From Certainty to Uncertainty: Foundations of Algorithmic Decision Making
Let us begin this journey into decision making by starting from the comfort of deterministic models and gradually stepping into the uncertainty of stochastic ones. A deterministic model is one where the outcome is completely predictable — the same input always produces the same output, like the mathematical fact that 2 + 2 equals 4. A stochastic model, on the other hand, involves randomness; rolling a die, for example, can yield any value from one to six. The goal of this discussion is to build the foundations for understanding decision making under uncertainty . At the heart of this problem lies a simple loop: an agent observes an environment and then takes an action based on what it observed. This is known as the observe–act cycle . Although this loop appears simple, each component (Environment, Agent, Observation, Action) is filled with uncertainty: The effects of our actions are uncertain The true state of the environment is uncertain The behavior ...
