A mathematical model for the behavior of pedestrians
Behavioral Science
Artificial Societies: The Computer Simulation of Social Life
Artificial Societies: The Computer Simulation of Social Life
Experiences creating three implementations of the repast agent modeling toolkit
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Simulating norm formation: an operational approach
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Coping with shame and sense of guilt: a Dynamic Logic Account
Autonomous Agents and Multi-Agent Systems
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Human social systems are the most complex systems that we can conceive of, as interaction among their members is by messages that have to interpreted before they can take any effect - and the interpretation of identical messages sent and received in identical situations can have divergent effects as human actors have long-term memories, which makes the effects highly path dependent. The emergent phenomena in human social systems are therefore even more difficult to understand than related phenomena in anthills or chemical systems. This paper introduces models of immergence and second-order emergence to explain the innovation of norms in human groups of moderate size - as opposed to the emergent phenomena in human crowds, which are quite similar to the phenomena that can be observed in physical and animal systems. Modeling the innovation of norms and norm systems makes it necessary for interactions between model agents to be manifold and path dependent, that is agents have a long memory and deliberation capabilities. This is shown with an example from modeling criminal or terrorist behavior.