Algorithms

Step-by-step procedures for solving specific classes of problems or performing computations—the fundamental building blocks of computer science and, more broadly, of any SYSTEMATIC approach to problem-solving. An algorithm takes defined INPUTS, applies a defined PROCESS, and produces defined OUTPUTS. Key properties: finiteness (must terminate), definiteness (each step unambiguous), effectiveness (each step executable). As a mental model: algorithmic thinking means converting vague procedures into EXPLICIT, REPEATABLE, TESTABLE sequences of steps—making implicit knowledge explicit and enabling delegation, automation, and improvement. The gap between 'I know how to do this' and 'I can write the algorithm for this' reveals the gap between tacit knowledge and actionable procedure.

When to use it

When implicit procedures need to be made explicit for delegation or automation; when process reliability requires unambiguous step-by-step specification; when the gap between tacit knowledge and explicit procedure needs closing; when optimization requires making each step visible and testable.

How it can help

Convert tacit knowledge into explicit algorithms wherever reliability, delegation, or scaling matters. The practice: (1) Document your process step-by-step so someone ELSE could follow it and produce the same result. (2) Identify decision points: where does the process BRANCH based on conditions? (3) Test the algorithm: give it to someone unfamiliar with the task and see if they produce the correct output. (4) Optimize: where are the bottlenecks, unnecessary steps, or error-prone branches? For entrepreneurs: the processes that ARE algorithmic should be documented and delegated; the processes that RESIST algorithmization are where your judgment adds irreplaceable value.