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Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

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Managing uncertainty in decision-making for conservation science.

Yakov Ben-Haim¹

¹Faculty of Mechanical Engineering, Technion-Israel Institute of Technology, Haifa, Israel.

Conservation Biology : the Journal of the Society for Conservation Biology

|August 8, 2023

Summary

This summary is machine-generated.

Responsible conservation decisions must manage uncertainty. The info-gap method, robust satisficing, prioritizes decisions that reliably achieve critical outcomes, even if suboptimal, demonstrating the value of new information for biodiversity.

Keywords:

conservation decisions decision methodology decisiones de conservación función del rendimiento reproductivo incertidumbre info-gaps metodología de decisiones reproductive output function robustness solidez uncertainty vacío de información 不确定性保护决策信息差决策方法论稳健性繁殖产出函数

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Area of Science:

Ecology
Conservation Biology
Decision Science

Background:

Science-based decision-making is challenged by incomplete and uncertain knowledge.
Managing uncertainty is crucial for effective policy, planning, and action in conservation.
Traditional info-gap theory often overlooks uncertainty in functional relationships.

Purpose of the Study:

To apply info-gap decision theory to manage deep uncertainty in conservation decision-making.
To explore robust satisficing as a strategy for prioritizing conservation actions.
To model functional uncertainty in endangered species translocation under climate change.

Main Methods:

Utilized info-gap models to address functional uncertainty, specifically in reproductive output functions.
Focused on two key uncertainties: future temperature fluctuations and species' reproductive success in new environments.
Applied the method to a generic endangered species translocation scenario.

Main Results:

Demonstrated the value of new information using the robustness function.
Showcased the potential for superior outcomes through the info-gap opportuneness function.
Highlighted that robust satisficing can lead to preferring decisions with less optimal but more reliable outcomes.

Conclusions:

Info-gap models can effectively manage functional uncertainty in conservation, particularly for species translocations.
Robust satisficing offers a framework for making reliable conservation decisions amidst deep uncertainty.
Understanding uncertainty, including functional relationships and climate impacts, is vital for successful biodiversity conservation.