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Glossary
Concepts and Definitions
Resources
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Definitions
Volley
Volley is coordinated interaction, and derives from the term volée which means flight. Often the elements of timing and precision are implied. Whether the term is used in the context of sports or war, a volley often represents the pinnacle of performance.
Decision Science
Decision Science is the collection of quantitative techniques used to inform decision-making at the individual and population levels. While most related fields focus on producing new knowledge, Decision Science is uniquely concerned with making optimal choices based on available information. (source: Harvard Center for Health Decision Science)
The interdisciplinary field of Decision Science seeks to understand and improve the judgment and decision-making of individuals, groups, and organizations. Carnegie Mellon is one of the leading centers for the study of Decision Science, and offers one of the only undergraduate and graduate programs that integrates analytical and behavioral approaches to decision making.
As part of the broader field of Decision Theory (or, the Theory of Choice), the study of an Agent's choices. Decision Theory can be broken into two branches: Normative Decision Theory, which analyzes the outcomes of decisions or determines the optimal decisions given constraints and assumptions, and Descriptive Decision Theory, which analyzes how agents actually make the decisions they do.
Decision Theory is closely related to the field of Game Theory and is an interdisciplinary topic, studied by economists, statisticians, data scientists, psychologists, biologists, political and other social scientists, philosophers and computer scientists.
Empirical applications of this rich theory are usually done with the help of statistical and econometric methods. (source: Wikipedia
Decision Sciences is also the name of a peer-reviewed academic journal covering research about decision making within the boundaries of an organization, as well as decisions involving inter-firm coordination. Decision Sciences is published by Wiley-Blackwell on behalf of the Decision Sciences Institute.
Analytical Hierarchy Process
Analytical Hierarchy Process (AHP), or the Analytical Hierarchy Process, is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology. It was developed by Thomas L. Saaty in the 1970s; Saaty partnered with Ernest Forman to develop software in 1983, and AHP has been extensively studied and refined since then. It represents an accurate approach to quantifying the weights of decision criteria. Individual experts’ experiences are utilized to estimate the relative magnitudes of factors through pairwise comparisons. Each of the respondents compares the relative importance each pair of items using a specially designed questionnaire. (adapted from: Wikipedia)
Multi-Criteria Decision-Making (MCDM)
Multi-Criteria Decision-Making (MCDM), or Multi-Criteria Decision Analysis (MCDA) is a sub-discipline of Operations Research that explicitly evaluates multiple conflicting criteria in decision making (both in daily life and in settings such as business, government and medicine). Conflicting criteria are typical in evaluating options: cost or price is usually one of the main criteria, and some measure of quality is typically another criterion, easily in conflict with the cost. In purchasing a car, cost, comfort, safety, and fuel economy may be some of the main criteria we consider – it is unusual that the cheapest car is the most comfortable and the safest one. In portfolio management, managers are interested in getting high returns while simultaneously reducing risks; however, the stocks that have the potential of bringing high returns typically carry high risk of losing money. In a service industry, customer satisfaction and the cost of providing service are fundamental conflicting criteria.
In their daily lives, people usually weigh multiple criteria implicitly and may be comfortable with the consequences of such decisions that are made based on only intuition. On the other hand, when stakes are high, it is important to properly structure the problem and explicitly evaluate multiple criteria. In making the decision of whether to build a nuclear power plant or not, and where to build it, there are not only very complex issues involving multiple criteria, but there are also multiple parties who are deeply affected by the consequences.
Structuring complex problems well and considering multiple criteria explicitly leads to more informed and better decisions. There have been important advances in this field since the start of the modern multiple-criteria decision-making discipline in the early 1960s. A variety of approaches and methods, many implemented by specialized decision-making software, have been developed for their application in an array of disciplines, ranging from politics and business to the environment and energy. (source: Wikipedia)
Multi-Criteria Decision Analysis
see: Multi-Criteria Decision-Making (MCDM)
Multi-Attribute Utility Theory
In decision theory, a multi-attribute utility function is used to represent the preferences of an agent over bundles of goods either under conditions of certainty about the results of any potential choice, or under conditions of uncertainty. (source: Wikipedia)
Analytic Network Process
Analytic Network Process (ANP) is a more general form of the Analytic Hierarchy Process (AHP) used in Multi-Criteria Decision Analysis.
AHP structures a decision problem into a hierarchy with a goal, decision criteria, and alternatives, while the ANP structures it as a network. Both then use a system of pairwise comparisons to measure the weights of the components of the structure, and finally to rank the alternatives in the decision. (source: Wikipedia)
see also: Analytic Network Process, Network Science
Behavioral Economics
Behavioral Economics studies the effects of psychological, cognitive, emotional, cultural and social factors on the decisions of individuals and institutions and how those decisions vary from those implied by classical economic theory.
Behavioral Economics is primarily concerned with the bounds of rationality of economic agents. Behavioral models typically integrate insights from psychology, neuroscience and microeconomic theory. The study of behavioral economics includes how market decisions are made and the mechanisms that drive public choice. (adapted from: Wikipedia)
Behavioral Science
Behavioral Science explores the cognitive processes within organisms and the behavioral interactions between organisms in the natural world. It involves the systematic analysis and investigation of human and animal behavior through naturalistic observation, controlled scientific experimentation and mathematical modeling. It attempts to accomplish legitimate, objective conclusions through rigorous formulations and observation. Examples of behavioral sciences include psychology, psychobiology, anthropology, and cognitive science. Generally, behavioral science primarily has shown how human action often seeks to generalize about human behavior as it relates to society and its impact on society as a whole. (source: Wikipedia)
Graph Theory
In mathematics, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations (pairings) between objects. A graph in this context is made up of vertices (also called nodes or points) which are connected by edges (also called links or lines). A distinction is made between undirected graphs, where edges link two vertices symmetrically, and directed graphs, where edges link two vertices asymmetrically. Graphs are one of the principal objects of study in discrete mathematics. (adapted from: Wikipedia)
see also: Weightings, Multiplex, Random Decision Forests
Network Effects
In economics, a Network Effect (also called network externality, or demand-side economies of scale) is the phenomenon by which the value or utility a user derives from a good or service depends on the number of users of compatible products. Network Effects are typically positive, resulting in a given user deriving more value from a product as other users join the same network. The adoption of a product by an additional user can be broken into two effects: an increase in the value to all other users ( "total effect") and also the enhancement of other non-users motivation for using the product ("marginal effect").
Network Effects can be direct or indirect. Direct Network Effects arise when a given user's utility increases with the number of other users of the same product or technology, meaning that adoption of a product by different users is complementary. This effect is separate from effects related to price, such as a benefit to existing users resulting from price decreases as more users join. Direct network effects can be seen with social networking services, telecommunications devices like the telephone, and closed messaging services. Indirect (cross-group, or "knock on") Network Effects arise when there are "at least two different customer groups that are interdependent, and the utility of at least one group grows as the other group(s) grow". For example, hardware may become more valuable to consumers with the growth of compatible software.
Network Effects are commonly mistaken for economies of scale, which describe decreasing average production costs in relation to the total volume of units produced. Economies of scale are a common phenomenon in traditional industries such as manufacturing, whereas network effects are most prevalent in new economy industries, particularly information and communication technologies. Network effects are the demand side counterpart of economies of scale, as they function by increasing a customer's willingness to pay due rather than decreasing the supplier's average cost.
Upon reaching critical mass, a bandwagon effect can result. As the network continues to become more valuable with each new adopter, more people are incentivised to adopt, resulting in a positive feedback loop. Multiple equilibria and market tipping are two key potential outcomes in markets that exhibit network effects. Consumer expectations are key in determining which outcomes will result. (source: Wikipedia)
In regards to Graph Theory, Network Effects state that as the number of nodes in a social graph is increased, the value of the network grows exponentially.
see also: Graph Theory, Network Science
Network Science
Network Science is an academic field of study regarding complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes (or vertices) and the connections between the elements or actors as links (or edges). The field draws on theories and methods including graph theory from mathematics, statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. The United States National Research Council defines Network Science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena." (source: Wikipedia)
Some interesting tidbits to keep in mind about Network Science:
Random Decision Forests
Random Decision Forests (or, random forests) are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean/average prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set. Random forests generally outperform decision trees, but their accuracy is lower than gradient boosted trees. However, data characteristics can affect their performance. (source: Wikipedia)
Red Team / Blue Team
"Red Teaming" (also known as red cell, adversary simulation, or Cyber Red Team), involves simulating real-world cyber attackers' tactics, techniques, and procedures (TTPs) to assess an organization's security posture). It is a technique that derives from the military to test the robustness of a strategy; the Red Team "attacks" by looking for and exploiting holes and weaknesses, while the Blue Team "defends" by assuming the strategy is sound; while the use of a Red Team / Blue Team approach is commonly associated with security and defense, the same can be effective in common decision-making scenarios
SDP organization
Society of Decision Professionals (SDP) hosts the annual DAAG conference
DAAG conference
Decision Analysis Affinity Group (DAAG), annual industry conference; affiliated with the Society of Decision Professionals (SDP)
INFORMS conference
INFORMS is the leading international association for professionals in operations research and analytics; hosts an annual Business Analytics conference
ISAHP conference
International Symposium on the Analytic Hierarchy Process (ISAHP), annual industry conference
Data Science
By the way, wondering what the difference between Decision Science and Data Science is? There's a great article on that topic.
Decision Analysis (DA)
Decision analysis (DA) is a systematic, quantitative, and visual approach to addressing and evaluating the important choices that businesses sometimes face. Ronald A. Howard, a professor of Management Science and Engineering at Stanford University, is credited with originating the term in 1964.
The idea is used by large and small corporations alike when making various types of decisions, including management, operations, marketing, capital investments, or strategic choices. Investopedia
Decision Quality (DQ)
Decision Quality (DQ) is the quality of a decision at the moment the decision is made, regardless of its outcome. Decision quality concepts permit the assurance of both effectiveness and efficiency in analyzing decision problems.[1] In that sense, decision quality can be seen as an extension to decision analysis. Decision quality also describes the process that leads to a high-quality decision. Properly implemented, the DQ process enables capturing maximum value in uncertain and complex scenarios. Wikipedia
Decision Analysis (DA)
Decision Analysis (DA) is the discipline comprising the philosophy, methodology, and professional practice necessary to address important decisions in a formal manner. Decision analysis includes many procedures, methods, and tools for identifying, clearly representing, and formally assessing important aspects of a decision; for prescribing a recommended course of action by applying the maximum expected-utility axiom to a well-formed representation of the decision; and for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker, and other corporate and non-corporate stakeholders. Wikipedia
Decision Theory
Decision Theory (or the theory of choice; not to be confused with choice theory) is a branch of applied probability theory concerned with the theory of making decisions based on assigning probabilities to various factors and assigning numerical consequences to the outcome.
There are three branches of decision theory:
Empirical applications of this theory are usually done with the help of statistical and econometric methods. Wikipedia
Terms
Barrier & Biases
Functional Fixedness Functional Fixedness refers to the cognitive bias that limits an individual in their problem-solving using objects. What this means is that people will associate a specific function with an object limiting the potential creativity to solve a problem. In the early 1900s psychologist Karl Duncker demonstrated this concept with an experiment. In this experiment, Duncker provided participants with a candle, a box of thumbtacks, and matches. He asked the participants to attach the candle to a wall over a table and for the candle not to drip down. Most of the participants tried to directly tack the candle to the wall; the out-of-the-box thinkers tried to melt some of the candle wax and stick it to the wall. None of the participants thought to tack the thumbtack box to the wall to serve as a candle holder. This was because they assumed that the box only served as a box to provide them with thumbtacks and subconsciously dismissed its functionality.
Design Fixation Design Fixation is essentially when you get really set on a certain way of approaching an idea and don't look at other ways to approach the problem. Other words for this that may sound familiar are getting set in your ways, tunnel vision, using old ideas, and being blind to the bigger picture. This mostly occurs when someone refuses to look at the big picture of an idea or they refuse to find alternative solutions to make the idea happen. A simple yet effective example would be a child who wants to get past a wall. The wall is only ten feet wide and it is easy to go around. The child decides to walk straight into the wall to get past the wall but he is pushed to the ground from this action. He gets back up and tries again but he never thinks to go around the wall. This is essentially what design fixation is. Now an example that can apply to our business world. Blockbuster was a massive video rental retailer in the late 20th century that had 1000s of stores across the country. In the 2000s, Netflix came along and decided to change the business by mailing rental movies to your door for cheap, and eventually, they would create the Netflix streaming platform. Blockbuster at first did not change its ways and stayed the course with its business model and was stubborn to change with its competitor. But Netflix took off and Blockbuster realized too late how their fixation on their business model had prevented them from being able to compete with Netflix. Blockbuster would soon go out of business in the early 2010s and Netflix became the dominant business of the two. See https://www.fastcompany.com/3044535/what-is-design-fixation-and-how-can-you-stop-it
Goal Fixedness Goal fixedness refers to how the way that a goal is phrased can narrow our thinking. It is a cognitive bias that limits a person's ability to see other alternatives or solutions. People become fixated on the purpose and overlook potential other functions. We have to frame the goal in more general terms to allow for more alternatives. (It is similar to functional fixedness) “For example, if you talk about ‘adhering’ to a product, people may assume that the only way to attach it is with glue. If you talk about ‘fastening’ a product, however, options such as nails, brackets, glues, and screws may come to mind”.
System 1 vs. System 2 thinking System 1 and System 2 thinking are concepts pioneered by psychologist Daniel Kahneman in his book Thinking, Fast and Slow. These two systems represent distinct modes of thinking that people use to process information and make decisions. System 1 Thinking: is fast, automatic, intuitive, and largely unconscious. It operates quickly and effortlessly, relying on heuristics and patterns to make quick judgments and decisions. It is responsible for our immediate reactions and responses to situations without requiring much mental effort. System 1 thinking is prone to biases and can lead to errors, but it is vital for quick decision-making and survival instincts.
Examples of System 1 Thinking:
<li>Recognizing familiar faces <li>Reacting to a sudden loud noise <li>Reading simple sentences or wordsPreventative Measures for System 1 Thinking Biases:
<li>Create awareness of biases <li>Slow down and deliberate <li>Seek diverse perspectivesSystem 2 Thinking: is slow, deliberate, analytical, and conscious. It involves lots of effort in processing information, logical reasoning, and critical thinking. It requires mental energy and attention, as it involves considering multiple factors, evaluating evidence, and making deliberate choices. It helps in overcoming biases and making more rational decisions.
Examples of System 2 Thinking:
<li>Solving complex math problems <li>Evaluating arguments in a debate <li>Learning a new skill that requires lots of focusPreventative Measures for System 2 Thinking Biases:
<li>Minimize your distractions <li>Break down complex problems into smaller parts <li>Take breaks and rechargePlanning Fallacy The planning fallacy is basically overoptimism towards one's estimates of how long an action takes time. Victims of the planning fallacy struggle to understand how much time will be needed to complete a future task and underestimate the time needed. When looking at planning for an event, people often consider the most optimistic outcomes and ignore the ramifications of any potential setbacks or unforeseen circumstances. Getting ready in five minutes might actually happen one day, but the chances are more likely that most days it will take a few more minutes than that. The planning fallacy says that people will believe that it truly takes those five minutes, which will in turn make them late as underestimated times add up fast. This can be seen in business through deadline setting, as companies often set deadlines that are over-ambitious and set teams up for failure from the start. See also Escalation of Commitment & Overconfidence Bias
Escalation of Commitment is a human behavior pattern in which an individual or group facing increasingly negative outcomes from a decision, action, or investment nevertheless continue the behavior instead of altering course. The actor maintains behaviors that are irrational, but align with previous decisions and actions.
Overconfidence Bias is the tendency for a person to overestimate their abilities. It may lead a person to think they're a better-than-average driver or an expert investor. Overconfidence bias may lead clients to make risky investments.
Base Rate Neglect, an important bias in estimating probability of uncertain events, describes humans' tendency to underweight base rate (prior) relative to individuating information (likelihood). However, the neural mechanisms that give rise to this bias remain elusive.
Cognitive Dissonance is the mental stress or discomfort that an individual experiences when they hold two or more contradictory beliefs, values, or perspectives at the same time. This can happen as someone’s behavior changes with their beliefs, or when new information starts to challenge their existing values. Examples include Cultural Beliefs: Someone who was raised in a culture with traditional beliefs might experience cognitive dissonance if they decide to move to a country with a different culture, i.e. Arranged vs Non-arranged marriages; Smoking: Someone who is aware of the dangers of smoking yet continues to smoke could experience cognitive dissonance. They might feel the discomfort of knowing that smoking is harmful, but they choose to do it anyway. A common way to combat cognitive dissonance is to change your beliefs and become more open-minded. This will allow you to better soak in new information so it doesn’t completely tarnish your values or perspectives.
Confirmation Bias is the Human tendency to search for, favor, and use information that confirms one’s pre-existing views on a certain subject. Examples include someone who might hold a stereotype towards a certain group of people. Any action that aligns with the stereotype will overlook other behaviors; a manager who has high expectations for an employee might focus more on their accomplishments rather than their bad qualities and mistakes. A common way to combat confirmation bias is to have more mental awareness. It is important to understand that your brain is naturally inclined to seek out information that confirms existing beliefs. See https://online.hbs.edu/blog/post/confirmation-bias-how-it-affects-your-organization-and-how-to-overcome-it
Fundamental Attribution Error The fundamental attribution error refers to an individual's tendency to attribute another's actions to their character or personality while attributing their behavior to external situational factors outside of their control. In other words, you tend to cut yourself a break while holding others 100 percent accountable for their actions. The fundamental attribution error is essentially when one perceives others' actions as being because of their personality, while their own behavior is attributed to external factors. For example, a "lazy employee." If a co-worker was late to an important meeting, you might be inclined to form a judgment of her character based on this one action alone. It's possible, that your co-worker's behavior is due to several external factors such as a family emergency or traffic jam, which have nothing to do with the quality of her character. Another example is when parents could blame other children's bad behavior on bad parenting. but then blame their own child's behavior on other things, such as lack of sleep. In action, forming impressions of a person's character based on limited information can have long-lasting effects. To prevent, when you become resentful of someone for a bad "quality" they demonstrate, try to make a list of five positive qualities the person also exhibits. This will help balance out your perspective and can help you view your co-worker as a whole person instead of through the lens of a single negative quality.
Parable of the Boiling Frog The parable of the boiling frog as it relates to business serves as a caution to not adapting to change and being complacent. It says that a frog placed in boiling water will simply jump out, however, a frog placed in cold water that is gradually warmed to boiling will not realize the danger until it is too late. This reinforces that in business it is vital to observe signs of change and adapt. It is easy to see water when it is boiling (major disruption/competition) but it is hard to predict when cold water will boil (prediction disruption). A small temperature increase (small change) can suggest the water will be boiling later (major disruption). A company that is complacent and firmly resting on its laurels is in danger of having its once-comfortable pot of water turn boiling too fast to escape. This parable reinforces the need for risk management, innovation & disruption, adaptation, and strong company identity and leadership.
Recency & Primacy Bias Recency bias is "the tendency to overemphasize the importance of recent experiences or the latest information we possess when estimating future events." If we have made a recent mistake within a computation error, we are likely going to put an overemphasis on fixing and not allowing for that error in the next project. It gives information to mislead a person to favor what has happened more recently to indicate what will happen next. Primacy Bias is the idea that we are going to remember the information that we first saw best. If you are playing a game of clues for example, if you find out which room was correct first, the other information in the middle of the game is harder to recall. Same if you are looking at a grocery list, you are likely to remember what you first read rather than what is down the list.
Self-serving bias Self-serving bias refers to the tendency of individuals to attribute their successes to internal factors or personal qualities while blaming external factors or circumstances for their failures. It is a cognitive bias that allows people to protect their self-esteem and maintain a positive self-image. An example of self-serving bias is when someone contributes their successes such as intelligence, preparation, or skills. But when failing or facing failure or negative experience contributes to bad luck, unfair circumstances, or the people's actions for their failures. Self-serving bias can lead to distorted thinking and poor decision-making. It can prevent individuals from taking responsibility for their failures, hinder self-improvement, and damage relationships by fostering a lack of accountability. In some cases, it can even lead to conflicts and disputes when people refuse to acknowledge their role in negative outcomes. A way to help overcome self-serving bias is by taking an outside view of the situation and your behavior, this requires you to really think about your actions and be accountable for the way you behave. You can also use digitization by tracking your KPI's or performance metrics to understand that what you're doing is either working or not working. By viewing things as numbers and statistics it can help you overcome the mental barrier of "that was someone else's fault and not my own" as you're using more "System 2" thinking instead of "System 1" thinking that would allow you to blame others based on emotion.
Anchoring & the Availability Heuristic The Anchoring bias is seen when one piece of information is used to justify one’s opinions or beliefs. This can be seen when comparing two similar items with different prices. Seeing the cheaper item first will “anchor” the idea that the other item is too expensive and vice-versa.
The Availability bias is using past events/information to judge how likely a similar outcome is to occur. For example, when people asked what is more lethal, sharks or bike accidents, most individuals would say shark attacks since their coverage is more available. However, shark attacks are very rare, but the reporting coverage makes the information more available.
Survivorship Bias Survivorship bias is basically when we only focus on people, projects, or things that were successful during a selection process while ignoring/overlooking the ones that failed. Two Examples: Start-up businesses that failed while bigger companies like Apple and Microsoft get an immense amount of recognition. College dropouts are never mentioned while college graduates are highly recognized at a university. To prevent this bias, be strategic and selective when picking your data sources to ensure accuracy in your observations. See https://thedecisionlab.com/biases/survivorship-bias
Blindspot Bias is a cognitive bias that fails to recognize the impact on one's own judgment, while at the same time noticing the impact of bias of judgments made by others. Also could be described as the tendency to see yourself as less biased than others. Individuals with Blindspot Bias may display a bias and not realize what they are doing is wrong, and then when someone else displays the exact same bias, they immediately recognize it as a fault on the other person. But what they are doing is not wrong because. they do not see faults within themselves. One of the problems faced with Blindside Bias is that individuals are unable to see their faults which take away from their ability to see their faults and in return grow from them.
Outcome Bias refers to the tendency to judge the quality of a decision or action based on its outcome rather than the decision-making process itself such as considering the current situation/information. For example, a football coach calls a risky play. If it is successful, it could result in a touchdown and the coach would be praised. However, if it fails, the coach would receive hate.
This evaluation based solely on the outcome represents outcome bias. It disregards the decision-making process, the available information, and the factors that led the coach to make the decision. To prevent, remove the outcome to an unbiased playing field. To do this, you can assess the quality of a decision based on the information and factors available at the time the decision was made, rather than relying on hindsight. Evaluate the decision based on the reasoning, analysis, and logic used in the decision-making process.
Placebo Bias refers to the influence of expectations and beliefs on the effectiveness of a treatment or intervention. It occurs when a person's belief in treatment leads to perceived positive effects, even if the treatment itself has no intrinsic therapeutic value. For example, researchers are conducting a study to see whether or not a new medicine improves cognitive function. One group receives the medicine and another group receives a placebo (fake medicine) that has no effects. If the placebo group shows improvements in cognitive performance, it may be due to their belief and expectation that the placebo will enhance their cognitive abilities, rather than any actual effects of the supplement. To prevent this, perform clinical trials that include a control group that receives a placebo. By comparing the effects of the active treatment to those of the control group, researchers can determine if the new drug is effective.
Abilene Paradox is a social phenomenon that describes a situation where a group of individuals collectively decide on a course of action that is counter to the preferences of any of the individuals in the group. This often results from a failure to communicate honestly or effectively, typically because individual members mistakenly believe their own preferences are out of step with the group. Consequently, everyone goes along with the decision, even though it may not be in anyone's best interest. For example, a group of friends decides where to eat for dinner. None of them want to eat at a particular restaurant, but they each incorrectly assume that the others want to go there. To avoid disappointing their friends, they all agree to go to that restaurant, even though no one in the group actually wants to. To prevent this each member's opinion should be valued and taken into consideration. This can be achieved by fostering a culture that respects diversity of thought and encourages the expression of individual preferences.
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