Accounting for uncertain educational outcomes in estimating the return to education /

"February 1990."

Mapping the consequences of an unanticipated drug supply change of uncertain origins: Response to the commentaries

No Abstract

Improved robust control of nonlinear stochastic systems using uncertain models

We introduce a technique for quantifying and then exploiting uncertainty in nonlinear stochastic control systems. The approach is suboptimal though robust and relies upon the approximation of the forward and inverse plant models by neural networks, which also estimate the intrinsic uncertainty. Sampling from the resulting Gaussian distributions of the inversion based neurocontroller allows us to introduce a control law which is demonstrably more robust than traditional adaptive controllers.

The application of methods of uncertain reasoning to the biological classification of river water quality

This thesis presents an investigation into the application of methods of uncertain reasoning to the biological classification of river water quality. Existing biological methods for reporting river water quality are critically evaluated, and the adoption of a discrete biological classification scheme advocated. Reasoning methods for managing uncertainty are explained, in which the Bayesian and Dempster-Shafer calculi are cited as primary numerical schemes. Elicitation of qualitative knowledge on benthic invertebrates is described. The specificity of benthic response to changes in water quality leads to the adoption of a sensor model of data interpretation, in which a reference set of taxa provide probabilistic support for the biological classes. The significance of sensor states, including that of absence, is shown. Novel techniques of directly eliciting the required uncertainty measures are presented. Bayesian and Dempster-Shafer calculi were used to combine the evidence provided by the sensors. The performance of these automatic classifiers was compared with the expert's own discrete classification of sampled sites. Variations of sensor data weighting, combination order and belief representation were examined for their effect on classification performance. The behaviour of the calculi under evidential conflict and alternative combination rules was investigated. Small variations in evidential weight and the inclusion of evidence from sensors absent from a sample improved classification performance of Bayesian belief and support for singleton hypotheses. For simple support, inclusion of absent evidence decreased classification rate. The performance of Dempster-Shafer classification using consonant belief functions was comparable to Bayesian and singleton belief. Recommendations are made for further work in biological classification using uncertain reasoning methods, including the combination of multiple-expert opinion, the use of Bayesian networks, and the integration of classification software within a decision support system for water quality assessment.

Agile or adaptable? Finding a paradigm for an uncertain world

This paper looks at the way in which, over recent years, paradigms for manufacturing management have evolved as a result of changing economic and environmental circumstances. The lean production concept, devised during the 1980s, proved robust only until the end of the bubble economy in Japan caused firms to re-examine the underlying principles of the lean production paradigm and redesign their production systems to suit the changing circumstances they were facing. Since that time a plethora of new concepts have emerged, most of which have been based on improving the way that firms are able to respond to the uncertainties of the new environment in which they have found themselves operating. The main question today is whether firms should be agile or adaptable. Both concepts imply a measure of responsiveness, but recent changes in the nature of the uncertainties have heightened the debate about what strategies should be adopted in the future.

Probabilistic control for uncertain systems

In this paper a new framework has been applied to the design of controllers which encompasses nonlinearity, hysteresis and arbitrary density functions of forward models and inverse controllers. Using mixture density networks, the probabilistic models of both the forward and inverse dynamics are estimated such that they are dependent on the state and the control input. The optimal control strategy is then derived which minimizes uncertainty of the closed loop system. In the absence of reliable plant models, the proposed control algorithm incorporates uncertainties in model parameters, observations, and latent processes. The local stability of the closed loop system has been established. The efficacy of the control algorithm is demonstrated on two nonlinear stochastic control examples with additive and multiplicative noise.

Sequencing Jobs with Uncertain Processing Times and Minimizing the Weighted Total Flow Time

We consider an uncertain version of the scheduling problem to sequence set of jobs J on a single machine with minimizing the weighted total flow time, provided that processing time of a job can take on any real value from the given closed interval. It is assumed that job processing time is unknown random variable before the actual occurrence of this time, where probability distribution of such a variable between the given lower and upper bounds is unknown before scheduling. We develop the dominance relations on a set of jobs J. The necessary and sufficient conditions for a job domination may be tested in polynomial time of the number n = |J| of jobs. If there is no a domination within some subset of set J, heuristic procedure to minimize the weighted total flow time is used for sequencing the jobs from such a subset. The computational experiments for randomly generated single-machine scheduling problems with n ≤ 700 show that the developed dominance relations are quite helpful in minimizing the weighted total flow time of n jobs with uncertain processing times.

Control Systems with Constraints and Uncertain Initial Conditions

AMS subject classification: 49N55, 93B52, 93C15, 93C10, 26E25.

Why (and When) are Preferences Convex? Threshold Effects and Uncertain Quality

It is often assumed (for analytical convenience, but also in accordance with common intuition) that consumer preferences are convex. In this paper, we consider circumstances under which such preferences are (or are not) optimal. In particular, we investigate a setting in which goods possess some hidden quality with known distribution, and the consumer chooses a bundle of goods that maximizes the probability that he receives some threshold level of this quality. We show that if the threshold is small relative to consumption levels, preferences will tend to be convex; whereas the opposite holds if the threshold is large. Our theory helps explain a broad spectrum of economic behavior (including, in particular, certain common commercial advertising strategies), suggesting that sensitivity to information about thresholds is deeply rooted in human psychology.

Sztochasztikus csődjátékok - avagy hogyan osszunk szét egy bizonytalan méretű tortát? = Stochastic bankruptcy games. How can a cake of uncertain dimensions be divided?

A kooperatív játékelmélet egyik legjelentősebb eredménye, hogy számos konfliktushelyzetben stabil megoldást nyújt. Ez azonban csak statikus és determinisztikus környezetben alkalmazható jól. Most megmutatjuk a mag egy olyan kiterjesztését - a gyenge szekvenciális magot -, amely képes valós, dinamikus, bizonytalan környezetben is eligazítást nyújtani. A megoldást a csődjátékok példájára alkalmazzuk, és segítségével megvizsgáljuk, hogy a pénzügyi irodalom ismert elosztási szabályai közül melyek vezetnek stabil, fenntartható eredményre. _______ One of the most important achievements of cooperative game theory is to provide a stable solution to numerous conflicts. The solutions it presents, on the other hand, have been limited to situations in a static, deterministic environment. The paper examines how the core can be extended to a more realistic, dynamic and uncertain scenario. The bankruptcy games studied are ones where the value of the estate and of the claims are stochastic, and a Weak Sequential Core is used as the solution concept for them. The author tests the stability of a number of well known division rules in this stochastic setting and finds that most are unstable, except for the Constrained Equal Awards rule, which is the only one belonging to the Weak Sequential Core.