers upward regardless of . These heuristics, while simplifying , frequently result in deviations from probabilistic reasoning. Irrational decision-making manifests in specific fallacies that perpetuate suboptimal choices. The sunk cost fallacy drives individuals to continue investments—such as persisting with a failing project—due to prior expenditures of time, money, or effort, rather than future prospects; experimental evidence shows participants allocate more resources to loss-making gambles after initial commitments. Similarly, overconfidence leads decision-makers to overestimate their knowledge or predictive accuracy, with studies revealing that most drivers rate themselves as safer and more skilled than average, fostering risky behaviors like underestimating project timelines. These patterns highlight how past commitments or inflated self-assessments distort objective evaluation. A foundational theory explaining such irrationality is prospect theory, developed by Kahneman and Tversky, which posits that people evaluate decisions relative to a reference point and exhibit loss aversion, where losses impact utility more than equivalent gains. Unlike expected utility theory, prospect theory describes a value function that is concave for gains (indicating risk aversion) and convex for losses (indicating risk-seeking), with steeper slopes for losses. The function is formalized as: v(x)={xαif x0λ(x)βif x<0v(x) = \begin{cases} x^{\alpha} & \text{if } x \geq 0 \\ -\lambda (-x)^{\beta} & \text{if } x < 0 \end{cases} Empirical estimates yield α=β0.88\alpha = \beta \approx 0.88 and λ2.25\lambda \approx 2.25, quantifying how losses loom approximately twice as large as gains. This framework accounts for phenomena like the endowment effect, where ownership inflates perceived value. Despite their risks, heuristics offer benefits in time-pressured or information-scarce environments, promoting speed and adaptability over perfection. Research on fast and frugal heuristics demonstrates that simple rules, such as recognizing familiar options or tallying cues without integration, can match or exceed complex models in accuracy for tasks like ecological inferences, as seen in emergency medical triage where quick pattern recognition saves lives. In uncertain settings, these shortcuts conserve cognitive resources, enabling effective decisions when full rationality is infeasible.

Biological and Psychological Influences

Neuroscience of Decision-Making

The of decision-making examines the brain's neural circuits and processes that underpin the , selection, and execution of choices, integrating sensory inputs, value assessments, and motor outputs. Key brain regions include the (PFC), which is crucial for planning, evaluating options, and exerting executive control over decisions; the , which processes emotional and risk-related aspects of choices; and the , which facilitate habitual and reward-driven selections through loops involving the . These structures interact via interconnected pathways, such as cortico-basal ganglia-thalamo-cortical loops, to resolve conflicts between immediate impulses and long-term goals. A prominent neural model highlights the role of in encoding reward prediction errors, which signal discrepancies between expected and actual outcomes to update value representations and guide learning. In this framework, neurons in the and fire phasically to compute the temporal difference error, formalized as: ΔV=r+γV(s)V(s)\Delta V = r + \gamma V(s') - V(s) where ΔV\Delta V is the prediction error, rr is the immediate reward, γ\gamma is the discount factor for future rewards, V(s)V(s) is the predicted value of the current state, and V(s)V(s') is the predicted value of the next state. This signal modulates in target areas like the and PFC, reinforcing adaptive choices while suppressing suboptimal ones. Functional magnetic resonance imaging (fMRI) studies provide evidence for value-based decision circuits, showing activation in the (vmPFC) during preference formation and subjective value computation. For instance, vmPFC activity correlates with the integrated value of options, integrating inputs from sensory and limbic regions to represent expected rewards and costs. These findings underscore how distributed networks, including the vmPFC and , enable flexible valuation across contexts like economic choices or social interactions. From an evolutionary perspective, decision-making mechanisms have adapted to enhance by balancing rapid, reflexive responses in simple environments with deliberative processes for complex, uncertain scenarios. Neural circuits mediating these behaviors, conserved across vertebrates, evolved under selective pressures favoring efficient and threat avoidance, as seen in neuroethological studies of and predation decisions in model organisms.00352-3)

Role of Emotions

Emotions play a pivotal role in decision-making by providing affective signals that guide choices, often complementing or even overriding purely rational analysis. According to Damasio's , emotions function as somatic markers—bodily-based signals that "tag" decision options with positive or negative valence, facilitating rapid evaluation and selection among complex alternatives. These markers arise from past experiences, where emotional responses to outcomes create gut feelings that bias future decisions toward advantageous paths and away from detrimental ones. In essence, without these emotional tags, individuals struggle to navigate efficiently, as the hypothesis posits that emotions are integral to adaptive reasoning rather than mere distractions. A classic illustration of this comes from the historical case of , a 19th-century railroad worker whose in 1848 led to profound changes in personality and decision-making. Prior to the accident, Gage was reliable and socially adept; afterward, he exhibited impulsivity and poor judgment, unable to weigh long-term consequences despite intact intellect. Damasio interpreted this as evidence that the damage disrupted somatic marker processing, resulting in decisions devoid of emotional guidance and thus maladaptive. Modern studies of patients with similar lesions confirm impaired decision-making in emotion-blunted states, where individuals fail to anticipate future regrets or rewards effectively.81144-0) The valence of emotions further modulates decision tendencies, with negative emotions like promoting to safeguard against potential losses. For instance, narrows cognitive focus toward threats, leading individuals to prefer safer options in uncertain scenarios, as demonstrated in experimental paradigms where induced increased conservative choices. In contrast, positive emotions such as broaden thought-action repertoires, encouraging exploration of novel options and creative problem-solving, per Barbara Fredrickson's . This theory argues that positive affects counteract the narrowing effects of negative emotions, building enduring psychological resources like resilience and social bonds over time. In group settings, emotions can spread through , influencing collective decisions by synchronizing affective states among members. Research shows that moods transfer rapidly in teams, with a leader's enthusiasm boosting group creativity or anxiety amplifying cautionary consensus. Similarly, anticipation of regret shapes individual choices by heightening sensitivity to potential downsides, often steering people away from decisions that might evoke post-hoc remorse, as seen in consumer and health behavior studies. Overall, emotions serve as evolved heuristics that integrate with rational processes, enhancing efficiency in real-world decisions where complete information is unavailable; their absence, as in Gage's case, underscores how emotionless cognition leads to suboptimal outcomes. This interplay highlights the 's brief role in tagging stimuli with emotional significance, linking affective processing to broader decision networks.81144-0)

Techniques and Strategies

Individual Techniques

Individual techniques for decision-making provide structured tools that individuals can apply independently to evaluate options, generate ideas, and refine choices in personal contexts. These methods range from simple qualitative approaches to more analytical ones incorporating probabilities and reflection, enabling solo decision-makers to navigate routine or complex scenarios like career transitions or financial . By focusing on personal application, these techniques emphasize self-directed without relying on group input. One foundational technique is the pros-and-cons list, which involves enumerating the advantages and disadvantages of each alternative to clarify trade-offs. This method promotes objective assessment by forcing explicit consideration of both positive and negative aspects, reducing the influence of unexamined preferences. Originating in multi-criteria evaluation practices, it is particularly effective for straightforward decisions where qualitative factors dominate. A more quantitative extension is the , also known as the Pugh matrix, where alternatives are scored against weighted criteria to identify the optimal choice. Developed by Stuart Pugh for concept selection, the process begins by listing options and criteria (e.g., cost, feasibility, impact), assigning weights to criteria based on importance, and rating each option on a scale (often +1 for better, 0 for neutral, -1 for worse relative to a baseline). Scores are multiplied by weights and summed to rank alternatives. This technique excels in personal decisions requiring balanced evaluation, such as selecting a new home or job. For generating options creatively, mind mapping serves as a visual brainstorming tool that radiates ideas from a central concept using branches for associations, keywords, and images. Invented by , it leverages nonlinear thinking to uncover novel alternatives, enhancing idea generation in exploratory phases of decision-making. Users start with a core question (e.g., "Career options") and expand outward, connecting related thoughts to reveal interconnections overlooked in linear lists. In probabilistic contexts, Bayesian updating offers an advanced method for revising beliefs based on new , formalized by in . As articulated in Leonard Savage's foundational work, it computes posterior probabilities as: P(HE)=P(EH)P(H)P(E)P(H|E) = \frac{P(E|H) P(H)}{P(E)} where P(H)P(H) is the of HH, P(EH)P(E|H) is the likelihood of EE given HH, and P(E)P(E) is the marginal probability of EE. Individuals apply this by starting with initial beliefs (priors) about outcomes—such as success rates for job applications—and them with incoming data, like feedback, to inform choices under uncertainty. This approach is ideal for personal risks involving incomplete information, such as investment decisions. Self-reflection tools further support individual decision-making by encouraging post-hoc analysis and foresight. Decision journaling involves recording the rationale, expected outcomes, and actual results of choices to build metacognitive awareness and identify patterns in past successes or errors. Studies in professional training demonstrate its role in enhancing and . Complementing this, requires envisioning multiple future narratives (e.g., best-case, worst-case) to test decision robustness against uncertainties. Pioneered by Pierre Wack at Shell for , it adapts to personal use by outlining "what-if" paths for decisions like , revealing vulnerabilities and opportunities. These tools are best employed after initial option evaluation to refine and learn from the process. These techniques suit routine personal decisions, such as daily budgeting, as well as complex ones like career paths, where integrating , evaluation, and reflection yields more informed outcomes. For instance, a professional contemplating a job switch might use mind mapping to brainstorm roles, a to score them, Bayesian updating for promotion probabilities, and journaling to track application reflections.

Group Decision-Making Techniques

techniques facilitate collaborative processes where multiple individuals contribute to collective choices, often enhancing outcomes through shared expertise and diverse perspectives. These methods structure interactions to promote idea generation, consensus-building, and evaluation while minimizing interpersonal barriers. Common approaches include structured ideation and iterative feedback mechanisms designed for both face-to-face and remote settings. One foundational technique is , developed by Alex Osborn in 1953 as a method to generate creative ideas in groups without initial critique. In brainstorming sessions, participants—typically 5 to 12 individuals with varied backgrounds—focus on producing a high quantity of ideas, adhering to rules such as deferring judgment, encouraging wild suggestions, and building on others' contributions. This approach counters "negative conference thinking" by prioritizing quantity over quality during the generation phase, with evaluation occurring afterward under guidance. Osborn's method has been widely adopted in organizational settings to stimulate innovative problem-solving in decision processes. The , originated by the in the 1950s, employs anonymous, iterative rounds of expert input to refine group judgments and forecast outcomes. Experts respond to questionnaires individually, receive aggregated feedback without revealing identities, and revise their opinions over multiple rounds—typically two to four—to converge on a consensus. reduces dominance by influential members and minimizes bias from group dynamics, making it suitable for complex, uncertain decisions like policy forecasting or . Experiments at RAND in 1968 demonstrated its effectiveness in eliciting reliable group opinions compared to unstructured discussions. Another structured approach is the (NGT), introduced by André L. Delbecq and Andrew H. Van de Ven in the early 1970s as a hybrid of individual and group input for exploratory decision-making. The process unfolds in four stages: silent idea generation, where participants independently list ideas; round-robin sharing, with each idea recorded without debate; clarification through brief discussions; and voting, involving ranking or rating to prioritize options. This method ensures equal participation and quantifies qualitative inputs, yielding more reliable priorities than traditional brainstorming. Originally applied in health studies to identify barriers, NGT generalizes to any scenario requiring balanced aggregation of views. Effective group decision-making often progresses through distinct developmental stages, as outlined by in 1965 based on analysis of small group literature. In the forming stage, members orient themselves, define roles, and depend on leaders to clarify the decision task. Storming follows, marked by conflicts over ideas and interpersonal tensions that challenge cohesion. Norming involves establishing norms and building trust, enabling open idea exchange. The performing stage sees the group function efficiently, focusing on task execution and consensus. Tuckman later added adjourning in 1977, where the group disbands, reviewing outcomes and addressing closure. These stages provide a framework for managing group evolution in decision contexts. Group techniques offer advantages such as diverse input, which pools varied experiences to overcome individual biases and improve accuracy—for instance, group averages in estimation tasks outperform solo judgments. However, challenges include the risk of , where pressures lead to flawed decisions by suppressing dissent, as seen in experiments where 40% of individuals aligned with incorrect group views. Techniques like and NGT mitigate these by structuring anonymity and equality. Real-world applications illustrate these techniques' utility. deliberations exemplify consensus-building, where 12 members discuss under unanimous rules, with majority factions dominating speech but minorities influencing through persistent input, with individual jurors changing their verdicts in about 32% of cases based on review. Corporate board meetings represent strategic , where directors collectively evaluate options via -based voting, leveraging diverse expertise to monitor management and reduce biases, achieving higher accuracy in complex judgments than individuals.

Developmental Aspects

Decision-Making in Children

Young children, particularly those in Piaget's preoperational stage (ages 2-7), exhibit , where they struggle to consider perspectives other than their own, leading to decisions heavily influenced by personal desires rather than logical evaluation. This egocentrism manifests in impulsive choices, as children focus on a single salient feature of a situation—a process known as —often prioritizing immediate gratification over long-term benefits. Classic studies on delay of gratification, such as the marshmallow test, demonstrate that preschoolers (ages 3-5) overwhelmingly opt for smaller, immediate rewards, reflecting limited and foresight. For instance, in experiments, 4-year-olds showed improved but still inconsistent ability to delay rewards compared to 3-year-olds, who frequently succumbed to . Parental guidance plays a pivotal role in shaping these early decisions, providing that helps children navigate choices amid their limited foresight. Parents often encourage consideration of consequences through verbal prompts and collaborative planning, which enhances in tasks like sharing resources. Young children also display a strong preference for tangible, concrete options over abstract ones; for example, in scenarios, 3- to 5-year-olds favor immediate physical rewards (e.g., a sticker now) rather than delayed or hypothetical benefits, limiting their ability to weigh intangible outcomes like future relationships or fairness. This bias stems from developmental constraints in executive function, which matures gradually after age 4, allowing better integration of multiple factors in decisions. Around ages 5-6, basic decision-weighing emerges, facilitated by the development of (ToM), which enables children to infer others' mental states and incorporate social perspectives into choices. Advanced ToM correlates with increased prosocial decision-making, such as equitable to peers, as children begin to balance self-interest with others' needs rather than defaulting to . Meta-analyses confirm that by this age, higher ToM proficiency predicts more and fairness in social dilemmas, marking a shift toward rudimentary pros-and-cons in interpersonal contexts. Educational interventions can foster these skills through age-appropriate strategies, such as guided discussions and visual aids to introduce simple pros-cons thinking. For children ages 5-7, programs using interactive quizzes or pictorial decision aids— like those for choices—improve understanding of options and encourage weighing immediate versus future impacts, with studies showing enhanced participation and reduced decisional conflict. Parental and teacher , including open-ended questions like "What might happen if...?", builds foundational habits of reflection without overwhelming young minds. These approaches, grounded in , prioritize concrete examples to gradually expand children's decision-making repertoire.

Decision-Making in Adolescents and Adults

Decision-making processes undergo significant maturation from to adulthood, reflecting neurodevelopmental changes and accumulated experiences. In adolescents, typically aged 12 to 18, risk-taking behaviors peak due to the underdeveloped , which is responsible for such as impulse control and long-term planning. This immaturity contrasts with the relatively earlier-maturing socioemotional brain systems, leading to heightened sensitivity to rewards and novelty. Peer influence plays a particularly strong role during this period, often amplifying risky choices in social contexts. The dual-systems model, proposed by Laurence Steinberg, explains this dynamic as an imbalance between a reactive reward-driven system (centered in the limbic areas) and a slower-developing control system (in the ), resulting in impulsive decisions that prioritize immediate gratification over potential consequences. In contrast, adults exhibit a more balanced integration of intuitive and analytical decision-making, drawing on life experiences to enhance judgment. This balance allows for effective , where familiar cues trigger rapid, yet informed, responses without overwhelming . The (RPD) model by Gary Klein illustrates how experienced adults simulate actions mentally based on past patterns, enabling efficient choices in complex situations. Unlike the heightened seen in children, which evolves into adolescent risk-seeking, adult processes emphasize informed by accumulated . Key differences between adolescents and adults lie in their orientations toward novelty versus long-term utility. Adolescents often favor novel, high-reward options, even at greater risk, while adults prioritize outcomes with sustained benefits, as evidenced in studies of financial decision-making where young adults show reduced compared to teens. For instance, research on risky choices under reveals that adolescents select riskier gambles more frequently than adults, who opt for safer, value-maximizing alternatives in economic tasks. Similar patterns appear in civic domains, such as simulated voting scenarios, where adults weigh broader implications more than the immediate social appeal that sways adolescents. Cultural variations further shape these processes; in individualistic societies like the , adolescents gain earlier in personal decisions, fostering independent , whereas in collectivist cultures such as those in , family interdependence delays full , emphasizing group-oriented choices into early adulthood.

Biases and Limitations

Cognitive and Personal Biases

Cognitive and personal biases represent systematic patterns of deviation from norm or in individual judgment, often leading to predictable errors in decision-making processes. These biases arise from cognitive shortcuts, or heuristics, that simplify complex processing but can distort perceptions and choices. , one of the most pervasive, involves the tendency to seek, interpret, and recall information in a way that confirms preexisting beliefs while ignoring contradictory evidence. In a seminal experiment, participants were given a rule to discover through testing but disproportionately selected confirming instances, demonstrating how this bias hinders falsification and objective evaluation. Hindsight bias, often termed the "knew-it-all-along" effect, occurs when individuals overestimate the predictability of an outcome after it has occurred, retrospectively viewing events as more foreseeable than they were . This impairs learning from past decisions by creating an illusion of foresight, as shown in studies where participants adjusted probability estimates upward upon learning outcomes, such as historical events or medical diagnoses. manifests as an exaggerated preference for maintaining the current state of affairs, even when alternatives might yield better results, due to perceived losses from change outweighing potential gains. Experimental evidence reveals that decision-makers select the default option far more often than when it is neutrally presented, illustrating in choices like investment allocations or policy selections. Personal factors amplify these cognitive distortions, with overconfidence bias leading individuals to overestimate their knowledge, skills, or predictive accuracy. For instance, of drivers rate themselves as safer and more skilled than , fostering undue risk-taking in domains requiring . Similarly, the causes people to value items they own more highly than equivalent items they do not, driven by where selling feels like a loss rather than forgoing a gain. experiments with mugs and tokens demonstrated this gap, as owners demanded higher selling prices than non-owners were willing to pay, persisting even in repeated market interactions. These biases collectively contribute to suboptimal decisions across contexts, particularly in investing where behavioral finance highlights their economic toll. Overconfidence prompts excessive trading, as seen in brokerage data where frequent traders underperform benchmarks by up to 1.4% annually in risk-adjusted returns, largely due to men exhibiting 45% higher turnover than women, correlating with gendered overconfidence patterns. Status quo bias exacerbates portfolio inertia, causing investors to cling to underperforming assets or default funds, while reinforces echo chambers in market analyses, and the inflates valuations of held stocks, delaying necessary sales. further compounds errors by discouraging post-mortem reviews, as investors retroactively justify poor choices. Overall, these distortions can lead to reductions in long-term wealth accumulation through missed opportunities and avoidable costs. To mitigate these biases, debiasing techniques emphasize deliberate analytical overrides of intuitive judgments. For , the "consider-the-opposite" strategy—explicitly generating and evaluating disconfirming evidence—has proven effective in reducing selective information seeking by up to 30% in experimental tasks. can be countered by prompting recall of foresight perspectives or attributing accessibility experiences to outcomes rather than inherent predictability, lowering retrospective adjustments in judgment studies. diminishes when defaults are reframed as active choices or partitioned into gains and losses, encouraging evaluation of alternatives without inertia's pull. Overconfidence responds to training, where individuals compare past predictions to outcomes to adjust self-assessments, while the weakens in competitive markets or through exercises that simulate non-ownership. Implementing checklists or seeking diverse viewpoints in decision protocols further embeds these countermeasures, fostering more rational outcomes across personal and professional settings.

Cognitive Limitations in Groups

In group decision-making, cognitive limitations often emerge from social dynamics that amplify flaws beyond those observed in individuals, such as pressures that suppress critical evaluation. One prominent phenomenon is , where cohesive groups prioritize consensus over rational analysis, leading to conformity that suppresses dissent and fosters illusions of unanimity. This process, first systematically analyzed by , manifests through symptoms like among members, of outsiders, and unquestioned belief in the group's moral superiority. represents another key limitation, in which discussions within a group cause members' opinions to shift toward more extreme positions than their initial individual views, often reinforcing risky or cautious tendencies. Seminal research by David G. Myers and Helmut Lamm demonstrated this effect across various domains, including ethical judgments and negotiations, attributing it to persuasive arguments encountered during interaction and social comparison processes. As a result, groups may endorse decisions that individual members would deem imprudent if considered alone. further hinders group decisions by diluting individual accountability, where members assume others will bear the burden of action or scrutiny, leading to inaction or suboptimal choices. John M. Darley and Bibb Latané's foundational experiments showed that the presence of multiple observers reduces the likelihood of intervention in emergencies, a dynamic that extends to decision contexts where shared responsibility obscures personal ownership. Contributing factors include , in which individuals exert less cognitive effort on collective tasks, believing their contributions are less identifiable in a group setting. Bibb Latané, Kipling D. Williams, and Stephen Harkins identified this through studies on group performance, linking it to reduced motivation when outputs are pooled. Hierarchical influences exacerbate these issues via , where lower-status members defer excessively to leaders, stifling diverse input and critical challenge. illustrated how perceived authority can compel compliance with flawed directives, even in group-like structures. A historical example of these limitations is the 1961 , where U.S. President 's advisory group succumbed to , ignoring dissenting intelligence on Cuban defenses due to pressures and overconfidence in the plan's success. Janis analyzed this fiasco as a case where hierarchical to the president and illusion of invulnerability led to a catastrophic policy error. To mitigate these cognitive limitations, strategies such as assigning roles—where a designated member challenges assumptions—can encourage dissent and uncover flaws. Additionally, fostering diverse group composition, including outsiders with varied expertise, helps counteract uniformity and promotes broader perspectives, as recommended by Janis in his revised framework.

Cognitive Styles

Optimizing vs. Satisficing

In decision-making, optimizing represents a rational approach aimed at identifying and selecting the option that yields the maximum possible or benefit, typically involving a comprehensive of all available alternatives, their probabilities, and outcomes. This style assumes access to and unlimited cognitive resources, leading to exhaustive search processes such as evaluating every feature and price in a major purchase like a or . In contrast, , a term coined by in , describes a where decision-makers choose the first alternative that meets a predefined acceptable threshold or aspiration level, rather than pursuing the absolute best option. This approach acknowledges —the limitations of human information processing and time—allowing individuals to halt search once a "good enough" solution is found, thereby conserving cognitive effort. The trade-offs between optimizing and satisficing highlight key practical considerations: while optimizing theoretically delivers superior outcomes, it is often time-consuming and resource-intensive, making it infeasible in complex or uncertain environments where full information is unavailable. , however, enables faster decisions at the potential cost of forgoing marginally better alternatives, proving more adaptive when speed and efficiency are prioritized over perfection. In applications such as consumer behavior, predominates in everyday choices.

Intuitive vs. Rational Styles

Decision-making styles can be broadly categorized into intuitive and rational approaches, reflecting distinct cognitive processes that individuals employ when faced with choices. Intuitive decision-making aligns with thinking, as described by psychologist , which operates quickly and automatically through unconscious and heuristics derived from experience. This style relies on gut feelings and rapid associations rather than exhaustive analysis, making it particularly effective in familiar domains where expertise allows for swift, accurate judgments. For instance, in chess, grandmasters often use to evaluate positions holistically, recognizing patterns from thousands of prior games to select optimal moves without deliberate . In contrast, rational decision-making corresponds to System 2 thinking, involving slower, effortful deliberation, , and systematic evaluation of alternatives. This approach is well-suited to situations or high-stakes choices where is high and outcomes require careful weighing of probabilities and , such as strategic investments or diagnoses under . Rational styles emphasize gathering complete information, assessing options step-by-step, and minimizing biases through structured processes. Individual preferences for these styles are influenced by personality traits and can be assessed through validated instruments such as the General Decision-Making Style (GDMS) questionnaire, which measures these tendencies across five dimensions, including rational (analytical) and intuitive (instinct-based) scales, revealing that most people exhibit a dominant style shaped by context and disposition. The Myers-Briggs Type Indicator (MBTI), a popular but controversial tool due to debates over its scientific validity, suggests that intuition-oriented types (N) favor abstract pattern-based judgments, while sensing types (S) prioritize concrete, factual data, affecting how decisions are approached in professional settings. Within these styles, sub-variations emerge, such as combinatorial and positional approaches, often analogous to chess strategies but applicable to broader decisions. Combinatorial styles involve holistic integration to achieve an envisioned end-state, akin to intuitive leaps that connect disparate elements creatively. Positional styles, conversely, proceed incrementally, evaluating each step methodically to build toward a , mirroring rational . Effective decision-makers often balance both styles in a hybrid manner, leveraging for initial insights and for validation, which enhances accuracy and adaptability in complex environments. This integration allows rational processes to refine intuitive hunches, as seen in fields where pure reliance on one style may overlook opportunities or risks.

References

Add your contribution
Related Hubs
User Avatar
No comments yet.