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Reviews of publications on Info-Gap decision theory

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Review # 15 (Posted: Ausugst 17, 2010; last update: August 27, 2010)

Reference:	Daniel Hine and Jim W. Hall Information gap analysis of flood model uncertainties and regional frequency analysis WATER RESOURCES RESEARCH, VOL. 46, W01514, doi:10.1029/2008WR007620, 2010
Abstract	Flood risk analysis is subject to often severe uncertainties, which can potentially undermine flood management decisions. This paper explores the use of information gap theory to analyze the sensitivity of flood management decisions to uncertainties in flood inundation models and flood frequency analysis. Information gap is a quantified nonprobabilistic theory of robustness. To analyze uncertainties in flood modeling, an energy-bounded information gap model is established and applied first to a simplified uniform channel and then to a more realistic 2-D flood model. Information gap theory is then applied to the estimation of flood discharges using regional frequency analysis. The use of an information gap model is motivated by the notion that hydrologically similar sites are clustered in the space of their L moments. The information gap model is constructed around a parametric statistical flood frequency analysis, resulting in a hybrid model of uncertainty in which natural variability is handled statistically while epistemic uncertainties are represented in the information gap model. The analysis is demonstrated for sites in the Trent catchment, United Kingdom. The analysis is extended to address ungauged catchments, which, because of the attendant uncertainties in flood frequency analysis, are particularly appropriate for information gap analysis. Finally, the information gap model of flood frequency is combined with the treatment of hydraulic model uncertainties in an example of how both sources of uncertainty can be accounted for using information gap theory in a flood risk management decision.
Acknowledgments	The research upon which this paper is based was funded by the UK Engineering and Physical Science Research Council grant GR/S18052 ‘‘CRANIUM: Climate change risk analysis, new impact and uncertainty methods.’’
Scores	TUIGF:100% SNHNSNDN:100% GIGO:100%

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Overview

This is a typical info-gap article. Typical in the sense that it is replete with all the misconceptions, errors and misleading rhetoric that are part and parcel of info-gap decision theory. The article's objective is to demonstrate an application of info-gap theory, in this case to water resources management. So, as an application of a fundamentally flawed method, the proposed application, as should have been expected, is "infected'' with all the ills afflicting the theory that it is based on.

Put another way, given that the analysis and results discussed in the paper are anchored in an erroneous theory, they can be only as good as the flawed theory on which they are based. For this reason there is no need to discuss here in detail the failings of the analysis and results outlined in the paper, as a my criticism of info-gap decision theory -- which shows it for what it is -- applies down to the last detail to this paper as well.

It is important, however, to take up a number of statements made in this paper because of their potential to mislead newcomers to this field, especially those who have not encountered the misleading info-gap phraseology and rhetoric before.

Maximin connection

The authors continue to propound the myth that info-gap decision theory is a distinct theory --- in their language "a quantified non probabilistic theory of robustness" --- and to justify this myth they fleetingly distinguish info-gap's robustness model from the Maximin model as follows (page 17, emphasis is mine):

While there is a superficial similarity with minimax decision making, no fixed bounds are imposed on the set of possibilities, leading to a comprehensive search of the set of possibilities and construction of functions that describe the results of that search.

It is hard to put across how grossly erroneous this statement is.

To begin with, not only is it downright false that there is only a "superficial similarity" between Info-gap's robustness model and the so called "minimax decision making" (read Wald's Maximin model). The truth of the matter is that info-gap's generic robustness model is in fact a simple instance of Wald's Maximin model, the most well-known model in decision-making under severe uncertainty, robust optimization, etc. The formal proof is short, simple, and straightforward (see FAQ-18).

I know for a fact that the second author is fully aware of the existence of such proofs, and I have every reason to believe that he is familiar with the proof showing that info-gap's generic model is a simple instance of Wald's Maximin model, as this proof has been available to the public at least since 2006. So, if the above statement is made on grounds that the authors take issue with this proof, they should have at least noted this fact and justified their objections.

In any event, it seems that the authors need reminding that, as a simple instance of a prototype (Wald's Maximin model), info-gap's robustness model cannot possibly possess capabilities that are absent from the prototype. Namely, whatever info-gap's robusntess model can do, it goes without saying that Wald's Maximin model can do.

In fact, it seems that the authors' erroneous explanation of the alleged dissimilarity between info-gap's robustness model and the Maximin model demonstrates not only a total lack of knowledge and/or misunderstanding of the Maximin's mode of operation, its capabilities, and its range. It demonstrates a lack of understanding of what info-gap's robustness model itself does. Thus, contrary to the authors' assertion, the Maximin model does not require imposing fixed bounds on the set of possibilities. In fact, there are simple, well known instances of the Maximin model where the variables' domains are unbounded. And insofar as info-gap's robustness model is concerned, the authors do not even realize that the performance requirement associated with this model does indeed impose bounds on the set of admissible possibilities. These bounds are imposed by virtue of the constraint that the performance requirement imposes on the admissible values of the horizon of uncertainty.

Indeed, if the horizon of uncertainty is really unbounded, as claimed by the authors, how is it that info-gap's robustness model seeks to maximize it?!?!?!

Thus, the harshest criticism should be directed the authors' grossly misleading claim that the Info-gap analysis provides for "a comprehensive search of the set of possibilities". This misleading claim is apparently due to the author's total lack of understanding of the true nature of info-gap's mode of operation. The fact of the matter is that info-gap's purported ability to conduct "a comprehensive search of the set of possibilities" due to its supposedly great attribute that "no fixed bounds are imposed on the set of possibilities" in effect comes to naught. This is so because info-gap's robustness model does not determine the robustness of a decision after "a comprehensive search of the set of possibilities". To the contrary, it determines the robustness of a decision solely with respect to a given critical level of the performance constraint. This means that info-gap's robustness model does not care two straws about the "set of all the possibilities": Its main mission is to satisfy the performance requirement.

The picture is this:

The large rectangle (U) represents the uncertainty space under consideration, the shaded area represents the points where the decision under consideration satisfies the performance requirement. The dot (û) represents the estimate and the bold blue circle represents the region of uncertainty around the estimate whose radius is equal to the robustness of the decision under consideration. Note that the radius of the circle (info-gap robustness) is not affected in the slightest by the performance of the decisions at points that are further from the circle. That is, the area outside the dashed circle is an info-gap No Man's Land: the performance of the decision outside this area has no impact whatsoever on the robustness of the decision. This means, of course, that info-gap's robustness analysis is not designed to explore the entire uncertainty space.

To make this obvious point even more explicit, consider this picture, in relation to the picture above

So, the authors better explain on what grounds do they claim that info-gap decision theory "explores" the entire uncertainty space? It most certainly does not do this, except in cases where robustness is not an issue. Namely in ( trivial) cases where a decision satisfies the performance requirement over the entire uncertainty space.

Remark:
Surely, the second author must be aware that info-gap's robustness analysis does not explore the entire uncertainty space. Because, consider the following statement from Hall and Ben-Haim (2007, p. 3):

Info-gap theory
An alternative approach is to start an uncertainty analysis at our best estimate and then examine how decision options perform as conditions depart increasingly from expectations, without identifying a worst case. If an option continues to be preferred, even at a very large horizon of uncertainty, then it is thought to be robust to uncertainty.

Note that, as the authors themselves make plain, the exploration of the horizon of uncertainty continues so long as at least one decision satisfies the performance requirement. If this requirement is not satisfied by any decision, the exploration terminates. Incidentally, the exploration terminating as a result of a violation of the constraint seems to explain why the authors’ erroneously conclude that info-gap decision theory does not identify a worst case.

In short, Hall and Ben-Haim's (2007) explanation makes it crystal clear: info-gap's robustness analysis is not based on a complete exploration of the entire uncertainty space.

It may be instructive to illustrate info-gap's robustness analysis in terms of an experiment with a gray balloon that automatically changes its color to blue once any point on its surface reaches a location that violates certain performance requirements. Consider then the following:

• A deflated gray balloon is placed at the nominal location (estimate).


• Making sure that its center remains at the nominal location (estimate), the balloon is slowly being inflated.


• The balloon turns blue once any point on its surface reaches a location that violates the performance requirement.


• We stop inflating the balloon once it turns blue.

At the point it turns blue, the radius of the balloon is the robustness of the balloon at the nominal location (estimate). Note that the balloon’s performance beyond the radius at which it turns blue is not explored. The set of locations in the uncertainty space that are beyond this radius is info-gap's No Man's Land.

Radius of stability connection

The authors are apparently unaware of the fact that info-gap's robustness model is a simple radius of stability model (circa 1960) and that, as such, it is designed to model/analyze small perturbations in a given value of the parameter of interest. The flaw in info-gap decision theory is that this model of local stability/robustness is misapplied to handle severe uncertainty that is, large perturbations. Consequently, the results yielded by this analysis, which obviously can have only local significance, are erroneously proclaimed by info-gap decision theory as having global significance.

The "remaining" assumption by Hall and Harvey (2009)

It is extremely interesting, thus certainly warranting comment in this review, that there is no mention in this article of the assumption introduced by Hall and Harvey (2009). The objective of the assumption in question was to rescue info-gap decision theory from the fate of a voodoo decision theory par excellence. Hence the assumption in question was phrased as follows (Hall and Harvey 2009, p. 2):

An assumption remains that values of u become increasingly unlikely as they diverge from û.

More on this can be found in my review of Hall and Harvey (2009).

To clarify why Hall and Harvey (2009) felt compelled to introduce this peculiar assumption note that:

• Info-gap decision theory (Ben-Haim 1996, 2001, 2006, 2010) puts forward the absurd proposition that a local analysis in the neighborhood of a wild guess (estimate) is a reliable method for identifying decisions that are robust against severe uncertainty over vast uncertainty spaces.


• So, in an attempt to eliminate, or at least mitigate, this absurd, Hall and Harvey (2009) introduced an assumption requiring the wild guess (estimate) to be the most likely value of the true value of the parameter of interest, and other possible values to become increasingly unlikely as they diverge from the wild guess (estimate). This attempt was a step in the right direction, but the result was an untenable contradiction which exposed info-gap decision theory, hence Hall and Harvey's (2009) application of it, to even harsher criticism (See Review # 6 for a discussion of this matter).

Incidentally, this contradiction was picked up in a report commissioned by ....

Department for Environment Food and Rural Affairs (DEFRA) report (2009)

The report I am referring to is the 2009 Department for Environment Food and Rural Affairs (DEFRA) report. To be precise I am referring to the following paragraph on page 75 of the report:

More recently, Info-Gap approaches that purport to be non-probabilistic in nature developed by Ben-Haim (2006) have been applied to flood risk management by Hall and Harvey (2009). Sniedovich (2007) is critical of such approaches as they adopt a single description of the future and assume alternative futures become increasingly unlikely as they diverge from this initial description. The method therefore assumes that the most likely future system state is known a priori. Given that the system state is subject to severe uncertainty, an approach that relies on this assumption as its basis appears paradoxical, and this is strongly questioned by Sniedovich (2007).

Mervyn Bramley, Ben Gouldby, Anthony Hurford, Jaap-Jeroen Flikweert
Marta Roca Collell, Paul Sayers, Jonathan Simm, Michael Wallis
Delivering Benefits Through Evidence
PAMS (Performance-based Asset Management System)
Phase 2 Outcome Summary Report (PDF File)
Project: SC040018/R1
Environment Agency -- December 2009
Department for Environment Food and Rural Affairs
UK

Due to its "diplomatic language", the point made by this paragraph may not be entirely clear to all readers .

So, let me clarify that the DEFRA report calls attention to the obvious: Hall and Harvey's (2009) assumption flies in the face of the assumption that the true value of the parameter of interest is subject to severe uncertainty.

You do not have to be an expert in Decision Theory to figure this out.

And to go back to the article under review:

The "Main Assumption"

In Hine and Hall (2010) a different assumption is introduced to get rid of, or mitigate, the above mentioned absurd in info-gap decision theory's prescription for the management of severe uncertainty. So, on page 2-3 we read the following:

The main assumption is that u, albeit uncertain, will to some extent be clustered around some central estimate û in the way described by U(α,û), though the size of the cluster (the horizon of uncertainty α) is unknown. In other words, there is no known or meaningfully bounded worst case. Specification of the info gap uncertainty model U(α,û) may be based upon current observations or best future projections.

Although this phrasing is an improvement on Hall and Harvey's (2009) quick-fix, just like its predecessor, this assumption amounts to a major indictment of info-gap decision theory. And, strictly speaking, it is misleading.

Let me explain.

• What the authors refer to here as the "main assumption", thereby creating the impression that this assumption is implicit in info-gap decision theory, is not in fact an info-gap decision theory assumption. It is not an integral part of info-gap decision theory -- as the theory is described in Ben-Haim (1996, 2001, 2006, 2010). There is no trace of it in any of these publications.


• This "main" assumption is an innovation introduced ad hoc by the authors in order to justify info-gap's absurd proposition to focus the analysis on a wild guess.


• Interestingly, there is no trace of this "main" assumption in Hall's earlier publications on info-gap decision theory. For example, consider the following quote (emphasis is mine):

  In info-gap theory the uncertainty in a system model is parametrised with an uncertainty parameter α (a positive real number), which defines a family of nested sets that bound regions or clusters of system behaviour. When α = 0 the prediction from the system model converges to a point, which is the anticipated system behaviour, given current available information. However, it is recognised that the system model is incomplete so there will be a range of variation around the nominal behaviour. Uncertainty, as defined by the parameter α, is therefore a range of variation of the actual around the nominal. No further commitment is made to the structure of uncertainty. α is not a normalised measure, so info-gap theory is fundamentally different to the family of methods based on measure theory shown in Figure 2.

  pp. 225-226
  Jim W. Hall
  Handling uncertainty in the hydroinformatic process
  Journal of Hydroinformatics, 5(4), 215-232, 2003

• The net outcome of this "main" assumption is that it implies that info-gap decision theory imposes some likelihood structure on the uncertainty space. This, however, flies in the face of the unequivocal statements by Ben-Haim -- the Father of info-gap decision theory -- that this theory is non-probabilistic and likelihood-free. Indeed, the assumption flies in the face of the authors' own statement in the abstract that "Information gap is a quantified non-probabilistic theory of robustness".


• The irony is that for all the havoc (inconsistencies, contradictions and the like) that it creates in info-gap decision theory itself, in the end the assumption comes to naught. Because, as in the case of the Hall and Harvey (2009) version, this assumption remains too weak to patch up the hole it was intended to mend. Namely, even in its present phrasing it does not guarantee that the true value of u is more likely to be in the neighborhood of the estimate to the extent that neighborhoods that are distant from the estimate can be excluded from the robustness analysis.

But more than anything else, this assumption changes completely the meaning of the concept "severe uncertainty" as it is used by the authors and it is inconsistent with the interpretation of this concept in info-gap decision theory.

And to see why, consider this:

Making Responsible Decisions (When it Seems that You Can't)
Engineering Design and Strategic Planning Under Severe Uncertainty

What happens when the uncertainties facing a decision maker are so severe that the assumptions in conventional methods based on probabilistic decision analysis are untenable? Jim Hall and Yakov Ben-Haim describe how the challenges of really severe uncertainties in domains as diverse as climate change, protection against terrorism and financial markets are stimulating the development of quantified theories of robust decision making.

Hall and Ben-Haim, 2007, p. 1

This is the opening paragraph of a paper posted on the web site FloodRiskNet in the UK since November 2007.

Are Hine and Hall (2010) referring to the same severe uncertainty that is considered by Hall and Ben-Haim (2007)? If so, how is it that the "main assumption" or a variation thereof, is not mentioned at all in Hall and Ben-Haim (2007)?

Remark:

• There seems to be an urgent need for an international search warrant for this "main assumption" as it is nowhere to be found, except in ... Hine and Hall's (2010) article.


• Should you come across this elusive statement elsewhere, please send me the exact bibliographic details.


• The objective of the authors' invention is to defend info-gap decision theory’s indefensible proposition to use of a local (radius of stability) model to determine the robustness of decisions against severe uncertainty.


• I challenge the authors to tell us where exactly can we find the origin of this "main assumption" in Ben-Haim's publications on info-gap decision theory.

Worst-Case Saga

The authors attempt to keep alive the myth that info-gap decision theory is not based on a worst-case approach to uncertainty. In page 3 we find:

the size of the cluster (the horizon of uncertainty α) is unknown. In other words, there is no known or meaningfully bounded worst case.

This, of course, is a gross error. Because, given that info-gap's robustness model is set to maximize the value of the horizon of uncertainty α≥0, the worst-case is clearly α=0. Furthermore, the worst-case approach underlying info-gap theory's attitude toward uncertainty is clearly manifested by the fact that the worst u in U(α,û) determines whether α is admissible. This is precisely the reason that info-gap's robustness model requires the decision variables to satisfy the performance requirement for all u in U(α,û) for any admissible value of α.

Normative utility theory

On page 3 the authors claim the following:

Info gap theory uses the concept of robust satisficing, in that it seeks to identify acts that perform acceptably well under a wide range of conditions, in contrast to normative decision theory, which seeks to maximize expected utility under assumed conditions.

This is a grossly misleading claim.

For starters, normative decision theory does not dictate to the analyst what "utility" is, nor does it dictate what he/she should consider as "utility". It is the user/analyst who decides what the "utility" under consideration is, or should be. In the case of info-gap decision theory, "utility" is the "robustness of the system". Hence, completely in line with normative decision theory, info-gap's robustness model maximizes the "utility" (robustness) of the system.

So where exactly is the contrast here with "normative decision theory"?

Secondly, the distinction between "optimizing" and "satisficing" is a matter of style, not of substance. The fact is that any "satisficing problem" can be expressed as an equivalent "optimization problem" (See my discussion on the "satisficing vs optimization" debate).

This is well known. For example, in the WIKIPEDIA entry of Satisficing, we find this:

In cybernetics, satisficing is optimization where all costs, including the cost of the optimization calculations themselves and the cost of getting information for use in those calculations, are considered.

Info-gap scholars' repeated futile attempts to establish the "uniqueness " of info-gap decision theory on the basis of alleged "fundamental" differences between satisficing and optimizing is yet another example of the confusion reigning in the info-gap literature about the basics of optimization theory, utility theory etc. all due to a lack of familiarity with the relevant literatures.

The sky is the limit!

Indeed, it is this lack of familiarity with the basics of decision theory, optimization theory, utility theory and I should add control theory and related fields, that explains why info-gap scholars do not realize that info-gap's generic robustness model is not just an instance of Wald's Maximin model but that it is a simple radius of stability model (circa 1960). This lack of familiarity also explains why they do not realize that the manner in which they apply this model is utterly unsuitable for the modeling/analysis and management of severe uncertainty.

However, since in the info-gap literature claims are not expected to be justified by solid arguments, there seems to be no limit to what can be said and promised. For instance consider the last paragraph in Hine and Hall (2010, emphasis is mine):

There are of course other sources of uncertainty in flood risk analysis, such as the uncertainties in depth damage functions and levee failure probabilities. Coastal and reservoir systems will require different treatment. Given the increasing need to demonstrate the robustness of flood risk management strategies and projects under a host of uncertainties, there is great potential for future extensions to the application of info gap theory.

So, we shall have to wait and see ...

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Other Reviews

1. Ben-Haim (2001, 2006): Info-Gap Decision Theory: decisions under severe uncertainty.


2. Regan et al (2005): Robust decision-making under severe uncertainty for conservation management.


3. Moilanen et al (2006): Planning for robust reserve networks using uncertainty analysis.


4. Burgman (2008): Shakespeare, Wald and decision making under severe uncertainty.


5. Ben-Haim and Demertzis (2008): Confidence in monetary policy.


6. Hall and Harvey (2009): Decision making under severe uncertainty for flood risk management: a case study of info-gap robustness analysis.


7. Ben-Haim (2009): Info-gap forecasting and the advantage of sub-optimal models.


8. Yokomizo et al (2009): Managing the impact of invasive species: the value of knowing the density-impact curve.


9. Davidovitch et al (2009): Info-gap theory and robust design of surveillance for invasive species: The case study of Barrow Island.


10. Ben-Haim et al (2009): Do we know how to set decision thresholds for diabetes?


11. Beresford and Thompson (2009): An info-gap approach to managing portfolios of assets with uncertain returns


12. Ben-Haim, Dacso, Carrasco, and Rajan (2009): Heterogeneous uncertainties in cholesterol management


13. Rout, Thompson, and McCarthy (2009): Robust decisions for declaring eradication of invasive species


14. Ben-Haim (2010): Info-Gap Economics: An Operational Introduction


15. Hine and Hall (2010): Information gap analysis of flood model uncertainties and regional frequency analysis


16. Ben-Haim (2010): Interpreting Null Results from Measurements with Uncertain Correlations: An Info-Gap Approach


17. Wintle et al. (2010): Allocating monitoring effort in the face of unknown unknowns


18. Moffitt et al. (2010): Securing the Border from Invasives: Robust Inspections under Severe Uncertainty


19. Yemshanov et al. (2010): Robustness of Risk Maps and Survey Networks to Knowledge Gaps About a New Invasive Pest


20. Davidovitch and Ben-Haim (2010): Robust satisficing voting: why are uncertain voters biased towards sincerity?


21. Schwartz et al. (2010): What Makes a Good Decision? Robust Satisficing as a Normative Standard of Rational Decision Making


22. Arkadeb Ghosal et al. (2010): Computing Robustness of FlexRay Schedules to Uncertainties in Design Parameters


23. Hemez et al. (2002): Info-gap robustness for the correlation of tests and simulations of a non-linear transient


24. Hemez et al. (2003): Applying information-gap reasoning to the predictive accuracy assessment of transient dynamics simulations


25. Hemez, F.M. and Ben-Haim, Y. (2004): Info-gap robustness for the correlation of tests and simulations of a non-linear transient


26. Ben-Haim, Y. (2007): Frequently asked questions about info-gap decision theory


27. Sprenger, J. (2011): The Precautionary Approach and the Role of Scientists in Environmental Decision-Making


28. Sprenger, J. (2011): Precaution with the Precautionary Principle: How does it help in making decisions


29. Hall et al. (2011): Robust climate policies under uncertainty: A comparison of Info-­-Gap and RDM methods


30. Ben-Haim and Cogan (2011) : Linear bounds on an uncertain non-linear oscillator: an info-gap approach


31. Van der Burg and Tyre (2011) : Integrating info-gap decision theory with robust population management: a case study using the Mountain Plover


32. Hildebrandt and Knoke (2011) : Investment decisions under uncertainty --- A methodological review on forest science studies.


33. Wintle et al. (2011) : Ecological-economic optimization of biodiversity conservation under climate change.


34. Ranger et al. (2011) : Adaptation in the UK: a decision-making process.

Recent Articles, Working Papers, Notes

Also, see my complete list of articles

Moshe's new book!

• Sniedovich, M. (2012) Fooled by local robustness, Risk Analysis, in press.


• Sniedovich, M. (2012) Black swans, new Nostradamuses, voodoo decision theories and the science of decision-making in the face of severe uncertainty, International Transactions in Operational Research, in press.


• Sniedovich, M. (2011) A classic decision theoretic perspective on worst-case analysis, Applications of Mathematics, 56(5), 499-509.


• Sniedovich, M. (2011) Dynamic programming: introductory concepts, in Wiley Encyclopedia of Operations Research and Management Science (EORMS), Wiley.


• Caserta, M., Voss, S., Sniedovich, M. (2011) Applying the corridor method to a blocks relocation problem, OR Spectrum, 33(4), 815-929, 2011.


• Sniedovich, M. (2011) Dynamic Programming: Foundations and Principles, Second Edition, Taylor & Francis.


• Sniedovich, M. (2010) A bird's view of Info-Gap decision theory, Journal of Risk Finance, 11(3), 268-283.


• Sniedovich M. (2009) Modeling of robustness against severe uncertainty, pp. 33- 42, Proceedings of the 10th International Symposium on Operational Research, SOR'09, Nova Gorica, Slovenia, September 23-25, 2009.


• Sniedovich M. (2009) A Critique of Info-Gap Robustness Model. In: Martorell et al. (eds), Safety, Reliability and Risk Analysis: Theory, Methods and Applications, pp. 2071-2079, Taylor and Francis Group, London.
.

• Sniedovich M. (2009) A Classical Decision Theoretic Perspective on Worst-Case Analysis, Working Paper No. MS-03-09, Department of Mathematics and Statistics, The University of Melbourne.(PDF File)


• Caserta, M., Voss, S., Sniedovich, M. (2008) The corridor method - A general solution concept with application to the blocks relocation problem. In: A. Bruzzone, F. Longo, Y. Merkuriev, G. Mirabelli and M.A. Piera (eds.), 11th International Workshop on Harbour, Maritime and Multimodal Logistics Modeling and Simulation, DIPTEM, Genova, 89-94.


• Sniedovich, M. (2008) FAQS about Info-Gap Decision Theory, Working Paper No. MS-12-08, Department of Mathematics and Statistics, The University of Melbourne, (PDF File)


• Sniedovich, M. (2008) A Call for the Reassessment of the Use and Promotion of Info-Gap Decision Theory in Australia (PDF File)


• Sniedovich, M. (2008) Info-Gap decision theory and the small applied world of environmental decision-making, Working Paper No. MS-11-08
  This is a response to comments made by Mark Burgman on my criticism of Info-Gap (PDF file )


• Sniedovich, M. (2008) A call for the reassessment of Info-Gap decision theory, Decision Point, 24, 10.


• Sniedovich, M. (2008) From Shakespeare to Wald: modeling wors-case analysis in the face of severe uncertainty, Decision Point, 22, 8-9.


• Sniedovich, M. (2008) Wald's Maximin model: a treasure in disguise!, Journal of Risk Finance, 9(3), 287-291.


• Sniedovich, M. (2008) Anatomy of a Misguided Maximin formulation of Info-Gap's Robustness Model (PDF File)
  In this paper I explain, again, the misconceptions that Info-Gap proponents seem to have regarding the relationship between Info-Gap's robustness model and Wald's Maximin model.


• Sniedovich. M. (2008) The Mighty Maximin! (PDF File)
  This paper is dedicated to the modeling aspects of Maximin and robust optimization.


• Sniedovich, M. (2007) The art and science of modeling decision-making under severe uncertainty, Decision Making in Manufacturing and Services, 1-2, 111-136. (PDF File) .


• Sniedovich, M. (2007) Crystal-Clear Answers to Two FAQs about Info-Gap (PDF File)
  In this paper I examine the two fundamental flaws in Info-Gap decision theory, and the flawed attempts to shrug off my criticism of Info-Gap decision theory.


• My reply (PDF File) to Ben-Haim's response to one of my papers. (April 22, 2007)

  This is an exciting development!

  • Ben-Haim's response confirms my assessment of Info-Gap. It is clear that Info-Gap is fundamentally flawed and therefore unsuitable for decision-making under severe uncertainty.
  
  
  • Ben-Haim is not familiar with the fundamental concept point estimate. He does not realize that a function can be a point estimate of another function.

    So when you read my papers make sure that you do not misinterpret the notion point estimate. The phrase "A is a point estimate of B" simply means that A is an element of the same topological space that B belongs to. Thus, if B is say a probability density function and A is a point estimate of B, then A is a probability density function belonging to the same (assumed) set (family) of probability density functions.

    Ben-Haim mistakenly assumes that a point estimate is a point in a Euclidean space and therefore a point estimate cannot be say a function. This is incredible!



• A formal proof that Info-Gap is Wald's Maximin Principle in disguise. (December 31, 2006)
  This is a very short article entitled Eureka! Info-Gap is Worst Case (maximin) in Disguise! (PDF File)
  It shows that Info-Gap is not a new theory but rather a simple instance of Wald's famous Maximin Principle dating back to 1945, which in turn goes back to von Neumann's work on Maximin problems in the context of Game Theory (1928).


• A proof that Info-Gap's uncertainty model is fundamentally flawed. (December 31, 2006)
  This is a very short article entitled The Fundamental Flaw in Info-Gap's Uncertainty Model (PDF File) .
  It shows that because Info-Gap deploys a single point estimate under severe uncertainty, there is no reason to believe that the solutions it generates are likely to be robust.


• A math-free explanation of the flaw in Info-Gap. ( December 31, 2006)
  This is a very short article entitled The GAP in Info-Gap (PDF File) .
  It is a math-free version of the paper above. Read it if you are allergic to math.


• A long essay entitled What's Wrong with Info-Gap? An Operations Research Perspective (PDF File) (December 31, 2006).
  This is a paper that I presented at the ASOR Recent Advances in Operations Research (PDF File) mini-conference (December 1, 2006, Melbourne, Australia).

Recent Lectures, Seminars, Presentations

If your organization is promoting Info-Gap, I suggest that you invite me for a seminar at your place. I promise to deliver a lively, informative, entertaining and convincing presentation explaining why it is not a good idea to use — let alone promote — Info-Gap as a decision-making tool.

Here is a list of relevant lectures/seminars on this topic that I gave in the last two years.

ASOR Recent Advances, 2011, Melbourne, Australia, November 16 2011. Presentation: The Power of the (peer-reviewed) Word. (PDF file).

• Alex Rubinov Memorial Lecture The Art, Science, and Joy of (mathematical) Decision-Making, November 7, 2011, The University of Ballarat. (PDF file).


• Black Swans, Modern Nostradamuses, Voodoo Decision Theories, and the Science of Decision-Making in the Face of Severe Uncertainty (PDF File) .
  (Invited tutorial, ALIO/INFORMS Conference, Buenos Aires, Argentina, July 6-9, 2010).


• A Critique of Info-Gap Decision theory: From Voodoo Decision-Making to Voodoo Economics(PDF File) .
  (Recent Advances in OR, RMIT, Melbourne, Australia, November 25, 2009)


• Robust decision-making in the face of severe uncertainty(PDF File) .
  (GRIPS, Tokyo, Japan, October 16, 2009)


• Decision-making in the face of severe uncertainty(PDF File) .
  (KORDS'09 Conference, Vilnius, Lithuania, September 30 -- OCtober 3, 2009)


• Modeling robustness against severe uncertainty (PDF File) .
  (SOR'09 Conference, Nova Gorica, Slovenia, September 23-25, 2009)


• How do you recognize a Voodoo decision theory?(PDF File) .
  (School of Mathematical and Geospatial Sciences, RMIT, June 26, 2009).


• Black Swans, Modern Nostradamuses, Voodoo Decision Theories, Info-Gaps, and the Science of Decision-Making in the Face of Severe Uncertainty (PDF File) .
  (Department of Econometrics and Business Statistics, Monash University, May 8, 2009).


• The Rise and Rise of Voodoo Decision Theory.
  ASOR Recent Advances, Deakin University, November 26, 2008. This presentation was based on the pages on my website (voodoo.moshe-online.com).


• Responsible Decision-Making in the face of Severe Uncertainty (PDF File) .
  (Singapore Management University, Singapore, September 29, 2008)


• A Critique of Info-Gap's Robustness Model (PDF File) .
  (ESREL/SRA 2008 Conference, Valencia, Spain, September 22-25, 2008)


• Robust Decision-Making in the Face of Severe Uncertainty (PDF File) .
  (Technion, Haifa, Israel, September 15, 2008)


• The Art and Science of Robust Decision-Making (PDF File) .
  (AIRO 2008 Conference, Ischia, Italy, September 8-11, 2008 )


• The Fundamental Flaws in Info-Gap Decision Theory (PDF File) .
  (CSIRO, Canberra, July 9, 2008 )


• Responsible Decision-Making in the Face of Severe Uncertainty (PDF File) .
  (OR Conference, ADFA, Canberra, July 7-8, 2008 )


• Responsible Decision-Making in the Face of Severe Uncertainty (PDF File) .
  (University of Sydney Seminar, May 16, 2008 )


• Decision-Making Under Severe Uncertainty: An Australian, Operational Research Perspective (PDF File) .
  (ASOR National Conference, Melbourne, December 3-5, 2007 )


• A Critique of Info-Gap (PDF File) .
  (SRA 2007 Conference, Hobart, August 20, 2007)


• What exactly is wrong with Info-Gap? A Decision Theoretic Perspective (PDF File) .
  (MS Colloquium, University of Melbourne, August 1, 2007)


• A Formal Look at Info-Gap Theory (PDF File) .
  (ORSUM Seminar , University of Melbourne, May 21, 2007)


• The Art and Science of Decision-Making Under Severe Uncertainty (PDF File) .
  (ACERA seminar, University of Melbourne, May 4, 2007)


• What exactly is Info-Gap? An OR perspective. (PDF File)
  ASOR Recent Advances in Operations Research mini-conference (December 1, 2006, Melbourne, Australia).

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