Customer anger and incentives for quality provision

Emotions are a significant determinant of consumer behaviour. A customer may get angry if he feels that he is being treated unfairly by his supplier and that anger may make him more likely to switch to an alternative provider. We model the strategic interaction between firms that choose quality levels and anger-prone customers who pick their supplier based on their expectations of suppliers' quality. Strategic interaction can allow for multiple equilibria including some in which no firm invests in high quality. Allowing customers to voice their anger on peer-review fora can eliminate low-quality equilibria, and may even support a unique equilibrium in which all firms choose high quality

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

Keeping a Clean Reputation: More Evidence on the Perverse Effects of Disclosure

When a principal relies on an agent, a conflict of interest can encourage the agent to provide biased advice. Conventional wisdom suggests that such behavior can be reduced through disclosure requirements. However, disclosure has been shown to exacerbate self-serving bias and can actually lead to greater harm for the principal in one-shot interactions. But in many naturally occurring settings, agents form reputations, a mechanism that could diminish the incentive to provide biased advice. We test for bias in the advice agents provide when faced with reputation concerns, and examine the impact of disclosure in such an environment. In controlled laboratory experiments, we find little evidence of self-serving bias in the absence of disclosure when (3) agents form reputations and (4) principals use that information in selecting agents. However, we find the introduction of disclosure leads to self-serving biased advice that is difficult for principals to detect. When the conflict of interest is endogenous, we find that agents overwhelmingly put themselves in the position of having a conflict of interest, but principals neither avoid conflicted agents nor differentially discount the advice such agents provide

Observable Reputation Trading

Is the reputation of a firm tradable when the change in ownership is observable? We consider a competitive market in which a share of owners must retire in each period. New owners bid for the firms that are for sale. Customers learn the owner’s type, which reflects the quality of the good or service provided, through experience. After observing an ownership change they may want to switch firm. However, in equilibrium, good new owners buy from good old owners and retain high-value customers. Hence reputation is a tradable intangible asset, although ownership change is observable

Selling Reputation When Going out of Business

Is the reputation of a firm tradeable when the previous owner has to retire even though ownership change is observable? We consider a competitive market in which a share of owners must retire in each period. New owners, observing only recent profits, bid for the firms on sale. Customers are concerned with the owners’ type, which reflects the quality of the good or service provided. When a customer observes an ownership change, he may have an incentive to switch to a different firm even if his past experience was good. However, we show that, in equilibrium, customers believe that also the new owner is of the good type. Hence reputation is tradeable, although ownership change is observable. In our model, reputation is an intangible asset, embodied in an attractive customer base. Firms owned by a good type sell at a premium.reputation, ownership change, intangible asset, theory of the firm

Observable Reputation Trading

Is the reputation of a firm tradable when the change in ownership is observable? We consider a competitive market in which a share of owners must retire in each period. New owners bid for the firms that are for sale. Customers learn the owner’s type, which reflects the quality of the good or service provided, through experience. After observing an ownership change they may want to switch firm. However, in equilibrium, good new owners buy from good old owners and retain high-value customers. Hence reputation is a tradable intangible asset, although ownership change is observable.Reputation; ownership change; intangible assets; theory of the firm.

Evaluating online trust using machine learning methods

Trust plays an important role in e-commerce, P2P networks, and information filtering. Current challenges in trust evaluations include: (1) fnding trustworthy recommenders, (2) aggregating heterogeneous trust recommendations of different trust standards based on correlated observations and different evaluation processes, and (3) managing efficiently large trust systems where users may be sparsely connected and have multiple local reputations. The purpose of this dissertation is to provide solutions to these three challenges by applying ordered depth-first search, neural network, and hidden Markov model techniques. It designs an opinion filtered recommendation trust model to derive personal trust from heterogeneous recommendations; develops a reputation model to evaluate recommenders\u27 trustworthiness and expertise; and constructs a distributed trust system and a global reputation model to achieve efficient trust computing and management. The experimental results show that the proposed three trust models are reliable. The contributions lie in: (1) novel application of neural networks in recommendation trust evaluation and distributed trust management; (2) adaptivity of the proposed neural network-based trust models to accommodate dynamic and multifacet properties of trust; (3) robustness of the neural network-based trust models to the noise in training data, such as deceptive recommendations; (4) efficiency and parallelism of computation and load balance in distributed trust evaluations; and (5) novel application of Hidden Markov Models in recommenders\u27 reputation evaluation

Modeling and evaluation of trusts in Multi-agent systems

Master'sMASTER OF ENGINEERIN

Characterizing eve: Analysing cybercrime actors in a large underground forum

Underground forums contain many thousands of active users, but the vast majority will be involved, at most, in minor levels of deviance. The number who engage in serious criminal activity is small. That being said, underground forums have played a significant role in several recent high-profile cybercrime activities. In this work we apply data science approaches to understand criminal pathways and characterize key actors related to illegal activity in one of the largest and longest- running underground forums. We combine the results of a logistic regression model with k-means clustering and social network analysis, verifying the findings using topic analysis. We identify variables relating to forum activity that predict the likelihood a user will become an actor of interest to law enforcement, and would therefore benefit the most from intervention. This work provides the first step towards identifying ways to deter the involvement of young people away from a career in cybercrime.Alan Turing Institut