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1、1Group Reputation Supports Beneficent Norms A case of “them” and “us”CPM Working Paper 02-101 (July 2002)David Hales Centre for Policy Modelling, The Business School Manchester Metropolitan University, Manchester, UK. E-mail: Web: AbstractThis paper demonstrates the role of group normative reputati
2、on 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 communic
3、ated 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
4、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 b
5、e a powerful mechanism, therefore, for the promotion of beneficent norms under the right conditions.1. IntroductionThe computational study of norms through artificial society simulation stretches back a good ten years (Shoham and Tanneholtz 1992, Conte and Castelfranchi 1995, Walker and Wooldridge 1
6、995, Castelfranchi, Conte and Paolucci 1998, Saam and Harrer 1999, Staller and Petta 2001, Flentge, Polani and Uthmann 2001,Conte and Paolucci 2002).It is important to note that no unified or generally accepted definition of a social norm exists and this is productively evidenced by the differences
7、in the models presented and conclusions drawn in previous computational work on norms1. However, in general, much of the work has tended to focus on prescriptive beliefs concerning proper behaviours within given social contexts. More specifically, interest is often focused on the kinds of norms that
8、 appear to contradict the narrow view of individual self-interest typified by classical rational actor theories2. The focus of this paper is also on these kinds of norms.1For an excellent overview of the different definitions of norms and their origins in the sociological literature and application
9、in recent computational models see Saam and Harrer (1999). 2For sure, these are only a subset of norms. Norms may “enable” choice rather than prescribe and do not need to be at odds with individualistic self-interested behaviour. It would seem that the preoccupation with this subset of social norms
10、is due to the seemingly incongruous nature of them when set against (classical conceptions of) self-interested behaviour and more recently myopic optimising (via say, some evolutionary process).2The resolution of social situations in which individual agent interests (or goals) conflict with group or
11、 system-level interests (or goals) are obviously a cornerstone of many theories of society (Hobbs 1962), but interestingly, are increasingly becoming issues within the agent engineering community (Jennings and Campos 1997, Kalenka and Jennings 1999). The vision of self-organising, open and productiv
12、e multi-agent software systems appears to demand new kinds of rigorous “computational social theory”. In the absence of any adequate analytical tools for dealing with complex adaptive systems, at the level of detail required, simulation and empirical analysis appears to offer the best way forward.Th
13、e aim of this paper is not to provide a complete or general solution to the problem of why self-interested agents might follow and / or enforce socially beneficial norms3. Rather it is to introduce the concept of group reputation and indicate how this kind of social cognition combined with communica
14、tion can support socially beneficent norms within given contexts.What can artificial societies bring to the discussion about norms? Two issues can be addressed; firstly, by precise specification and experimentation (incrementally adding agent abilities) an understanding can be gained of the impact o
15、f individual processes. Secondly, by realising the processes computationally, findings can potentially be applied (at least as an initial guide) within the computational agent engineering community which is becoming increasingly concerned with how to control large, possibly open systems of software
16、agents (Moss 2002).The computational model presented here is an extension of a framework first introduced by Conte and Castelfranchi (1995)4. We present this basic framework (section 2) and then give replication results from a slightly changed re- implementation of the model5. We then replicate studies with an extended model (Castelfranchi, Conte and Paolucci 1998) that includes reputation and communication of reputation (section 4). The model is further
