This paper analyzes decisions on emissions of a stock pollutant under uncertainty in a two period model. Decisions are based on a weighted average of expected utility (EU) and the MaxiMin criterion. I first show that more weight on the worst case (less weight on EU) may lead to increased first period emissions. The effect of learning possibilities on emissions is not clear in general, but depen...

We consider the problem of multi-robot task-allocation when robots have to deal with uncertain utility estimates. Typically an allocation is performed to maximize expected utility; we consider a means for measuring the robustness of a given optimal allocation when robots have some measure of the uncertainty (e.g., a probability distribution, or moments of such distributions). We introduce the I...

The α–maxmin model is a prominent example of preferences under Knightian uncertainty as it allows to distinguish ambiguity and ambiguity attitude. These preferences are dynamically inconsistent for nontrivial versions of α. In this paper, we derive a recursive, dynamically consistent version of the α–maxmin model. In the continuous–time limit, the resulting dynamic utility function can be repre...

Most planners constructed up to now are qualitative: they deal with uncertainty by considering all possible outcomes of each plan, without quantifying their relative likelihood. They then choose a plan that deals with the worst-case scenario. However, it is clearly infeasible to plan for every possible contingency. Even beyond the purely computational considerations, planning for highly unlikel...

In a variety of contexts, randomization is regarded as an effective technique to conceal sensitive in-formation. We model randomization mechanisms as information-theoretic channels. Our starting point isa semantic notion of security that expresses absence of any privacy breach above a given level of serious-ness , irrespective of any background information, represented as a prio...

We review recent results on the new concept of worst-case portfolio optimization, i.e. we consider the determination of portfolio processes which yield the highest worst-case expected utility bound if the stock price may have uncertain (down) jumps. The optimal portfolios are derived as solutions of non-linear differential equations which itself are consequences of a Bellman principle for worst...

A buyer makes an offer to a privately informed seller for a good of uncertain quality. Quality determines both the seller’s valuation and the buyer’s valuation, and the buyer evaluates each contract according to its worst-case performance over a set of probability distributions. This paper demonstrates that the contract that maximizes the minimum payoff over all possible probability distributio...

This paper addresses the search control problem of selecting which plan to refine next for decision-theoretic planners, a choice point common to the decision theoretic planners created to date. Such planners can make use of a utility function to calculate bounds on the expected utility of an abstract plan. Three strategies for using these bounds to select the next plan to refine have been propo...

Practical experience building Gaia (Active Spaces), TEEVE (Tele-immersive Environment for Everybody), and Matrix (A Samsung sponsored project examining Active Spaces on PDAs) suggest certain common models, patterns and best practices aid the construction of pervasive and ubiquitous systems. While sharing many concerns with other software systems, these kinds of systems have particular problems....

In this expository paper we show how one can, in a uniform way, calculate the weight distributions of some well-known binary cyclic codes. The codes are related to certain families of curves, and the weight distributions are related to the distribution of the number of rational points on the curves.