The Art and Science of Modeling Decision-Making Under Severe Uncertainty

Sniedovich, Moshe

doi:10.7494/dmms.2007.1.2.111

Cited by 39 publications

(52 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The selection of an appropriate starting point (ũ) within a theoretically unbounded region of uncertainty is a highly debated subject (Sniedovich 2007;2012). For this analysis the median scenarios of supply and demand (following rankings as stated above) are selected for the primary IG run (defined as U mid ).…”

Section: Info-gap Decision Theory (Ig)mentioning

confidence: 99%

“…The number of start points selected for examination is deemed appropriate given the complexity of the case study and range of uncertainty examined. The range in supply and demand uncertainty is selected with great care and by considering a wide array of different data/information sources to produce a range of genuinely likely scenarios, as advised by Sniedovich (2007), detailed fully in section 3.2.…”

Section: Info-gap Decision Theory (Ig)mentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Roach

Kapelan

Ledbetter

et al. 2016

J. Water Resour. Plann. Manage.

View full text Add to dashboard Cite

This paper evaluates two established decision making methods and analyses their performance and suitability within a Water Resources Management (WRM) problem. The methods under assessment are Info-Gap decision theory (IG) and Robust Optimisation (RO). The methods have been selected primarily to investigate a contrasting local vs global method of assessing water system robustness to deep uncertainty but also to compare a robustness model approach (IG) with a robustness algorithm approach (RO), whereby the former selects and analyses a set of prespecified strategies and the latter uses optimisation algorithms to automatically generate and evaluate solutions. The study presents a novel area-based method for IG robustness modelling and assesses the applicability of utilising the Future Flows climate change projections in scenario generation for water resource adaptation planning. The methods were applied to a case study 2 resembling the Sussex North Water Resource Zone in England, assessing their applicability at improving a risk-based WRM problem and highlighting the strengths and weaknesses of each method at selecting suitable adaptation strategies under climate change and future demand uncertainties. Pareto sets of robustness to cost are produced for both methods and highlight RO as producing the lower costing strategies for the full range of varying target robustness levels. IG produced the more expensive Pareto strategies due to its more selective and stringent robustness analysis, resulting from the more complex scenario ordering process.

show abstract

Section: Info-gap Decision Theory (Ig)mentioning

confidence: 99%

Section: Info-gap Decision Theory (Ig)mentioning

confidence: 99%

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Roach

Kapelan

Ledbetter

et al. 2016

J. Water Resour. Plann. Manage.

View full text Add to dashboard Cite

show abstract

“…• Probabilistic reasoning using Indicators of Impact and other evidence -This can include pattern recognition, classification, inductive reasoning from evidence, and reasoning about uncertainty (Schum 2001, Barker and Haimes 2009, Loch et al 2008, Masys 2012, Sniedovich 2007, van Asselt and Rotmans 2002, Ferrin et al 2009). These insights and inferences should be very informative for Steps 5, 6, and 7, above.…”

Section: Model Selection and Estimation Methodsmentioning

confidence: 99%

How Bad is it? – A Branching Activity Model to Estimate the Impact of Information Security Breaches

Thomas¹,

Antkiewicz²,

Florer³

et al. 2013

SSRN Journal

View full text Add to dashboard Cite

“…An information-gap model does quantify the possible range of uncertainty, but without any measure function. Sniedovich (2007) is critical of any information-gap models, which, he asserts, generically resemble Wald's Maximin models (Wald, 1945(Wald, , 1950 and, therefore, can lead to only locally optimal and, therefore, rationally limited decisions. However, this paper demonstrates that this is a very doubtful, if not outrightly wrong, assertion, since information gap decision models do not use a probabilistic measure function: they focus on the incompleteness or lack of information.…”

Section: Introductionmentioning

confidence: 99%

“…Only recently I learned that mathematicians Ben-Haim and Sniedovich are currently probing similar non-probabilistic decision-making theory issues more generally (Ben-Haim, 2006;Sniedovich, 2007). Ben-Haim's information (info-) gap decision theory is a non-probabilistic decision theory seeking to optimize robustness to failure, or opportunities for pro…t, under severe uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Investment Model Uncertainty and Fair Pricing

Los

Tungsong

2008

SSRN Journal

View full text Add to dashboard Cite

Modern investment theory takes it for granted that a Security Market Line (SML) is as certain as its "corresponding" Capital Market Line. (CML). However, it can be easily demonstrated that this is not the case. Knightian non-probabilistic, information gap uncertainty exists in the security markets, as the bivariate "Galton's Error" and its concomitant information gap proves (Journal of Banking & Finance, 23, 1999, 1793-1829. In fact, an SML graph needs (at least) two parallel horizontal beta axes, implying that a particular mean security return corresponds with a limited Knightian uncertainty range of betas, although it does correspond with only one market portfolio risk volatility. This implies that a security' risk premium is uncertain and that a Knightian uncertainty range of SMLs and of fair pricing exists. This paper both updates the empirical evidence and graphically traces the …nancial market consequences of this model uncertainty for modern investment theory. First, any investment knowledge about the securities risk remains uncertain. Investment valuations carry with them epistemological ("modeling") risk in addition to the Markowitz-Sharpe market risk. Second, since idiosyncratic, or …rm-speci…c, risk is limited-uncertain, the real option value of a …rm is also limited-uncertain This explains the simultaneous coexistence of di¤erent analyst valuations of investment projects, particular …rms or industries, included a category "undecided." Third, we can now distinguish between "buy", "sell" and "hold" trading orders based on an empirically determined collection of SMLs, based this Knightian modeling risk. The coexistence of such simultaneous value signals for the same security is necessary for the existence of a market for that security! Without epistemological investment uncertainty, no ongoing markets for securities could exist. In the absence of transaction costs and other ine¢ ciencies, Knightian uncertainty is the necessary energy for market trading, since it creates potential or perceived arbitrage (= trading) opportunities, but it is also necessary for investors to hold securities. Knightian uncertainty provides a possible reason why the SEC can't obtain consensus on what constitutes "fair pricing." The paper also shows that Malkiel's recommended CML-based investments are extremely conservative and non-robust.

show abstract

The Art and Science of Modeling Decision-Making Under Severe Uncertainty

Cited by 39 publications

References 11 publications

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

How Bad is it? – A Branching Activity Model to Estimate the Impact of Information Security Breaches

Investment Model Uncertainty and Fair Pricing

Contact Info

Product

Resources

About