The Effects of Information Dynamics on Trust and Reputation

上下文现代系统越来越复杂，具有多个独立的、决策的代理。这些代理包括物联网、自动驾驶汽车和人类。单个系统可以包括多种类型的代理。代理人的目标是在他们的最大利益范围内行事，需要某种机制来选择在不确定情况下与谁互动。类似的有机系统以信任的形式提供了这一点。对于以密码学为范式的计算机科学来说，信任并不新鲜。然而，传统的加密方法往往不可行或不适合复杂的社会。因此，现代计算信任的概念旨在捕捉普遍的现象来描述现有的系统并构建改进的系统。在人口稠密的环境中，代理需要某种方式来建立信任。一个代理不可能安全地与每个潜在的合作伙伴进行交互和判断。声誉是用来克服这一点的。代理将在整个网络中共享关于其他人的信息，以建立比他们单独使用更丰富的社区想法。因此，在所有此类系统上信任的基础是消息传递。当试图理解消息的影响时，很容易将重点放在消息的内容上。然而，了解信息何时、何地和如何传播对于了解其影响至关重要。恶意实体可以利用网络中的信息流进行攻击的方式有很多。无论是利用缓慢、错误的信息传播来“隐藏”其行为的后果，还是利用快速但未经验证的信息传播来传播消息，信息动力学都是任何声誉系统的弱点。本项目旨在研究信息动力学的一些基本原理及其对多智能体决策的影响，重点关注单个智能体和整个社区的开发和保护。研究方法一般计算信任的研究本身是相当新颖的。捕捉这一现象的正式尝试甚至更新。因此，要研究信任的一个新方面，如信息动力学，至少需要对现有方法进行新的应用。分析网络(网络理论)和相互作用的战略主体(博弈论)的正式工具是存在的，但它们在现代计算信任概念中的应用即使存在，也是稀少的。因此，这个项目努力借鉴来自各种领域的既定正式方法，以解决我们对复杂系统中的信任和社会决策的理解中目前尚未解决但根本的问题。更确切地说，该项目的目的是：-了解网络结构如何影响信息在其上的流动，以及这对网络成员的影响。+这可用于构建不太容易受到此类问题影响的网络。-分析恶意实体如何利用这些动态(例如，利用信息传播中的延迟来“隐藏”后果)。+这可以用来为开发减少此类网络上的恶意行为的机制提供信息。-分析无辜方的策略，这些策略或多或少可能使他们容易受到恶意实体的攻击。+在工程系统的情况下，这可以用于构建更健壮的代理。+在社交网络的情况下，这可以为围绕人类行为者的教育和政策决策提供信息(例如，当信任其他个人、网站或在线新闻来源时)。EPSRC战略数学科学；全球不确定性；数字经济合作者虽然到目前为止还没有正式的合作者，但一些公司已经表达了他们对这项工作的分支和结果的兴趣。谈判正在进行中。

ContextModern systems are increasingly complex, feature multiple independent, decision-making agents. These agents including IoT, autonomous vehicles and human beings. A single system can include multiple types of agent. Agents aim to act within their best interests, requiring some mechanism for selecting who to interact with under uncertainty. Similar organic systems provide this in the form of trust. Trust is not novel to computer science with cryptography being the paradigm. However, conventional cryptographic methods are often infeasible or unsuitable for complex societies. As such, the modern conception of computational trust aims to capture the generalized phenomenon to describe existing systems and construct improved ones.In highly populated environments, agents needs some way of establishing trust "off the ground". It is infeasible for an agent to safely interact with and judge every potential partner. Reputation is used to overcome this. Agents will share information about others throughout the network to build a richer idea of the community than they could alone. As such, the basis for trust on all such systems is message passing. It is easy to focus on the content of a message when trying to understand its impact. However, knowing when, where and how a message spreads is crucial to understanding its influence. There are many unexplored ways in which a malicious entity can exploit information flow in a network. Whether exploiting slow, faulty information propagation to "hide" from the consequences of their actions or exploiting fast but unverified information propagation to spread a message, information dynamics are a vulnerability of any reputation system. This project aims to look at some fundamental tenets of the study of information dynamics and its influence on multi-agent decision-making, with a focus on the exploitation and protection of individual agents and the community as a whole.Research MethodologyThe study of general computational trust is itself quite novel. Formal attempts at capturing this phenomenon are even newer. As such, to study a novel aspect of trust such as information dynamics would require at least the novel application of existing methods. Formal tools for analysing networks (network theory) and interacting, strategic agents (game theory) exist but their application to the modern conception of computational trust is sparse if at all present. As such, this project endeavours to draw from established formal methods from a variety of fields to tackle a currently unresolved but fundamental issue in our understanding of trust and social decision-making in complex systems. More precisely, the project aims for the following:- Understanding how the structure of a network affects the movement of information on it and the impact of this on the members of the network. + This can be used to then construct networks which are less vulnerable to such issues.- Analysing how malicious entities may exploit these dynamics (e.g. exploiting delays in information propagation to "hide" from consequences). + This can be used to inform the development of mechanisms to mitigate malicious behaviour on such networks.- Analysing strategies of innocent parties that are more or less likely to make them vulnerable to the endeavours of a malicious entity. + In the case of engineered systems, this can be used to construct more robust agents. + In the case of social networks, this could inform education and policy decisions around human actors (e.g. when trusting other individuals, websites or news sources online).EPSRC StrategiesMathematical Sciences; Global Uncertainties; Digital EconomyCollaboratorsWhile there are no formal collaborators as of yet, some companies have expressed their interest in the ramifications and outcomes of this work. Talks are ongoing.