Elsevier

Cognitive Systems Research

Volume 64, December 2020, Pages 164-173
Cognitive Systems Research

Evaluating agents’ trustworthiness within virtual societies in case of no direct experience

https://doi.org/10.1016/j.cogsys.2020.08.005Get rights and content

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.

Introduction

While digital infrastructures keep spreading, even social interaction is facing a paradigm shift (Liang and Shi, 2008) (Martin, 2008). We are living a major change, started decades ago with the introduction of the Web, and that will transform in an ever more deep way the interactional and communicative modalities as well as its (human or artificial) actors. Thanks to the Internet, more and more people are connected to each other, creating virtual societies that “facilitate global and simultaneous interaction, create a common context for collaboration, combine different tools for communication and enhance knowledge and knowing processes” (Mueller et al., 2011). However, in many environments a substantial part of the communications occur between people (or artificial agents) who know little or nothing about each other (Haythornthwaite, 2005) (Liu et al., 2009) (Resnick and Zeckhauser, 2002), such as in e-markets, forums, cloud systems, and multi-agent systems in general. In these contexts, an individual may rely on one or more members of the network to carry out a service for him: realizing an action, reporting information, providing goods, and so on. It becomes essential to identify methodologies able to evaluate our interlocutors (Abdul-Rahman and Hailes, 2000) and to select adequate partners.

If in the classical social paradigm we mainly rely on direct experience, there may be situation in which this dimension may not be enough. In fact, the more populated the network, the more complicated knowing sufficiently well its members and their performance. In this context, characterized by a high number of agents and a high dynamism, we need practical alternatives to further generalize the knowledge we have.

At the same time, a high level of interconnection is not necessarily drawback, since it increases the possibility to retrieve indirect information, such as trust evaluations produced by other agents in the network. In this respect, a few trust models made a further step forward, showing that combining inferential processes with recommendations (Conte and Paolucci, 2002) (De Meo et al., 2018) (Yolum and Singh, 2003) represents a powerful instrument that in many situations can be more effective than simple individual recommendations. However, there is still a lot to say about the limits and the situation in which this approach actually represents an advantage. The current studies just show that their global performance is better than the previous models, but they do not deepen the relationship between individual and category recommendation within their model.

Starting from the assumption that direct experience is an irreplaceable resource, in this work we are interested in studying what happens when there is a complete lack of direct experience. In this case, the trustor, namely the agent who needs to find a partner to rely on for a specific task, is forced to make use of external information. This is anything but an uncommon situation, happening each time the trustor joins a new network, or even if it has already interacted with someone in the network for tasks that are completely different from those needed at the moment, so that he cannot use the old knowledge.

Under these conditions, we aim to identify when it is more convenient to use individual recommendation rather than category recommendation. In particular, we target two specific parameter: the turnover in the network and the recommenders' trustworthiness. We are not interested in realizing a new trust model, we just want to compare and analyze the performance of these two different approaches.

With respect to the current literature, our contributes are:

  • 1.

    To show that the presence of turnover advantages category recommendations over individual recommendations. This effect, although not astonishing, represents a further element supporting the usefulness of category recommendations in virtual societies, precisely because they are often characterized by a high turnover.

  • 2.

    To prove that, in the presence of unreliable recommenders, category recommendations offer a much more robust trust assessment than the individual ones, especially when the recommenders’ trustworthiness is not known a priori.

The article is organized as follows. In Section 2 we introduce the concept of category. In Section 3 we analyze related work. Section 4 describes the simulations and their workflow. In Section 5 we study the effect of turnover in the network, while in Section 6 we introduce unreliable recommenders. In conclusion, Section 7 summarizes the results of the whole work.

Section snippets

The categories

A category is defined by a set of specific features: an agent belongs to a category if it possesses the features defining that category. Thus, a set of distinctive features, which can be determined through a set of visible and non-deceptive signs, identifies a category. In the case of human beings, they could be somatic traits. For instance, the authors of (FeldmanHall et al., 2018) show that we tend to identify the somatic traits of the most reliable individuals and, in the absence of other

Related work

The importance of categories/stereotypes has broadly been shown in the literature. It has been widely used for user modeling (Middleton et al., 2004), and more recently for trust modeling.

Tirloe (Tirloe, 1996) starts modeling group reputation as an aggregate of individual reputations and proposes a few inferential strategies. Later Sun (Sun et al., 2005) proposes a group-based reputation system. Here an average performance Pi for the group is computed, which is used as an initial trust value

Simulations

Once we defined the framework, we used the simulations in order to verify the utility of categories in different situations.

Among the various available platform for agent-based simulation (Abar et al., 2017) (Kravari and Bassiliades, 2015); our choice fell on NetLogo (Wilensky, 1999), a multi-agent framework designed specifically for simulations. In fact, besides being one of the most used tool for social simulation, it is free, open source, cross-platform, and continuously supported/updated.

First part: CV and uncertainty on categories in the absence of turnover

In the first part of this experiment, we analyze how the variability of the standard deviation σ of a category affects the CV. As stated above, one can define categories at various levels of abstraction and, in this sense, σ represents a useful index to measure their appropriateness. Thus, the following analysis will also allow us to determine the proper values of σ for this specific network, which we will then use in the following experiments. Indeed, in a simulation we can capitalize on this

Second experiment: Untrustworthy recommenders

This second experiment is meant to study the effect of untrustworthy recommenders on category and individual recommendations, with the purpose of understanding whether one of these two approach grants a better outcome. In general, each agent possesses a partial and subjective knowledge of the network, which he can faithfully report to the trustor or not, providing a different result. We will not focus on the motivation beyond this action (which can be due to the aspect of competence, honesty or

Discussion and conclusion

Starting from the assumption that direct experience is an irreplaceable resource, in this work we analyzed what happens when there is a complete lack of direct experience, forcing the trustor to make use of external information to find a partner, i.e. recommendations. There is still a lot to say about how to efficiently exploit recommendations within a digitally infrastructured society. We support the idea that the category recommendations play a fundamental and complementary role to that of

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work is partially supported by the project CLARA—CLoud plAtform and smart underground imaging for natural Risk Assessment, funded by the Italian Ministry of Education, University and Research (MIUR-PON).

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