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VERSION:2.0
PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
BEGIN:VEVENT
DTSTAMP:20260411T111346Z
UID:Seminar-dept-375@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Lutz Oettershagen:MAILTO:Lutz.Oettershagen@liverpool.ac.uk
DTSTART:20150421T130000
DTEND:20150421T140000
SUMMARY:School Seminar Series
DESCRIPTION:Dr Steve Phelps: Dynamic social networks and reciprocity: comparing agent-based models and behavioural studies\n\nMany models of social network formation implicitly assume that network\n\nproperties are static in steady-state. In contrast, actual social networks are\n\nhighly dynamic: allegiances and collaborations expire and may or may not be\n\nrenewed at a later date. Moreover, empirical studies show that human social\n\nnetworks are dynamic at the individual level but static at the global level:\n\nindividuals' degree rankings change considerably over time, whereas network\n\nlevel metrics such as network diameter and clustering coefficient are relatively\n\nstable. There have been some attempts to explain these properties of empirical\n\nsocial networks using agent-based models in which agents play social dilemma\n\ngames with their immediate neighbours, but can also manipulate their network\n\nconnections to strategic advantage. However, such models cannot\n\nstraightforwardly account for reciprocal behaviour based on reputation scores\n\n("indirect reciprocity"), which is known to play an important role in many\n\neconomic interactions. In order to account for indirect reciprocity, we model\n\nthe network in a bottom-up fashion: the network emerges from the low-level\n\ninteractions between agents. By so doing we are able to simultaneously account\n\nfor the effect of both direct reciprocity (e.g. "tit-for-tat") as well as\n\nindirect reciprocity (helping strangers in order to increase one's reputation).\n\nWe test the implications of our model against a longitudinal dataset of\n\nChimpanzee grooming interactions in order to determine which types of\n\nreciprocity, if any, best explain the data. We discuss the importance of the\n\ntemporal and micro-properties of the data in analysing reciprocity: in\n\nparticular determining the length of window over which direct reciprocity\n\noccurs, and the importance of network-motifs in detecting patterns of indirect\n\nreciprocity.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=375
LOCATION:Ashton Lecture Theater
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