11.2 Decision-Making Models
2 min read•july 25, 2024
Decision-making models in cognitive psychology explore how we make choices. Normative models prescribe ideal decisions, while descriptive models explain real-world choices. These approaches highlight the gap between rational ideals and human limitations.
Rational choice theory assumes clear preferences and complete information. However, bounded rationality recognizes our cognitive constraints. This leads to and using . Different models suit various contexts, from quick personal choices to complex professional decisions.
Decision-Making Models in Cognitive Psychology
Normative vs descriptive decision models
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- Normative models prescribe ideal decision-making based on logic and math principles assume full rationality
- Descriptive models explain actual decision-making based on empirical observations account for cognitive biases and limitations
- Key differences: ideal vs realistic approaches, theoretical vs practical applications, prescriptive vs explanatory nature
- Examples: (normative) vs (descriptive)
Components of rational choice theory
- Preferences represent clear stable set of desires guide decision-making process
- Options encompass available choices or alternatives to be evaluated
- Consequences outline potential outcomes associated with each option
- Utility measures satisfaction or value derived from each consequence
- Assumptions include complete information, transitivity of preferences, independence of irrelevant alternatives
- Decision-making process involves:
- Identifying all possible options
- Evaluating consequences of each option
- Assigning utilities to consequences
- Choosing option with highest expected utility
- Examples: Consumer choosing between products, investor selecting stocks
Bounded rationality in decision-making
- Limited rationality due to cognitive constraints and environmental factors affects real-world decisions
- Satisficing involves choosing first satisfactory option rather than optimal one (job search)
- Heuristics serve as mental shortcuts to simplify complex decisions ()
- Cognitive limitations restrict information processing capacity impact decision quality
- Time constraints pressure quick decisions may lead to suboptimal choices (emergency situations)
- Limited information results in incomplete knowledge affects decision accuracy (medical diagnoses)
- Cognitive biases cause systematic deviations from rationality influence judgments ()
- Implications: decisions may not be optimal, emphasis on "good enough" solutions, recognition of human limitations
Effectiveness of decision models
- Personal contexts: Intuitive models effective for familiar, low-stakes decisions (choosing lunch)
- Pros: Quick, based on experience
- Cons: Prone to biases, may overlook important factors
- Professional contexts: Analytical models suitable for complex, high-stakes decisions (business strategy)
- Pros: Systematic, evidence-based
- Cons: Time-consuming, may overlook intuitive insights
- Societal domains: Collaborative models effective for decisions affecting diverse groups (public policy)
- Pros: Inclusive, considers multiple perspectives
- Cons: Slow, difficult to reach consensus
- Effectiveness criteria: decision quality, efficiency, adaptability, ethical considerations
- Hybrid approaches combine multiple models for balanced decision-making integrate intuitive and analytical thinking
- Examples: Using both data analysis and expert opinions in healthcare decisions, combining cost-benefit analysis with stakeholder input in urban planning
Key Terms to Review (18)
Amos Tversky: Amos Tversky was a cognitive psychologist renowned for his groundbreaking work in the field of decision-making, particularly in understanding how people assess risk and make choices. His research laid the foundation for the development of decision-making models and highlighted the role of cognitive biases and heuristics in everyday judgment. Along with his collaborator Daniel Kahneman, Tversky's insights revolutionized our understanding of human behavior and the psychology behind decision-making processes.
Analytical decision-making: Analytical decision-making is a systematic process that involves evaluating information, weighing alternatives, and using logical reasoning to reach a conclusion. This approach emphasizes data-driven analysis and structured methodologies to solve problems and make informed choices. It often contrasts with intuitive decision-making, where choices are made based on gut feelings or personal experience.
Availability heuristic: The availability heuristic is a mental shortcut that relies on immediate examples that come to mind when evaluating a specific topic, concept, method, or decision. This cognitive process often leads individuals to overestimate the importance or frequency of an event based on how easily they can recall similar instances, influencing problem-solving and decision-making in various contexts.
Bounded rationality model: The bounded rationality model is a concept in decision-making that suggests individuals are limited in their ability to process information, leading them to make decisions that are rational within the constraints of their cognitive limitations. This model recognizes that people often rely on heuristics or rules of thumb to simplify complex choices, resulting in satisfactory rather than optimal outcomes.
Confirmation bias: Confirmation bias is the tendency to search for, interpret, favor, and recall information in a way that confirms one's preexisting beliefs or hypotheses. This bias can significantly affect various cognitive processes, leading individuals to overlook contradictory evidence and reinforcing their current perspectives.
Daniel Kahneman: Daniel Kahneman is a psychologist known for his groundbreaking work in the fields of judgment, decision-making, and behavioral economics. He introduced concepts that highlight the ways people think and make choices, especially in uncertain situations, and distinguished between different forms of reasoning and decision-making processes that people use in their daily lives.
Decision trees: Decision trees are a graphical representation used to visualize the decision-making process, showcasing different choices and their possible consequences in a structured way. They help individuals and groups systematically evaluate the potential outcomes of various options, guiding them toward the best choice. Decision trees also illustrate the uncertainty involved in decisions, making them a valuable tool for understanding complex problems and assessing risks.
Expected utility theory: Expected utility theory is a fundamental concept in decision-making that posits individuals make choices based on the expected outcomes of their actions, weighing the potential benefits against the probabilities of those outcomes. This theory assumes that people aim to maximize their utility, or satisfaction, when faced with uncertainty and risk, leading them to evaluate decisions rationally by calculating expected values. It serves as a key framework for understanding how choices are made in various contexts, including economics and psychology.
Groupthink: Groupthink is a psychological phenomenon where the desire for harmony and conformity within a group leads to irrational or dysfunctional decision-making. It occurs when group members prioritize consensus over critical analysis, which can result in poor decisions as alternative viewpoints and dissenting opinions are suppressed. This phenomenon is often intensified in cohesive groups and can significantly impact decision-making processes, social interactions, and overall cognitive functioning.
Heuristics: Heuristics are mental shortcuts or rules of thumb that simplify decision-making and problem-solving processes. They enable individuals to make quick judgments and decisions without having to analyze every detail, often leading to satisfactory solutions based on limited information.
Intuitive Decision-Making: Intuitive decision-making refers to the process of making choices based on instinct, gut feelings, or immediate perception without the need for extensive reasoning or analysis. This approach often relies on subconscious cues and past experiences, allowing individuals to arrive at conclusions quickly. Intuitive decision-making can be particularly beneficial in complex situations where time is limited, but it can also lead to biases if not tempered with rational thought.
Loss aversion: Loss aversion refers to the psychological phenomenon where people prefer to avoid losses rather than acquiring equivalent gains. This means that the pain of losing something is psychologically more powerful than the pleasure of gaining something of equal value. This concept impacts various decision-making processes, biases, and real-world choices, influencing how individuals evaluate risks and rewards.
Majority rule: Majority rule is a decision-making principle where the option that receives more than half of the votes or support is selected as the final choice. This concept is fundamental in democratic systems, as it ensures that decisions reflect the preferences of the larger group, promoting fairness and equality in group dynamics.
Multi-criteria decision analysis: Multi-criteria decision analysis (MCDA) is a structured approach used to evaluate and prioritize multiple conflicting criteria in decision-making. It provides a framework for individuals and organizations to assess complex choices by breaking them down into simpler components, allowing for a more comprehensive comparison of alternatives. This method is particularly useful when decisions involve trade-offs among different factors, making it essential in various fields such as business, healthcare, and environmental management.
Prospect theory: Prospect theory is a behavioral economic theory that describes how individuals evaluate potential losses and gains when making decisions under risk. It highlights that people tend to be loss-averse, meaning they are more sensitive to losses than to equivalent gains, leading to irrational decision-making in uncertain situations. This theory contrasts with traditional expected utility theory, which assumes that individuals make rational choices based on objective probabilities and outcomes.
Rational choice model: The rational choice model is a theoretical framework used to understand decision-making processes, where individuals make choices by systematically evaluating available options based on their preferences and the expected outcomes. This model assumes that people act logically and in their own self-interest, weighing the potential benefits and costs of each option before arriving at a decision.
Risk aversion: Risk aversion is a behavioral economics concept that describes the tendency of individuals to prefer outcomes that are certain over those that involve uncertainty, even when the uncertain option may lead to a better expected outcome. This inclination reflects an emotional response to potential losses, where people often weigh losses more heavily than equivalent gains. Understanding risk aversion helps in analyzing decision-making processes, particularly in scenarios involving financial choices or uncertain outcomes.
Satisficing: Satisficing is a decision-making strategy that aims for a satisfactory or adequate solution rather than an optimal one. This approach acknowledges the limitations of human cognitive processing and time constraints, leading individuals to settle for a choice that meets their minimum criteria instead of exhaustively searching for the best possible option. Satisficing recognizes that people often prioritize efficiency over perfection in their decision-making processes.