What is Decision Models?
They are decision mechanisms that automatically determine the most logical or profitable option by processing data and specific business rules.
Overview
Decision models are algorithmic structures that analyze available inputs in complex problem situations and reach a conclusion through specific logical steps or probability calculations. They encompass both classical rule-based systems (if-then logic) and modern AI-based prediction models. Their primary purpose is to ensure that decisions are repeatable, consistent, and optimized by reducing human bias.
How it works
Input data is cleaned and transformed into variables that the model can understand. The model calculates a score for each possible outcome using predefined rules or mathematical weights learned from historical data. The option that provides the highest benefit or lowest risk is passed to the output as the final decision.
Where it is used
They are used in approving credit applications, stock trading strategies, logistics route optimization, and real-time maneuvering choices of autonomous vehicles.
Commonly confused with
They differ from pure prediction models; while prediction models tell 'what will happen tomorrow', decision models determine 'what needs to be done based on this prediction'.
Questions and answers
Do decision models always use artificial intelligence?
No. Decision models can also be built using entirely rule-based deterministic methods such as decision trees and rule tables.
Do autonomous agents operate using decision models?
Yes. Autonomous AI agents decide which tool or step to choose based on the data they collect from their environment through decision models.
Related terms
This explanation was written in plain language for TreScout with AI and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →