Allocating Security Expenditures under Knightian Uncertainty: an Info-Gap Approach

We apply the information gap approach to resource allocation under Knightian (non-probabilistic) uncertainty in order to study how best to allocate public resources betweencompeting defense measures. We demonstrate that when determining the level and composi-tion of defense spending in an environment of extreme uncertaintyvis-a-visthe likelihood ofarmed conflict and its outcomes, robust-satisficing expected utility will usually be preferableto expected utility maximisation. Moreover, our analysis suggests that in environments withunreliable information about threats to national security and their consequences, a desirefor robustness to model misspecification in the decision making process will imply greaterexpenditure on certain types of defense measures at the expense of others. Our results alsoprovide a positivist explanation of how governments seem to allocate security expendituresin practice

Allocating Security Expenditures under Knightian Uncertainty: an Info-Gap Approach

We apply the information gap approach to resource allocation under Knightian (non-probabilistic) uncertainty in order to study how best to allocate public resources between competing defense measures. We demonstrate that when determining the level and composition of defense spending in an environment of extreme uncertainty vis-a-vis the likelihood of armed conflict and its outcomes, robust-satisficing expected utility will usually be preferable to expected utility maximization. Moreover, our analysis suggests that in environments with unreliable information about threats to national security and their consequences, a desire for robustness to model misspecification in the decision making process will imply greater expenditure on certain types of defense measures at the expense of others. Our results also provide a positivist explanation of how governments seem to allocate security expenditures in practice

Allocating Security Expenditures under Knightian Uncertainty: an Info-Gap Approach

We apply the information gap approach to resource allocation under Knightian (non-probabilistic) uncertainty in order to study how best to allocate public resources between competing defense measures. We demonstrate that when determining the level and composition of defense spending in an environment of extreme uncertainty vis-a-vis the likelihood of armed conflict and its outcomes, robust-satisficing expected utility will usually be preferable to expected utility maximization. Moreover, our analysis suggests that in environments with unreliable information about threats to national security and their consequences, a desire for robustness to model misspecification in the decision making process will imply greater expenditure on certain types of defense measures at the expense of others. Our results also provide a positivist explanation of how governments seem to allocate security expenditures in practice

Identification of certain polynomial nonlinear structures by adaptive selectively-sensitive excitation

This paper presents a method for identification of certain polynomial nonlinear dynamic systems by adaptive vibrational excitation. The identification is based on Introduction In complaining about undue adherence to linear theories, Erwin Schrodinger once wrote to Max Born about the plethora of models in which " [a]ll is linear, linear-linear in the nth power I would say, if that was not a contradiction." [1, p. 381]. While much modern engineering is rooted in linear models, today's scientific engineer is intensely aware of the nonlinearities which abound in nature as well as in technology. Identification of mechanical damage in civil structures, for example, is just one of many fields in which the multiplicity of nonlinear phenomena is recognized Two classes of identification problems can be defined [3]: (7) the structure of the model (for linear mechanical systems this refers to the number of degrees of freedom and trie type of damping), and (2) values of the model parameters (stiffnesses, inertias and so on). The identification of linear elastic structures has traditionally exploited modal properties which are characteristic of such systems. The complexities inherent in modal analysis of a nonlinear system occur for several reason

A lower bound on CNF encodings of the at-most-one constraint

Constraint "at most one" is a basic cardinality constraint which requires that at most one of its $n$ boolean inputs is set to $1$. This constraint is widely used when translating a problem into a conjunctive normal form (CNF) and we investigate its CNF encodings suitable for this purpose. An encoding differs from a CNF representation of a function in that it can use auxiliary variables. We are especially interested in propagation complete encodings which have the property that unit propagation is strong enough to enforce consistency on input variables. We show a lower bound on the number of clauses in any propagation complete encoding of the "at most one" constraint. The lower bound almost matches the size of the best known encodings. We also study an important case of 2-CNF encodings where we show a slightly better lower bound. The lower bound holds also for a related "exactly one" constraint.Comment: 38 pages, version 3 is significantly reorganized in order to improve readabilit

Don't know, can't know: Embracing deeper uncertainties when analysing risks

This article is available open access through the publisher’s website at the link below. Copyright @ 2011 The Royal Society.Numerous types of uncertainty arise when using formal models in the analysis of risks. Uncertainty is best seen as a relation, allowing a clear separation of the object, source and ‘owner’ of the uncertainty, and we argue that all expressions of uncertainty are constructed from judgements based on possibly inadequate assumptions, and are therefore contingent. We consider a five-level structure for assessing and communicating uncertainties, distinguishing three within-model levels—event, parameter and model uncertainty—and two extra-model levels concerning acknowledged and unknown inadequacies in the modelling process, including possible disagreements about the framing of the problem. We consider the forms of expression of uncertainty within the five levels, providing numerous examples of the way in which inadequacies in understanding are handled, and examining criticisms of the attempts taken by the Intergovernmental Panel on Climate Change to separate the likelihood of events from the confidence in the science. Expressing our confidence in the adequacy of the modelling process requires an assessment of the quality of the underlying evidence, and we draw on a scale that is widely used within evidence-based medicine. We conclude that the contingent nature of risk-modelling needs to be explicitly acknowledged in advice given to policy-makers, and that unconditional expressions of uncertainty remain an aspiration

Evolution of constrained layer damping using a cellular automaton algorithm

Constrained layer damping (CLD) is a highly effective passive vibration control strategy if optimized adequately. Factors controlling CLD performance are well documented for the flexural modes of beams but not for more complicated mode shapes or structures. The current paper introduces an approach that is suitable for locating CLD on any type of structure. It follows the cellular automaton (CA) principle and relies on the use of finite element models to describe the vibration properties of the structure. The ability of the algorithm to reach the best solution is demonstrated by applying it to the bending and torsion modes of a plate. Configurations that give the most weight-efficient coverage for each type of mode are first obtained by adapting the existing 'optimum length' principle used for treated beams. Next, a CA algorithm is developed, which grows CLD patches one at a time on the surface of the plate according to a simple set of rules. The effectiveness of the algorithm is then assessed by comparing the generated configurations with the known optimum ones

<i>Vibrio coralliilyticus</i> sp. nov., a temperature-dependent pathogen of the coral <i>Pocillopora damicornis</i>

Vibrio sp. YB1T (=ATCC BAA-450T =LMG 20984T), the aetiological agent of tissue lysis of the coral Pocillopora damicornis, was characterized as a novel Vibrio species on the basis of 16S rDNA sequence, DNA-DNA hybridization data (G+C content is 45·6 mol%), AFLP and GTG5-PCR genomic fingerprinting patterns and phenotypic properties, including the cellular fatty acid profile. The predominant fatty acids were 16 : 0 and 18 : 1?7c. The name Vibrio coralliilyticus sp. nov. is proposed for the novel coral-pathogenic species. In addition to strain YB1T, which was isolated from the Indian Ocean, five additional strains of V. coralliilyticus have been isolated, three from diseased P. damicornis in the Red Sea, one from diseased oyster larvae (Kent, UK) and one from bivalve larvae (Brazil). The six V. coralliilyticus strains showed high genotypic and phenotypic similarities and all were pathogenic to P. damicornis. The closest phylogenetic neighbours to V. coralliilyticus are Vibrio tubiashii, Vibrio nereis and Vibrio shilonii</i

Robust decision-making under severe uncertainty for conservation management

In-conservation biology it is necessary to make management decisions for endangered and threatened species under severe uncertainty. Failure to acknowledge and treat uncertainty can lead to poor decisions. To illustrate the importance of considering uncertainty, we reanalyze a decision problem for the Sumatran rhino, Dicerorhinus sumatrensis, using information-gap theory to propagate uncertainties and to rank management options. Rather than requiring information about the extent of parameter uncertainty at the outset, information-gap theory addresses the question of how much uncertainty can be tolerated before our decision would change. It assesses the robustness of decisions in the face of severe uncertainty. We show that different management decisions may result when uncertainty in utilities and probabilities are considered in decision-making problems. We highlight the importance of a full assessment of uncertainty in conservation management decisions to avoid, as much as possible, undesirable outcomes