scholarly journals A Satisficing Framework for Environmental Policy Under Model Uncertainty

2021 ◽  
Author(s):  
Stergios Athanasoglou ◽  
Valentina Bosetti ◽  
Laurent Drouet
Keyword(s):  
Environmental Policy ◽  
Model Uncertainty ◽  
Probability Distributions ◽  
Success Probability ◽  
Economic Assessment ◽  
Decision Criterion ◽  
Probability Criterion ◽  
Change Context ◽  
Future Consumption ◽  
Point Of Departure

AbstractWe propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

scholarly journals Robustness Computation of Dynamic Controllability in Probabilistic Temporal Networks with Ordinary Distributions

2020 ◽  
Author(s):  
Michael Saint-Guillain ◽  
Tiago Stegun Vaquero ◽  
Jagriti Agrawal ◽  
Steve Chien
Keyword(s):  
Probability Distributions ◽  
Dynamic Control ◽  
Success Probability ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Temporal Networks ◽  
Fixed Parameter ◽  
Probability Of Success ◽  
Dynamic Policy ◽  
Simple Temporal Networks

Most existing works in Probabilistic Simple Temporal Networks (PSTNs) base their frameworks on well-defined probability distributions. This paper addresses on PSTN Dynamic Controllability (DC) robustness measure, i.e. the execution success probability of a network under dynamic control. We consider PSTNs where the probability distributions of the contingent edges are ordinary distributed (e.g. non-parametric, non-symmetric). We introduce the concepts of dispatching protocol (DP) as well as DP-robustness, the probability of success under a predefined dynamic policy. We propose a fixed-parameter pseudo-polynomial time algorithm to compute the exact DP-robustness of any PSTN under NextFirst protocol, and apply to various PSTN datasets, including the real case of planetary exploration in the context of the Mars 2020 rover, and propose an original structural analysis.

Pacific Salmon Environmental and Life History Models: Advancing Science for Sustainable Salmon in the Future

2009 ◽  
Keyword(s):  
Model Uncertainty ◽  
Pacific Salmon ◽  
Probability Distributions ◽  
Cultural Resources ◽  
Policy Makers ◽  
Recovery Planning ◽  
Habitat Recovery ◽  
Alternative Scenarios ◽  
The One

<em>Abstract.</em>—Natural resource management requires difficult decisions, broad societal costs, and sacrifices from private landowners and public agencies. With so many financial, ecological and cultural resources at stake, policy-makers, managers, and citizens need scientific predictions that can help resolve conflicts and balance the often competing needs of ecosystems and communities. Modeled information is essential for meeting this need. The words “model uncertainty” are often misinterpreted as describing a lack of knowledge about model output. In fact, they describe knowledge, not only of the one most likely modeled estimate, but also of all the other possible estimates that the model might have provided, and their likelihood. We present six case studies, from salmon habitat recovery planning, illustrating how scientists can provide more useful products by describing distributions of possible outcomes as formal probability distributions, as confidence intervals, or as descriptions of alternative scenarios. In terms of management effectiveness, the communication and use of model uncertainty can be at least as important as the quality of the original model.

Robust Optimization With Mixed Interval and Probabilistic Parameter Uncertainties, Model Uncertainty, and Metamodeling Uncertainty

2019 ◽  
Author(s):  
Yanjun Zhang ◽  
Tingting Xia ◽  
Mian Li
Keyword(s):  
Robust Optimization ◽  
Real World ◽  
Parameter Uncertainty ◽  
Model Uncertainty ◽  
Optimization Problems ◽  
Probability Distributions ◽  
Parameter Uncertainties ◽  
True Value ◽  
Uncertainty Model ◽  
The Difference

Abstract Various types of uncertainties, such as parameter uncertainty, model uncertainty, metamodeling uncertainty may lead to low robustness. Parameter uncertainty can be either epistemic or aleatory in physical systems, which have been widely represented by intervals and probability distributions respectively. Model uncertainty is formally defined as the difference between the true value of the real-world process and the code output of the simulation model at the same value of inputs. Additionally, metamodeling uncertainty is introduced due to the usage of metamodels. To reduce the effects of uncertainties, robust optimization (RO) algorithms have been developed to obtain solutions being not only optimal but also less sensitive to uncertainties. Based on how parameter uncertainty is modeled, there are two categories of RO approaches: interval-based and probability-based. In real-world engineering problems, both interval and probabilistic parameter uncertainties are likely to exist simultaneously in a single problem. However, few works have considered mixed interval and probabilistic parameter uncertainties together with other types of uncertainties. In this work, a general RO framework is proposed to deal with mixed interval and probabilistic parameter uncertainties, model uncertainty, and metamodeling uncertainty simultaneously in design optimization problems using the intervals-of-statistics approaches. The consideration of multiple types of uncertainties will improve the robustness of optimal designs and reduce the risk of inappropriate decision-making, low robustness and low reliability in engineering design. Two test examples are utilized to demonstrate the applicability and effectiveness of the proposed RO approach.

scholarly journals Quantum algorithms for computing general discrete logarithms and orders with tradeoffs

2021 ◽  
Vol 15 (1) ◽  
pp. 359-407
Author(s):  
Martin Ekerå
Keyword(s):  
Quantum Algorithms ◽  
Probability Distributions ◽  
Success Probability ◽  
Discrete Logarithms ◽  
Group Order ◽  
Shor's Algorithm

Abstract We generalize our earlier works on computing short discrete logarithms with tradeoffs, and bridge them with Seifert's work on computing orders with tradeoffs, and with Shor's groundbreaking works on computing orders and general discrete logarithms. In particular, we enable tradeoffs when computing general discrete logarithms. Compared to Shor's algorithm, this yields a reduction by up to a factor of two in the number of group operations evaluated quantumly in each run, at the expense of having to perform multiple runs. Unlike Shor's algorithm, our algorithm does not require the group order to be known. It simultaneously computes both the order and the logarithm. We analyze the probability distributions induced by our algorithm, and by Shor's and Seifert's order-finding algorithms, describe how these algorithms may be simulated when the solution is known, and estimate the number of runs required for a given minimum success probability when making different tradeoffs.

scholarly journals Where that emissions went? Characterising the uncertainty of characterisation factors of underspecified emissions.

2020 ◽  
Author(s):  
Miguel Fernández Astudillo
Keyword(s):  
Life Cycle ◽  
Model Uncertainty ◽  
Probability Distributions ◽  
Random Errors ◽  
Discrete Probability ◽  
Continuous Approach ◽  
Imperfect Knowledge ◽  
Characterisation Factors ◽  
Discrete Probability Distributions ◽  
The Impact

Life cycle impact assessment (LCIA) methods use unspecified archetypes to model situations of imperfect knowledge. There is an inherent uncertainty in characterisation factors to unspecified archetypes, and this study proposes an estimation of this uncertainty using continuous and discrete probability distributions. The extent of “archetype uncertainty” is analysed for several methods and the impact on LCIA scores is quantified for all activities in the database ecoinvent. Results indicate that this source of uncertainty can be very large, introducing systematic as well as random errors in LCIA scores. Based on this research we recommend using undefined archetypes only when needed and quantify this source of uncertainty by default. The continuous and discrete approaches to model uncertainty give similar results, but the continuous approach is easier to implement.

scholarly journals Probabilistic Temporal Networks with Ordinary Distributions: Theory, Robustness and Expected Utility

2021 ◽  
Vol 71 ◽  
pp. 1091-1136
Author(s):  
Michael Saint-Guillain ◽  
Tiago Vaquero ◽  
Steve Chien ◽  
Jagriti Agrawal ◽  
Jordan Abrahams
Keyword(s):  
Expected Utility ◽  
Probability Distributions ◽  
Dynamic Control ◽  
Success Probability ◽  
Computation Method ◽  
Temporal Networks ◽  
Fixed Parameter ◽  
Simple Temporal Networks ◽  
Strong Dynamic ◽  
Utility Measures

Most existing works in Probabilistic Simple Temporal Networks (PSTNs) base their frameworks on well-defined, parametric probability distributions. Under the operational contexts of both strong and dynamic control, this paper addresses robustness measure of PSTNs, i.e. the execution success probability, where the probability distributions of the contingent durations are ordinary, not necessarily parametric, nor symmetric (e.g. histograms, PERT), as long as these can be discretized. In practice, one would obtain ordinary distributions by considering empirical observations (compiled as histograms), or even hand-drawn by field experts. In this new realm of PSTNs, we study and formally define concepts such as degree of weak/strong/dynamic controllability, robustness under a predefined dispatching protocol, and introduce the concept of PSTN expected execution utility. We also discuss the limitation of existing controllability levels, and propose new levels within dynamic controllability, to better characterize dynamic controllable PSTNs based on based practical complexity considerations. We propose a novel fixed-parameter pseudo-polynomial time computation method to obtain both the success probability and expected utility measures. We apply our computation method to various PSTN datasets, including realistic planetary exploration scenarios in the context of the Mars 2020 rover. Moreover, we propose additional original applications of the method.

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