Arlette van Wissen

Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions

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. As an application of the model, an agent-based analysis was made of patterns in crowd behaviour, in particular to simulate a real-life incident that took place on May 4, 2010 in Amsterdam. From available video material and witness reports, useful empirical data were extracted. Similar patterns were achieved in simulations, whereby some of the parameters of the model were tuned to the case addressed, and most parameters were assigned default values. The results show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion.

Intelligent mobile support for therapy adherence and behavior change

Mobile applications have proven to be promising tools for supporting people in adhering to their health goals. Although coaching and reminder apps abound, few of them are based on established theories of behavior change. In the present work, a behavior change support system is presented that uses a computational model based on multiple psychological theories of behavior change. The system determines the users reason for non-adherence using a mobile phone app and an online lifestyle diary. The user automatically receives generated messages with persuasive, tailored content. The system was designed to support chronic patients with type 2 diabetes, HIV, and cardiovascular disease, but can be applied to many health and lifestyle domains. The main focus of this work is the development of the model and the underlying reasoning method. Furthermore, the implementation of the system and some preliminary results of its functioning will be discussed.

Modelling the interplay of emotions, beliefs and intentions within collective decision making based on insights from social neuroscience

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.

An Agent-Based Model for the Interplay of Information and Emotion in Social Diffusion

The spread of information and emotion within groups is studied in models of social diffusion. Evidence has been found that the emotional states of humans affect their information processing abilities, and hence, may affect the spread of information as well. This paper introduces an agent-based model that simulates the spread of information and emotion among a group of agents. The model takes both the influence of emotions upon the spread of information and the influence of information on emotions into account. The approach is exemplified by means of a case study in the domain of emergency evacuation.

Agent-based modelling of the emergence of collective states based on contagion of individual states in groups

This paper introduces a neurologically inspired computational model for the dynamics and diffusion of agent states within groups. The model combines an individual model based on Damasios Somatic Marker Hypothesis with mutual effects of group members on each other via mirroring of individual states such as emotions, beliefs and intentions. The obtained model shows how this combination of assumed neural mechanisms can form an adequate basis for the emergence of common group beliefs and intentions, while, in addition there is a positive feeling with these common states amongst the group members. A particular issue addressed is how certain types of states may affect other types of states, for example, emotions have an effect on beliefs and intentions, and beliefs may effect emotions.

Analysis and support of lifestyle via emotions using social media

Using recent insights from Cognitive, Affective and Social Neuroscience, this paper addresses how affective states in social interactions can be used through social media to analyze and support lifestyle behaviour. A computational model is provided that integrates both mechanisms for the impact of ones emotions on behaviour, and for the impact of emotions of others on ones own emotion. The model is used to assess the state of a user with regard to a lifestyle goal (such as exercising frequently), based on extracted information of emotions exchanged in social interaction. Support is provided by proposing ways to affect these social interactions, which will indirectly influence the impact of the emotions of others. An ambient intelligent system based on this model has been implemented for the social medium Twitter.

Modelling the emergence of group decisions based on mirroring and somatic marking

This paper introduces a neurologically inspired computational model for the emergence of group decisions. The model combines an individual decision making model based on Damasios Somatic Marker Hypothesis with mutual effects of group members on each other via mirroring of emotions and intentions. The obtained model shows how this combination of assumed neural mechanisms can form an adequate basis for the emergence of common group decisions, while, in addition, there is a feeling of wellness with these common decisions amongst the group members.

A computational model of habit learning to enable ambient support for lifestyle change

Agent-based applications have the potential to assist humans in their lifestyle change, for instance eliminating addictive behaviours or adopting new healthy behaviours. In order to provide adequate support, agents should take into consideration the main mechanisms underlying behaviour formation and change. Within this process habits play a crucial role: automatic behaviours that are developed unconsciously and may persist without the presence of any goals. Inspired by elements from neurological literature, a computational model of habit formation and change was developed as a basis for support agents able to assist humans in lifestyle and behaviour change. Simulations are presented showing that the model exhibits realistic human-like behaviour.

Contagion of Habitual Behaviour in Social Networks: An Agent-Based Model

This paper introduces a computational model for contagion and change of habitual behaviour in a social network. It considers both the level of individual mechanisms of habit formation, involving a persons attitudes, goals, intentions and relevant environmental cues, and the level of social mechanisms for the impact of others in a social network. Simulation experiments with the model for small-world networks show that the model exhibits realistic behaviour. The model can be used as an aid for physicians and policymakers addressing lifestyle formation and spread in a population under considered policies.

Computational Analysis of the Impacts of Emotion on Learning in a Social Context

This paper addresses how affective states and social interactions can strengthen learning processes. Using recent insights from Cognitive, Affective and Social Neuroscience it provides a computational model of learning processes, integrating mechanisms for the impact of a persons own emotions on these processes, as well as for the impact of emotions of others. By means of simulation experiments it is shown how these impacts strengthen the learning process. Based on these insights, and the presented computational model, support is provided for designing and testing (in silico) learning environments that enable and promote these impacts.