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SocInfo 2018: Social Informatics: 10th International Conference on Social Informatics (St. Petersburg, Russia, September 25-28, 2018), Item does not contain fulltext, Human evacuation experiments can trigger distress, be unethical and present high costs. As a solution, computer simulations can predict the effectiveness of new emergency management procedures. This paper applies multi-agent simulation to measure the influence of staff members with diverse training levels on evacuation time. A previously developed and validated model was extended with explicit mechanisms to simulate staff members helping people to egress. The majority of parameter settings have been based on empirical data acquired in earlier studies. Therefore, simulation results are expected to be realistic. Results show that staff are more effective in complex environments, especially when trained. Not only specialised security professionals but, especially, regular workers of shopping facilities and offices play a significant role in evacuation processes when adequately trained. These results can inform policy makers and crowd managers on new emergency management procedures.

SocInfo 2018: Social Informatics: 10th International Conference on Social Informatics (St. Petersburg, Russia, September 25-28, 2018), Item does not contain fulltext, In this paper, a multi-agent simulation model is presented to investigate the dynamics of 'mutual-support networks': online social networks consisting of both humans and artificial agents. Via such networks, human users who are coping with stress can share their problems, via text messages, with human peers as well as 'artificial friends'. Even though not everybody feels comfortable sharing personal problems with artificial agents, a bot is always available to help human users, and does not face any negative consequence of providing help to stressed peers. Using the simulation model, the dynamics of social networks consisting of an arbitrary combination of humans and agents have been explored under various circumstances. This exploration resulted in several insights that are useful for shaping our vision on artificial friends: (1) humans can provide less emotional support than artificial agents because they have limited emotional resources, (2) the type of support that is provided has a large impact on the human’s stress level, and (3) the more open users are to receiving automated support, the more effective the support is in reducing their stress level. The model was internally validated by means of a mathematical verification.