Multi-Stakeholder Consensus Decision-Making Framework Based on Trust and Risk

Indiana University-Purdue University Indianapolis (IUPUI)This thesis combines human and machine intelligence for consensus decision-making, and it contains four interrelated research areas. Before presenting the four research areas, this thesis presents a literature review on decision-making using two criteria: trust and risk. The analysis involves studying the individual and the multi-stakeholder decision-making. Also, it explores the relationship between trust and risk to provide insight on how to apply them when making any decision. This thesis presents a grouping procedure of the existing trust-based multi-stakeholder decision-making schemes by considering the group decision-making process and models. In the first research area, this thesis presents the foundation of building multi-stakeholder consensus decision-making (MSCDM). This thesis describes trust-based multi-stakeholder decision-making for water allocation to help the participants select a solution that comes from the best model. Several criteria are involved when deciding on a solution such as trust, damage, and benefit. This thesis considers Jain's fairness index as an indicator of reaching balance or equality for the stakeholder's needs. The preferred scenario is when having a high trust, low damages and high benefits. The worst scenario involves having low trust, high damage, and low benefit. The model is dynamic by adapting to the changes over time. The decision to select is the solution that is fair for almost everyone. In the second research area, this thesis presents a MSCDM, which is a generic framework that coordinates the decision-making rounds among stakeholders based on their influence toward each other, as represented by the trust relationship among them. This thesis describes the MSCDM framework that helps to find a decision the stakeholders can agree upon. Reaching a consensus decision might require several rounds where stakeholders negotiate by rating each other. This thesis presents the results of implementing MSCDM and evaluates the effect of trust on the consensus achievement and the reduction in the number of rounds needed to reach the final decision. This thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in the stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the distance of the choices made by the stakeholders. Trust is useful in decreasing the distances. In the third research area, this thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the perturbation in the rating matrix. Trust is useful in increasing the rating matrix perturbation. Such perturbation helps to decrease the number of rounds. Therefore, trust helps to increase the speed of agreeing upon the same decision through the influence. In the fourth research area, this thesis presents Rating Aggregation operators in the implemented MSCDM framework. This thesis addresses the need for aggregating the stakeholders' ratings while they negotiate on the round of decisions to compute the consensus achievement. This thesis presents four aggregation operators: weighted sum (WS), weighted product (WP), weighted product similarity measure (WPSM), and weighted exponent similarity measure (WESM). This thesis studies the performance of those aggregation operators in terms of consensus achievement and the number of rounds needed. The consensus threshold controls the performance of these operators. The contribution of this thesis lays the foundation for developing a framework for MSCDM that facilitates reaching the consensus decision by accounting for the stakeholders' influences toward one another. Trust represents the influence

Product characteristics and reputation effects in the wine market

This dissertation analyzes the relationships between wine attributes and prices, with a focus on reputation effects. Contributions are made in the fields of industrial organization and econometrics, developing a model of firm behavior in the presence of collective and individual reputation incentives, and a technique broadly applicable to the task of estimating class-specific parametric models in the presence of class uncertainty. Data from California and Washington wines are analyzed. In a dynamic optimization framework, a theoretical model analyzes the firm's choice in maximizing the present value of its profits in a market in which the return of investing in quality is two-fold: collective (associated with the region of production) and firm reputation (associated with the brand or label). The results indicate that markets with fewer firms with both collective and firm reputation are conducive to the highest levels of quality.; The empirical part of the dissertation analyzes the effect of wine attributes on prices using hedonic models, while taking account of extreme product heterogeneity. It is hypothesized that multiple product classes exist. To identify and estimate class-specific hedonic models, two approaches are taken. The first approach uses price to segment the wine market, while the second uses all information to segment the market. In the price segmented model, accounting for multiple wine classes results in a greater ability to explain the variability in the data and produces more accurate and interpretable results regarding the implicit prices of the attributes. For the latter application, an innovative econometric technique is developed. First, a hedonic model for wine is estimated nonparametrically via local polynomial regression. Differences in the hedonic function across neighborhoods of data reflect changes in the underlying supply and demand functions. Data are then aggregated into groups of observations that share functionally similar estimates of the (local) hedonic functions. In this way, wine segments are endogenously determined on the basis of similarities in market equilibria. Using this methodology, four differentiated wine markets are identified: commercial, semi-premium, premium, and ultra-premium. Finally, parametric hedonic functions specific to each wine class are estimated, revealing significant differences in implicit prices of the attributes across classes

A trust framework for peer-to-peer interaction in ad hoc networks

PhDAs a wider public is increasingly adopting mobile devices with diverse applications, the idea of who to trust while on the move becomes a crucial one. The need to find dependable partners to interact is further exacerbated in situations where one finds oneself out of the range of backbone structures such as wireless base stations or cellular networks. One solution is to generate self-started networks, a variant of which is the ad hoc network that promotes peer-to-peer networking. The work in this thesis is aimed at defining a framework for such an ad hoc network that provides ways for participants to distinguish and collaborate with their most trustworthy neighbours. In this framework, entities create the ability to generate trust information by directly observing the behaviour of their peers. Such trust information is also shared in order to assist those entities in situations where prior interactions with their target peers may not have existed. The key novelty points of the framework focus on aggregating the trust evaluation process around the most trustworthy nodes thereby creating a hierarchy of nodes that are distinguished by the class, defined by cluster heads, to which they belong. Furthermore, the impact of such a framework in generating additional overheads for the network is minimised through the use of clusters. By design, the framework also houses a rule-based mechanism to thwart misbehaving behaviour or non-cooperation. Key performance indicators are also defined within this work that allow a framework to be quickly analysed through snapshot data, a concept analogous to those used within financial circles when assessing companies. This is also a novel point that may provide the basis for directly comparing models with different underlying technologies. The end result is a trust framework that fully meets the basic requirements for a sustainable model of trust that can be developed onto an ad hoc network and that provides enhancements in efficiency (using clustering) and trust performance

QoS-Aware Middleware for Web Services Composition

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online Business-to-Business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different Quality of Service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Reputations, Relationships and the Enforcement of Incomplete Contracts

This paper discusses the literature on the enforcement of incomplete contracts. It compares legal enforcement to enforcement via relationships and reputations. A number of mechanisms, such as the repeat purchase mechanism (Klein and Leffler (1981)) and efficiency wages (Shapiro and Stiglitz (1984)), have been offered as solutions to the problem of enforcing an incomplete contract. It is shown that the efficiency of these solutions is very sensitive to the characteristics of the good or service exchanged. In general, neither the repeat purchase mechanism nor efficiency wages is the most efficient in the set of possible relational contracts. In many situations, total output may be increased through the use of performance pay and through increasing the quality of law.contract, law and economics, reputation, repeated games, incomplete contracts, transactions costs, institutional economics, contract enforcement

Reputation risk contagion

The effects of the reputation of any single member of a group of agents on all the others in the group are calculated by modeling the spread of reputation contagion in a DeGroot network. The reputation of individual agents is measured by compiling a reputation index for each agent over an extended period. Transition probabilities within the network are assessed by considering extreme reputational events using a Bayesian approach. The results indicate that consensus is reached quickly, and influential agents can be easily identified. Agents in the network with a very positive reputation serve to mitigate the negative reputation of other agents in the network. Approximately 10–15% of the reputation of any agent in the network is attributable to network effects; positive reputations are deflated and negative reputations are inflated. The network effect on the sales of any single agent can be estimated once the reputation score has been translated to sale