The rational model is based on the assumption that you have a clear and specific goal, a set of well-defined criteria, and complete and accurate information about all the possible options. The rational model involves four steps: identify the problem, generate alternatives, evaluate alternatives, and select the best option. Data and analytics can help you in each step by providing relevant facts, insights, and evidence to support your reasoning and judgment. The rational model is ideal for situations where you have a high degree of certainty and control, and where you can quantify and compare the costs and benefits of each option.
1. Define the problem. What is the decision you need to make? Once you have defined your problem, you can start to brainstorm and come up with possible solutions. 2. Retrieve clean & processed data and analytics about the issue and the potential options. Consolidate your data to an easily digestible format i.e., a dashboard that can be refreshed automatically. 3. Weigh the pros and cons. Once you have gathered data, reviewed the analytics, and heard the SMEs, created the actionable steps for each option, you need to list the pros and cons of each option. 4. Fact-based decision. Based on your analysis, select an option, and take action. Collect data to track your progress. This way you will stay on track to achieve your goal!
The Rational Model, often deemed the 'economic man model', champions logical and systematic decision-making. Data fuels each step - diagnosing problems, generating alternatives, evaluating options, and making the final choice. Perfect for high-certainty environments, it thrives on quantifiable data to weigh pros and cons. Think of the Rational Model as your financial modeling process. You define the problem (like identifying key financial metrics), generate and evaluate alternatives (various scenarios), and select the best option (optimal financial path). Data and analytics are indispensable throughout, making this model apt for data-rich, controlled environments. It flourishes when the situation is controlled and data is abundant.
The intuitive model is based on the assumption that you have a vague or complex goal, a limited or ambiguous set of criteria, and incomplete or uncertain information about the possible options. The intuitive model involves relying on your gut feeling, experience, and intuition to make a quick and confident decision. Data and analytics can help you in this model by providing a general overview, a sense of direction, and a validation of your intuition. The intuitive model is ideal for situations where you have a low degree of certainty and control, and where you need to act fast and adapt to changing circumstances.
Think of the Intuitive Model as jazz improvisation. The goal is to make great music, but there's no clear path. You trust your instincts, drawing from experience, but data provides the rhythm, guiding and confirming your choices. This model is ideal in situations with high uncertainty and a need for swift decision-making. Your intuition guides your actions, but data serves as your compass, offering a sense of direction and validating your gut feelings. It's valuable when rapid decisions are required in uncertain environments.
The analytic hierarchy process (AHP) is based on the assumption that you have a multiple and conflicting goals, a large and diverse set of criteria, and a complex and dynamic set of options. The AHP involves breaking down the decision problem into a hierarchy of subproblems, assigning weights to the criteria and the options, and calculating the overall scores and rankings of the options. Data and analytics can help you in this model by providing a structured and consistent framework, a quantitative and qualitative analysis, and a sensitivity and robustness test. The AHP is ideal for situations where you have a high degree of complexity and uncertainty, and where you need to balance multiple perspectives and trade-offs.
The Analytic Hierarchy Process (AHP) is like navigating a multi-level maze. It breaks down complex decisions into subproblems, assigns weights based on data and then calculates the optimal path. This model, assisted by data and analytics for structure, analysis, and testing, thrives when complexity and uncertainty are high and multiple perspectives need balancing. Think of AHP as a project manager juggling multiple tasks. It breaks complex decisions into manageable parts, weighs them based on importance, and calculates the best option. Data and analytics act as the project management tool, giving structure, conducting analysis, and testing robustness. This model is apt for complex situations with multiple competing goals.
The decision tree is based on the assumption that you have a sequential and conditional decision problem, where each option leads to a different outcome or another decision. The decision tree involves drawing a diagram that represents the decision problem as a series of nodes (decisions or outcomes) and branches (options or probabilities). Data and analytics can help you in this model by providing a visual and logical representation, a probabilistic and expected value calculation, and a risk and scenario analysis. The decision tree is ideal for situations where you have a high degree of variability and contingency, and where you need to anticipate the consequences and implications of each option.
With the exception of the rational model that assumes a specific goal, each of these models can benefit from the Value-Focused Thinking method. This approach focuses on the essential qualitative analysis that must be done prior to making choices. VFT puts values at the center of decision-making and identification of them should precede evaluation of alternatives. The use of explicit values makes ranking existing alternatives easier and it helps generate new alternatives. A VFT approach likewise makes communicating alternative choices and negotiating over them easier. Using the VFT method may also help to identify new decision opportunities. “Smart Choices” by Hammond, Keeney and Raiffa is a good primer resource.
I’m “old school” and rely on the KT Decision Analysis process, KT Problem Analysis process and Root Cause Analysis. Have even used a hybrid of the KT PA and RCA processes. They are still very effective tools for decision making and problem solving.