Refining Indeterministic Choice: Imprecise Probabilities and Strategic Thinking

Jorge Castro, Joaquim Gabarro, Maria Serna
2020 Vietnam Journal of Computer Science  
Often, uncertainty is present in processes that are part of our routines. Having tools to understand the consequences of unpredictability is convenient. We introduce a general framework to deal with uncertainty in the realm of distribution sets that are descriptions of imprecise probabilities. We propose several non-biased refinement strategies to obtain sensible forecasts about results of uncertain processes. Initially, uncertainty on a system is modeled as the non-deterministic choice of its
more » ... ossible behaviors. Our refinement hypothesis translates non-determinism into imprecise probabilistic choices. Imprecise probabilities allow us to propose a notion of uncertainty refinement in terms of set inclusions. Later on, unpredictability is tackled through a strategic approach using uncertainty profiles and angel/daemon games ([Formula: see text]-games). Here, imprecise probabilities form the set of mixed strategies and Nash equilibria corresponds to natural uncertainty refinements. We use this approach to study the performance of Web applications — in terms of response times — under stress conditions.
doi:10.1142/s2196888820500256 fatcat:cg3kzziiwbcojkccqcplrjphxi