Rational Trust Modeling

Trust models are widely used in various computer science disciplines. The main purpose of a trust model is to continuously measure trustworthiness of a set of entities based on their behaviors. In this article, the novel notion of "rational trust modeling" is introduced by bridging trust management and game theory. Note that trust models/reputation systems have been used in game theory (e.g., repeated games) for a long time, however, game theory has not been utilized in the process of trust model construction; this is where the novelty of our approach comes from. In our proposed setting, the designer of a trust model assumes that the players who intend to utilize the model are rational/selfish, i.e., they decide to become trustworthy or untrustworthy based on the utility that they can gain. In other words, the players are incentivized (or penalized) by the model itself to act properly. The problem of trust management can be then approached by game theoretical analyses and solution concepts such as Nash equilibrium. Although rationality might be built-in in some existing trust models, we intend to formalize the notion of rational trust modeling from the designer's perspective. This approach will result in two fascinating outcomes. First of all, the designer of a trust model can incentivise trustworthiness in the first place by incorporating proper parameters into the trust function, which can be later utilized among selfish players in strategic trust-based interactions (e.g., e-commerce scenarios). Furthermore, using a rational trust model, we can prevent many well-known attacks on trust models. These two prominent properties also help us to predict behavior of the players in subsequent steps by game theoretical analyses

Trust from a trait perspective: a theoretical framework and empirical test

Corresponding to the pivotal role of trust for all kinds of social interactions and inter-personal relationships, trust has been the target of abundant research across scientific disci-plines. However, an integration of the huge literature is currently missing – thus hampering a common understanding of trust and a synthesis between the fields. Furthermore, from a psy-chological perspective, there is an insufficient understanding of the (basic) personality traits accounting for individual variation in trust. Therefore, the overall objective of this thesis is to bridge the gap between different lines of trust research and to uncover the dispositional de-terminants of trust. To that end, a behavioral view on trust is adopted, defining trust in terms of a risky choice to depend on another. Based on a broad review of the literature, a theoretical framework is distilled, identifying the situational features and personality characteristics un-derlying trust. Specifically, trust is considered to be a function of (1) attitudes toward risky prospects (risk and loss aversion), (2) trustworthiness expectations, and (3) betrayal sensitivity. These determinants are, in turn, rooted in different traits (i.e., anxiety/fear, trustworthiness, and forgiveness) which can be localized in the space defined by basic personality models. Here, the HEXACO model provides particularly clear-cut hypotheses on the basic traits driv-ing trust, including a unique factor for each of the proposed (specific) trait determinants. Building on this reasoning, the empirical part of this thesis presents first evidence on the link between the HEXACO dimensions and trust. As a starting point, the focus was on the Honesty-Humility factor, representing the unique feature of the HEXACO model compared to more established models of personality (e.g., the Five-Factor Model). In line with the pro-posed theoretical framework, two sets of studies provided support for a social projection path from trait trustworthiness to trustworthiness expectations. Specifically, high levels of Hones-ty-Humility predicted more optimistic trustworthiness expectations and – as necessitated by social projection – were also positively linked to trustworthy behavior. As such, the findings not only identify a trait source of trust, but also clarify the dispositional determinants of trust-worthiness. Overall, the theoretical framework and empirical evidence presented in this thesis suggests the fruitfulness to take a closer look at trust – from a trait perspective

Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems

Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind?s Alpha Go Zero [1]) are an impressive example of an artificial intelligence system calculating results that even a human expert for the game can hardly retrace [2]. But this is, quite literally, a toy example. In reality, intelligent algorithms are encroaching more and more into our everyday lives, be it through algorithms that recommend products for us to buy, or whole systems such as driverless vehicles. We are delegating ever more aspects of our daily routines to machines, and this trend looks set to continue in the future. Indeed, continued economic growth is set to depend on it. The nature of human-computer interaction in the world that the digital transformation is creating will require (mutual) trust between humans and intelligent, or seemingly intelligent, machines. But what does it mean to trust an intelligent machine? How can trust be established between human societies and intelligent machines

Cognitive Systems For Revenge and Forgiveness

Minimizing the costs that others impose upon oneself and upon those in whom one has a fitness stake, such as kin and allies, is a key adaptive problem for many organisms. Our ancestors regularly faced such adaptive problems (including homicide, bodily harm, theft, mate poaching, cuckoldry, reputational damage, sexual aggression, and the infliction of these costs on one\u27s offspring, mates, coalition partners, or friends). One solution to this problem is to impose retaliatory costs on an aggressor so that the aggressor and other observers will lower their estimates of the net benefits to be gained from exploiting the retaliator in the future. We posit that humans have an evolved cognitive system that implements this strategy - deterrence - which we conceptualize as a revenge system. The revenge system produces a second adaptive problem: losing downstream gains from the individual on whom retaliatory costs have been imposed. We posit, consequently, a subsidiary computational system designed to restore particular relationships after cost-imposing interactions by inhibiting revenge and motivating behaviors that signal benevolence for the harmdoer. The operation of these systems depends on estimating the risk of future exploitation by the harmdoer and the expected future value of the relationship with the harmdoer. We review empirical evidence regarding the operation of these systems, discuss the causes of cultural and individual differences in their outputs, and sketch their computational architecture

How to Make the Perfect Citizen? Lessons from China\u27s Social Credit System

How to make the perfect citizen? This has been one of the questions driving the construction of the Chinese Social Credit System: a technology-driven project that aims to assess, evaluate, and steer the behavior of Chinese citizens. After presenting social credit systems in China\u27s public and private sectors (Part II), the Article provides normative standards to distinguish the Chinese system from comparable systems in liberal democracies (Part III). It then discusses the concept of civic virtue, as implemented by the Social Credit System, claiming that it creates a new form of governance, cybernetic citizenship, which fundamentally changes the essence of citizenship and the political role of the state (Part IV). On the whole, the Article demonstrates how the Chinese . Social Credit System redefines the institution of citizenship and warns against similar patterns that are mushrooming in liberal democracies

Trusted community : a novel multiagent organisation for open distributed systems

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Cause and Effect in Victim Sensitivity: Analyses of Associated Social-Cognitive Processes

Victim sensitivity (VS) – as one facet of justice sensitivity – is conceptualized as a personality disposition reflecting a combination of a strong need to trust others together with a latent expectation that other people are malevolent and untrustworthy. Previous research has linked victim sensitivity to a wide range of emotional and conduct problems that affect the respective individual as well as its social environment. However, there is still a lack of knowledge about how exactly VS shapes information processing and behavior. This thesis adopts a social-cognitive perspective on victim sensitivity in order to gain a more complete understanding of the psychological processes that drive victim-sensitive people’s reactions. To analyze underlying causes and effects, four studies were conducted that used timely and highly state-of-the-art procedures (e.g., eye tracking or virtual reality technology). Two important conclusions can be drawn from this research: first, the results confirm once again that victim sensitivity shapes emotions, cognitions, and behavior in a dysfunctional way. More specifically, the results suggested that VS is associated with hostile information processing (Study II) and contributes to even pathological forms of interpersonal dysfunction (Study I). Second, however, the present research also illustrates that it is possible to alleviate any adverse consequences of victim sensitivity. Study III, for example, found that individuals high in VS are able to overcome their habitual distrust if a sense of control is reestablished. In addition, Study IV demonstrated that persons high in VS allocate preferential attention toward information violating their negative social expectations, which might reduce these expectations in the long run. Taken together, the research described in this thesis extends our knowledge about social-cognitive and motivational processes underlying victim sensitivity and therefore has important implications for research on justice-related personality dispositions

A framework for decentralised trust reasoning.

Recent developments in the pervasiveness and mobility of computer systems in open computer networks have invalidated traditional assumptions about trust in computer communications security. In a fundamentally decentralised and open network such as the Internet, the responsibility for answering the question of whether one can trust another entity on the network now lies with the individual agent, and not a priori a decision to be governed by a central authority. Online agents represent users' digital identities. Thus, we believe that it is reasonable to explore social models of trust for secure agent communication. The thesis of this work is that it is feasible to design and formalise a dynamic model of trust for secure communications based on the properties of social trust. In showing this, we divide this work into two phases. The aim of the first is to understand the properties and dynamics of social trust and its role in computer systems. To this end, a thorough review of trust, and its supporting concept, reputation, in the social sciences was carried out. We followed this by a rigorous analysis of current trust models, comparing their properties with those of social trust. We found that current models were designed in an ad-hoc basis, with regards to trust properties. The aim of the second phase is to build a framework for trust reasoning in distributed systems. Knowledge from the previous phase is used to design and formally specify, in Z, a computational trust model. A simple model for the communication of recommendations, the recommendation protocol, is also outlined to complement the model. Finally an analysis of possible threats to the model is carried out. Elements of this work have been incorporated into Sun's JXTA framework and Ericsson Research's prototype trust model