Suchergebnisse

Researchers and commissions contend that the risk of human extinction is high, but none of these estimates have been based upon a rigorous methodology suitable for estimating existential risks. This article evaluates several methods that could be used to estimate the probability of human extinction. Traditional methods evaluated include: simple elicitation; whole evidence Bayesian; evidential reasoning using imprecise probabilities; and Bayesian networks. Three innovative methods are also considered: influence modeling based on environmental scans; simple elicitation using extinction scenarios as anchors; and computationally intensive possible‐worlds modeling. Evaluation criteria include: level of effort required by the probability assessors; level of effort needed to implement the method; ability of each method to model the human extinction event; ability to incorporate scientific estimates of contributory events; transparency of the inputs and outputs; acceptability to the academic community (e.g., with respect to intellectual soundness, familiarity, verisimilitude); credibility and utility of the outputs of the method to the policy community; difficulty of communicating the method's processes and outputs to nonexperts; and accuracy in other contexts. The article concludes by recommending that researchers assess the risks of human extinction by combining these methods.

As science and technology change our world, anticipating the unintended consequences becomes critical. This article presents a framework for identifying unintended consequences, especially unanticipated-unintended consequences, and prioritizing the necessary actions to mitigate or adapt. Content for the framework, and the distinctions among the anticipated-intended, anticipated-unintended, and unanticipated-unintended consequences, are generated with four scenario types: evolution over time, market-saturation, interventions in tightly coupled systems, and existential risk of human extinction. To validate the framework, each scenario type is applied to historical and emerging technologies to anticipate the unintended consequences, including the causes, initiators, effects, actions to mitigate or adapt, and unmet obligations to future generations.