This study investigates the role of artificial intelligence (AI) in recruitment, with a specific emphasis on small and medium enterprises (SMEs) and cultural diversity, two dimensions frequently underrepresented in existing research. The objective is to evaluate the benefits, challenges, and strategies for the responsible adoption of AI in recruitment. To achieve this, a Multivocal Literature Review (MLR) was conducted, systematically synthesising peer-reviewed studies and grey literature published from 2018 onwards. Following Kitchenham’s systematic review guidelines and Garousi’s multivocal extensions, academic and practitioner perspectives were analysed to capture both theoretical insights and real-world practices. The findings indicate that AI can streamline recruitment processes, improve decision-making accuracy, and enhance candidate experience through tools such as résumé screening, predictive analytics, and generative AI applications. However, issues of algorithmic bias, limited transparency, data quality, regulatory compliance, and workforce scepticism persist, particularly in SMEs that face resource constraints. Although much of the available evidence reflects Western contexts, this review broadens the scope by integrating global perspectives and highlighting how cultural and regional factors influence AI acceptance. The novelty of this study lies in combining academic and industry evidence to propose actionable strategies—such as bias audits, explainable AI frameworks, and human-in-the-loop approaches—for more inclusive, sustainable, and globally relevant adoption of AI in recruitment.

This paper proposes a multi-agent based simulation (MABS) framework to construct an artificial electric power market where heterogeneous, rationally bounded and learning agents co-evolve dynamically. The proposed framework aims to facilitate the integration of two MABS constructs: (i) the design of the environmental physical properties and entities, and (ii) the simulation models of resources and both the decision-making (deliberative) and reactive agents. The framework is materialized in an experimental setup involving distinct power generator companies which operate in the market and search for the trading strategies that best exploit their generating unitspsila resources. The experimental results show a coherent market behavior that emerges from the overall simulated environment.

This paper describes an exploratory work that aims to identify a semantic basis underlying a mental model from the field of emergency management using a collaborative approach. An experimental application of the distance ratio measure was used to compare individual networks among themselves and to analyze the aggregated network representing the group mental model. The data was obtained by surveying a group of Civil Protection graduates and aggregating all the answers (shared mental model). The data allowed us to deepen the analysis of the resulting network in order to understand the main group concepts and their relations within this domain.

Looking to the operation of an agent architecture, ie. its goal generation and maintenance processing, is relevant to understand fully how a moral based agent takes appropriate and diverse decisions within social situations of serious games. How decision does happen is a complex issue and the major motivation of this paper, and our answer, the proposal of a new architecture, is supported on the clarification of the organization and structure of an agent, ie. the interpretation of agent actions (moral-driven behaviour) under the pressure of severe constraints.