Simon's Bounded Rationality. Origins and use in economic theory

The paper aims to show how Simon's notion of bounded rationality should be interpreted in the light of its connection with artificial intelligence. This connection points out that bounded rationality is a highly structured concept, and sheds light on several implications of Simon's general views on rationality. Finally, offering three paradigmatic examples, the artic1e presents the view that recent approaches, which refer to Simon's heterodox theory, only partially accept the teachings of their inspirer, splitting bounded rationality from the context of artificl al intelligence.

Project resources leveling using software agents

Different approaches to project planning and scheduling have been developed. The Operational Research (OR) approach provides two major planning techniques: CPM and PERT. Artificial Intelligence (AI) initially promoted the automatic planner concept. In order to plan a project, the automatic application of predefined operators is required. However, most domains are not so easily formalized in the form of predefined planning operators. The paper focus is on the agent-based approach to project planning and scheduling, especially in Resource Leveling issues. The authors have developed and implemented the ResourceLeveler system, an agent-based model for leveling project resources. The objective of Resource Leveler is to find a scheduling of resources similar to the optimal theoretical solution which takes into consideration all constraints stemming from the relationships between projects, activity calendars, resource calendars, resource allotment to the activities and resource availability. ResourceLeveler was developed in C# as a plug-in for Microsoft Project.project management, agent-based models, artificial intelligence, project resource leveling

Defining and Explorting the Intelligence Space

Intelligence is a difficult concept to define, despite many attempts at doing so. Rather than trying to settle on a single definition, this article introduces a broad perspective on what intelligence is, by laying out a cascade of definitions that induces both a nested hierarchy of three levels of intelligence and a wider-ranging space that is built around them and approximations to them. Within this intelligence space, regions are identified that correspond to both natural -- most particularly, human -- intelligence and artificial intelligence (AI), along with the crossover notion of humanlike intelligence. These definitions are then exploited in early explorations of four more advanced, and likely more controversial, topics: the singularity, generative AI, ethics, and intellectual property.Comment: May ultimately appear as a journal article and/or a book chapte

A conceptual framework for intelligent real-time information processing

By combining artificial intelligence concepts with the human information processing model of Rasmussen, a conceptual framework was developed for real time artificial intelligence systems which provides a foundation for system organization, control and validation. The approach is based on the description of system processing terms of an abstraction hierarchy of states of knowledge. The states of knowledge are organized along one dimension which corresponds to the extent to which the concepts are expressed in terms of the system inouts or in terms of the system response. Thus organized, the useful states form a generally triangular shape with the sensors and effectors forming the lower two vertices and the full evaluated set of courses of action the apex. Within the triangle boundaries are numerous processing paths which shortcut the detailed processing, by connecting incomplete levels of analysis to partially defined responses. Shortcuts at different levels of abstraction include reflexes, sensory motor control, rule based behavior, and satisficing. This approach was used in the design of a real time tactical decision aiding system, and in defining an intelligent aiding system for transport pilots

The regulation of artificial intelligence

Before embarking on a discussion of the regulation of artificial intelligence (AI), it is first necessary to define the subject matter regulated. Defining artificial intelligence is a difficult endeavour, and many definitions have been proposed over the years. Although more than 70 years have passed since it was adopted, the most convincing definition is still nonetheless that proposed by Turing; in any case, it is important to be mindful of the risk of anthropomorphising artificial intelligence, which may arise in particular from its very definition. Once we have established the subject matter regulated, we must ask ourselves whether lawmakers should pursue an approach that seeks to regulate artificial intelligence as a whole, or whether by contrast they should regulate applications of artificial intelligence in specific sectors or individual areas. The proposal for a regulation on artificial intelligence published on 21 April 2021 implements the former approach whilst also pursuing geopolitical goals. After providing an initial overview of the notion of artificial intelligence, this article investigates the geopolitical context to the proposal for a regulation, and then goes on to illustrate the regulatory model embraced by the proposal as well as related critical aspects

Artificial Intelligent Enabled Supply Chains as a Competitive Advantage

The focus of this paper is on the topics of artificial intelligence and supply chain management and how artificial intelligence-enabled supply chains provide organizations with competitive advantages. The supply chain’s adoption of data collection technologies as part of digital transformation and movements of industry 4.0 creates a strong foundation for artificial intelligence analytics. Artificial intelligence has three branches sensing and interacting, decision-making, and learning. Each branch uses its algorithms and serves a different purpose for the business. Artificial intelligence-enabled supply chains create unique, inimitable competitive advantages that fit Michael Porter’s five forces

Artificial Intelligence is a Character? Exploring design scenarios to build interface behaviours

The paper aims to illustrate the qualitative results of the first phase of the scenario research about voice interfaces, examining whether it is possible to design them as if they were a theatrical or cinematographic character. The research field intersects interaction design with character design, intended as the narrative construction of a character, and theatrical performances. The experimentation takes advantage of theatre workshops that aims to show, and understand, which are the main characteristics of a vocal interface and how to design them according to a performance approach. The paper ends illustrating how design can address actual opportunities and criticalities about emerging technologies, following a relations-based approach

A Conexionist Intelligent System for Accounting

Neural networks are a computing paradigm developed from artificial intelligence and brain modelling’s fields, which lately has become very popular in business. Many researchers are seeing neural networks systems as solutions to business problems like modelling and forecasting, but accounting and audit were also touched by the new technology. The purpose of this paper is to present the ability of an artificial neural networks model to forecast and recognize patterns while analyzing company’s sales evolution. The monthly sales evolutions are considered a time-series and the target is to observe the ability of the investigated model to make predictions.accounting, neural networks, predictions, time-series, hybrid intelligent systems

Recruitment systems nowadays: how XAI can improve trust

openThe use of artificial intelligence systems has a strong impact on people’s lives. One of the fields of application in which these systems are being tested is job recruitment. The use of artificial intelligence allows to manage a more complex number of data and to automate some phases of the management of these, making the recruitment process more fluid. It is necessary, therefore, that both the candidate and the human resource manager can trust the choices made by the system. In this thesis we develop the topic of artificial intelligence, focusing in particular on the use of XAI (eXplainable Artificial Intelligence). The implementation of XAI systems significantly improves the level of trust that people have in AI systems. Finally, we offer food for thought on the minimum technical measures to be taken at the design stage so that these systems can operate on European territory, following the guidelines set out in the AI ACT, act promoted by the European Commission to regulate in the field of Artificial Intelligence