Computational Trust Model for Repeated Trust Games Quang-Vinh Dang, Claudia-Lavinia Ignat
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Computational Trust Model for Repeated Trust Games Quang-Vinh Dang, Claudia-Lavinia Ignat To cite this version: Quang-Vinh Dang, Claudia-Lavinia Ignat. Computational Trust Model for Repeated Trust Games. The 15th IEEE International Conference on Trust, Security and Privacy in Computing and Commu- nications (TrustCom-16), Aug 2016, Tianjin, China. hal-01351250 HAL Id: hal-01351250 https://hal.inria.fr/hal-01351250 Submitted on 3 Aug 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Computational Trust Model for Repeated Trust Games Quang-Vinh Dang Claudia-Lavinia Ignat Universite´ de Lorraine, LORIA, F-54506 Inria, F-54600 Inria, F-54600 Universite´ de Lorraine, LORIA, F-54506 CNRS, LORIA, F-54506 CNRS, LORIA, F-54506 [email protected] [email protected] Abstract—Trust game is a money exchange game that has The trust game could be one-shot, i.e. the game ends after been widely used in behavioral economics for studying trust and one round of money exchange, or repeated, i.e. it lasts several collaboration between humans. In this game, exchange of money rounds [4], [5], [6]. The pairs of users could be fixed [7] or is entirely attributable to the existence of trust between users. The trust game could be one-shot, i.e. the game ends after one re-assigned before each round [8]. The total number of rounds round of money exchange, or repeated, i.e. it lasts several rounds. in the experiment can be reported to users [9], [10] or not [6]. Predicting user behavior in the repeated trust game is of critical These games provide different kinds of partner information to importance for the next movement of the partners. However, players, such as their gender, age and income [11], or their existing behavior prediction approaches uniquely rely on players past interaction history [12], [8]. Some of these games allow personal information such as their age, gender and income and do not consider their past behavior in the game. communication between players [9], [13] or specification of In this paper, we propose a computational trust metric that user contracts [14], [15]. is uniquely based on users past behavior and can predict the Predicting partner behavior in the repeated trust game is future behavior in repeated trust game. Our trust metric can of critical importance. In e-commerce, for instance, it is distinguish between users having different behavioral profiles very important to predict the behavior of the partners before and is resistant to fluctuating user behavior. We validate our model by using an empirical approach against data sets collected transaction completion. Game theory predicts that, in trust from several trust game experiments. We show that our model game, sender will send 0 and receiver will send back 0 [16]. is consistent with rating opinions of users, and our model can However, in experimental game theory we usually do not provide higher accuracy on predicting users’ behavior compared observe this user behavior. In fact, the sending behavior of with other naive models. users in large-scale settings follows the normal distribution Index Terms—trust management, computational trust, non- cooperative games, behavioral game theory [3]. Existing approaches on predicting behavior of participants in one-shot trust game were proposed using additional infor- I. INTRODUCTION mation related to players, such as the personal information Studying cooperation between users is a main topic of re- (age, gender, income, etc.) or the data collected through some search in economy, psychology and computer science. Invest- tests / questionnaires. However, the only reliable available in- ment game or trust game proposed by Berg [1] is an important formation for predicting users behavior is their behavior during tool to study trust and collaboration [2]. The experiment has previous transactions. No other work proposed prediction of been repeated thousands of times all over the world1. In this user behavior based on analysis of previous interactions. game two users exchange money in anonymous mode. The In this paper, we present a computational trust metric, which sender sends an amount to the receiver. This amount is tripled serves as a model for user behavior, to reflect and predict when it arrives to the receiver. The receiver then selects an user’s behavior in repeated trust games. We claim that, based amount that should not exceed the sum received to send back on users past behavior in the repeated trust game, we can to the sender. This time, the amount is not tripled when it model and predict their next behavior. We only focus on arrives to the sender. The money exchange between users is repeated version of original anonymous trust game, i.e. the entirely attributable to the existence of trust between them. players have no information about their partners. We show that The model of trust game can be applied for the cooperation our model is robust to several types of attacks. We validate between users that are located in different places that do not our model by using an empirical approach against data sets know each other. For instance, in e-commerce, buyers usually collected from several trust game experiments. We prove that need to pay before they can receive the goods, so they need our trust metric is consistent with users’ opinion about partners to trust the sellers. trustworthiness. The paper is structured as follows. We present related works 1According to Google Scholar, the paper has been cited 3,810 times, as on 27-Jun-2016. An incomplete study counted that there are more than 23,000 in Section II. In Section III we present our trust metric and in participants involved in trust game experiments [3]. section IV we describe its evaluation. Section V presents some limitations of our metric. Concluding remarks are presented in 2) The trust value can distinguish between different types Section VI. of users. 3) The trust value considers user behavior over time. II. RELATED WORK 4) The trust value encourages a stable behavior rather than Several approaches on predicting behavior of participants a fluctuating one. in one-shot trust game were proposed using additional infor- 5) The trust value is robust against attacks. mation related to players, such as their personal information A. Parameters initial values (e.g. age, gender, income) or evaluation collected through The values of the parameters used for the trust metric some tests or questionnaires users needed to fill in before the computation are displayed in Table I. The left side of the table experiment. contains the initial values of the corresponding parameters, Gunnthorsdottir et al. [17] used the Mach test on users while the right side of the table contains the constant values personality to predict their behavior in the game. Evans et of the corresponding parameters. al. [18] predicted users behavior based on the results of Propensity to Trust Survey. Using a similar idea, Yamagishi et TABLE I al. [19] defined a new model called attitudinal trust to predict PARAMETER INITIAL VALUES. users’ behavior in one-shot trust game. The attitudinal trust is calculated based on data collected through a questionnaire. α0 0. 0.3 Yen [20] claimed that users with higher income send more β0 0. φ 0.1 to their partners than users with lower income. Falk et al. [21] atf0 0. MAX ATF 2. confirmed this suggestion by showing that, students tend to expect trust0 0. threshold 0.25 send less than other social groups. trend factor0 0. c 0.9 current trust 0. Ashraf et al. [22] used the data collected in the dictator 0 aggregate trust0 0. game to predict the behavior of the same users in the trust change rate0 0. game. trust value0 0.5 In real world applications, sociometric information of users is usually not available [23]. Even if this information is available, such as a user login to a website by using Facebook B. Current trust account, it is not always reliable as users can declare false As we described in Section I, in each round, two users personal information. Our method to model and predict users’ interact by sending a non-negative amount. For senders, the behavior does not require any additional personal data. maximum amount they can send is set to 10, and for receivers, We are not aware of any prior work that predicted users’ the maximum amount they can send is the amount they behavior in repeated trust game. The theoretical prediction is received from the sender (i.e. three times of what the sender that in the repeated trust game participants keep exchanging sent). For both roles, we normalize the send proportiont as an amount of 0 [16]. the sending proportion of a user at round t, with t ≥ 1: Glaeser et al. [24] used average value of previous sending amount as the trust measurement of users in trust game. This sending amountt send proportiont = (1) trust metric as average of previous sending amounts was used maximum sending amountt later on by other research works [8], [3], [25]. However, It is obvious that 8t; 0 ≤ send proportiont ≤ 1. this average trust metric can not deal with user fluctuating We define next the trust metric for a single interaction behavior, as we discuss in Section IV.