This paper provides a theory on staged or step-by-step privatization. We show that such an approach can be efficient, in the sense that it can successfully transform a state-owned enterprise into an efficient market-based firm by the time when the reform is complete. It may explain the popularity of staged privatization around the world. We have also conducted empirical analysis, which yields s...

In this research, recognition of karstic water-bearing zones using the management of exploration data in Kal-Qorno valley, situated in the Tepal area of Shahrood, has been considered. For this purpose, the sequential exploration method was conducted using geological evidences and applying remote sensing and geoelectrical resistivity methods in two major phases including the regional and local s...

Reinforcement learning (RL) is a paradigm for learning sequential decision making tasks. However, typically the user must hand-tune exploration parameters for each different domain and/or algorithm that they are using. In this work, we present an algorithm called leo for learning these exploration strategies on-line. This algorithm makes use of bandit-type algorithms to adaptively select explor...

In this paper we investigate human exploration/exploitation behavior in sequential-decision making tasks. Previous studies have suggested that people are suboptimal at scheduling exploration, and heuristic decision strategies are better predictors of human choices than the optimal model. By incorporating more realistic assumptions about subject’s knowledge and limitations into models of belief ...

Growing availability of data has enabled practitioners to tailor decisions at the individuallevel. This involves learning a model of decision outcomes conditional on individual-specific covariates or features. Recently, contextual bandits have been introduced as a framework to study these online and sequential decision making problems. This literature predominantly focuses on algorithms that ba...

As an important method to solve sequential decisionmaking problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to largescale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitat...

Oracle’s system for Trec8 was the interMedia Text retrieval engine integrated with the Oracle8i database and SQL query language. interMedia Text supports a novel theme-based document retrieval capability using an extensive lexical knowledge base. Trec8 queries constructed by extracting themes from topic titles and descriptions were manually refined. Queries were simple and intuitive. Oracle’s r...