Introducing Quantum-Like Influence Diagrams for Violations of the Sure Thing Principle

It is the focus of this work to extend and study the previously proposed quantum-like Bayesian networks to deal with decision-making scenarios by incorporating the notion of maximum expected utility in influence diagrams. The general idea is to take advantage of the quantum interference terms produced in the quantum-like Bayesian Network to influence the probabilities used to compute the expected utility of some action. This way, we are not proposing a new type of expected utility hypothesis. On the contrary, we are keeping it under its classical definition. We are only incorporating it as an extension of a probabilistic graphical model in a compact graphical representation called an influence diagram in which the utility function depends on the probabilistic influences of the quantum-like Bayesian network. Our findings suggest that the proposed quantum-like influence digram can indeed take advantage of the quantum interference effects of quantum-like Bayesian Networks to maximise the utility of a cooperative behaviour in detriment of a fully rational defect behaviour under the prisoner's dilemma game

Multifaceted modelling of complex business enterprises

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control

Flexible Nets: a modeling formalism for dynamic systems with uncertain parameters

Funder: FP7 People: Marie-Curie Actions; doi: https://doi.org/10.13039/100011264; Grant(s): 623995Abstract: The modeling of dynamic systems is frequently hampered by a limited knowledge of the system to be modeled and by the difficulty of acquiring accurate data. This often results in a number of uncertain system parameters that are hard to incorporate into a mathematical model. Thus, there is a need for modeling formalisms that can accommodate all available data, even if uncertain, in order to employ them and build useful models. This paper shows how the Flexible Nets (FNs) formalism can be exploited to handle uncertain parameters while offering attractive analysis possibilities. FNs are composed of two nets, an event net and an intensity net, that model the relation between the state and the processes of the system. While the event net captures how the state of the system is updated by the processes in the system, the intensity net models how the speed of such processes is determined by the state of the system. Uncertain parameters are accounted for by sets of inequalities associated with both the event net and the intensity net. FNs are not only demonstrated to be a valuable formalism to cope with system uncertainties, but also to be capable of modeling different system features, such as resource allocation and control actions, in a facile manner

A Set of Prescribed Activities for Enhancing Requirements Engineering in the Development of Usable E-Government Applications

Over the last years, e-Government applications have become indispensable in every country as they help stakeholders carry out tasks with the administration. However, and despite their growing usage, most of these applications are created through a developercentered approach instead of a user-centered one, using traditional development processes that do not fit well with the diversity of stakeholders and existing legislation that involve e-Government applications today. Besides, usability is an important clue in the development of such solutions, so a user-centered approach, combined with a successful stakeholder and legislation analysis, should be considered overall. This paper is focused on addressing these concerns, and it provides a set of prescribed activities, tasks and products to be carried through a user-centered process in order to design usable web-based e-Government solutions. Specifically, our approach considers requirements engineering activities enhancing usability by analyzing the diversity and interests of the stakeholders involved, as well as the specific legislation as a source of organizational requirements. In addition, a validation is provided through a case study, showing the feasibility of the approach presentedThis work has been partially supported by the funding projects «Flexor» (grant number TIN2014-52129-R) granted by the Spanish Government and «eMadrid» (grant number S2013/ICE-2715) granted by the Madrid Research Council

Multifaceted Modelling of Complex Business Enterprises

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control

Implementing Influence Diagram Concepts to Optimize Border Patrol Operations using Unmanned Aerial Vehicles

The most common approach for border patrol operations is the use of human personnel and manned ground vehicles, which is expensive, at times inefficient and sometimes even hazardous to people involved. The length of the US border, mostly covering unpopulated areas, with harsh atmospheric conditions makes it more susceptible to illegal human activities. Automated border surveillance by unattended, fixed, ground sensors forming an electronic fence has proven expensive, inefficient and was prone to unacceptable rate of false alarms. A better approach would be using Unmanned Aerial Vehicles (UAVs) in combination with such ground sensors. This would help improve the overall effectiveness of the surveillance system as a UAV could first scan the alert area before sending in personnel and vehicles, if deemed necessary. In this thesis, we are proposing border surveillance using multiple Unmanned Aerial Vehicles (UAVs) in combination with alert stations consisting of Unattended Ground Sensors (UGSs) along the border line or fence. Upon detecting an event, an alert would be triggered by any UGS. We simulate this process by reading probability data for different timestamps from a text file. And, based on utility values of each stations, two UAVs decide on which alert stations to service

A creativity support system based on causal mapping.

Theory development is a very complex process that requires creativity and highly specialized analytical skills. This article presents a new algorithm, based on causal mapping, for assisting in the creation of qualitative theories. This algorithm is able to conjecture and prove new theorems, to test for consistency and completeness of the theory, and to derive meta-theorems comparing the different concepts in it. The use of the algorithm is exemplified in developing a theory to explain structural inertia in organizations