Emotion estimation in crowds: a survey <s> Emotion Regulation and Contagion <s> Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in Social Neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes. <s> BIB001 </s> Emotion estimation in crowds: a survey <s> Emotion Regulation and Contagion <s> Social decision making under stressful circumstances may involve strong emotions and contagion from others. Recent developments in Social Neuroscience have revealed neural mechanisms by which social contagion of cognitive and emotional states can be realised. In this paper, based on these mechanisms, an agent-based computational model is proposed. Furthermore, it is demonstrated how the proposed cognitive model can be transformed into an equivalent behavioural model without any cognitive states. As an application of the model, a computational analysis was performed of patterns in crowd behaviour, in particular by agent-based simulation of a real-life incident that took place on May 4, 2010 in Amsterdam. The results of the model analysis show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion. <s> BIB002 </s> Emotion estimation in crowds: a survey <s> Emotion Regulation and Contagion <s> To avoid the development of negative emotion in their teams, team leaders may benefit from being aware of the emotional dynamics of the team members. To this end, the use of intelligent computer systems that analyze emotional processes within teams is a promising direction. As a first step toward the development of such systems, this paper uses an agent-based approach to formalize and simulate emotion contagion processes within groups, which may involve absorption or amplification of emotions of others. The obtained computational model is analyzed both by explorative simulation and by mathematical analysis. In addition, to illustrate the applicability of the model, it is shown how the model can be integrated within a computational ‘ambient agent model’ that monitors and predicts group emotion levels over time and proposes group support actions based on that. Based on this description, a discussion is provided of the main contribution of the model, as well as the next steps needed to incorporate it into real-world applications. <s> BIB003 </s> Emotion estimation in crowds: a survey <s> Emotion Regulation and Contagion <s> This paper addresses an agent-based computational social agent model for the integration of emotion regulation, emotion contagion and decision making in a social context. The model integrates emotion-related valuing, in order to analyse the role of emotions in socially affected decision making. The agent-based model is illustrated for the interaction between two persons. Simulation experiments for different kinds of scenarios help to understand how decisions can be affected by regulating the emotions involved, and how these emotions are affected by emotion regulation and contagion. <s> BIB004
An important aspect in understanding the dynamics of emotions in groups and crowds concerns the problem of whether and how these spread or amplify across individuals in a group. Inspired on neural mechanisms revealed by the recent work of Damasio  , the authors of BIB001 and BIB002 propose ASCRIBE, an agent-based model to describe the interplay of mental states (emotions, beliefs and intentions) of individuals in the decisionmaking process under stressful situations. ASCRIBE is defined as having an external and internal level of operation; at the external level it incorporates mechanisms for mirroring mental states between individuals, at the internal level it describes how emotions and beliefs affect each other and how both affect a person's intentions. The model was put to the test by simulations and an empirical study case that compared four models and showed the ASCRIBE model to yield higher prediction accuracy. Expanding the concepts applied in the ASCRIBE model, the multi-agent-based model presented in BIB003 formalizes several concepts of emotion contagion spirals based on fundamental aspects at the individual level: the senders current emotional state and the extent to which the sender expresses the emotion; the strength of the communication channel from sender to receiver; the receiver current emotional state, its openness or sensitivity for the received emotion, bias to adapt emotions upward or downward and tendency to amplify emotions. Although no empiric validation is provided, the model is tested with simulations and mathematical analysis, and it produced interesting emerging patterns identified in psychology literature such as the upward and downward emotion spirals. Further studying the role of emotions in the decision-making process under stressful situations, and with similar concepts to ASCRIBE, an adaptive agent model for affective social decision making is proposed by Manzoor et al in  and later extended in BIB004 to account for emotion regulation and contagion. This model incorporates Hebbian learning principles to adapt the agent's decision-making process, but as found in the experiments conducted, it did not yield significant discrimination in the agent's decisions. Regulation is approached by antecedentfocused strategy (regulation before an emotional response has an effect on behavior), modeling it as a dynamic interaction between internal mental states and contagion is implemented as described in BIB003 .