The Spin Stops Here!
Decision-Making Under Severe Uncertainty  
Faqs | Help | @ | Contact | home  
voodoostan info-gap decision theory info-gap economics severe uncertainty mighty maximin robust decisions responsible decisions

Reviews of publications on Info-Gap decision theory

Review # 32 (Posted: June 25, 2011)

Reference: Patrick Hildebrandt and Thomas Knoke
Investment decisions under uncertainty --- A methodological review on forest science studies
Forest Policy and Economics, 13 (2011) 1–15.
Abstract

Several parametric and non-parametric approaches have been developed to value financial assets. Yet, financial valuation techniques have only slowly percolated into disciplines concerned with the management of ecosystems. Particularly in forest management, decision-makers often find themselves confronted with extremely long time horizons and severely uncertain information. This requires careful valuation approaches, which are often underrepresented or even completely lacking in forest management. This paper gives a comprehensive overview on techniques for financial decision-making under uncertainty and develops future research needs. First, we analyse different approaches from the expected utility framework as well as option pricing models and robust optimisation techniques as possible approaches to make decisions on forest investments and giving a short review regarding forestry-related applications. Afterwards we discuss the suitability of the presented approaches to support decisions in forestry and conclude that robust optimisation techniques should be developed further, especially since erroneous financial data is likely to occur, as well as deviations from the assumption of normality. Currently, the maximization of financial robustness is probably the most adequate approach for many long-term decisions in forestry, such as selecting the optimum tree species composition. Further development of this approach appears possible and necessary. Finally, we come to the conclusion that even though it is intuitively clear that many long-term decisions should consider uncertainty, adequate financial valuation is not sufficiently developed within forest science. In the case of Central Europe, this may be an effect of ecological research dominating in forest science. Consequently, an intensification of the analysis of uncertainty in forest decision-making is necessary..

Keywords Uncertainty Diversification, Expected utility, Mean-variance, Option pricing, Stochastic dominance, Downside risk. Lower partial moment, Information-gap decision theory, Robust optimisation.
Acknowledgment This research was supported by the German Research Foundation (DFG), projects KN 586/4-1 and KN 586/7-1. For language editing we wish to thank Mrs. Kristin Dzurella. Furthermore, we gratefully acknowledge the anonymous review for the comments, which improved this manuscript significantly.
Scores TUIGF:100%
SNHNSNDN:100%
GIGO:100%

My principal reason for reviewing this article is the authors' references to my criticism of info-gap decision theory. My review will therefore be confined to a brief commentary on their statements on this matter.

The issues

The following statement is made in the article in the section entitled Robust Optimization immediately after a short overview of the literature:

We will discuss information-gap decision theory (IGDT; Ben-Haim, 2006) in greater detail, because this technique has obtained the greatest popularity in ecosystem management so far. However, we also acknowledge the criticism to IGDT (Sniedovich, 2007), which classifies IGDT as a special instance of Wald's maximin model and as a local sensitivity analysis (see Section 4).
Hildebrandt and Knoke (2011, p. 7)

So, the good news is that the authors recognize the fact that info-gap decision theory is a theory whose concern is robust optimization problems.

And the authors correctly identify two key ingredients of my criticism of info-gap decision theory, namely:

It is a pity, though, that the authors do not spell out more clearly the nature of my criticism. Furthermore, they also neglect to point out the following important point:

That said, let us now examine what the authors write about my criticism in Section 4 of their article.

The details

For the record, here is the compelete paragraph in Section 4 where my criticism of info-gap decision theory is discussed:

The approach that offers specific information about sensitivity to sampling errors or unexpected changes is the IGDT. This approach is slowly percolating into ecological research fields and its acceptance seems to be growing. Its high acceptance in ecological studies was the reason why we chose to devote so much space to introducing IGDT. However, IGDT has also provoked heated and emotional criticism. Sniedovich (2007) has launched a campaign against IGDT, not seeing the opportunity of IGDT to form a bridge that connects various disciplines. Sniedovich (2007) criticizes IGDT as being only a simple instance of Wald's maximin model and as local by its nature (Sniedovich, 2010). Naturally, that uncertainty set what is searched for by IGDT, i.e. that special set of parameters, which would still allow for acceptable outcomes, may only be a subset of the actual, but usually unknown uncertainty set. But nevertheless information about this subset is most welcome and informative to compare alternative decisions. However, the critique by Sniedovich (2010), although often presented overly harsh, should be taken seriously. It should be an indication to consider, besides IGDT, also the numerous existing approaches on robust optimisation (e.g. Goldfarb and Iyengar, 2003; Bertsimas and Sim, 2004; Ben-Tal et al., 2006a). However, these approaches are not always easy to adopt for non-mathematicians.
Hildebrandt and Knoke (2011, p. 12)

While on the face of it I should have perhaps commended the authors for mentioning in their article my criticism of info-gap decision theory, it is important to make it clear that their comments on my criticism not only do not convey the real reasons for my criticism being "overly harsh". But, the fact is that their justification (however apologetic) for the reasons for turning to info-gap decision theory, ends blunting the real point of my criticism of this fundamentally flawed theory. For one thing, the authors do not bother to explain why they judge my criticism to be "overly harsh".

So first, let me respond to the statement:

However, the critique by Sniedovich (2010), although often presented overly harsh, should be taken seriously.
Hildebrandt and Knoke (2011, p. 12)

It is important to take note that:

The point is then that as the authors failed to mention the real reasons for the "overly harsh" nature of my criticism, I remind the authors and the readers, of the reasons for my criticms being so harsh.

Regarding the first point.

One would have expected the authors to make it clear that

So keeping these facts in mind, let us examine this statement:

Sniedovich (2007) has launched a campaign against IGDT, not seeing the opportunity of IGDT to form a bridge that connects various disciplines. Sniedovich (2007) criticizes IGDT as being only a simple instance of Wald's maximin model and as local by its nature (Sniedovich, 2010).
Hildebrandt and Knoke (2011, p. 12)

In view of what I say above, I find the authors' claim that I fail to see "the opportunity of IGDT to form a bridge that connects various disciplines" most amusing.

Because, will the authors kindly explain:

This being so, the "connecting bridge" that info-gap decision theory can provide will at best be a rickety bridge. And to see how rickety a bridge it is, I remind the authors that because this theory has irresponsibly been promoted in the last few years as a reliable tool for the management of severe uncertainty characterized by unbounded uncertainty spaces, it is now being proposed by applied ecologists as a means for managing Black Swans and Unknown unknowns in sound environmental management (see Review 17).

These are the real reasons for my criticism of info-gap decision theory, and of scholars promoting it from the pages of peer-reviewed journals, being harsh.

Indeed, in view of the way info-gap decision theory is presented in the article under review, it appears that I ought to intensify my criticism, as it turn out that it is not sufficiently harsh!

And to give you an example of what I have in mind, I want to go back to this statement:

It should be an indication to consider, besides IGDT, also the numerous existing approaches on robust optimisation (e.g. Goldfarb and Iyengar, 2003; Bertsimas and Sim, 2004; Ben-Tal et al., 2006a). However, these approaches are not always easy to adopt for non-mathematicians.
Hildebrandt and Knoke (2011, p. 12)

My question to the authors is very simple:

Are the authors seriously suggesting that a theory that is a muddle through and through ought to be recommended to the "non-mathematians" in say, applied ecology, because this is all they might be able to cope with, seeing that they are not up to handling the "real stuff" in say Ben-tal et al. (2009)?!

I suggest that the authors throw in for good measure the suggestion that taking up info-gap decision theory would give the "non-mathematitians" in applied ecology an extra bonus: a wealth of buzzwords, empty rhetoric, indeed a rhetoric for every occasion, and endless spin. And this, the authors should point out to applied ecologists, would certainly stand them in good stead in the applied ecology journals. Because the buzzword "info-gap" apparently suffices to have an article published in applied ecology journals.

Indeed, I put it to the authors that it is only on grounds of the buzzwords, phraseology, etc. that:

This approach is slowly percolating into ecological research fields and its acceptance seems to be growing. Its high acceptance in ecological studies was the reason why we chose to devote so much space to introducing IGDT.
Hildebrandt and Knoke (2011, p. 12)

Finally, to the bottom line:

Sniedovich (2007) criticizes IGDT as being only a simple instance of Wald's maximin model and as local by its nature (Sniedovich, 2010). Naturally, that uncertainty set what is searched for by IGDT, i.e. that special set of parameters, which would still allow for acceptable outcomes, may only be a subset of the actual, but usually unknown uncertainty set. But nevertheless information about this subset is most welcome and informative to compare alternative decisions.
Hildebrandt and Knoke (2011, p. 12)

This is a gross misrepresentation of what my criticism of info-gap decision theory is all about.

For even if, for argument's sake, we accept that "... information about this subset is most welcome and informative to compare alternative decisions ... ", this has got nothing to do with my criticism of info-gap decision theory. My criticism has to do with the fact that info-gap decision theory is being hailed as a reliable methodology for the management of severe uncertainty characterized by vast uncertainty spaces, poor estimates and likelihood-free models of uncertainty. This means that info-gap decision theory must therefore be evaluated/assessed only as such, and not as a tool for local sensitivity analysis that may provide useful information to a decision maker.

So let me re-iterate the basic facts about my criticism:

Regarding the last point.

As I clearly explain in my articles (e.g. Sniedovich 2010, 2011), I use the term "voodoo" in precisely the same manner as this term is used in the popular phrases: "voodoo economics", "voodoo mathematics", "voodoo statistics", "voodoo ecology", and so on. That is, I use the phrase "voodoo decision theory" to describe a decision theory, such as info-gap decision theory, that is based on unsupported claims, unproven results, self-contradictions, and so on.

In the case of info-gap decision theory, from a methodological point of view, the most jarring unsupported claim is that an inherently local Radius of Stability model provides a reliable tool for the management of severe uncertainty that is characterized by an unbounded uncertainty space and a poor point estimate. Counter-examples to this claim are provided in Sniedovich (2010, 2011).

I also remind the authors that in the paper

Ben-Tal, A., Golany, B., Shtern, S. (2009) Robust multi-echelon multi-period inventory control.
European Journal of Operational Research, 199(3), 922-935

the authors use the term `a somewhat "irresponsible" decison maker' to describe a decision maker who restricts the robustness analysis to the "normal range" of values of the uncertainty paramater, instead of conducting the analysis on the "full range" of values.

More on this in Sniedovich (2010, 2011).

So, the authors must now ask themselves: do they support the claim that info-gap's robustness model provides a reliable tool for the management of severe uncertainty characterized by unbounded uncertainty spaces, a poor estimate, and a likelihood-free quantification of uncertainty?

Square wheels

In Section 3.3 Robust Optimization, we read (emphasis added)

Robust optimisation techniques take into account the uncertainty involved in long-term decision-making so common in forest and ecosystem management. Nevertheless, in general, applications up to now are scarce as the techniques only recently evolved; most have been performed in the IGDT framework, but other developments in robust optimisation seem promising as well for decision making in forestry or ecosystem management. We can be sure they will be applied more frequently and for different purposes in future studies.
Hildebrandt and Knoke (2011, p. 11)

Let me then remind the authors that robust optimization techniques date back to the early 1970s and that the first book on this topic, entitled Robust Discrete Optimization and Its Applications by Kouvelis and Yu, was published in 1997. In short, "robust optimization" pre-dates info-gap decision theory. But even more important is the fact that the Radius of Stability model dates back to the 1960s, and Maximin models to the 1940s.

So it is important that the authors consider this:

circa 2001 circa 1960
Generic info-gap robustness model Generic Radius of stability model
max {α ≥ 0: rc ≤ r(q,u), ∀u ∈U(α,û)} max {α ≥ 0: con(q,u), ∀u∈U(α,û) }

where con(q,u) denotes the list of stability/robustness requirements on alternative q.

By inspection then, info-gap's robustness model is a Radius of Stability model characterized by a single performance constraint of the form rc ≤ r(q,u).

So ... in what sense is info-gap robustness model "easier" (for non-mathematicians) than the Radius of Stability model??

The inevitable conclusion is that the authors are also unaware of the fact that info-gap's robustness model is a simple instance of the Radius of Stability model, and that they are not familiar with Hinrichsen and Pritchard's (1986, 2005) seminal work in the field of control theory. They should therefore consider this:

Robustness analysis has played a prominent role in the theory of linear systems. In particular the state-state approach via stability radii has received considerable attention, see [HP2], [HP3], and references therein. In this approach a perturbation structure is defined for a realization of the system, and the robustness of the system is identified with the norm of the smallest destabilizing perturbation. In recent years there has been a great deal of work done on extending these results to more general perturbation classes, see, for example, the survey paper [PD], and for recent results on stability radii with respect to real perturbations.
Paice and Wirth (1998, p. 289)
here: HP2 = Hinrichsen and Pritchard (1990), HP3 = Hinrichsen and Pritchard (1992) and PD= Packard and Doyle (1993).

The authors are also reminded that, like any other Radius of Stability model, info-gap's robustness model deals with small perturbations in a nominal value of a parameter, not with severe uncertainty.

We then introduce the stability radius as a measure of the smallest perturbation for which the perturbed system no longer satisfies the constraints. This is a worst case robustness measure expressed by a single number that provides an efficient tool for assessing the robustness of the stability of a given system.
Hinrichsen, and Pritchard (2005, p.519)

The stability radius is a worst case measure of robustness. It measures the size of the smallest perturbation for which the perturbed system is either not well-posed or does not have spectrum in Cg.

Hinrichsen and Pritchard (2005, p.585)

In short, what the authors should have emphasized in their article is that IGDT therefore does not have the capabilities to deal with severe uncertainty manifested in unbounded uncertainty spaces. And that its prescription to apply a Radius of stability model to this end renders IGDT a voodoo decision theory. And for extra flair they might throw in that it is a re-invened wheel, and a square one at that.

Of course, some square wheels have interesting practical applications. Like this one.

Summary

In their review article the authors had an opportunity to explain to the readers of Forest Policy and Economics the real hard facts about info-gap decisions theory that render it "problematic", and they could have clarified its true role and place in decision theory and robust optimization. Instead, the authors used my criticism as a platform for presenting a distorted view of the state of the art in decision-making under severe uncertainty and robust optimization.

The authors seem unwilling to accept the fact --- and a fact it is --- that info-gap's robustness model is a muddled re-invention of a well established wheel.

As things stand then, info-gap adherents and promoters can be credited with two achievements:

The article under review here is an example.

And so is Knoke's (2010) review of Ben-Haim's (1010) book Info-Gap Economics: An Operational Introduction, which I briefly discuss below.

These, no doubt are significant marketing achievements.

So the real question is this:

How long will peer-reviewed journals allow reinvented square wheels to be promoted from their pages as new and radically different.

Post Script

As a final note. I want to touch on a question, raised by this article, that from my perspective is the most interesting of all in this matter. The question is this:

How does the discussion in this article sit with Knoke's (2010) review of Info-Gap Economics, given that in this review he does not even hint at the existence of a criticism of info-gap decision theory?

I am referring to:

Thomas Knoke (2010). Book review in Ecological Economics, 2010, Volume 70, Issue 3, pp. 567-568:
Yakov Ben-Haim, Info-Gap Economics: An Operational Introduction, 2010,
Palgrave Macmillan, ISBN: 978-0-230-22804-7, 245 pp.

The question is then: how serious is Knoke (2010) about his recent advice

However, the critique by Sniedovich (2010), although often presented overly harsh, should be taken seriously.
Hildebrandt and Knoke (2011, p. 12)

Be that as it may, the point that really screams out for comment in Knoke's (2010) review is the gross misrepresentation in this review, and in Ben-Haim (2010), of the state of the art in robust optimization and economics. For instance, consider this

While certainly useful, in practice robust optimization is not always applicable for the needs of many scientists in the community. A common problem is that the true size and shape of the uncertainty set are unknown and difficult or impossible to predict. In lieu of this, Yakov Ben-Haim carefully introduces Info-Gap Economics readers to the uncertainty modelling approach, which is based on “Info-gaps”. An Info-gap is the disparity between what the analyst knows and what needs to be known in order to make a reliable decision. A worst-case scenario is not presumed, nor is the probability distribution reliably known, although some probabilistic information may exist and can be integrated.
Knoke (2010, p. 567)

Is Knoke (2010) seriously claiming that the rich literature on robust optimization does not offer models of robustness whose uncertainty spaces are at least as "simple" and "easy" to predict as those of info-gap's robustness model?

I put it to Knoke (2010) that he would be hard pressed to substantiate this claim because, after all, what is info-gap's robustness model but a ... simple instance of the Radius of Stability model, hence a simple instance of Wald's maximin model. In which case, does Knoke (2010) propose that a (mis)application of these models --- as prescribed by info-gap decision theory --- has an edge (or, whatever) on what is being done in Robust Optimization?!

Indeed, I put it to Knoke (2010) that his readers should have been advised that Radius of Stability models and maximin models have been staple models of robustness analysis for decades and that the body of knowledge associated with these models is enormous. Which means that the mere suggestion that info-gap's robustness model does what models in Robust Optimization cannot do borders on the absurd!

Also, is Knoke (2010) seriously suggesting that the basic difference between info-gap decision theory and say, Robust Optimization, is in the former's purported new "uncertainty modelling approach", which is based on "Info-gaps"? Is Knoke (2010) unaware of the fact that the buzzword "info-gap" is no more and no less than what is known to the rest of the world as "an error", or "a deviation", or "a perturbation"? So that, as a Radius of Stability analysis, all that info-gap's robustness analysis does is to seek a decision that provides the largest safe "perturbation" of a (poor) estimate?

Does Knoke(2010) suggest that the challenging task of decision-making under severe uncertainty is likely to be accomplished by pure rhetoric? That all that it would take to accomplish this task would be to market the old and trusted Radius of Stability model as an "X-Z" model?

And finally I would like to ask Knoke (2010) the following question:

How is it that a book on robust decision-making in economics, namely Info-Gap Economics: An Operational Introduction (Ben-Haim 2010), does not make so much as a single reference to the extensive work by Hansen and Sargent on the use of robustness models in ... economics? In particular, does not Knoke find it odd that there is no reference at all in Ben-Haim (2010) to the book entitled Robustness (Hansen and Sargent 2007)?

Note that the first endorsement of Hansen and Sargent's (2007) book reads as follows:

"Best policies can be evaluated, in theory at least, given an economy. But macroeconomists have only model economies at their disposal and necessarily these economies are abstractions. A concern then is that the model economy used to evaluate policy will provide poor guidance in practice. This leads to the search for policy that performs well for a broad class of economies. This is what robust control theory is all about. In this book, Hansen and Sargent greatly extend robust control theory to make it useful in the macro policy setting. This is a major contribution to macroeconomics."
-- Edward C. Prescott, Nobel Prize-winning economist

And while we are at it: how is it that not a single reference is made to Robust Optimization in the three books on info-gap decision theory (Ben-Haim 2001, 2006, 2010)?

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.

Disclaimer: This page, its contents and style, are the responsibility of the author (Moshe Sniedovich) and do not represent the views, policies or opinions of the organizations he is associated/affiliated with.


Last modified: