Quantifying Trust

Trust is a central concept in public-key cryptography infrastruc- ture and in security in general. We study its initial quantification and its spread patterns. There is empirical evidence that in trust-based reputation model for virtual communities, it pays to restrict the clusters of agents to small sets with high mutual trust. We propose and motivate a mathematical model, where this phenomenon emerges naturally. In our model, we separate trust values from their weights. We motivate this separation using real examples, and show that in this model, trust converges to the extremes, agreeing with and accentuating the observed phenomenon. Specifically, in our model, cliques of agents of maximal mutual trust are formed, and the trust between any two agents that do not maximally trust each other, converges to zero. We offer initial practical relaxations to the model that preserve some of the theoretical flavor

Product Specification: Distributed Trust Model System (DOE-PSU-0000922-4)

A Distributed Trust Model (DTM) System is a supervisory component within an energy grid of things. The role of a DTM System is to implement the trust aspects of an energy services interface. The DTM System augments existing security measures by monitoring the communication between the various EGoT System actors and quantifying metrics of trust of each actor

Consumers' Trust in Government and Their Attitudes Towards Genetically Modified Food: Empirical Evidence from China

Understanding the determinants of consumer's acceptance towards genetically modified food (GMF) is critical important for future biotechnology development. Among many factors, consumers' trust in government has increasingly received great attentions in the literature. However, accurately quantifying impact of consumers' trust in government on their GMF attitudes is difficult because researchers often encounter many difficulties in empirical estimation. Overall goal of this study is to empirically quantify the impact of consumers' trust in government on their attitudes towards GMF in China. An econometric model on consumer's trust in government and their attitude towards GMF is developed and estimated based on a unique data set collected by the authors in 2002 and 2003 in 11 cities of China. This study shows that the consumers' acceptance of GMF is high in urban China. Among many factors, consumers' trust in government is found to have significantly positive impact on their acceptance of GMFs, which has important implications for any government who wants to pursue the development of GMFs. Our study also shows that fail to consider the endogeneity of consumers trust in government will lead to serious underestimation of its impacts on consumers' acceptance of GMFs. This is, as the best of our knowledge, the first study on the impact of consumers' trust in government with consideration the endogenous problems that are often embodied in the consumer perception studies.Trust in government, Genetically modified food, Consumer's attitude, Acceptance, China, Food Consumption/Nutrition/Food Safety, Research and Development/Tech Change/Emerging Technologies, Q13, Q18, O13,

Relational quality: A dynamic framework for assessing the role of trust in strategic alliances

The organizational literature has always posited that «trust» plays a key role in economic exchanges, particularly when one or another party is subject to the risk of opportunistic behaviour, incomplete monitoring, or when moral hazard problems arise. These conditions are almost always present in the case of alliances and joint ventures between independent parties. This paper explores the concept of «relational quality» in one such inter-organizational form ­dyadic alliances­ where past experience and the shadow of the future play an important role. Relational quality is important, as it affects the extent to which partners substitute reliance on trust for more formal control mechanisms. Building on theory, case studies and survey data, we develop a framework for thinking about trust in dynamic and practical terms. We define three elements affecting relational quality in alliances: the initial conditions surrounding the exchange, the cumulative experiences of the parties with each other's behaviours as they interact, and the impact that external events have on perceptions of behaviour and attitudes of the parties about each other's trustworthiness. We use data on a sample of alliances with one Spanish partner to explore the relative impact of these elements and develop a more precise set of propositions from this framework. The paper should guide further work towards quantifying the role of trust as a control mechanism in the performance of strategic alliances.Alliances; economic exchanges; joint ventures;

Off-Street Vehicular Fog for Catering Applications in 5G/B5G: A Trust-based Task Mapping Solution and Open Research Issues

One of the key enablers in serving the applications requiring stringent latency in 5G networks is fog computing as it is situated closer to the end users. With the technological advancement of vehicles’ on-board units, their computing capabilities are becoming robust, and considering the underutilization of the off-street vehicles, we envision that the off-street vehicles can be an enormously useful computational source for the fog computing. Additionally, clustering the vehicles would be advantageous in order to improve the service availability. As the vehicles become highly connected, trust is needed especially in distributed environments. However, vehicles are made from different manufacturers, and have different platforms, security mechanisms, and varying parking duration. These lead to the unpredictable behavior of the vehicles where quantifying trust value of vehicles would be difficult. A trust-based solution is necessary for task mapping as a task has a set of properties including expected time to complete, and trust requirements that need to be met. However, the existing metrics used for trust evaluation in the vehicular fog computing such as velocity and direction are not applicable in the off-street vehicle fog environments. In this paper, we propose a framework for quantifying the trust value of off-street vehicle fog computing facilities in 5G networks and forming logical clusters of vehicles based on the trust values. This allows tasks to be shared with multiple vehicles in the same cluster that meets the tasks’ trust requirements. Further, we propose a novel task mapping algorithm to increase the vehicle resource utilization and meet the desired trust requirements while maintaining imposed latency requirements of 5G applications. Results obtained using iFogSim simulator demonstrate that the proposed solution increases vehicle resource utilization and reduces task drop noticeably. This paper presents open research issues pertaining to the study to lead..

Quantifying Carbon Stocks within the Androscoggin Land Trust

Greenhouse gas (GHG) emissions from the burning of fossil fuels for transportation and electricity, land use and deforestation, agriculture, and industry are causing unprecedented rates of climate change. The climate emergency is intensifying as temperature extremes increase, snow and ice cover declines, and the oceans warm and acidify. The need to mitigate climate change is rapidly increasing and many mitigation efforts focus on the reduction of GHG outputs into the atmosphere, primarily the development of new renewable energy technologies in order to reduce the burning of fossil fuels. While a decreased reliance on fossil fuels is a key climate solution, a largely underappreciated method of mitigation is through enhancing the Earth’s carbon stocks, the carbon sequestered and stored in the plant and soil biomass of forested landscapes. Implementing sustainable land management strategies to protect against land development and improve the land’s sequestration potential provides an opportunity to increase the uptake of CO2 and reduce the atmospheric carbon concentration. While land trusts might not immediately come to mind when thinking about climate change mitigation, their work to protect natural ecosystems, improve vegetation cover and prevent deforestation and development is important in carbon sequestration. The benefits of land trusts extends beyond protecting biodiversity, enhancing and protecting vulnerable ecosystems, and providing recreation opportunities for the community to also providing a natural climate solution. This report is and exploration into the carbon sequestered and stored on land conserved by the Androscoggin Land Trust (ALT) in order to quantify ALT’s mitigation benefits. ALT conserves 52 properties throughout the Androscoggin Watershed in Maine, working with local landowners and organizations to protect significant land that gives this region character. We quantified the carbon held in each of the four land types in which the properties can be characterized: mixedwood forest, mixedwood forest and wetland, mixedwood forest and agricultural land, and mixedwood forest and meadow/field. We intend for the data, educational information about the carbon cycle and sequestration, and methodology that resulted from this project to be used to continue to quantify carbon held in more ALT properties, educate and inform the public about this benefit of ALT, and shared with other land trusts to allow them to complete similar projects. We provide ALT, and other Land Trusts, with the tools to increase the effectiveness of communicating their role in carbon sequestration and allow the value of land trusts in climate change mitigation to be fully appreciated and supported

Quantifying Divergence for Human-AI Collaboration and Cognitive Trust

Predicting the collaboration likelihood and measuring cognitive trust to AI systems is more important than ever. To do that, previous research mostly focus solely on the model features (e.g., accuracy, confidence) and ignore the human factor. To address that, we propose several decision-making similarity measures based on divergence metrics (e.g., KL, JSD) calculated over the labels acquired from humans and a wide range of models. We conduct a user study on a textual entailment task, where the users are provided with soft labels from various models and asked to pick the closest option to them. The users are then shown the similarities/differences to their most similar model and are surveyed for their likelihood of collaboration and cognitive trust to the selected system. Finally, we qualitatively and quantitatively analyze the relation between the proposed decision-making similarity measures and the survey results. We find that people tend to collaborate with their most similar models -- measured via JSD -- yet this collaboration does not necessarily imply a similar level of cognitive trust. We release all resources related to the user study (e.g., design, outputs), models, and metrics at our repo

Is Social Media a Threat or Can It Be a Trusted Agent?

There is a prevailing belief within the United States Department of Defense (DOD) that social media is a threat to national security, leading to restrictions in workplace use of social-media applications. However, instead of dismissing social media as a threat, leaders should be asking whether or not the information received via social media can be trusted, thus leveraging the information-sharing capabilities of social media. This article presents a theoretical case for quantifying social media trustworthiness by exploring the factors that influence trust in social media and proposing a trust framework to be used to quantify trustworthiness

Reasoning about Cognitive Trust in Stochastic Multiagent Systems

We consider the setting of stochastic multiagent systems modelled as stochastic multiplayer games and formulate an automated verification framework for quantifying and reasoning about agents’ trust. To capture human trust, we work with a cognitive notion of trust defined as a subjective evaluation that agent A makes about agent B’s ability to complete a task, which in turn may lead to a decision by A to rely on B. We propose a probabilistic rational temporal logic PRTL*, which extends the probabilistic computation tree logic PCTL* with reasoning about mental attitudes (beliefs, goals, and intentions) and includes novel operators that can express concepts of social trust such as competence, disposition, and dependence. The logic can express, for example, that “agent A will eventually trust agent B with probability at least p that B will behave in a way that ensures the successful completion of a given task.” We study the complexity of the automated verification problem and, while the general problem is undecidable, we identify restrictions on the logic and the system that result in decidable, or even tractable, subproblems