Étiquette : uncertainty

Dealing with Uncertainty in Cyberspace

There are five different common reactions to dealing with, or taming, this #uncertainty in #cyberspace: (1) using #riskmanagement to control uncertainty; (2) recovering from uncertainty through #resilience; (3) mitigating uncertainty through the use of #laws and #regulations; (4) suspending uncertainty by engaging in trust; and (5) ignoring uncertainty through inaction. Lire

Quantifying Uncertainty and Sensitivity in Climate Risk Assessments: Varying Hazard, Exposure and Vulnerability Modelling Choices

“We present a novel approach to quantify the uncertainty and sensitivity of risk estimates, using the CLIMADA open-source climate risk assessment platform. This work builds upon a recently developed extension of CLIMADA, which uses statistical modelling techniques to better quantify climate model ensemble uncertainty. Here, we further analyse the propagation of hazard, exposure and vulnerability […]

Bayesian Model Selection and Prior Calibration for Structural Models in Economic Experiments: Some Guidance for the Practitioner

“Bayesian estimates from experimental data can be influenced by highly diffuse or “uninformative” priors. This paper discusses how practitioners can use their own expertise to critique and select a prior that (i) incorporates our knowledge as experts in the field, and (ii) achieves favorable sampling properties. I demonstrate these techniques using data from eleven experiments […]

Cyber Risk: Hyperconnectivity and the Political Economy of Uncertainty

“This paper explores the notion of ‘cyber risk’, asking how we might understand it through a sociotechnical lens. It pays specific attention to how we can theorise cyber risk as an assemblage of sociotechnical ‘riskscapes’, in which our understanding of risk goes beyond organisational imperatives of ‘risk management’ and into treating cyber risk as a set of productive knowledges and practices within a […]

Fat Tails, Tipping Points and Asymmetric Time Horizons: Dealing With Systemic Climate-Related Uncertainty in the Prudential Regime

“Even pioneering forward-looking stress tests cannot feasibly capture all possible tail risks. We propose supplementing the existing capital requirements regime by giving it a stronger precautionary and macroprudential focus, paying particular attention to the prevention of environmental tipping points to avoid systemic and catastrophic impacts on the financial system and macroeconomy.”    Lire

Regulatory Complexity, Uncertainty, and Systemic Risk: are Regulators Hehogs or Foxes?

“Rebalancing regulation towards simplicity may produce Pareto-improving solutions, and encourage better decision making by authorities and regulated entities. However, addressing systemic risk in a complex financial system should not entail the replacement of overly complex rules with overly simple or less stringent regulations.” Lire

Catastrophic Uncertainty and Regulatory Impact Analysis

“Cost-benefit analysis embodies techniques for the analysis of possible harmful outcomes when the probability of those outcomes can be quantified with reasonable confidence. But when those probabilities cannot be quantified (“deep uncertainty”), the analytic path is more difficult. The problem is especially acute when potentially catastrophic outcomes are involved, because ignoring or marginalizing them could […]

Information, Uncertainty and Espionage

“Decision theory, both orthodox and behavioural, depicts decision rather narrowly as a prioritisation task undertaken within a delineated problem space where the probabilities “sum to one”. From such a perspective, certain perennial challenges in intelligence and counterintelligence appear resolvable when in fact they are not, at least not when approached from the usual direction.” Lire

The Road Less Travelled: Keynes and Knight on Probability and Uncertainty

“The possibilities of a Keynesian-Knightian synthesis as a way forward are considered by comparing these signposts. It is argued that, although there is some common ground between Knight and Keynes, there are fundamental differences particularly associated with Keynes’s concept of weight of argument.” Lire

Cyber Loss Model Risk Translates to Premium Mispricing and Risk Sensitivity

“The standard statistical approaches to assessment of insurability and potential mispricing are enhanced in several aspects involving consideration of model risk … We demonstrate how to quantify the effect of model risk in this analysis by incorporating various robust estimators for key model parameter estimates that apply in both marginal and joint cyber risk loss […]

Distributionally Robust Reinsurance with Value-at-Risk and Conditional Value-at-Risk

“Our model handles typical stop-loss reinsurance contracts. We show that a three-point distribution achieves the worst-case VaR of the total retained loss of the insurer, from which the closed-form solutions of the worst-case distribution and optimal deductible are obtained. Moreover, we show that the worst-case Conditional Value-at-Risk of the total retained loss of the insurer is equal to the worst-case VaR, […]