Value Of Distrust Research Articles

Online travel agency or blockchain-based platform?

Online travel agency has been used by most hotels to attract customers. However, hefty commissions and unverified reviews reduce hotels' cooperation intentions. Armed with unique advantages, blockchain-based platforms with higher blockchain-adoption costs can alternatively offer peer-to-peer booking service. This paper investigates the hotels' choice strategies on online travel platforms, blockchain-adoption conditions, and effects of blockchain-based platform. The results show that marginal blockchain cost, commission rate and customers' perceived distrust value of online reviews have great influence on the hotels' decisions. Although blockchain-based platforms can create win-win situations for hotel(s) and customers, they are not widely available as it is considered imperfect and costly. Alarmingly, there exists an invalidation effect of online travel platforms in a competitive market, resulting in non-equilibrium.

Scepticism and the value of distrust

ABSTRACT Faced with urgent calls for more trust in experts, especially in high impact and politically sensitive domains, such as climate science and COVID-19, the complex nature of public trust in experts and the need for a more critical approach to the topic are easy to overlook. Scepticism – at least in its Humean mitigated form that encourages independent, questioning attitudes – can prove valuable to democratic governance, but stands in opposition to the cognitive dependency entailed by epistemic trust. In this paper, we investigate the tension between the value of mitigated scepticism and the need for trust in experts. We offer four arguments in favour of mitigated scepticism: the argument from loss of intellectual autonomy; the argument from democratic deficit; the argument from the normative failures of science; and the argument from past and current injustices. One solution, which we reject, is the idea that reliance, rather than trust, is sufficient for accommodating experts in policy matters. The solution we endorse is to create a ‘climate of trust’, where questioning experts and expertise is welcomed, but the epistemic trust necessary for acting upon information which the public cannot obtain first-hand is enabled and encouraged through structural, institutional and justice-based measures.

A Geometric-Based LSGDM Method for Tourism Project Decision Optimization with Trust-Distrust Relationships.

In this paper, we discuss the decision optimization of tourism projects in Hebei Province, China. To improve the process of analyzing tourism projects, we introduce a model that includes multiple decision makers as subjects based on a standard four-dimensional evaluation system. In order to improve the effectiveness of decision-making results, we will increase the number of decision makers to 40. A novel large-scale group decision-making (LSGDM) algorithm that incorporates the trust–distrust asymmetric relationships between decision makers is proposed. This model contains three main innovations: firstly, in the evaluation of decision makers’ social network relations, the trust–distrust value is introduced as a new carrier, and a weighted directed network and data integration operator are constructed based on the evaluation between decision makers; secondly, an extended Girvan-Newman (GN) algorithm is constructed to cluster the decision makers from this weighted network; thirdly, the interval-valued intuitionistic fuzzy number (IVIFN) is used to evaluate the alternatives, studying the IVIFN’s geometric significance by placing in a rectangular coordinate system. Finally, a new LSGDM model is proposed. Using the development of a cultural tourism project in a township as an example, the effectiveness of the proposed model is illustrated. By comparing the results of our method to those of a LSGDM algorithm that does not incorporate trust relationships, we assess the performance of the new model.

Building trust/distrust relationships on signed social service network through privacy-aware link prediction process

With the ever-increasing popularity of social software, we can easily establish a signed social network (SSN) by capturing users’ attitudes (i.e., trust/distrust, friend/enemies, consent/opposition) toward other people. However, the social relationships among users are often very sparse in an SSN, which impede the effective extension of the users’ social circle significantly. To tackle this issue, researchers often use link prediction methods to search for missing links and predict new links in the network. However, existing link prediction methods cannot protect user’s private information well. Considering this shortcoming, we propose a Simhash-based link prediction method with privacy-preservation. Concretely, we first apply Simhash to build less-sensitive user indices and then determine the ”probably similar” friends (i.e., candidates) of a target user based on his or her indices. Through theoretical analysis, it can be known that the method proposed in this paper can effectively protect users’ proprietary information. Second, for each candidate, we calculate his/her trust and distrust values with the target user. Third, we use Social Balance Theory to evaluate the possibility of building a link between the candidate and the target user based on the trust and distrust values. Finally, we conducted a set of experiments on the real-world Epinions dataset. Experimental results prove the advantages of our proposal in terms of overcoming the sparsity problem, compared to other competitive approaches.

Human dignity

Although human dignity has caught the attention of scholars in many different fields, planners remain unresponsive. Three reasons may be given for planners’ neglect of human dignity: (1) so far, nobody has inspired planners to cherish human dignity as a planning value or goal; (2) planners respect and protect human dignity, but do not use the term; (3) planners distrust values that are not too complex for laypersons. Two cases illustrate the possible consequences of ‘planning for dignity’ – the case of the knitting ladies of Blikkiesdorp (Cape Town), and the poor door controversy of 2014. The most important conclusions are that planners, in order to align their plans with human dignity (or ubuntu), must co-produce their selves within local communities affected by their plans. Moreover, planners must recognize the tension that exists between social justice and human dignity: A plan that is socially just can still be humiliating.

QMM-VANET: An efficient clustering algorithm based on QoS and monitoring of malicious vehicles in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) are considered as a subset of mobile ad hoc networks (MANETs) that can be used in the transportation field. These networks considerably improve the traffic safety and accident prevention. Because of the characteristics of VANETs such as self-organization, frequent link disconnections and rapid topology changes, developing efficient routing protocols is a challenging task. To address this issue, clustering is an appropriate approach in a mobile environment. Clustering aims to partition the vehicles into a number of clusters based on some predefined metrics such as velocity, distance and location. In this paper, a clustering routing protocol, named QMM-VANET, which considers Quality of Service (QoS) requirements, the distrust value parameters and mobility constraints, is proposed. This protocol specifies a reliable and stable cluster and increases the stability and connectivity during communications. This protocol is composed of three parts: (1) computing the QoS of vehicles and electing a trustier vehicle as a cluster-head, (2) selecting a set of proper neighboring nodes as gateways for retransmitting the packets and (3) using gateway recovery algorithm to choose another gateway in case of failure of the link. NS-2 simulator is utilized to illustrate the performance of our proposed protocol in a highway scenario. The performance analyses display that the QMM-VANET protocol can achieve low end-to-end delay and high packet delivery ratio and maintain the network stability.

Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation

Evaluating trust and distrust between users in online social networks is an important research problem. To address this problem, we provide a method for estimating continuous trust /distrust value between unconnected users. Our method is based on co-citation and transpose trust propagation. We determine on average how differently two users trust or are trusted by other users, and how differently a user trusts another user from how it is trusted by that user. Using these differences, we estimate four partial trust estimates and compute the final trust value from trustor to trustee as the weighted average of these partial estimates. We propose a basic framework that maximizes accuracy, robustness and coverage and show how we can further improve the accuracy at a lower coverage. We perform experiments on real world trust related networks that show that our proposed method outperforms recent state of the art trust computation methods in terms of accuracy and robustness on commonly used datasets.

A dynamic trust model in internet of things

Indicating trust or distrust of a node is a key issue in the trust management of IoT. However, there are some challenges for the management with anonymous nodes, inaccurate communication, etc. Due to huge number of nodes in IoT, one of the possible solutions is predicting trust and distrust values. In this paper, we propose a dynamic trust model based on direct and indirect trust computation, and the most important part of a model, trust prediction. The prediction method mainly depends on the combination of exponential smoothing and a Markov chain. We employ exponential smoothing to predict trustiness and a Markov chain to fix the deviation of the prediction. To test our scheme, we create a simulation to evaluate its performance and effectiveness. The simulation results are encouraging.

A two-fold feedback mechanism to support consensus-reaching in social network group decision-making

This study focuses on consensus-reaching in social network group decision-making. Using Archimedean t-norms and t-conorms as the basis, this study introduces a family of interval-valued dual trust propagation (IVDTP) operators. A perfect knowledge degree-induced ordered weighted averaging (PKD-IOWA) aggregation operator is also put forward for obtaining the overall trust and distrust values. The proposed PKD-IOWA aggregation operator makes full use of the knowledge in each trust path, which can effectively overcome the limitations of existing trust aggregation operators. The weights of the experts can then be derived by combining the IVDTP operators and the PKD-IOWA aggregation operator. Three levels of consensus indices are defined to identify the incompatible experts in a group. A two-fold feedback mechanism comprising a judgment feedback mechanism (JFM) and a weight feedback mechanism (WFM), is designed to reach consensus. In each round of this interaction model, the most incompatible expert is advised to modify his/her judgments on the basis of the JFM. If the expert rejects the suggestion, then his/her weight will be reduced according to the WFM, which can act as an incentive or penalty to force him/her to accept the judgment feedback suggestion. Two illustrative examples are used to validate the feasibility and effectiveness of the proposed approach, followed by comparative analysis and discussion.

Diagnosa Penyakit Osteoporosis Menggunakan Metode Certainty Factor

Tujuan dari penelitian ini adalah untuk membangun aplikasi Sistem Pakar untuk diagnosis penyakit Osteoporosis. Penelitian ini menggunakan metode Certainty Factor karena dalam metode ini terdapat suatu nilai berupa nilai kepercayaan (Measure Of Belief) dan nilai ketidakpercayaan (Measure Of Disabelief) pada suatu gejala, dimana nantinya nilai tersebut dapat menghasilkan nilai CF (Certainty Factor) sebagai tolak ukur, semakin besar nilai CF (Certainty Factor) yang diperoleh maka semakin besar peluang penyakit itu akan menyerang kita, dimana hasilnya ditampilkan dalam bentuk kondisi pengguna yang terkait dengan penyakit Osteoporosis. Hasil penelitian ini juga dilengkapi dengan solusi penyakit dan perawatan yang ditampilkan dalam bentuk website dengan menggunakan pemrograman PHP dan juga berguna untuk melakukan diagnosa awal terhadap suatu penyakit yang dirasakan oleh user, sehingga membantu user dalam mengenali gejala-gejala penyakit Osteoporosis yang mereka rasakan, serta dengan adanya sistem pakar ini dapat dijadikan solusi alternatif bagi masyarakat untuk melakukan diagnosa dini terhadap gejala-gejala penyakit Osteoporosis yang mereka rasakan sebelum melakukan konsultasi langsung dengan pakar dalam hal ini dokter spesialis tulang. Sistem ini mampu menyimpan representasi pengetahuan pakar berdasarkan nilai kepercayaan (certainty factor) dengan keakuratan sebesar 80%.

A visual interaction consensus model for social network group decision making with trust propagation

A theoretical visual interaction framework to model consensus in social network group decision making (SN-GDM) is put forward with following three main components: (1) construction of trust relationship; (2) trust based recommendation mechanism; and (3) visual adoption mechanism. To do that, dual trust propagation is investigated to connect incomplete trust relationship by trusted third partners, in a way that it can fit our intuition in these cases: trust values decrease while distrust values increase. Trust relationship is proposed to be used in determining the trust degree of experts and in aggregating individual opinions into a collective one. Three levels of consensus degree are defined and used to identify the inconsistent experts. A trust based recommendation mechanism is developed to generate advices according to individual trust relationship, making recommendations more likeable to be implemented by the inconsistent experts to achieve higher levels of consensus. Therefore, it has an advantage with respect to existing interaction models because it does not force the inconsistent experts to accept advices irrespective of their trust on them. Finally, a visual adoption mechanism, which provides visual information representations on experts’ individual consensus positions before and after adopting the recommendation advices, is presented and analysed theoretically. Experts can select their appropriate feedback parameters to achieve a balance between group consensus and individual independence. Consequently, the proposed visual interaction model adds real and needed flexibility in guiding the consensus reaching process in SN-GDM.

Influences of Distrust (and Trust) on Decision Making

Our study highlights how distrust can influence decision making under uncertainty. Experiment 1 compared decision makers' behaviors when uncertainty was attributed to benevolent or malevolent sources. We found that compared to those who expected the source of information to help, those who expected the source to attempt to harm them (i) showed a stronger inclination not to respond with a decision and (ii) were more accurate in their decisions, especially when using the reject response. Experiment 2 extended these findings by revealing that participants were more willing to take the advice of people after recounting an experience of distrust, but were more inclined to discount the advice of others after recounting an experience of trust. Taken together, our findings highlight the value of distrust to decision quality.

(White) Tyranny and the Democratic Value of Distrust

This paper makes an argument for the democratic value of distrust. It begins by analyzing distrust, since distrust is not merely the negation of trust. The account that it develops is based on Martin Luther King Jr.’s work in Why We Can’t Wait . Distrust is the confident belief that another individual, a group of individuals, or an institution will not act justly or as justice requires. This paper argues that distrust’s democratic value lies in its ability to temper tyranny. The main example discussed is King’s involvement in the Birmingham Campaign during the Black Civil Rights movement in America.

Is Trust Always Better than Distrust? The Potential Value of Distrust in Newer Virtual Teams Engaged in Short-Term Decision-Making

The debate on the benefits of trust or distrust in groups has generated a substantial amount of research that points to the positive aspects of trust in groups, and generally characterizes distrust as a negative group phenomenon. Therefore, many researchers and practitioners assume that trust is inherently good and distrust is inherently bad. However, recent counterintuitive evidence obtained from face-to-face (FtF) groups indicates that the opposite might be true; trust can prove detrimental, and distrust instrumental, to decision-making in groups. By extending this argument to virtual teams (VTs), we examined the value of distrust for VTs completing routine and non-routine decision tasks, and showed that the benefits of distrust can extend to short-term VTs. Specifically, VTs seeded with distrust significantly outperformed all control groups in a non-routine decision-making task. In addition, we present quantitative evidence to show that the decision task itself can significantly affect the overall levels of trust/distrust within VTs. In addition to its practical and research implications, the theoretical contribution of our study is that it extends to a group level, and then to a VT setting, a theory of distrust previously tested in the psychology literature in the context of completing non-routine and routine decision tasks at an individual level.

Effect of Health Care System Distrust on Breast and Cervical Cancer Screening in Philadelphia, Pennsylvania

We investigated whether health care system distrust is a barrier to breast and cervical cancer screening and whether different dimensions of distrust-values and competence-have different impacts on cancer screening. We utilized data on 5268 women aged 18 years and older living in Philadelphia, Pennsylvania, and analyzed their use of screening services via logistic and multinomial logistic regression. High levels of health care system distrust were associated with lower utilization of breast and cervical cancer screening services. The associations differed by dimensions of distrust. Specifically, a high level of competence distrust was associated with a reduced likelihood of having Papanicolaou tests, and women with high levels of values distrust were less likely to have breast examinations within the recommended time period. Independent of other covariates, individual health care resources and health status were associated with utilization of cancer screening. Health care system distrust is a barrier to breast and cervical cancer screening even after control for demographic and socioeconomic determinants. Rebuilding confidence in the health care system may improve personal and public health by increasing the utilization of preventive health services.

Detection of malicious vehicles (DMV) through monitoring in Vehicular Ad-Hoc Networks

Vehicular Ad Hoc Networks (VANETs) are appropriate networks that can be applied to intelligent transportation systems. In VANET, messages exchanged among vehicles may be damaged by attacker nodes. Therefore, security in message forwarding is an important factor. We propose the Detection of Malicious Vehicles (DMV) algorithm through monitoring to detect malicious nodes that drop or duplicate received packets and to isolate them from honest vehicles, where each vehicle is monitored by some of it trustier neighbors called verifier nodes. If a verifier vehicle observes an abnormal behavior from vehicle V, it increases distrust value of vehicle V. The ID of vehicle V is then reported to its relevant Certificate Authority (CA) as a malicious node when its distrust value is higher than a threshold value. Performance evaluation shows that DMV can detect most existence abnormal and malicious vehicles even at high speeds.

VWCA: An efficient clustering algorithm in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) are appropriate networks that can be used in intelligent transportation systems. Among challenges in VANET, scalability is a critical issue for a network designer. Clustering is one solution for the scalability problem and is vital for efficient resource consumption and load balancing in large scale networks. As our first algorithm, we propose a novel clustering algorithm, vehicular clustering based on the weighted clustering algorithm (VWCA) that takes into consideration the number of neighbors based on dynamic transmission range, the direction of vehicles, the entropy, and the distrust value parameters. These parameters can increase stability and connectivity and can reduce overhead in network. On the other hand, transmission range of a vehicle is important for forwarding and receiving messages. When a fixed transmission range mechanism is used in VANET, it is likely that vehicles are not located in the range of their neighbors. This is because of the high-rate topology changes and high variability in vehicles density. Thus, we propose an adaptive allocation of transmission range (AATR) technique as our second algorithm, where hello messages and density of traffic around vehicles are used to adaptively adjust the transmission range among them. Finally, we propose a monitoring of malicious vehicle (MMV) algorithm as our third algorithm to determine a distrust value for each vehicle used in the VWCA. The effectiveness of the proposed algorithms is illustrated in a highway scenario.

The Value of Distrust in Computer-Based Decision-Making Groups

We present a creative approach to improve the effectiveness of decision-making groups in solving nonroutine problems by introducing increased distrust into the group. Our theory is proposed through an extension and empirical test of Schul et al.’s [1] distrust model in an online group decision-making domain. We find that an increase in distrust among group members led to an increase in group decision accuracy for nonroutine problems. This demonstrates that through a distrust stimulus, members of decision-making groups can better solve nonroutine problems. This paper also demonstrates how simple environmental abnormalities can increase distrust and trust in computer-based decision-making groups. Various trust- and distrust-related measures were also tested and validated to further research in this area.

The value of distrust

We assume that a state of distrust is the mental system’s signal that the environment is not normal—things may not be as they appear. Hence, individuals sense they should be on guard. In particular, they are likely to avoid routine strategies, ones proven to be optimal and regularly used in normal environments, because these strategies are easily anticipated by whoever may be seeking to deceive them. Conversely, a state of trust is associated with a feeling of safety. The environment is as it normally is and things really are as they appear to be. Thus, individuals see no reason to refrain from doing what they routinely do. Accordingly, we hypothesize that figuring out a new situation depends on the type of environment and the actor’s state of mind: in normal environments, where routine strategies are optimal, individuals who trust should outperform those who distrust; however, in unusual environments, where non-routine strategies are optimal, individuals who distrust should outperform those who trust. This paper reports three experiments that manipulate distrust via orienting tasks that participants perform prior to attempting to predict a series of events (Experiments 1 and 2) or solve matchstick arithmetic problems (Experiment 3). Performance success depends on discovering and implementing an appropriate rule. We found that, as predicted, the manipulation of distrust sensitized participants to the existence of non-routine contingencies, that is, contingencies that were not expected.