Abstract A great deal of effort has been made to introduce trust models to assess trustworthiness within virtual societies. The great majority of them makes extensive use of direct experience as the main source of information, considering recommendation/reputation and inferential processes just later, as a secondary mechanism to refine trust assessment. In this kind of networks, unfortunately, direct experience might not always represent the best solution to assess trustworthiness. In fact, their highly dynamic structure promotes an increase of the average number of interconnections among agents. This in turn negatively affects the degree of knowledge the agents possess about each specific individual, i.e. direct experience. To date, however, it has not been said much about how to face these situations. It is fundamental to find an effective approach for trust assessment even in lack of direct experience, which is the central focus of this research. By the means of a multi-agent social simulation, we consider the situation in which an agent can just access indirect knowledge for trust assessment, namely recommendations of specific individuals or whole categories of individuals. Then, we compare the efficiency of these two approaches in order to identify when it is more convenient to rely on the first or on the second one. As expected, our results confirm that the dynamic nature of these networks strongly affects the role of categories. We modeled this feature introducing the “turnover” in the simulations, whereby the higher is the turnover the more convenient it is relying on categories. Besides this confirmatory result, our simulations highlight the higher degree of robustness of categories in the presence of unreliable recommenders. Such a result is even more significant if there is no available information about how reliable the recommenders are. The results we obtained are in accordance with the current literature and can be of important interest for the development of this sector.

中文翻译：

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在没有直接经验的情况下评估代理在虚拟社会中的可信度

摘要 为了引入信任模型来评估虚拟社会中的可信度，已经做出了大量努力。他们中的绝大多数广泛使用直接经验作为主要信息来源，将推荐/声誉和推理过程考虑在之后，作为完善信任评估的辅助机制。不幸的是，在这种网络中，直接经验可能并不总是代表评估可信度的最佳解决方案。事实上，它们的高度动态结构促进了代理之间平均互连数量的增加。这反过来又会对代理人对每个特定个体的知识程度产生负面影响，即直接经验。然而，迄今为止，关于如何面对这些情况还没有说太多。即使在缺乏直接经验的情况下，找到有效的信任评估方法也很重要，这是本研究的核心重点。通过多代理社会模拟，我们考虑代理只能访问间接知识进行信任评估的情况，即特定个人或整个类别的个人的推荐。然后，我们比较这两种方法的效率，以确定何时依赖第一种或第二种方法更方便。正如预期的那样，我们的结果证实这些网络的动态性质强烈影响类别的作用。我们对该特征进行了建模，在模拟中引入了“营业额”，营业额越高，它依赖类别就越方便。除了这个验证结果，我们的模拟突出了在存在不可靠推荐者的情况下类别的更高程度的鲁棒性。如果没有关于推荐者有多可靠的可用信息，这样的结果甚至更重要。我们获得的结果与现有文献一致，对这一领域的发展具有重要意义。