An Adaptive Trust Model Based on Fuzzy Logic

In cooperative environments is common that agents delegate tasks to each other to achieve their goals since an agent may not have the capabilities or resources to achieve its objectives alone. However, to select good partners, the agent needs to deal with information about the abilities, experience, and goals of their partners. In this situation, the lack or inaccuracy of information may affect the agent's judgment about a given partner; and hence, increases the risk to rely on an untrustworthy agent. Therefore, in this work, we present a trust model that combines different pieces of information, such as social image, reputation, and references to produce more precise information about the characteristics and abilities of agents. An important aspect of our trust model is that it can be easily configured to deal with different evaluation criteria. For instance, as presented in our experiments, the agents are able to select their partners by availability instead of the expertise level. Besides, the model allows the agents to decide when their own opinions about a partner are more relevant than the opinions received from third parties, and vice-versa. Such flexibility can be explored in dynamic scenarios, where the environment and the behavior of the agents might change constantly.

Measuring the Welfare Effects of Shame and Pride

Public recognition is frequently used to motivate desirable behavior, yet its welfare effects—such as costs of shame or gains from pride— are rarely measured. We develop a portable empirical methodology for measuring and monetizing social image utility, and we deploy it in experiments on exercise and charitable behavior. In all experiments, public recognition motivates desirable behavior but creates highly unequal image payoffs. High-performing individuals enjoy significant utility gains, while low-performing individuals incur significant utility losses. We estimate structural models of social signaling, and we use the models to explore the social efficiency of public recognition policies. (JEL C93, D64, D82, D91)

Social Image of Autism and the Need for Educational Research

In this paper, we present the results of an online survey concerning the social perception of people with Autism Spectrum Disorders (ASD). The analysis of the online survey conducted in Poland from March to May 2020, in which 355 Polish speaking respondents took part and which consisted of two parts: closed-ended questions and open-ended statements – shows that there is a cognitively interesting discrepancy between the relatively high level of knowledge declared by respondents and the more negative and stereotypical attitude towards people with ASD visible in the open-ended responses. Particularly noteworthy is the fact that relations between the respondents and neurotypical people are lined with fear, anxiety and insecurity. The survey results seem to unequivocally indicate the necessity for further educational projects that deepen knowledge and raise awareness of people with ASD.

Honesty in the Digital Age

Modern communication technologies enable efficient exchange of information but often sacrifice direct human interaction inherent in more traditional forms of communication. This raises the question of whether the lack of personal interaction induces individuals to exploit informational asymmetries. We conducted two experiments with a total of 848 subjects to examine how human versus machine interaction influences cheating for financial gain. We find that individuals cheat about three times more when they interact with a machine rather than a person, regardless of whether the machine is equipped with human features. When interacting with a human, individuals are particularly reluctant to report unlikely and therefore, suspicious outcomes, which is consistent with social image concerns. The second experiment shows that dishonest individuals prefer to interact with a machine when facing an opportunity to cheat. Our results suggest that human presence is key to mitigating dishonest behavior and that self-selection into communication channels can be used to screen for dishonest people. This paper was accepted by Axel Ockenfels, decision analysis.