Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, t...

We introduce a novel generative model for interpretable subgroup analysis for causal inference applications, Causal Rule Sets (CRS). A CRS model uses a small set of short rules to capture a subgroup where the average treatment effect is elevated compared to the entire population. We present a Bayesian framework for learning a causal rule set. The Bayesian framework consists of a prior that favo...

The paper undertakes a conceptual scrutiny of Bayes’ rule in terms of the nature and interpretation of its probabilistic components, which reveals that there is nothing obvious or self-evident about it. First, it is not an instantiation of the conditional probability formula because (i) it involves conditioning on the unobservable event (a hypothesis), and (ii) it ignores the gap between Plato...

We consider the performance and incentive compatibility of voting rules in a Bayesian environment with independent private values and at least three alternatives. It is shown that every (ex-ante) Pareto efficient ordinal rule among neutral rules is incentive compatible under a symmetry assumption on alternatives. Furthermore, we prove that there exists an incentive compatible cardinal rule whic...

The Ellsberg paradox demonstrates that people’s belief over uncertain events might not be representable by subjective probability. We argue that Uncertainty Aversion may be viewed as a case of “Rule Rationality”. This paradigm claims that people’s decision making has evolved to simple rules that perform well in most regular environments. Such an environment consists of replicas of some basic si...

Explicit information-seeking actions are needed to evaluate alternative actions in problem-solving tasks. Information-seeking costs are often traded off against the utility of information. We present three experiments that show how subjects adapt to the cost and information structures of environments in a map-navigation task. We found that subjects often stabilize at suboptimal levels of perfor...

In knowledge discovery and data mining many measures of interestingness have been proposed in order to measure the relevance and utility of the discovered patterns. Among these measures, an important role is played by Bayesian confirmation measures, which express in what degree a premise confirms a conclusion. In this paper, we are considering knowledge patterns in a form of “if..., then...” ru...

This paper argues that software testing can be less thorough yet more efficient if applied in a well-managed, empirical manner across the entire Software Development Life Cycle (SDLC). To ensure success, testing must be planned and executed within an Earned Value Management (EVM) paradigm. A specific example of empirical software testing is given: the Empirical Bayesian Stopping Rule (EBSR). Th...

Rule learning has the major advantage of understandability by human experts when performing knowledge discovery within the biomedical domain. Many rule learning algorithms require discrete data in order to learn the IF-THEN rule sets. This requirement makes the selection of a discretization technique an important step in rule learning. We compare the performance of one standard technique, Fayya...

Probabilistic models based on Bayes' rule are an increasingly popular approach to understanding human cognition. Bayesian models allow immense representational latitude and complexity. Because they use normative Bayesian mathematics to process those representations, they define optimal performance on a given task. This article focuses on key mechanisms of Bayesian information processing, and pr...