Decision Making Frameworks

Making Better Decisions, Systematically

I’ve been collecting decision-making frameworks for years, partly out of professional necessity and partly because I find it genuinely interesting how different fields have solved the same fundamental problem: how do you choose well when you don’t have all the information?

What I’ve learned is that no single framework works everywhere. The right approach depends on the situation: how much time you have, how much data is available, whether you’re deciding alone or as a group, and what the stakes are. This series covers the range, from formal analytical methods to the rapid intuitive approaches used by firefighters and emergency room nurses.

Foundations

The logical starting point is Decision Strategies: Beyond Expected Value, which looks at why the standard expected value calculation breaks down in practice and what alternatives exist. From there, Decision Making Under Uncertainty digs into the practical challenges of making decisions when you genuinely don’t know what’s going to happen.

Structured Frameworks

Several posts explore specific frameworks you can put to work immediately. Simon’s Decision Framework breaks complex decisions into three phases: Intelligence, Design, and Choice. It’s simple enough to remember and structured enough to actually help.

Build-Measure-Learn covers the Lean Startup’s iterative approach to decisions, while PDCA Quality Control applies the Plan-Do-Check-Act cycle from manufacturing to modern decision-making. Both are about building feedback loops so your decisions get better over time.

Decision Journals borrows from high-reliability organizations like hospitals and nuclear plants. The idea is simple: write down what you decided, why, and what you expected to happen, then go back and check. It’s the most effective way I’ve found to learn from your own decisions.

Decisions in Context

Some of the most interesting decision-making research comes from high-pressure environments. Group Decision Making examines how teams make critical decisions in life-or-death situations, from aviation crews to trauma teams. The lessons about authority, communication, and healthy conflict apply far beyond those settings.

The Nursing Decision Cycle looks at clinical judgment under pressure, where nurses must assess, plan, implement, and evaluate in rapid succession. Recognition-Primed Decisions covers Gary Klein’s research on how experts like firefighters and military commanders make fast, effective decisions by pattern-matching against experience rather than analyzing options.

The Meta-Questions

A few posts in the series step back and ask bigger questions about how we think about decisions. Data-Driven vs. Data-Justified Decisions tackles the uncomfortable truth that much of what passes for data-driven decision-making is really about finding data to support decisions already made.

Decision-Modeling vs. Process-Modeling explores when you should focus on improving the decision itself versus improving the process around it. Getting this distinction right can save a lot of wasted effort.