Induced on rcor

Observation and Reporting in Conditional Probability — One Ace versus the Ace of Hearts

Tue, 02 Jun 2026 00:00:00 +0000

In practice, decision making rarely depends on the full information that was originally observed¹. Instead, decisions are often based only on the information that was eventually reported. This distinction is fundamental.

An observer may possess richer latent information than the information communicated through the reporting protocol. Consequently, two situations that appear equivalent at the reporting level may correspond to very different informational structures.

Conditional probability is usually introduced theoretically through abstract events and sigma-algebras. However, in practical applications, conditioning frequently depends on:

Towards a Unified View of Probability

Fri, 29 Aug 2025 00:00:00 +0000

Probability is a fundamental concept that transcends disciplinary boundaries, including mathematics, statistics, physics, philosophy, and finance. Over time, different disciplines have developed distinct interpretations, each with specific strengths and limitations.

The table below offers a unified view of probability, presenting the main interpretations, associated formulas, contexts where they are appropriate, as well as the advantages and limitations of each. The aim is not merely to list the concepts, but also to highlight how some frameworks integrate or generalize others, thereby enabling a coherent perspective that is applicable across domains.

Seismic Risks in a Changing World: Induced vs. Triggered Events and Their Business Implications

Sun, 23 Mar 2025 00:00:00 +0000

The distinction between induced and triggered seismicity is based on whether all or only a portion of the strain energy released was anthropogenic. The term induced is used when the majority of the energy was anthropogenic, while triggered is employed when it was predominantly of natural origin.

The distinction between induced and triggered events has profound implications for the insurance industry. A clear understanding of the underlying causes can inform policy structuring, risk modeling, and claims handling.