Talking Nets: A Multi-Agent Connectionist Approach to Communication and Trust between Individuals

A multi-agent connectionist model is proposed that consists of a collection of individual recurrent networks that communicate with each other, and as such is a network of networks. The individual recurrent networks simulate the process of information uptake, integration and memorization within individual agents, while the communication of beliefs and opinions between agents is propagated along connections between the individual networks. A crucial aspect in belief updating based on information from other agents is the trust in the information provided. In the model, trust is determined by the consistency with the receiving agents’ existing beliefs, and results in changes of the connections between individual networks, called trust weights. Thus activation spreading and weight change between individual networks is analogous to standard connectionist processes, although trust weights take a specific function. Specifically, they lead to a selective propagation and thus filtering out of less reliable information, and they implement Grice’s (1975) maxims of quality and quantity in communication. The unique contribution of communicative mechanisms beyond intra-personal processing of individual networks was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions

Trust beyond reputation: A computational trust model based on stereotypes

Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"- essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information

Screening with an Approximate Type Space

We re-visit the single-agent mechanism design problem with quasi-linear preferences, but we assume that the principal knowingly operates on the basis of only an approximate type space rather than the (potentially complex) truth. We propose a two-step scheme, the profit-participation mechanism, whereby: (i) the principal .takes the model seriously and computes the optimal menu for the approximate type space; (ii) but she discounts the price of each allocation proportionally to the profit that the allocation would yield in the approximate model. We characterize the bound to the profit loss and show that it vanishes smoothly as the distance between the approximate type space and the true type space converges to zero. Instead, we show that it is not a valid approximation to simply act as if the model was correct.

Bootstrapping trust evaluations through stereotypes

Publisher PD

TURTLE-P: a UML profile for the formal validation of critical and distributed systems

The timed UML and RT-LOTOS environment, or TURTLE for short, extends UML class and activity diagrams with composition and temporal operators. TURTLE is a real-time UML profile with a formal semantics expressed in RT-LOTOS. Further, it is supported by a formal validation toolkit. This paper introduces TURTLE-P, an extended profile no longer restricted to the abstract modeling of distributed systems. Indeed, TURTLE-P addresses the concrete descriptions of communication architectures, including quality of service parameters (delay, jitter, etc.). This new profile enables co-design of hardware and software components with extended UML component and deployment diagrams. Properties of these diagrams can be evaluated and/or validated thanks to the formal semantics given in RT-LOTOS. The application of TURTLE-P is illustrated with a telecommunication satellite system

Responsibility for implicit bias

Research programs in empirical psychology from the past two decades have revealed implicit biases. Although implicit processes are pervasive, unavoidable, and often useful aspects of our cognitions, they may also lead us into error. The most problematic forms of implicit cognition are those which target social groups, encoding stereotypes or reflecting prejudicial evaluative hierarchies. Despite intentions to the contrary, implicit biases can influence our behaviours and judgements, contributing to patterns of discriminatory behaviour. These patterns of discrimination are obviously wrong and unjust. But in remedying such wrongs, one question to be addressed concerns responsibility for implicit bias. Unlike some paradigmatic forms of wrongdoing, such discrimination is often unintentional, unendorsed, and perpetrated without awareness; and the harms are particularly damaging because they are cumulative and collectively perpetrated. So, what are we to make of questions of responsibility? In this article, we outline some of the main lines of recent philosophical thought, which address questions of responsibility for implicit bias. We focus on (a) the kind of responsibility at issue; (b) revisionist versus nonrevisionist conceptions of responsibility as applied to implicit bias; and (c) individual, institutional, and collective responsibility for implicit bias

Responsibility for implicit bias

Research programs in empirical psychology from the past two decades have revealed implicit biases. Although implicit processes are pervasive, unavoidable, and often useful aspects of our cognitions, they may also lead us into error. The most problematic forms of implicit cognition are those which target social groups, encoding stereotypes or reflecting prejudicial evaluative hierarchies. Despite intentions to the contrary, implicit biases can influence our behaviours and judgements, contributing to patterns of discriminatory behaviour. These patterns of discrimination are obviously wrong and unjust. But in remedying such wrongs, one question to be addressed concerns responsibility for implicit bias. Unlike some paradigmatic forms of wrongdoing, such discrimination is often unintentional, unendorsed, and perpetrated without awareness; and the harms are particularly damaging because they are cumulative and collectively perpetrated. So, what are we to make of questions of responsibility? In this article, we outline some of the main lines of recent philosophical thought, which address questions of responsibility for implicit bias. We focus on (a) the kind of responsibility at issue; (b) revisionist versus nonrevisionist conceptions of responsibility as applied to implicit bias; and (c) individual, institutional, and collective responsibility for implicit bias