What type of reasoning employs likelihood and uncertainty rather than strict rules to make decisions?

Probability-Based Reasoning utilizes statistical methods to assess the likelihood of various outcomes, allowing for more nuanced decision-making in situations filled with uncertainty. This form of reasoning contrasts with Logic-Based Reasoning, which relies heavily on predetermined rules and absolute truths, often leading to deterministic conclusions.

In many real-world applications, especially in data science and artificial intelligence, decisions must be made under conditions where complete information is not available, and outcomes can vary. Probability-Based Reasoning acknowledges this variability, leveraging tools such as probability distributions, Bayesian inference, or machine learning models to infer conclusions based on available data. As a result, it provides a robust framework for handling uncertainty, making it particularly useful in fields that require predictive analytics or risk assessment, where conditions are dynamic and complex.

By focusing on likelihood and varying degrees of confidence rather than adhering strictly to established rules, Probability-Based Reasoning allows for more flexible and adaptive decision-making that can better align with real-world complexities.