Arbitrage and Walrasian equilibrium economies with limited information

Equilibrium Theory;economic theory

Visualization with hierarchically structured trees for an explanation reasoning system

This work is concerned with an application of drawing hierarchically structured trees. The tree drawing is applied to an explanation reasoning system. The reasoning is based on synthetic abduction (hypothesis) that gets a case from a rule and a result. In other words, the system searches a proper environment to get a desired result. In order that the system may be reliably related to the amount of rules which are used to get the answer, we visualize a process of reasoning to show how rules have concern with the process. Since the process of reasoning in the system makes a hierarchically structured tree, the visualization of reasoning is a drawing of a hierarchically structured tree. We propose a method of visualization that is applicable to the explanation reasoning system.</p

Interpretable Vector AutoRegressions with Exogenous Time Series

The Vector AutoRegressive (VAR) model is fundamental to the study of multivariate time series. Although VAR models are intensively investigated by many researchers, practitioners often show more interest in analyzing VARX models that incorporate the impact of unmodeled exogenous variables (X) into the VAR. However, since the parameter space grows quadratically with the number of time series, estimation quickly becomes challenging. While several proposals have been made to sparsely estimate large VAR models, the estimation of large VARX models is under-explored. Moreover, typically these sparse proposals involve a lasso-type penalty and do not incorporate lag selection into the estimation procedure. As a consequence, the resulting models may be difficult to interpret. In this paper, we propose a lag-based hierarchically sparse estimator, called "HVARX", for large VARX models. We illustrate the usefulness of HVARX on a cross-category management marketing application. Our results show how it provides a highly interpretable model, and improves out-of-sample forecast accuracy compared to a lasso-type approach.Comment: Presented at NIPS 2017 Symposium on Interpretable Machine Learnin

An approach to safety analysis of clinical workflows

A clinical workflow considers the information and processes that are involved in providing a clinical service. They are safety critical since even minor faults have the potential to propagate and consequently cause harm to a patient, or even for a patient's life to be lost. Experiencing these kinds of failures has a destructive impact on all the involved parties. Due to the large number of processes and tasks included in the delivery of a clinical service, it can be difficult to determine the individuals or the processes that are responsible for adverse events, since such an analysis is typically complex and slow to do manually. Using automated tools to carry out an analysis can help in determining the root causes of potential adverse events and consequently help in avoiding preventable errors through either the alteration of existing workflows, or the design of a new workflow. This paper describes a technical approach to safety analysis of clinical workflows, utilising a safety analysis tool (Hierarchically-Performed Hazard Origin and Propagation Studies (HiP-HOPS)) that is already in use in the field of mechanical systems. The paper then demonstrates the applicability of the approach to clinical workflows by applying it to analyse the workflow in a radiology department. We conclude that the approach is applicable to this area of healthcare and provides a mechanism both for the systematic identification of adverse events and for the introduction of possible safeguards in clinical workflows

Temporal verification in secure group communication system design

The paper discusses an experience in using a real-time UML/SysML profile and a formal verification toolkit to check a secure group communication system against temporal requirements. A generic framework is proposed and specialized for hierarchical groups

Centralised Versus Market-Based Control Under Environment Uncertainty: Case of the Mobile Task Allocation Problem (MTAP)

This paper aims at comparing the centralised versus the market-based approach. This is done in the context of the mobile task allocation problem (MTAP) from the perspective of environmental uncertainty. MTAP is defined as an optimization problem for planning the assignment of service tasks to mobile workers. Environmental uncertainty is introduced through the injection of stochastic tasks and dynamic travel delays. A multi-agent simulator is employed to experiment the behaviour of each approach in reaction to different uncertainty levels. Preliminary results suggest a tentative conceptual model to evaluate the suitability of each approach to address MTAP in function of uncertainty. It is suggested that uncertainty’s effect on achieved performance is moderated by the timeliness of decision making, workers’ degree of local knowledge, and problem’s complexity and size