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.

Figure 1: The foundational Observe–Act Cycle in Decision Making.

Although this loop appears simple, each component (Environment, Agent, Observation, Action) is filled with uncertainty:

Action Results The effects of our actions are uncertain.

State Awareness The true state of the environment is uncertain.

Multi-Agent Behavior The behavior of other agents is uncertain.

Model Accuracy Even our model of the problem may be uncertain.

Handling these uncertainties is central to the field of Artificial Intelligence. This foundational framework appears in real‑world systems such as aircraft collision avoidance, autonomous driving, breast cancer screening, financial consumption modeling, and portfolio allocation.

Methods for Building Decision‑Making Agents

There are several distinct approaches we can take to build these systems:

• • Explicit Programming: Anticipating all scenarios and specifying exactly what the agent should do in each case.
• • Supervised Learning: Learning patterns and making predictions from labeled examples.
• • Optimization: Searching for the best strategy through evaluation and simulation.
• • Planning: Determining the specific sequence of steps required to reach an optimal solution.
• • Reinforcement Learning: Learning strategies through direct, trial-and-error interaction with the environment.

Going forward, we will focus on decision making through the lenses of Supervised Learning, Optimization, Planning, and Reinforcement Learning.

#Algorithm #ArtificialIntelligence #DecisionMaking #MachineLearning #Foundations