Researchers from the Norwegian University of Life Sciences (NMBU) explored the possibilities of introducing risk into forest sector models (FSM). They have found a step-wise procedure, which has one main goal:
help decision makers to facilitate better decisions.

Researchers from Norway found that for incorporating risk in FSM, fuzzy set theory and robust optimization techniques seem promising new approaches, alongside methods that already are in use, like Monte Carlo simulation and, in particular, scenario and sensitivity analysis.

Researchers proposed a step-wise procedure combining deterministic optimization, sensitivity analysis, Monte Carlo simulation and scenario analysis, in order to achieve one main goal:

Help decision makers to facilitate better decisions across their organizations.

The use of forest sector models (FSM), which include forestry, forest industries and market interactions between them, has strongly increased in the last decade. Nearly all of the FSM share the common feature of being deterministic – i.e. risks or uncertainties are not explicitly considered. Deterministic models are “first-pass attempts” in assessment modeling, and are often missing quantitative risk descriptions important for decision-making.

You can access the full article via following link: Incorporating risk in forest sector modeling – state of the art and promising paths for future research

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