Recognize that decision outcomes are not deterministic; many things could occur with varying likelihoods. Understanding this probabilistic nature is crucial for better decision-making.
Do not judge the quality of a decision solely by its outcome, especially in situations with high uncertainty. A good decision can lead to a bad outcome, and vice-versa.
After any outcome (good or bad), actively consider alternative scenarios that could have occurred, including those that didn’t happen but were possible. This deep dive into “what if” scenarios is crucial for significant progress and understanding decision quality.
To plan effectively, envision having already achieved your goal (e.g., “it’s a year and a day, and I achieved X”). Then, look backward and ask, “How did I actually achieve this?” to identify the steps and factors, including luck, that led to success.
Before starting a project or pursuing a goal, imagine it’s a year later and you’ve failed. Then, work backward to identify all the potential reasons for that failure, including luck, allowing you to proactively mitigate risks.
After identifying potential lucky or unlucky events (via backcasting/premortem), proactively ask: Can I increase good luck? Can I decrease bad luck? Can I hedge against bad luck? And most importantly, what will be my pre-planned response if bad luck occurs, to ensure calm, effective decision-making.
Progress from attributing losses to luck and wins to skill (Level 1) to critically analyzing all four quadrants (good/bad decision, good/bad outcome) equally (Level 3), and ultimately questioning even successful outcomes for even better paths (Level 4).
To assess decision quality, analyze all possible outcomes, their likelihoods, and your preferences for each. Compare this expected value against other potential decisions to choose the path with the highest chance of a preferred outcome.
Aim to identify and correct errors in thinking (e.g., overconfidence, deterministic thinking) more quickly, as even small improvements in this area significantly enhance decision-making.
When exploring new activities, try many low-risk options, then quickly abandon those that don’t yield positive expected value (happiness, health, etc.) to free up time for things you truly love and want to commit to.
Just as negative outcomes trigger analysis, unexpectedly good outcomes should also be thoroughly examined. This helps identify overlooked risks, refine models, and learn from successful strategies that may not have been fully understood.
Before an activity, forecast your expected performance. Afterward, analyze deviations: if you perform significantly below or above your forecast, investigate the underlying reasons to learn and improve.
To reach higher levels of decision analysis, be willing to challenge your core beliefs about your competence, even if it means re-evaluating a successful outcome and potentially “turning a win into a loss” for deeper learning.
Cultivate a hunger for feedback on your decisions and outcomes. This proactive approach helps you understand potential futures, anticipate reactions, and learn from your experiences to continuously improve.
Be aware that fear of blame or a desire to avoid being seen as an “idiot” can lead to making status quo decisions rather than optimal ones, even when data suggests otherwise.
To encourage innovation and thinking outside the box, leaders and individuals should reduce the fear of being labeled an “idiot” for unexpected decisions that lead to bad outcomes, as this fear suppresses progress.
When evaluating investments or decisions, focus not just on potential returns but also on the risk involved (risk-adjusted return on capital) to make more robust and sustainable choices.
In post-mortems for negative events, expand the inquiry beyond “Could we have done better?” to also ask “Should we have done worse?” This helps identify situations where the decision process was actually worse than the outcome suggested, offering deeper learning.
After an outcome, consider what you would have done with perfect information. This reveals if you underplayed or overplayed a situation, helping to refine your strategy regardless of the actual result.
Poker, with its incomplete information and strong influence of luck, is a better model for real-world decision-making than chess, which has perfect information and no luck. This helps understand the nuances of decisions in uncertain environments.
When personal accountability (e.g., using your own money, reputation) is high, it creates a stronger incentive to engage in higher-level, more objective decision-making and analysis.
To accelerate decision-making skill, seek environments with high “skin in the game,” a significant blend of luck and skill, and short feedback loops, as these conditions force rapid learning or failure.
In decisions with extended feedback loops, systematic and probabilistic thinking becomes even more critical, as longer time horizons offer greater opportunity for self-deception and bias to go unchecked.
For decisions with long feedback loops, analyze them with the same rigor as if you would receive immediate feedback, like in chess. This helps counteract the tendency for self-deception over time.
When decisions lead to positive but suboptimal outcomes, resist the urge to stop analyzing. Dig deeper to identify the truly optimal path and capture the full potential value that was missed.
Understanding a robust decision-making framework allows you to decide quickly on routine matters and also to navigate complex, uncertain situations more efficiently, avoiding paralysis by recognizing when a “good enough” path is sufficient.