Decision Making Models – execbeat.com

Rational Decision-Making Model: A structured approach emphasizing logical reasoning and systematic analysis of choices.
Bounded Rationality Model: Acknowledges limitations in cognitive processing and information availability.
Satisficing Model: Focuses on finding a solution that is “good enough” rather than the optimal one.
Incremental Decision-Making Model: Makes decisions in small steps, adjusting as new information becomes available.

Decision Tree Analysis: A visual representation of decisions and their possible consequences.
Cost-Benefit Analysis: Compares costs and benefits of alternatives to determine the most viable option.
Multi-Criteria Decision Analysis (MCDA): Evaluates multiple conflicting criteria to find the best option among alternatives.
Analytic Hierarchy Process (AHP): Breaks down complex decisions into a hierarchy, allowing for systematic evaluation of options.

Availability Heuristic: Relies on immediate examples that come to mind when evaluating a specific topic.
Representativeness Heuristic: Judging the probability of an event based on its similarity to known categories.
Anchoring Bias: The tendency to rely heavily on the first piece of information encountered.
Confirmation Bias: The inclination to search for, interpret, and remember information that confirms pre-existing beliefs.

Nominal Group Technique (NGT): A structured process for brainstorming that prioritizes ideas through individual ranking.
Delphi Method: Gathers expert opinions through iterative rounds of questionnaires to reach a consensus.
Consensus Decision-Making: A collaborative process where all participants agree on a decision.
Brainstorming: A creative group activity aimed at generating a large number of ideas for problem-solving.

Risk Analysis Matrix: Evaluates risks based on likelihood and impact to prioritize which risks to address.
Failure Mode and Effects Analysis (FMEA): Identifies potential failure points in a process for improvement.
Monte Carlo Simulation: Uses statistical modeling to predict the probability of different outcomes in uncertain situations.
Scenario Analysis: Examines potential future events by considering alternative outcomes and their impacts.

PDCA Cycle (Plan-Do-Check-Act): A continuous improvement model guiding problem-solving through iterative testing.
Root Cause Analysis (RCA): Identifies fundamental causes of problems to address issues effectively.
Fishbone Diagram (Ishikawa): A visual tool categorizing potential causes of a problem for thorough analysis.
5 Whys Technique: A questioning method used to explore the cause-and-effect relationships underlying a problem.

SWOT Analysis: Identifies strengths, weaknesses, opportunities, and threats related to a decision or strategy.
Porter’s Five Forces Framework: Analyzes industry competitiveness to inform strategic positioning.
Ansoff Matrix: Evaluates growth strategies by considering market penetration, product development, market development, and diversification.
Value Chain Analysis: Examines activities within an organization to identify areas for efficiency and competitive advantage.

Prospect Theory: Describes how people make decisions based on perceived gains or losses rather than final outcomes.
Decision-Making Under Uncertainty: Explores how individuals choose among alternatives when outcomes are uncertain.
Framing Effect: The way information is presented influences decision-making; decisions can change based on framing.
Cognitive Dissonance Theory: Examines how conflicting beliefs can affect decision-making and attitudes.

Vroom-Yetton Decision Model: Guides leaders on the appropriate level of team involvement in decision-making based on the situation.
Decision-Making Process Model: Outlines steps including problem identification, analysis, selection, implementation, and evaluation.
The OODA Loop (Observe, Orient, Decide, Act): A cyclical model for rapid decision-making in dynamic environments.
DMAIC (Define, Measure, Analyze, Improve, Control): A data-driven quality strategy used to improve processes.

Machine Learning Algorithms: Utilize statistical techniques to enable computers to learn from data and make predictions or decisions.
Predictive Analytics: Analyzes historical data to forecast future outcomes, aiding informed decision-making.
Decision Automation Systems: Employ algorithms to automate repetitive decision-making processes for efficiency.
Big Data Analytics: Uses large datasets to uncover patterns and insights that inform strategic decisions.

Utilitarianism: Focuses on maximizing overall happiness or utility when making ethical decisions.
Kantian Ethics: Emphasizes duty and adherence to moral laws when making ethical decisions.
Virtue Ethics: Centers on character and virtues as the basis for ethical decision-making.

Integrative Negotiation Model: Focuses on collaboration and mutual benefit in negotiation processes.
Interest-Based Relational Approach: Emphasizes maintaining relationships while addressing underlying interests in conflict resolution.