Decision Support System

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The concept of Decision Support Systems emerged in the late 1960s as a response to the increasing complexity of decision-making processes in organizations. Initially developed for business applications, DSS have since evolved to address decision-making challenges in fields such as healthcare, finance, logistics, and public administration. Decision Support Systems integrate data, models, and analytical tools to facilitate informed decision making at different levels of an organization.

Key Components:

Several key components underpin Decision Support Systems:

  • Data Management: Decision Support Systems rely on data management systems to collect, store, and organize relevant data from internal and external sources. Data can include structured data from databases, unstructured data from text documents or social media, and semi-structured data from sensors or IoT devices.
  • Modeling and Analysis: Decision Support Systems employ modeling techniques and analytical tools to analyze data, identify patterns, and generate insights relevant to decision making. These may include statistical analysis, data mining, machine learning, optimization, simulation, and scenario analysis.
  • User Interface: The user interface of Decision Support Systems provides interactive visualization tools and intuitive interfaces for users to explore data, interact with models, and generate reports or visualizations. User-friendly interfaces enhance user engagement and facilitate decision-making processes.

Functionality:

Decision Support Systems offer a range of functionalities to support decision making:

  • Data Integration: DSS integrate data from multiple sources and formats, enabling users to access comprehensive and up-to-date information relevant to their decision-making needs.
  • Analysis and Visualization: DSS facilitate data analysis and visualization, allowing users to explore trends, patterns, and relationships within the data through interactive charts, graphs, maps, and dashboards.
  • What-If Analysis: DSS enable What-If analysis, where users can simulate different scenarios, assumptions, or decision alternatives to assess their potential outcomes and implications.
  • Collaboration and Communication: DSS support collaboration and communication among decision-makers by providing shared workspaces, discussion forums, and real-time collaboration tools.

Implementation:

The implementation of Decision Support Systems involves several steps:

  • Needs Assessment: Organizations identify decision-making challenges, information needs, and user requirements to determine the scope and objectives of the DSS implementation.
  • Data Collection and Integration: Data from internal and external sources are collected, cleaned, and integrated into a unified data repository compatible with the DSS.
  • Model Development: Analytical models and algorithms are developed or selected based on the specific decision-making context and objectives of the DSS.
  • User Training and Adoption: Users receive training on how to use the DSS effectively, including data entry, analysis, interpretation, and decision-making processes.

Impact:

Decision Support Systems have a significant impact on organizations and decision-making processes:

  • Improved Decision Quality: DSS provide decision-makers with timely, relevant, and accurate information, leading to improved decision quality and outcomes.
  • Enhanced Efficiency and Productivity: DSS streamline decision-making processes, reduce manual effort, and enable faster response times, leading to increased efficiency and productivity.
  • Risk Mitigation: DSS help identify risks, uncertainties, and potential consequences associated with decision alternatives, allowing organizations to make more informed and risk-aware decisions.
  • Strategic Planning and Competitive Advantage: DSS support strategic planning and analysis, enabling organizations to anticipate market trends, identify opportunities, and gain a competitive advantage.

Contemporary Relevance:

In today’s data-driven and fast-paced business environment, Decision Support Systems remain highly relevant:

  • Big Data and Analytics: The proliferation of big data and advanced analytics technologies has expanded the capabilities and applications of Decision Support Systems, enabling organizations to derive actionable insights from large and diverse datasets.
  • Artificial Intelligence and Machine Learning: Advances in artificial intelligence and machine learning have enabled the development of intelligent Decision Support Systems capable of adaptive learning, predictive analytics, and prescriptive recommendations.
  • Cloud Computing and Mobility: Cloud computing and mobile technologies have made Decision Support Systems more accessible, scalable, and flexible, allowing users to access and interact with DSS anytime, anywhere, using a variety of devices.

Conclusion:

Decision Support Systems play a vital role in facilitating informed decision making and improving organizational performance across various domains. By integrating data, models, and analytical tools, DSS empower decision-makers to navigate complex decision-making challenges, mitigate risks, and capitalize on opportunities in today’s dynamic and competitive business environment.

FrameworkDescriptionWhen to Apply
Artificial Intelligence (AI) Integration– Artificial Intelligence (AI) Integration: Decision support systems can leverage artificial intelligence (AI) technologies to enhance decision-making processes by analyzing large datasets, identifying patterns, and generating insights. Understanding AI integration helps organizations harness the power of machine learning algorithms, natural language processing, and predictive analytics to provide personalized recommendations and optimize decision outcomes. Interventions may involve implementing AI-powered chatbots, recommendation engines, and predictive models to augment human decision-making capabilities and improve the efficiency and accuracy of decision support systems.– Enhancing decision-making processes with AI-powered insights through machine learning algorithms or predictive analytics, in data-driven decision-making environments where large datasets are involved, in implementing chatbots or recommendation engines that provide personalized recommendations, in adopting approaches that optimize decision outcomes through AI integration principles.
Data Visualization Tools– Data Visualization Tools: Decision support systems can benefit from data visualization tools that transform complex data into visual representations, making it easier for decision-makers to interpret and analyze information. Understanding data visualization tools helps organizations present insights and trends in intuitive formats, facilitating data-driven decision-making. Interventions may involve using tools such as dashboards, heatmaps, and interactive charts to visualize key performance indicators, trends, and correlations, empowering decision-makers to explore data visually and gain actionable insights more effectively.– Facilitating data-driven decision-making with dashboards or interactive charts, in contexts where visualizing key performance indicators or trends is essential, in implementing heatmaps or scatter plots to identify correlations or patterns, in adopting approaches that empower decision-makers to explore data visually through data visualization tool principles.
Collaborative Decision Support– Collaborative Decision Support: Decision support systems can promote collaboration among stakeholders by providing shared platforms for data sharing, analysis, and decision-making. Understanding collaborative decision support helps organizations foster teamwork, transparency, and consensus-building in decision processes. Interventions may involve using collaborative tools such as groupware, workflow systems, and virtual meeting platforms to facilitate communication, coordination, and knowledge sharing among decision-makers, enhancing the effectiveness and inclusivity of decision support systems.– Fostering teamwork and transparency in decision processes through groupware or virtual meeting platforms, in collaborative decision-making environments where stakeholder involvement is crucial, in implementing workflow systems that streamline communication and coordination, in adopting approaches that promote inclusivity and consensus-building through collaborative decision support principles.
Predictive Analytics Models– Predictive Analytics Models: Decision support systems can leverage predictive analytics models to forecast future outcomes, identify potential risks, and optimize resource allocation. Understanding predictive analytics models helps organizations anticipate trends, assess scenarios, and make proactive decisions based on data-driven insights. Interventions may involve developing models such as regression analysis, time series forecasting, and machine learning algorithms to predict market trends, customer behavior, and business performance, enabling decision-makers to anticipate changes and adapt strategies accordingly.– Anticipating trends and assessing scenarios with regression analysis or machine learning algorithms, in dynamic environments where proactive decision-making is critical, in implementing time series forecasting techniques to predict future outcomes, in adopting approaches that optimize resource allocation through predictive analytics model principles.
Real-time Data Integration– Real-time Data Integration: Decision support systems can integrate real-time data streams to provide up-to-date information and insights for decision-makers. Understanding real-time data integration helps organizations respond quickly to changes, identify emerging trends, and make informed decisions in dynamic environments. Interventions may involve connecting decision support systems to data sources such as IoT devices, social media platforms, and sensors to capture real-time information, enabling decision-makers to access timely insights and adjust strategies in response to evolving conditions.– Responding quickly to changes with real-time data streams from IoT devices or social media platforms, in fast-paced industries or dynamic environments where agility is crucial, in implementing sensors or monitoring systems that capture real-time information, in adopting approaches that enable timely decision-making through real-time data integration principles.
Scenario Planning Techniques– Scenario Planning Techniques: Decision support systems can facilitate scenario planning exercises to explore alternative futures, assess risks, and develop contingency plans. Understanding scenario planning techniques helps organizations anticipate uncertainties, test assumptions, and prepare for potential outcomes. Interventions may involve using techniques such as scenario analysis, sensitivity testing, and war-gaming to simulate different scenarios and evaluate their potential impact on business operations, enabling decision-makers to make more informed and resilient decisions in uncertain environments.– Exploring alternative futures and assessing risks with scenario analysis or sensitivity testing, in strategic planning or risk management processes where uncertainty is high, in implementing war-gaming exercises to simulate potential outcomes, in adopting approaches that enhance resilience and preparedness through scenario planning techniques.
Decision Trees and Bayesian Networks– Decision Trees and Bayesian Networks: Decision support systems can employ decision trees and Bayesian networks to model complex decision problems, analyze dependencies, and evaluate options probabilistically. Understanding decision trees and Bayesian networks helps organizations structure decision processes, quantify uncertainties, and identify optimal courses of action. Interventions may involve building decision trees, Markov models, and probabilistic graphical models to represent decision problems, facilitating decision-making under uncertainty and complexity.– Structuring decision processes and quantifying uncertainties with decision trees or Bayesian networks, in decision-making contexts where complexity or ambiguity is a challenge, in implementing Markov models that analyze dependencies and transitions, in adopting approaches that identify optimal courses of action through decision tree and Bayesian network principles.
Optimization Algorithms– Optimization Algorithms: Decision support systems can utilize optimization algorithms to solve complex decision problems, allocate resources efficiently, and optimize performance. Understanding optimization algorithms helps organizations identify the best solutions to decision problems given constraints and objectives. Interventions may involve using algorithms such as linear programming, genetic algorithms, and simulated annealing to find optimal solutions, improving decision outcomes and resource utilization.– Allocating resources efficiently and optimizing performance with linear programming or genetic algorithms, in decision-making processes where resource constraints or conflicting objectives exist, in implementing simulated annealing techniques to find optimal solutions, in adopting approaches that improve decision outcomes through optimization algorithm principles.
Decision Support System Evaluation– Decision Support System Evaluation: Decision support systems require ongoing evaluation to assess their effectiveness, usability, and impact on decision-making processes. Understanding DSS evaluation helps organizations identify strengths, weaknesses, and areas for improvement in decision support systems. Interventions may involve conducting user surveys, usability tests, and performance evaluations to gather feedback and insights from decision-makers, ensuring that DSSs meet user needs and contribute to organizational goals effectively.– Assessing effectiveness and usability of decision support systems through user surveys or usability tests, in continuous improvement processes or system upgrades, in implementing performance evaluations that measure impact on decision-making, in adopting approaches that align DSSs with user needs and organizational goals through evaluation principles.
Knowledge Management Systems– Knowledge Management Systems: Decision support systems can integrate knowledge management systems to capture, organize, and share tacit and explicit knowledge within organizations. Understanding knowledge management systems helps organizations leverage internal expertise, best practices, and lessons learned to support decision-making processes. Interventions may involve implementing knowledge bases, expert systems, and collaborative platforms to facilitate knowledge sharing, collaboration, and learning among decision-makers, improving decision quality and organizational performance.– Leveraging internal expertise and best practices with knowledge bases or expert systems, in decision-making contexts where knowledge sharing is critical, in implementing collaborative platforms that facilitate learning and collaboration, in adopting approaches that enhance decision quality and organizational performance through knowledge management system principles.
Risk Management Frameworks– Risk Management Frameworks: Decision support systems can integrate risk management frameworks to identify, assess, and mitigate risks associated with decision outcomes. Understanding risk management frameworks helps organizations anticipate potential threats, implement controls, and monitor risk exposure throughout decision processes. Interventions may involve using frameworks such as COSO, ISO 31000, and PMI Risk Management to establish risk management processes, assess risk appetite, and develop risk mitigation strategies, enhancing decision resilience and ensuring business continuity.– Anticipating potential threats and implementing controls with COSO or ISO 31000 frameworks, in decision-making contexts where risk management is essential, in implementing PMI Risk Management techniques to develop mitigation strategies, in adopting approaches that enhance decision resilience and business continuity through risk management framework principles.

Connected Thinking Frameworks

Convergent vs. Divergent Thinking

convergent-vs-divergent-thinking
Convergent thinking occurs when the solution to a problem can be found by applying established rules and logical reasoning. Whereas divergent thinking is an unstructured problem-solving method where participants are encouraged to develop many innovative ideas or solutions to a given problem. Where convergent thinking might work for larger, mature organizations where divergent thinking is more suited for startups and innovative companies.

Critical Thinking

critical-thinking
Critical thinking involves analyzing observations, facts, evidence, and arguments to form a judgment about what someone reads, hears, says, or writes.

Biases

biases
The concept of cognitive biases was introduced and popularized by the work of Amos Tversky and Daniel Kahneman in 1972. Biases are seen as systematic errors and flaws that make humans deviate from the standards of rationality, thus making us inept at making good decisions under uncertainty.

Second-Order Thinking

second-order-thinking
Second-order thinking is a means of assessing the implications of our decisions by considering future consequences. Second-order thinking is a mental model that considers all future possibilities. It encourages individuals to think outside of the box so that they can prepare for every and eventuality. It also discourages the tendency for individuals to default to the most obvious choice.

Lateral Thinking

lateral-thinking
Lateral thinking is a business strategy that involves approaching a problem from a different direction. The strategy attempts to remove traditionally formulaic and routine approaches to problem-solving by advocating creative thinking, therefore finding unconventional ways to solve a known problem. This sort of non-linear approach to problem-solving, can at times, create a big impact.

Bounded Rationality

bounded-rationality
Bounded rationality is a concept attributed to Herbert Simon, an economist and political scientist interested in decision-making and how we make decisions in the real world. In fact, he believed that rather than optimizing (which was the mainstream view in the past decades) humans follow what he called satisficing.

Dunning-Kruger Effect

dunning-kruger-effect
The Dunning-Kruger effect describes a cognitive bias where people with low ability in a task overestimate their ability to perform that task well. Consumers or businesses that do not possess the requisite knowledge make bad decisions. What’s more, knowledge gaps prevent the person or business from seeing their mistakes.

Occam’s Razor

occams-razor
Occam’s Razor states that one should not increase (beyond reason) the number of entities required to explain anything. All things being equal, the simplest solution is often the best one. The principle is attributed to 14th-century English theologian William of Ockham.

Lindy Effect

lindy-effect
The Lindy Effect is a theory about the ageing of non-perishable things, like technology or ideas. Popularized by author Nicholas Nassim Taleb, the Lindy Effect states that non-perishable things like technology age – linearly – in reverse. Therefore, the older an idea or a technology, the same will be its life expectancy.

Antifragility

antifragility
Antifragility was first coined as a term by author, and options trader Nassim Nicholas Taleb. Antifragility is a characteristic of systems that thrive as a result of stressors, volatility, and randomness. Therefore, Antifragile is the opposite of fragile. Where a fragile thing breaks up to volatility; a robust thing resists volatility. An antifragile thing gets stronger from volatility (provided the level of stressors and randomness doesn’t pass a certain threshold).

Ergodicity

ergodicity
Ergodicity is one of the most important concepts in statistics. Ergodicity is a mathematical concept suggesting that a point of a moving system will eventually visit all parts of the space the system moves in. On the opposite side, non-ergodic means that a system doesn’t visit all the possible parts, as there are absorbing barriers

Systems Thinking

systems-thinking
Systems thinking is a holistic means of investigating the factors and interactions that could contribute to a potential outcome. It is about thinking non-linearly, and understanding the second-order consequences of actions and input into the system.

Vertical Thinking

vertical-thinking
Vertical thinking, on the other hand, is a problem-solving approach that favors a selective, analytical, structured, and sequential mindset. The focus of vertical thinking is to arrive at a reasoned, defined solution.

Metaphorical Thinking

metaphorical-thinking
Metaphorical thinking describes a mental process in which comparisons are made between qualities of objects usually considered to be separate classifications.  Metaphorical thinking is a mental process connecting two different universes of meaning and is the result of the mind looking for similarities.

Maslow’s Hammer

einstellung-effect
Maslow’s Hammer, otherwise known as the law of the instrument or the Einstellung effect, is a cognitive bias causing an over-reliance on a familiar tool. This can be expressed as the tendency to overuse a known tool (perhaps a hammer) to solve issues that might require a different tool. This problem is persistent in the business world where perhaps known tools or frameworks might be used in the wrong context (like business plans used as planning tools instead of only investors’ pitches).

Peter Principle

peter-principle
The Peter Principle was first described by Canadian sociologist Lawrence J. Peter in his 1969 book The Peter Principle. The Peter Principle states that people are continually promoted within an organization until they reach their level of incompetence.

Straw Man Fallacy

straw-man-fallacy
The straw man fallacy describes an argument that misrepresents an opponent’s stance to make rebuttal more convenient. The straw man fallacy is a type of informal logical fallacy, defined as a flaw in the structure of an argument that renders it invalid.

Google Effect

google-effect
The Google effect is a tendency for individuals to forget information that is readily available through search engines. During the Google effect – sometimes called digital amnesia – individuals have an excessive reliance on digital information as a form of memory recall.

Streisand Effect

streisand-effect
The Streisand Effect is a paradoxical phenomenon where the act of suppressing information to reduce visibility causes it to become more visible. In 2003, Streisand attempted to suppress aerial photographs of her Californian home by suing photographer Kenneth Adelman for an invasion of privacy. Adelman, who Streisand assumed was paparazzi, was instead taking photographs to document and study coastal erosion. In her quest for more privacy, Streisand’s efforts had the opposite effect.

Compromise Effect

compromise-effect
Single-attribute choices – such as choosing the apartment with the lowest rent – are relatively simple. However, most of the decisions consumers make are based on multiple attributes which complicate the decision-making process. The compromise effect states that a consumer is more likely to choose the middle option of a set of products over more extreme options.

Butterfly Effect

butterfly-effect
In business, the butterfly effect describes the phenomenon where the simplest actions yield the largest rewards. The butterfly effect was coined by meteorologist Edward Lorenz in 1960 and as a result, it is most often associated with weather in pop culture. Lorenz noted that the small action of a butterfly fluttering its wings had the potential to cause progressively larger actions resulting in a typhoon.

IKEA Effect

ikea-effect
The IKEA effect is a cognitive bias that describes consumers’ tendency to value something more if they have made it themselves. That is why brands often use the IKEA effect to have customizations for final products, as they help the consumer relate to it more and therefore appending to it more value.

Ringelmann Effect 

Ringelmann Effect
The Ringelmann effect describes the tendency for individuals within a group to become less productive as the group size increases.

The Overview Effect

overview-effect
The overview effect is a cognitive shift reported by some astronauts when they look back at the Earth from space. The shift occurs because of the impressive visual spectacle of the Earth and tends to be characterized by a state of awe and increased self-transcendence.

House Money Effect

house-money-effect
The house money effect was first described by researchers Richard Thaler and Eric Johnson in a 1990 study entitled Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice. The house money effect is a cognitive bias where investors take higher risks on reinvested capital than they would on an initial investment.

Heuristic

heuristic
As highlighted by German psychologist Gerd Gigerenzer in the paper “Heuristic Decision Making,” the term heuristic is of Greek origin, meaning “serving to find out or discover.” More precisely, a heuristic is a fast and accurate way to make decisions in the real world, which is driven by uncertainty.

Recognition Heuristic

recognition-heuristic
The recognition heuristic is a psychological model of judgment and decision making. It is part of a suite of simple and economical heuristics proposed by psychologists Daniel Goldstein and Gerd Gigerenzer. The recognition heuristic argues that inferences are made about an object based on whether it is recognized or not.

Representativeness Heuristic

representativeness-heuristic
The representativeness heuristic was first described by psychologists Daniel Kahneman and Amos Tversky. The representativeness heuristic judges the probability of an event according to the degree to which that event resembles a broader class. When queried, most will choose the first option because the description of John matches the stereotype we may hold for an archaeologist.

Take-The-Best Heuristic

take-the-best-heuristic
The take-the-best heuristic is a decision-making shortcut that helps an individual choose between several alternatives. The take-the-best (TTB) heuristic decides between two or more alternatives based on a single good attribute, otherwise known as a cue. In the process, less desirable attributes are ignored.

Bundling Bias

bundling-bias
The bundling bias is a cognitive bias in e-commerce where a consumer tends not to use all of the products bought as a group, or bundle. Bundling occurs when individual products or services are sold together as a bundle. Common examples are tickets and experiences. The bundling bias dictates that consumers are less likely to use each item in the bundle. This means that the value of the bundle and indeed the value of each item in the bundle is decreased.

Barnum Effect

barnum-effect
The Barnum Effect is a cognitive bias where individuals believe that generic information – which applies to most people – is specifically tailored for themselves.

Anchoring Effect

anchoring-effect
The anchoring effect describes the human tendency to rely on an initial piece of information (the “anchor”) to make subsequent judgments or decisions. Price anchoring, then, is the process of establishing a price point that customers can reference when making a buying decision.

Decoy Effect

decoy-effect
The decoy effect is a psychological phenomenon where inferior – or decoy – options influence consumer preferences. Businesses use the decoy effect to nudge potential customers toward the desired target product. The decoy effect is staged by placing a competitor product and a decoy product, which is primarily used to nudge the customer toward the target product.

Commitment Bias

commitment-bias
Commitment bias describes the tendency of an individual to remain committed to past behaviors – even if they result in undesirable outcomes. The bias is particularly pronounced when such behaviors are performed publicly. Commitment bias is also known as escalation of commitment.

First-Principles Thinking

first-principles-thinking
First-principles thinking – sometimes called reasoning from first principles – is used to reverse-engineer complex problems and encourage creativity. It involves breaking down problems into basic elements and reassembling them from the ground up. Elon Musk is among the strongest proponents of this way of thinking.

Ladder Of Inference

ladder-of-inference
The ladder of inference is a conscious or subconscious thinking process where an individual moves from a fact to a decision or action. The ladder of inference was created by academic Chris Argyris to illustrate how people form and then use mental models to make decisions.

Goodhart’s Law

goodharts-law
Goodhart’s Law is named after British monetary policy theorist and economist Charles Goodhart. Speaking at a conference in Sydney in 1975, Goodhart said that “any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” Goodhart’s Law states that when a measure becomes a target, it ceases to be a good measure.

Six Thinking Hats Model

six-thinking-hats-model
The Six Thinking Hats model was created by psychologist Edward de Bono in 1986, who noted that personality type was a key driver of how people approached problem-solving. For example, optimists view situations differently from pessimists. Analytical individuals may generate ideas that a more emotional person would not, and vice versa.

Mandela Effect

mandela-effect
The Mandela effect is a phenomenon where a large group of people remembers an event differently from how it occurred. The Mandela effect was first described in relation to Fiona Broome, who believed that former South African President Nelson Mandela died in prison during the 1980s. While Mandela was released from prison in 1990 and died 23 years later, Broome remembered news coverage of his death in prison and even a speech from his widow. Of course, neither event occurred in reality. But Broome was later to discover that she was not the only one with the same recollection of events.

Crowding-Out Effect

crowding-out-effect
The crowding-out effect occurs when public sector spending reduces spending in the private sector.

Bandwagon Effect

bandwagon-effect
The bandwagon effect tells us that the more a belief or idea has been adopted by more people within a group, the more the individual adoption of that idea might increase within the same group. This is the psychological effect that leads to herd mentality. What in marketing can be associated with social proof.

Moore’s Law

moores-law
Moore’s law states that the number of transistors on a microchip doubles approximately every two years. This observation was made by Intel co-founder Gordon Moore in 1965 and it become a guiding principle for the semiconductor industry and has had far-reaching implications for technology as a whole.

Disruptive Innovation

disruptive-innovation
Disruptive innovation as a term was first described by Clayton M. Christensen, an American academic and business consultant whom The Economist called “the most influential management thinker of his time.” Disruptive innovation describes the process by which a product or service takes hold at the bottom of a market and eventually displaces established competitors, products, firms, or alliances.

Value Migration

value-migration
Value migration was first described by author Adrian Slywotzky in his 1996 book Value Migration – How to Think Several Moves Ahead of the Competition. Value migration is the transferal of value-creating forces from outdated business models to something better able to satisfy consumer demands.

Bye-Now Effect

bye-now-effect
The bye-now effect describes the tendency for consumers to think of the word “buy” when they read the word “bye”. In a study that tracked diners at a name-your-own-price restaurant, each diner was asked to read one of two phrases before ordering their meal. The first phrase, “so long”, resulted in diners paying an average of $32 per meal. But when diners recited the phrase “bye bye” before ordering, the average price per meal rose to $45.

Groupthink

groupthink
Groupthink occurs when well-intentioned individuals make non-optimal or irrational decisions based on a belief that dissent is impossible or on a motivation to conform. Groupthink occurs when members of a group reach a consensus without critical reasoning or evaluation of the alternatives and their consequences.

Stereotyping

stereotyping
A stereotype is a fixed and over-generalized belief about a particular group or class of people. These beliefs are based on the false assumption that certain characteristics are common to every individual residing in that group. Many stereotypes have a long and sometimes controversial history and are a direct consequence of various political, social, or economic events. Stereotyping is the process of making assumptions about a person or group of people based on various attributes, including gender, race, religion, or physical traits.

Murphy’s Law

murphys-law
Murphy’s Law states that if anything can go wrong, it will go wrong. Murphy’s Law was named after aerospace engineer Edward A. Murphy. During his time working at Edwards Air Force Base in 1949, Murphy cursed a technician who had improperly wired an electrical component and said, “If there is any way to do it wrong, he’ll find it.”

Law of Unintended Consequences

law-of-unintended-consequences
The law of unintended consequences was first mentioned by British philosopher John Locke when writing to parliament about the unintended effects of interest rate rises. However, it was popularized in 1936 by American sociologist Robert K. Merton who looked at unexpected, unanticipated, and unintended consequences and their impact on society.

Fundamental Attribution Error

fundamental-attribution-error
Fundamental attribution error is a bias people display when judging the behavior of others. The tendency is to over-emphasize personal characteristics and under-emphasize environmental and situational factors.

Outcome Bias

outcome-bias
Outcome bias describes a tendency to evaluate a decision based on its outcome and not on the process by which the decision was reached. In other words, the quality of a decision is only determined once the outcome is known. Outcome bias occurs when a decision is based on the outcome of previous events without regard for how those events developed.

Hindsight Bias

hindsight-bias
Hindsight bias is the tendency for people to perceive past events as more predictable than they actually were. The result of a presidential election, for example, seems more obvious when the winner is announced. The same can also be said for the avid sports fan who predicted the correct outcome of a match regardless of whether their team won or lost. Hindsight bias, therefore, is the tendency for an individual to convince themselves that they accurately predicted an event before it happened.

Read Next: Biases, Bounded Rationality, Mandela Effect, Dunning-Kruger Effect, Lindy Effect, Crowding Out Effect, Bandwagon Effect.

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