The Power of Assumptions
The Power of Assumptions
Navigating Uncertainty in Decision-Making
In most of our decisions, we are not betting against another person. Rather, we are betting against all the future versions of ourselves that we are not choosing.
- Annie Duke
Introduction
In an increasingly complex and uncertain world, making well-informed decisions often requires navigating through incomplete information and noise from information overflow. Assumptions play a pivotal role in navigating complexity in decision making. Properly using assumptions helps simplify problems by filling in the missing information, thus enabling us to tackle complex issues more easily. They also encourage us to consider alternative perspectives, leading to more objective decisions.
Assumptions are statements accepted as true without definitive proof, serving as starting points for reasoning or action. They are essential tools that help us formulate strategies, plan for the future, and adapt to changing circumstances. However, unchecked or unexamined assumptions can lead to misunderstandings, flawed strategies, and unintended consequences. Therefore, it's important that when using assumptions that you approach them with a balance of confidence and skepticism.
This article delves into the role of assumptions in decision-making—their benefits, pitfalls, and how to effectively incorporate them into your decision-making process. We'll explore techniques for formulating clear assumptions, assigning confidence levels to them, validating them through evidence and show how to incorporate principles like Bayesian probability and counterfactuals to superpower our assumptions.
Assumptions: The Good and the Bad
Advantages of Using Assumptions in Your Decision-Making
Simplification of Complex Problems: Assumptions reduce complexity by filling in the gaps of information, they help make difficult and problems that contain high levels of uncertainty more manageable.
Efficiency in Decision-Making: They allow for quicker decisions by enabling progress without waiting for all data.
Encouragement of Creative Thinking: Assumptions open up possibilities beyond the known facts, fostering innovation and alternative solutions.
Preparation for Uncertainty: By considering different assumptions, you can plan for various scenarios, enhancing adaptability.
Disadvantages of Using Assumptions in Your Decision-Making
Risk of Inaccuracy: Incorrect assumptions can lead to flawed strategies and poor outcomes.
Overlooking Important Data: Relying too heavily on assumptions may cause neglect of crucial information.
Reinforcement of Biases: Personal biases can influence assumptions, leading to subjective decisions. Make sure that you consider biases that might be creeping in to your assumptions, and ultimately into your decision-making process.
Resistance to New Information: Fixed assumptions may hinder the acceptance of new evidence that contradicts them.
False Sense of Security: Unquestioned assumptions might create unwarranted confidence in uncertain situations.
It’s essential to hold your assumptions loosely, viewing them as temporary placeholders that might change with the acquisition of new information (See Bayesian probability).
Using Assumptions in Your Decision-Making Process
Formulate Assumptions
Identify Gaps: Recognize where information is missing and consciously decide on assumptions to fill those gaps.
Be Explicit: Clearly state your assumptions to yourself and to your team to ensure everyone is on the same page.
Rate Confidence Levels: Assess how confident you are in each assumption. Are they based on solid data, past experience ( see definition: base rates), or are they speculative? Assigning confidence levels helps gauge the reliability of your assumptions and helps keep you honest.
Incorporate Counterfactual Thinking: For each assumption, consider counterfactuals by asking "what if" questions about alternative scenarios. This helps identify potential risks and opportunities.
Example:
Assumption: Our new product will meet high market demand.
Counterfactual: What if the market demand is lower than expected due to emerging competitors?
Validate Assumptions
Question Regularly: Continually challenge your assumptions by asking critical questions. What evidence supports this assumption? What if it isn’t true?
Seek Evidence: Look for data, feedback, or expert opinions that can confirm or refute your assumptions.
Consider Alternatives: Explore different scenarios where your assumptions might not hold. This prepares you for unexpected outcomes.
Example:
Assumption Validation: Conduct market research to test the demand for your product.
Counterfactual Exploration: Analyze the impact on your business if a key competitor releases a similar product.
Iterate, Iterate, Iterate
Feedback Loop: Establish mechanisms to collect feedback on the outcomes of decisions based on your assumptions.
Measure Against Expectations: Compare actual outcomes with what you expected if your assumptions were correct.
Adjust Accordingly: Use new information and results to refine your assumptions. Be willing to revise or abandon assumptions that no longer serve you.
Explore Counterfactual Scenarios: Analyze how different outcomes could have occurred if alternative assumptions were made. Ask yourself what factors led to the actual result versus the expected one.
Learn and Adapt: Use discrepancies between expected and actual outcomes to refine your assumptions and decision-making processes.
Example:
Assumption: A marketing campaign will increase sales by 20%.
Outcome: Sales increased by only 10%.
Counterfactual Analysis: What if the campaign had targeted a different demographic? What if a competitor hadn't launched a promotion simultaneously?
Action: Adjust future campaigns based on these insights.
Apply Bayesian Probability
Regularly Update Your Beliefs and Assumptions Based on New Information
Incorporate principles from Bayesian Probability, which provides a mathematical framework for updating beliefs in light of new evidence. Bayesian thinking emphasizes that our initial beliefs (prior probabilities) should be adjusted (posterior probabilities) as we receive new data. The main takeaway is not the mathematical calculations, but the concept of rethinking your assumptions whenever there is new information or evidence. This approach leads to more accurate and reliable decisions over time, as it systematically incorporates new information. A perfect example of using assumptions as temporary guideposts.
Conclusion
Instead of trying to predict a single future outcome, prepare for a range of possible futures based on different assumptions and their counterfactuals. This approach acknowledges uncertainty and equips you to adapt as situations evolve. Embracing flexibility, continuously updating your beliefs, and being willing to adjust your assumptions will lead to more resilient and effective decision-making.
Definitions
Assumptions: Ideas or statements accepted as true without definitive proof, used as a starting point for further reasoning or action. In decision-making, assumptions fill informational gaps and provide a foundation for developing strategies.
Base Rate: The general probability of an event occurring within a given context, based on historical data or general knowledge.
Bayesian Probability is a framework for interpreting probability as a measure of belief or confidence, rather than as a long-term frequency of events. It allows for the updating of probabilities as new information becomes available, using Bayes' Theorem.
Beliefs: Convictions or acceptances that certain things are true or real, often held without complete evidence. Beliefs are shaped by personal experiences, cultural contexts, and values, influencing how we interpret information and make decisions.
Bets: Decisions made with incomplete information, based on the best available evidence and assumptions. Betting in decision-making involves weighing the probabilities and potential outcomes to make informed choices under uncertainty.
Counterfactuals: Hypothetical scenarios that consider what might have happened if past events had occurred differently. They involve thinking about alternative realities and are useful for understanding causality, exploring different outcomes, and improving future decision-making by learning from past possibilities.