A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems

Trust and reputation models for distributed, collaborative systems have been studied and applied in several domains, in order to stimulate cooperation while preventing selfish and malicious behaviors. Nonetheless, such models have received less attention in the process of specifying and analyzing formally the functionalities of the systems mentioned above. The objective of this paper is to define a process algebraic framework for the modeling of systems that use (i) trust and reputation to govern the interactions among nodes, and (ii) communication models characterized by a high level of adaptiveness and flexibility. Hence, we propose a formalism for verifying, through model checking techniques, the robustness of these systems with respect to the typical attacks conducted against webs of trust.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200

On the Modeling and Verification of Collective and Cooperative Systems

none1noThe formal description and verification of networks of cooperative and interacting agents is made difficult by the interplay of several different behavioral patterns, models of communication, scalability issues. In this paper, we will explore the functionalities and the expressiveness of a general-purpose process algebraic framework for the specification and model checking based analysis of collective and cooperative systems. The proposed syntactic and semantic schemes are general enough to be adapted with small modifications to heterogeneous application domains, like, e.g., crowdsourcing systems, trustworthy networks, and distributed ledger technologies.Aldini, AlessandroAldini, Alessandr

ILR Research in Progress 2011-12

The production of scholarly research continues to be one of the primary missions of the ILR School. During a typical academic year, ILR faculty members published or had accepted for publication over 25 books, edited volumes, and monographs, 170 articles and chapters in edited volumes, numerous book reviews. In addition, a large number of manuscripts were submitted for publication, presented at professional association meetings, or circulated in working paper form. Our faculty's research continues to find its way into the very best industrial relations, social science and statistics journals.Research_in_Progress_2011_12.pdf: 46 downloads, before Oct. 1, 2020

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

The Web as an Adaptive Network: Coevolution of Web Behavior and Web Structure

Much is known about the complex network structure of the Web, and about behavioral dynamics on the Web. A number of studies address how behaviors on the Web are affected by different network topologies, whilst others address how the behavior of users on the Web alters network topology. These represent complementary directions of influence, but they are generally not combined within any one study. In network science, the study of the coupled interaction between topology and behavior, or state-topology coevolution, is known as 'adaptive networks', and is a rapidly developing area of research. In this paper, we review the case for considering the Web as an adaptive network and several examples of state-topology coevolution on the Web. We also review some abstract results from recent literature in adaptive networks and discuss their implications for Web Science. We conclude that adaptive networks provide a formal framework for characterizing processes acting 'on' and 'of' the Web, and offers potential for identifying general organizing principles that seem otherwise illusive in Web Scienc

The Agent-Based Modeling Approach through Some Foundational Monographs

L’article analyse quelques monographies fondamentales qui mettent en évidence la pertinence de la simulation multi-agents pour l’analyse sociologique. Ces ouvrages ont été sélectionnés au sein de travaux qui portent sur la coopération, les dynamiques sociales et les normes. Ils montrent l’importance de modéliser les comportements complexes des acteurs et leurs interactions pour comprendre les régularités sociales ainsi que les raisons pour lesquelles la modélisation et l’abstraction sont importantes pour l’analyse sociologique. La modélisation multi-agents peut nous aider à produire des théories des phénomènes sociaux plus cohérentes et vérifiables et nous permet de mieux organiser les théories avant de les tester et en vue de les répliquer. Enfin, dans l’esprit d’une approche collaborative, cet article argumente en faveur du besoin de liens plus étroits entre les approches expérimentales et la sociologie

Inter-Organizational Learning and Collective Memory in Small Firms Clusters: an Agent-Based Approach

Literature about Industrial Districts has largely emphasized the importance of both economic and social factors in determining the competitiveness of these particular firms\' clusters. For thirty years, the Industrial District productive and organizational model represented an alternative to the integrated model of fordist enterprise. Nowadays, the district model suffers from competitive gaps, largely due to the increase of competitive pressure of globalization. This work aims to analyze, through an agent-based simulation model, the influence of informal socio-cognitive coordination mechanisms on district\'s performances, in relation to different competitive scenarios. The agent-based simulation approach is particularly fit for this purpose as it is able to represent the Industrial District\'s complexity. Furthermore, it permits to develop dynamic analysis of district\'s performances according to different types of environment evolution. The results of this work question the widespread opinion that cooperative districts can answer to environmental changes more effectively that non-cooperative ones. In fact, the results of simulations show that, in the presence of turbulent scenarios, the best performer districts are those in which cooperation and competition, trust and opportunism balance out.Firm Networks, Collective Memory, Agent Based Models, Uncertainty

Dynamics, robustness and fragility of trust

Trust is often conveyed through delegation, or through recommendation. This makes the trust authorities, who process and publish trust recommendations, into an attractive target for attacks and spoofing. In some recent empiric studies, this was shown to lead to a remarkable phenomenon of *adverse selection*: a greater percentage of unreliable or malicious web merchants were found among those with certain types of trust certificates, then among those without. While such findings can be attributed to a lack of diligence in trust authorities, or even to conflicts of interest, our analysis of trust dynamics suggests that public trust networks would probably remain vulnerable even if trust authorities were perfectly diligent. The reason is that the process of trust building, if trust is not breached too often, naturally leads to power-law distributions: the rich get richer, the trusted attract more trust. The evolutionary processes with such distributions, ubiquitous in nature, are known to be robust with respect to random failures, but vulnerable to adaptive attacks. We recommend some ways to decrease the vulnerability of trust building, and suggest some ideas for exploration.Comment: 17 pages; simplified the statement and the proof of the main theorem; FAST 200

Towards a global participatory platform: Democratising open data, complexity science and collective intelligence

The FuturICT project seeks to use the power of big data, analytic models grounded in complexity science, and the collective intelligence they yield for societal benefit. Accordingly, this paper argues that these new tools should not remain the preserve of restricted government, scientific or corporate élites, but be opened up for societal engagement and critique. To democratise such assets as a public good, requires a sustainable ecosystem enabling different kinds of stakeholder in society, including but not limited to, citizens and advocacy groups, school and university students, policy analysts, scientists, software developers, journalists and politicians. Our working name for envisioning a sociotechnical infrastructure capable of engaging such a wide constituency is the Global Participatory Platform (GPP). We consider what it means to develop a GPP at the different levels of data, models and deliberation, motivating a framework for different stakeholders to find their ecological niches at different levels within the system, serving the functions of (i) sensing the environment in order to pool data, (ii) mining the resulting data for patterns in order to model the past/present/future, and (iii) sharing and contesting possible interpretations of what those models might mean, and in a policy context, possible decisions. A research objective is also to apply the concepts and tools of complexity science and social science to the project's own work. We therefore conceive the global participatory platform as a resilient, epistemic ecosystem, whose design will make it capable of self-organization and adaptation to a dynamic environment, and whose structure and contributions are themselves networks of stakeholders, challenges, issues, ideas and arguments whose structure and dynamics can be modelled and analysed. Graphical abstrac