(6 articles matched your search)
Journal of Artificial Societies and Social Simulation 1 (4) 2
Abstract: Using the "meme" conception (Dawkins 1976) of cultural transmission and computer simulations, an exploration is made of the relationship between agents, their beliefs about their environment, communication of those beliefs, and the global behaviours that emerge in a simple artificial society. This paper builds on previous work using the Minimeme model (Bura 1994). The model is extended to incorporate open-mindedness meta-memes (memes about memes). In the scenarios presented such meta-memes have dramatic effects, increasing the optimality of population distribution and the accuracy of existing beliefs. It is argued that artifical society experimentation offers a potentially fruitful response to the inherent problems of building new meme theory.
Journal of Artificial Societies and Social Simulation 5 (4) 4
Abstract: This paper demonstrates the role of group normative reputation in the promotion of an aggression reducing possession norm in an artificial society. A previous model of normative reputation is extended such that agents are given the cognitive capacity to categorise other agents as members of a group. In the previous model reputational information was communicated between agents concerning individuals. In the model presented here reputations are projected onto whole groups of agents (a form of "stereotyping"). By stereotyping, norm followers outperform cheaters (who do not follow the norm) under certain conditions. Stereotyping, by increasing the domain of applicability of a piece of reputational information, allows agents to make informed decisions concerning interactions with agents which no other agent has previously met. However, if conditions are not conducive, stereotyping can completely negate norm following behaviour. Group reputation can be a powerful mechanism, therefore, for the promotion of beneficent norms under the right conditions.
David Hales, Juliette Rouchier and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 6 (4) 5
Abstract: In recent years there has been an explosion of published literature utilising Multi-Agent-Based Simulation (MABS) to study social, biological and artificial systems. This kind of work is evidenced within JASSS but is increasingly becoming part of mainstream practice across many disciplines. However, despite this plethora of interesting models, they are rarely compared, built-on or transferred between researchers. It would seem there is a dearth of "model-to-model" analysis. Rather researchers tend to work in isolation, designing all their models from scratch and reporting their results without anyone else reproducing what they found. Although the opposite extreme, where all that seems to happen is the next twist on an existing model, is not to be wished for, there are considerable dangers if everybody only works on their own model. Part of the reason for this is that models tend to be very seductive – especially to the person who has built the model. What is needed is a third person to check the results. However it is not always clear how people who are not the modeller can interpret or utilise such results, because it is very difficult to replicate simulation models from what is reported in papers. It was for these reasons that we called on the MABS community to submit papers for a model-to-model (M2M) workshop. The aim of the workshop was to gather researchers in MABS who were interested in understanding and furthering the transferability of knowledge between models. We received fourteen submissions from which (after a process of peer review) eight were presented at the workshop. Of the six articles that comprise this special issue, five were presented at the workshop.
Bruce Edmonds and David Hales
Journal of Artificial Societies and Social Simulation 6 (4) 11
Abstract: A published simulation model (Riolo et al. 2001) was replicated in two independent implementations so that the results as well as the conceptual design align. This double replication allowed the original to be analysed and critiqued with confidence. In this case, the replication revealed some weaknesses in the original model, which otherwise might not have come to light. This shows that unreplicated simulation models and their results can not be trusted – as with other kinds of experiment, simulations need to be independently replicated.
Bruce Edmonds and David Hales
Journal of Artificial Societies and Social Simulation 7 (2) 9
Abstract: We present a computational simulation which captures aspects of negotiation as the interaction of agents searching for an agreement over their own mental model. Specifically this simulation relates the beliefs of each agent about the action of cause and effect to the resulting negotiation dialogue. The model highlights the difference between negotiating to find any solution and negotiating to obtain the best solution from the point of view of each agent. The later case corresponds most closely to what is commonly called "haggling". This approach also highlights the importance of what each agent thinks is possible in terms of actions causing changes and in what the other agents are able to do in any situation to the course and outcome of a negotiation. This simulation greatly extends other simulations of bargaining which usually only focus on the case of haggling over a limited number of numerical indexes. Three detailed examples are considered. The simulation framework is relatively well suited for participatory methods of elicitation since the "nodes and arrows" representation of beliefs is commonly used and thus accessible to stakeholders and domain experts.
Shade T. Shutters and David Hales
Journal of Artificial Societies and Social Simulation 16 (1) 4
Abstract: Cooperation is essential for complex biological and social systems and explaining its evolutionary origins remains a central question in several disciplines. Tag systems are a class of models demonstrating the evolution of cooperation between selfish replicators. A number of previous models have been presented but they have not been widely explored. Though previous researchers have concentrated on the effects of one or several parameters of tag models, exploring exactly what is meant by cheating in a tag system has received little attention. Here we re-implement three previous models of tag-mediated altruism and introduce four definitions of cheaters. Previous models have used what we consider weaker versions of cheaters that may not exploit cooperators to the degree possible, or to the degree observed in natural systems. We find that the level of altruism that evolves in a population is highly contingent on how cheaters are defined. In particular when cheaters are defined as agents that display an appropriate tag but have no mechanism for participating in altruistic acts themselves, a population is quickly invaded by cheaters and all altruism collapses. Even in the intermediate case where cheaters may revert back to a tag-tolerance mode of interaction, only minimal levels of altruism evolve. Our results suggest that models of tag-mediated altruism using stronger types of cheaters may require additional mechanisms, such as punishment strategies or multi-level selection, to evolve meaningful levels of altruism.