This research tries to propose a general construct for computational models handling affect dedicated to complex and collective decision-making. The importance of integrating emotional, personal, and social intelligences, in complex individual and collective decision-making, is highlighted. Complex decision-making is approached from human to computational perspectives with the main perspective of complex problem solving. The objective of this paper is hence to: 1) examine how emotional, personal, and social intelligences capabilities contribute to effective collective decision-making in complex environments, 2) investigate how these capabilities can be computationally modeled to enable agents to build internal representations of the systems they manage, learn to process and respond to highly complex and dynamic information, and execute deliberate, prioritized cognitive and behavioral strategies to achieve desired outcomes in real-world problem solving, 3) identify current methodologies and approaches that integrate these forms of intelligence in agent-based systems, and 4) highlight promising future research directions and alternatives emerging from initial findings in this field. The main results are that this study identifies seven core mechanisms through which individual and group affect influence complex collective decision-making, integrating bottom-up and top-down emotional workflows into a single agent-based model. The implications of this study are that by combining affective, cognitive, and environmental parameters — weighted using statistical, knowledge-based, and machine learning methods — the model enables more adaptive, human-like behavior in artificial general intelligence systems.

When developing a research roadmap for human behaviour in fires, it is necessary to identify areas that require additional research. A general overview – from a multidisciplinary perspective – of gaps in human behaviour in fires research across multiple contexts is missing. The goal of this paper was to perform a scoping review to identify research gaps and themes in all aspects of human behaviour in fires across contexts. This scoping review included 17 articles. In total, 37 research gaps and 11 research themes for the built environment and community context were identified. The main research gaps are related to cognitive factors, behavioural responses, environmental factors and physical/physiological factors. Also, for all research themes, additional research involving heterogenous populations is required. Furthermore, there is an imbalance in human behaviour in fires studies: most articles were focused on the built environment rather than the community context. Finally, the topic of intoxication has received limited research attention, and data collection methods lack diversity. Future research should not only be done from a multidisciplinary perspective but also interdisciplinary research efforts are required. The availability of more data and knowledge on human behaviour and responses in fires could be beneficial to simulation model developers/users, the general public and fire safety managers.

The goals of this chapter are to provide: (1) an overview of the various human factors in pedestrian planning research, and (2) an understanding of how these human factors affect pedestrian behaviors relevant for pedestrian planning research. The interdisciplinary field of human factors combines engineering, psychology, and physiology to study the relationship between humans and technology from a system's perspective. Pedestrian planning research is also an interdisciplinary field at the intersection of engineering, transport, architecture, psychology, and sociology. It studies how pedestrians perform in specific contexts – such as at crossings, in travel choices, during wayfinding, and in egress – in both routine and emergency situations. Both fields share a focus on efficiency and safety, and therefore complement each other well. However, despite this, engineers who practice pedestrian planning and design or use technologies that can monitor or interact with pedestrians, do not typically have extensive experience in human factors. Consequently, such pedestrian systems rarely incorporate deep understanding of human behavior, some of which can be counterintuitive. To address these limitations, this chapter first introduces a framework to understand human factors in pedestrian planning research, including the dimensions: (1) observable versus non-observable behaviors, (2) conscious versus unconscious behaviors, (3) physical versus psychological crowds, (4) routine versus emergency situations, and (5) urban versus rural environments. Next, the most common human factors in pedestrian planning research and their influence on operational, tactical, and strategical pedestrian behaviors are considered at three levels: (1) individual, (2) social, and (3) environmental human factors.

Building fires can be considered a risk to the health and safety of occupants. Environmental factors in building fires might affect the speed of an evacuation. Therefore, in this study participants (N = 153) were tested in an experimental design for the effects of (1) a fire alarm, (2) darkness and (3) the use of emergency exit signs on building evacuation time. In addition, the effects of age and gender on evacuation time were investigated. The main results indicate that the combination of a fire alarm, darkness and not illuminated emergency exit signs had a significant negative influence on evacuation time, namely an increase in evacuation time of 26.6% respectively 28.1%. Another important finding is that age had a significant negative effect on evacuation time. The increase in evacuation time was at least 30.4% for participants aged 56 years or older compared to participants aged 18–25 years. For gender no significant effect was found. Building and safety managers can use these results by including longer evacuation time considerations – based on darkness and older age – in their evacuation plans. Future research should focus further on investigating the effects of personal and psychological characteristics on evacuation behaviour and evacuation time.

To improve communication during emergencies, this research introduces an agent-based modeling (ABM) method to test the effect of psychological emergency communication strategies on evacuation performance. We follow a generative social science approach in which agent-based simulations allow for testing different candidate solutions. Unlike traditional methods, such as laboratory experiments and field observations, ABM simulation allows high-risk and infrequent scenarios to be empirically examined before applying the lessons in the real world. This is essential, as emergency communication with diverse crowds can be challenging due to language barriers, con-flicting social identities, different cultural mindsets, and crowd demographics. Improving emergency communication could therefore improve evacuations, reduce injuries, and ultimately save lives. We demonstrate this ABM method by determining the effectiveness of three communication strategies for different crowd compositions in transport terminals: (1) dynamic emergency exit floor lighting directing people to exits, (2) staff guiding people to exits with verbal and physical instructions, and (3) public announcements in English. The simulation results indicated that dynamic emergency exit floor lighting and staff guiding people to exits were only beneficial for high-density crowds and those unfamiliar with the environment. Furthermore, English public announcements actually slowed the evacuation for mainly English-speaking crowds, due to simultaneous egress causing congestion at exits, but improved evacuation speed in multicultural, multilingual crowds. Based on these results, we make recommendations about which communication strategies to apply in the real world to demonstrate the utility of this ABM simulation approach for risk assessment practice.

Emergencies such as fires and terrorist attacks pose risks of injuries and fatalities, which can be exacerbated by delayed, ill-informed, or unmanaged responses. Effective emergency communication strategies could be used to better inform people and reduce these risks. This research analyzes videos of real-world emergencies to: (a) identify people's observed behaviors that increase risk during evacuations, and (b) examine which emergency communication strategies might reduce risk behaviors. We analyzed 126 publicly available videos of emergency evacuations in different emergencies (e.g., fire, terror attack, evacuation alarm, perceived threat). We found evidence of three types of risk behaviors (delayed response, filming, running) and four emergency communication strategies (evacuation alarm, staff guiding people to exits, general prerecorded message, live announcement). Our analyses suggest that having staff guide people to exits is the most effective strategy for promoting faster and more effective responses. However, neither live announcements nor pre-recorded messages were associated with delayed responses, while evacuation alarms were associated with more delayed responses than other communication strategies. Although people filming the incident was unrelated to staff interactions, it occurred more with alarms sounding and prerecorded messages, suggesting that these emergency communications might not prevent filming. Compared to no communications, all emergency communication strategies reduced running during evacuations. We discuss the implications of this research for identifying effective emergency communication strategies and reducing risk-increasing evacuation behaviors.

According to the Department of Energy, demand response provides an opportunity for end-users to play a significant role in the efficiency, reliability, resilience, and sustainability of a power grid. This is made possible owing to the existence of storage devices and diversity of energy sources at the customer level and the advent of the Internet of Things. Social influences and psychological traits of consumers affect their behavior and decision-making. Consequently, there is a necessity to bring the influences of humans, organizations, and societies on the power system together through computational social science into a cyber-physical-social system. Hence, in this brief, we introduce our development of an artificial society of the social demand response of a power system, a well-known approach in computational sociology based on a bottom-up approach, starting from theory. We assume that consumers can engage in demand response to fulfill two aims: save their cost or enhance the sustainability of a power system. The literature concerning sustainability-based demand response is limited to only considering CO2, NOX, and SO2. In addition to NOX, and SO2, we examine the impact of power systems on water pollution, disability-adjusted loss of life year, and exergy in demand response, and provide an environomic-based social demand response. We show that when the level of satisfaction and cooperation of end-user is low, the marginal level of load shaving and improvement in sustainability cannot be fulfilled.

Contemporary ergonomics problems are increasing in scale, ambition, and complexity. Understanding and creating solutions for these multi-faceted, dynamic, and systemic problems challenges traditional methods. Computational modelling approaches can help address this methodological shortfall. We illustrate this potential by describing applications of computational modelling to: (1) teamworking within a multi-team engineering environment; (2) crowd behaviour in different transport terminals; and (3) performance of engineering supply chains. Our examples highlight the benefits and challenges for multi-disciplinary approaches to computational modelling, demonstrating the need for socio-technical design principles. Our experience highlights opportunities for ergonomists as designers and users of computational models, and the instrumental role that ergonomics can play in developing and enhancing complex socio-technical systems. Recognising the challenges inherent in designing computational models, we reflect on practical issues and lessons learned so that computational modelling and simulation can become a standard and valuable technique in the ergonomists’ toolkit. Practitioner summary: This paper argues that computational modelling and simulation is currently underutilised in ergonomics research and practice. Through example applications illustrating the benefits, limitations, and opportunities of such approaches, this paper is a point of reference for researchers and practitioners using computational modelling to explore complex socio-technical systems.