risikovurderingsmodeller

Risikovurderingsmodeller, or risk assessment models, are systematic methods used to identify, analyze, evaluate, and communicate risks within a system, process, or organization. They aim to estimate the likelihood of adverse events and their potential impacts, enabling prioritization of mitigation measures and allocation of resources. Models combine data, historical records, and expert judgment to support decision making under uncertainty.

Approaches range from qualitative to quantitative. Qualitative tools include risk matrices and scoring systems. Semi-quantitative methods such as FMEA (Failure Mode and Effects Analysis) provide structured scoring. Quantitative and probabilistic techniques include fault-tree analysis, event-tree analysis, probabilistic risk assessment, Bayesian networks, and Monte Carlo simulations. Multi-criteria decision analysis helps rank risks when several objectives exist.

Inputs usually cover hazard identification, exposure, vulnerability, and potential consequence, together with data sources and assumptions. Uncertainty is addressed through scenario analysis, sensitivity analysis, and confidence assessment. Where data are limited, expert elicitation and historical information are used to calibrate models and to bound risk estimates.

Risikovurderingsmodeller are applied across sectors such as industry, energy, finance, healthcare, environment, and cyber security. They are often embedded in risk management frameworks like ISO 31000 and ISO 31010, and support outputs such as risk registers, design decisions, and ongoing monitoring of residual risk.

Limitations include model uncertainty, data gaps, and biases in expert judgment. Validation against independent data, transparent documentation, and routine sensitivity analyses are essential to maintain credibility. The overall aim is to inform decisions and resource allocation, rather than to provide precise predictions.