Factors that determine construction health and safety agent collaboration on construction projects: A Delphi study

The construction industry (CI) continues to be the cause of injuries and illnesses to many workers worldwide. Collaboration between the construction health and safety agent (CHSA) and other built environment professionals may improve the impact of the CHSA on health and safety (H&S) performance. However, no study has identified the factors that determine CHSA collaboration on construction sites. A three rounds Delphi study was conducted to identify the factors that determine CHSA collaboration. A panel of 14 experts serving the CI were selected from four continents and were asked to identify additional factors and validate the factors identified from literature. Microsoft Excel 2016 was used to analyse the data; group medians were calculated to reach consensus, and open question responses were summarised qualitatively. The experts confirmed the existence of the factors identified in the literature. The factors that determine CHSA collaboration on construction projects include mutuality, trust, enabling environment, personal characteristics, common purpose, institutional support, and project context. Drawing from the findings, the study suggests that these seven factors can influence CHSA collaboration. The study is limited to 14 experts and more experts could have provided more information. The factors that determine CHSA collaboration identified in this study may not be exhaustive and another study may provide different factors. Further research could adopt other research methods such as the quantitative method, in order to determine the impact of these factors on CHSA collaboration. Factors that determine CHSA collaboration on construction projects should be identified, implemented, and monitored, in order to increase the influence of CHSA on H&S performance.

Dynamic Model of Collaboration in Multi-Agent System Based on Evolutionary Game Theory

Multi-agent collaboration is greatly important in order to reduce the frequency of errors in message communication and enhance the consistency of exchanging information. This study explores the process of evolutionary decision and stable strategies among multi-agent systems, including followers, leaders, and loners, involved in collaboration based on evolutionary game theory (EGT). The main elements that affected the strategies are discussed, and a 3D evolution model is established. The evolutionary stability strategy (ESS) and stable conditions were analyzed subsequently. Numerical simulation results were obtained through MATLAB simulation, and they manifested that leaders play an important role in exchanging information with other agents, accepting agents’ state information, and sending messages to agents. Then, with the positivity of receiving and feeding back messages for followers, implementing message communication is profitable for the system, and the high positivity can accelerate the exchange of information. At the behavior level, reducing costs can strengthen the punishment of impeding the exchange of information and improve the positivity of collaboration to facilitate the evolutionary convergence toward the ideal state. Finally, the EGT results revealed that the possibility of collaboration between loners and others is improved, and the rewards are increased, thereby promoting the implementation of message communication that encourages leaders to send all messages, improve the feedback positivity of followers, and reduce the hindering degree of loners.

Too Many cooks: Bayesian inference for coordinating Multi-agent Collaboration

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub-tasks to work on in parallel. Here, we develop Bayesian Delegation, a decentralized multi-agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. We test Bayesian Delegation in a suite of multi-agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high-level plans (e.g. what sub-task they should work on) and their low-level actions (e.g. avoiding getting in each other’s way). In a self-play evaluation, Bayesian Delegation outperforms alternative algorithms. Bayesian Delegation is also a capable ad-hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience.

Gender Equality in Business Action: A Multi-Agent Change Management Approach

Critical to social sustainability and organizations’ growth, at present, is gender equality. Yet, egalitarian principles are difficult to apply in the practice, particularly in private firms. Acknowledging previous calls that research should respond to these concerns and support practitioners, we provide a theory-grounded conceptual framework to address change management in this field, aimed at providing applicable guidelines in the organizational practice. Integrating utilitarian and social justice perspectives about gender action, we call for multi-agent collaboration involving coordinated action from policymakers, private firms and gender experts. We provide an overview of how public policies and legislation guide organizational action by providing key statutory norms and procedures. We then address the relevance of organizational commitment and the alignment of gender goals with the organizational strategy and decision-making, involving managers. Finally, we underscore the benefits of implementing evidence-based action based on academic and consultancy collaboration. The implementation of these principles is illustrated with a multi-agent practice developed in the Basque Country (Spain) between gender equality change agents, suited to apply academic principles to real-world organizational practices. Recommendations for gender equality and corporate social action are provided.

Who Is to Blame? The Appearance of Virtual Agents and the Attribution of Perceived Responsibility

Virtual agents have been widely used in human-agent collaboration work. One important problem with human-agent collaboration is the attribution of responsibility as perceived by users. We focused on the relationship between the appearance of a virtual agent and the attribution of perceived responsibility. We conducted an experiment with five agents: an agent without an appearance, a human-like agent, a robot-like agent, a dog-like agent, and an angel-like agent. We measured the perceived agency and experience for each agent, and we conducted an experiment involving a sound-guessing game. In the game, participants listened to a sound and guessed what the sound was with an agent. At the end of the game, the game finished with failure, and the participants did not know who made the mistake, the participant or the agent. After the game, we asked the participants how they perceived the agents’ trustworthiness and to whom they attributed responsibility. As a result, participants attributed less responsibility to themselves when interacting with a robot-like agent than interacting with an angel-like robot. Furthermore, participants perceived the least trustworthiness toward the robot-like agent among all conditions. In addition, the agents’ perceived experience had a correlation with the attribution of perceived responsibility. Furthermore, the agents that made the participants feel their attribution of responsibility to be less were not trusted. These results suggest the relationship between agents’ appearance and perceived attribution of responsibility and new methods for designs in the creation of virtual agents for collaboration work.