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.
%0 Journal Article
%1 Bosse:2012:aamas
%A Bosse, Tibor
%A Hoogendoorn, Mark
%A Klein, Michel C. A.
%A Treur, Jan
%A van der Wal, C. Natalie
%A van Wissen, Arlette
%D 2012
%J Autonomous Agents and Multi-Agent Systems
%K imported thesis
%R 10.1007/s10458-012-9201-1
%T Modelling collective decision making in groups and crowds: Integrating
social contagion and interacting emotions, beliefs and intentions
%X 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.
@article{Bosse:2012:aamas,
abstract = {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.},
added-at = {2017-03-16T11:50:55.000+0100},
affiliation = {Department of Artificial Intelligence, VU University, De Boelelaan,
1081 HV Amsterdam, The Netherlands},
author = {Bosse, Tibor and Hoogendoorn, Mark and Klein, Michel C. A. and Treur, Jan and van der Wal, C. Natalie and van Wissen, Arlette},
biburl = {https://www.bibsonomy.org/bibtex/24674c13118c6519af7f3f7d45c01a4d5/krevelen},
doi = {10.1007/s10458-012-9201-1},
interhash = {1cfc79b87ca69c9fad36389e57971454},
intrahash = {4674c13118c6519af7f3f7d45c01a4d5},
issn = {1387-2532},
journal = {Autonomous Agents and Multi-Agent Systems},
keyword = {Computer Science},
keywords = {imported thesis},
month = jun,
owner = {Rick},
timestamp = {2017-03-16T11:54:14.000+0100},
title = {Modelling collective decision making in groups and crowds: Integrating
social contagion and interacting emotions, beliefs and intentions},
year = 2012
}