Evolution Of Trust Research Articles

Shocks to the System: Electoral Manipulation, Protests and the Evolution of Political Trust in Russia

Abstract How do electoral manipulation and resulting anti-fraud protests influence political trust in non-democratic contexts? I leverage the plausibly exogenous variation in the timing of a series of original surveys fielded on nationally representative samples in Russia to understand the impact of political shocks – particularly allegations of electoral fraud and post-election protests – on the evolution of trust in political institutions and individuals. This study demonstrates that allegations of excessive, blatant electoral fraud decrease trust in the autocrat. However, trust rebounds following attendant post-election protests. Finally, I examine the conditional impacts of fraud and protest on trust, finding that updating occurs primarily among those with weak political affiliation.

Dynamic clustering-based consensus model for large-scale group decision-making considering overlapping communities

Consensus-reaching strategy is crucial in large-scale group decision-making (LSGDM) as it serves as an effective approach to reducing group conflicts. Meanwhile, the common social network relationships in large groups can affect information exchange, thereby influencing the consensus-reaching process (CRP) and decision results. Therefore, how to leverage social network information in LSGDM to obtain an agreed solution has received widespread attention. However, most existing research assumes relative independence between communities in the dimension reduction process of LSGDM and neglects the possibility of different overlaps between them. Moreover, the impact of overlapping communities on CRP has not been adequately explored. Besides, the dynamic variations in clusters and their weights caused by evaluation updates need to be further studied. To address these issues, this paper proposes a dynamic clustering-based consensus-reaching method for LSGDM considering the impact of overlapping communities. First, the LINE-based label propagation algorithm is designed to cluster decision makers (DMs) and detect overlapping communities with social network information. An overlapping community-driven feedback mechanism is then developed to enhance group consensus by utilizing the bridging role of overlapping DMs. During CRP, clusters and their weights are dynamically updated with trust evolution due to the evaluation iteration. Finally, a case study using the Film Trust dataset is conducted to verify the effectiveness of the proposed method. Simulation experiments and comparative analysis demonstrate the capability of our method in modeling practical scenarios and addressing LSGDM problems under social network contexts.

Payoff-driven migration promotes the evolution of trust in networked populations

Establishing and maintaining trustworthy behaviors in large populations of self-interested individuals is a significant problem which received extensive attentions from research fields such as evolutionary biology, economics, sociology and engineering. As a well-known model that captures social trust dilemmas, the N-player trust game is adopted to investigate the evolution of trust in groups consisting of investors and trustees. In addition, to model real scenarios in dynamic environments, we introduce a novel migration mechanism, called adaptive environment-benefit-driven migration, which considers the influence of individuals’ payoffs on migration. Through theoretical analysis and Monte Carlo simulations, we demonstrate the effectiveness of the migration mechanism on facilitating trust behaviors in the framework of evolutionary game theory. We find that the population density plays a crucial role in establishing trust. Simulation results reveal that trust can be fostered in a dynamic environment influenced by individual migration with the medium vacancy rate. This work significantly contributes to the understanding of mutual trust behaviors in networked social systems from a more realistic perspective.

Trust in Automation (TiA): Simulation Model, and Empirical Findings in Supervisory Control of Maritime Autonomous Surface Ships (MASS)

Over the past three decades, Trust in Automation (TiA) has been the subject of extensive research. However, a large portion of the research takes a “static” approach to modeling trust and views trust as a linear unidirectional phenomenon. This view fails to recognize that trust is a dynamic construct that changes over time as an outcome of prolonged interaction with automation. The present study aims to address this gap and explore the nonlinear dynamic nature of trust by developing a simulation model of Trust in Automation (TiA) that can demonstrate trust evolution, deterioration, and recovery within the context of supervisory control of Maritime Autonomous Surface Ships (MASS). Employing System Dynamics (SD) approach, the model captures trust’s non-linear and reciprocal nature through dynamic feedback loops, producing behavioral patterns consistent with empirical observations of trust. The simulation results showcase the crucial role of initial trust conditions and the alignment of expectations with system performance in fostering trust and effective automation use. The study also explores the timing of system malfunctions, revealing that early faults have a greater negative impact on trust compared to later faults of the same magnitude. We tested a segment of the proposed model in an experimental study involving 30 human participants to investigate the effects of automation malfunctions on operators’ trust and behavioral responses during the supervisory control of MASS. Results not only validated the proposed model but demonstrated a significant decline in perceived reliability and trust in automation as well as the monitoring strategy after the automation malfunction.

Evolution of trust in N-player trust games with loss assessment.

Trust plays a crucial role in social and economic interactions, serving as the foundation for social stability and human cooperation. Previous studies have explored the evolution of trust between investors and trustees by constructing trust game models, incorporating factors such as network structure, reputation, and incentives. However, these studies often assume that investors consistently maintain their investment behavior, neglecting the potential influence of the investment environment on investment behavior. To address this gap, we introduce a loss assessment mechanism and construct a trust game model. Specifically, investors first allocate their investment amount to an assessment agency, which divides the amount into two parts according to a certain allocation ratio. One part is used for investment assessment, and the results are fed back to the investors. If the payoff from this portion exceeds the investors' expected value, the remaining amount is invested; otherwise, it is returned to the investors. The results indicate that investors with moderate expectations are more likely to form alliances with trustworthy trustees, thereby effectively promoting the evolution of trust. Conversely, lower or higher expectations yield opposite results. Additionally, we find that as investors' expected values increase, the corresponding allocation ratio should also increase to achieve higher payoffs.

Automated or human: Which driver wins the race for the passengers’ trust? Examining passenger trust in human-driven and automated vehicles following a dangerous situation

Automated vehicles (AVs) provide numerous advantages over manually operated vehicles, but the extent of these benefits depends on whether we engage with AVs safely and efficiently. To achieve such interactions with AVs, an appropriate – or calibrated – level of trust in AVs especially during critical scenarios, is fundamental. The trust level also impacts individuals' decisions regarding the utilisation of AV technology. This study investigates trust calibration and factors that influence how trust develops in AVs compared to human drivers. Two groups of participants underwent a driving simulation, experiencing either a ride in a human-driven taxi or an AV, during which a dangerous situation occurred. Before, during and after the simulation, the passengers’ trust was measured.Pre-simulation trust was higher in the human driver than in the AV, but this difference disappeared after the simulation. Noticeably, during the simulation trust did not differ between the groups. Instead, the critical situation significantly influenced trust: following the dangerous incident, trust levels in both conditions dropped but recovered until the simulation ended. Additionally, self-esteem, which has been associated with trust in the past, was investigated. However, no significant relationship between self-esteem and trust was found in this study. Overall, the findings indicate that the dangerous situation prompted heightened caution among participants. A process of trust calibration was initiated in which the participants’ trust was highly susceptible to the driving style of the driver/AV. Moreover, the comparable evolution of trust in the human-driven vehicle and the AV, sheds light on the dynamics underlying attitudes towards AVs.

Catalysing cooperation: the power of collective beliefs in structured populations

Collective beliefs can catalyse cooperation in a population of selfish individuals. We study this transformative power of collective beliefs, an effect that intriguingly persists even when beliefs lack moralising components. Besides the process itself, we consider the structure of human populations explicitly. We incorporate the intricate structure of human populations into our model, acknowledging the bias brought by social and cultural identities in interaction networks. Hence, we develop our model by assuming a heterogeneous group size and structured population. We recognise that beliefs, typically complex story systems, might not spontaneously emerge in society, resulting in different spreading rates for actions and beliefs within populations. As the degree of connectedness can vary among individuals perpetuating a belief, we examine the speed of trust build-up in networks with different connection densities. We then scrutinise the timing, speed and dynamics of trust and belief spread across specific network structures, including random Erdös-Rényi networks, scale-free Barabási-Albert networks, and small-world Newman-Watts-Strogatz networks. By comparing these characteristics across various network topologies, we disentangle the effects of structure, group size diversity, and evolutionary dynamics on the evolution of trust and belief.

The evolution of the trust relationship between the family and its external advisors throughout generations: A family business perspective

AbstractThis study delves into the evolving dynamics of the trust relationship between the family and its external advisors across generations in family firms, a crucial, yet underexplored topic in the family firm literature. Anchored in the resource‐based view, family firms leverage external advisors to compensate for internal resource deficits. Employing a qualitative multiple case study approach, our research unveils the nuanced evolution of trust between the family and these external advisors across generations. The findings reveal a distinctive trust evolution, transitioning from interpersonal trust in the first generation to a more sophisticated reliance on the advisor's competencies and trust in the processes of the advisor's firm in subsequent generations. This transformation in the trust relationship is marked by a shift from emotionally charged relationships to more business‐oriented relationships, openness becoming less self‐evident but more calculated, neutrality assuming importance where it was initially absent, and competence being subjected to critical assessment rather than presumed. With this, our study makes a notable contribution to the family firm literature by providing a dynamic perspective on the trust relationship between the family and its external advisors. It also addresses whether family firm decision makers prioritize interpersonal trust in the advisor over trust in competencies or the system, providing valuable insights for practitioners and scholars alike. Overall, this study enriches our understanding of the sustained and evolving nature of trust as a critical resource for competitive advantage in family firms.

Postnormal Trust: The Case of the Media

This article is the first installment of a two-part paper aimed at assessing whether the theory of postnormal times provides an appropriate framework for examining the evolution of public trust in the media. Building upon previous studies that have explored the applicability of postnormal times theory in explaining changes in the new century, this paper specifically examines communication as a new case study, delving into factors that either foster or impede trust in the media. This paper argues that communication, both as a theoretical disclipine and as a human activity, exhibits characteristics of postnormality. It contends that not only does communication adhere to the tenets of postnormal times, but it also serves as one of the principal indicators of postnormality on a global scale, particularly concerning trust in the media. The article evaluates the suitability of postnormal times theory to study the recent evolution of communication. It then conducts a review of existing literature on trust in the media and proposes a roadmap for constructing prospective scenarios through the Three Tomorrows’ approach.

Evolution of trust in the N-player trust game with the margin system

Investigations into trust behavior between investors and trustees using trust games have gained significant attention in recent years. However, understanding the process of trust evolution remains challenging. The margin system, widely employed in the financial industry as an economic and behavioral incentive mechanism, aims to maintain order in financial markets and protect participants' rights and interests. Despite its extensive use, the precise influence of the margin system on trust evolution dynamics remains unclear. To address this gap, we propose an N-player trust game model that incorporates a margin system within an infinite well-mixed population. In our model, a third-party institution collects a proportionate amount of margin from trustees, which is returned upon fulfilling their obligations but forfeited otherwise. By utilizing replicated dynamic equations, we find that implementing the margin system can establish a stable coexistence state between investors and trustworthy trustees, representing a significant advancement in trust evolution. Additionally, an intriguing finding arises from our observation of an inverted U-shaped relationship between the magnitude of the attraction domain for coexistence and the coefficient of the margin. This suggests that excessively high margin proportions can discourage trustees' participation, while excessively low proportions may fail to effectively deter dishonest behavior. Optimal margin proportions can effectively promote trust evolution.

Modelling and Measuring Trust in Human–Robot Collaboration

Recognizing trust as a pivotal element for success within Human–Robot Collaboration (HRC) environments, this article examines its nature, exploring the different dimensions of trust, analysing the factors affecting each of them, and proposing alternatives for trust measurement. To do so, we designed an experimental procedure involving 50 participants interacting with a modified ‘Inspector game’ while we monitored their brain, electrodermal, respiratory, and ocular activities. This procedure allowed us to map dispositional (static individual baseline) and learned (dynamic, based on prior interactions) dimensions of trust, considering both demographic and psychophysiological aspects. Our findings challenge traditional assumptions regarding the dispositional dimension of trust and establish clear evidence that the first interactions are critical for the trust-building process and the temporal evolution of trust. By identifying more significant psychophysiological features for trust detection and underscoring the importance of individualized trust assessment, this research contributes to understanding the nature of trust in HRC. Such insights are crucial for enabling more seamless human–robot interaction in collaborative environments.

Evolution of trust in structured populations

The trust game, derived from an economics experiment, has recently attracted interest in the field of evolutionary dynamics. In a recent version of the evolutionary trust game, players adopt one of three strategies: investor, trustworthy trustee, or untrustworthy trustee. Trustworthy trustees enhance and share the investment with the investor, whereas untrustworthy trustees retain the full amount, betraying the investor. Following this setup, we investigate a two-player trust game, which is analytically feasible under weak selection. We explore the evolution of trust in structured populations, factoring in four strategy updating rules: pairwise comparison (PC), birth-death (BD), imitation (IM), and death-birth (DB). Comparing structured populations with well-mixed populations, we arrive at two main conclusions. First, in the absence of untrustworthy trustees, there is a saddle point between investors and trustworthy trustees, with collaboration thriving best in well-mixed populations. The collaboration diminishes sequentially from DB to IM to PC/BD updating rules in structured populations. Second, an invasion of untrustworthy trustees makes this saddle point unstable and leads to the extinction of investors. The 3-strategy system stabilizes at an equilibrium line where the trustworthy and untrustworthy trustees coexist. The stability span of trustworthy trustees is maximally extended under the PC and BD updating rules in structured populations, while it decreases in a sequence from IM to DB updating rules, with the well-mixed population being the least favorable. This research thus adds an analytical lens to the evolution of trust in structured populations.

Trust Management in Cloud Computing Using Blockchain Technology: a taxonomy, review & future directions

Through visualization and resource integration, cloud computing has expanded its service its service area and offers a better user experience than the traditional platform, along with us business operation model bringing huge economic and social benefits. However, a large amount of evidence shows that cloud computing is facing with serious security and trust crises, and building a trust / enabled transaction environment has become its key factor. The traditional cloud trust model transparency and traceability trust evolution results cannot be fully recognized by all participants. Its unique features in operating rules and traceability of records ensure the integrity, undesirability and security of the transaction data. Therefore Blockchain is very suitable for constructing distributed and decentralized trust architecture. Based on a novel Cloud- Edge trust management framework and a double Blochkchain structure based cloud transaction model , it identifies the open challenges and gives direction for future research field.

Trust evolution based minimum adjustment consensus framework with dynamic limited compromise behavior for probabilistic linguistic large scale group decision-making

Trust evolution based minimum adjustment consensus framework with dynamic limited compromise behavior for probabilistic linguistic large scale group decision-making

Three-dimensional dynamic trust-driven consensus model for social network group decision-making with application to sustainable supplier selection in a circular economy

The trust degree between individuals plays an important role in social network group decision-making (SNGDM). The majority of current literature assumes that the trust degree between individuals is constant. However, the trust values among decision-makers (DMs) are subject to change over time. Thus, it is necessary to identify potential dynamic trust that is compatible with the actual SNGDM to support the consensus reaching. For this purpose, this article investigates consensus building that considers dynamic trust among DMs in SNGDM with linguistic distribution assessments (LDAs). Firstly, the three-dimensional trust degree among DMs is constructed from three perspectives: the trust relationship from social networks, the confidence level of DMs, and the similarity of DMs' preferences. Secondly, an optimization model is developed with the objective of maximizing consensus to determine how trust degree is assigned to each perspective, thereby determining the weights of DMs. Then, a double feedback-based consensus mechanism incorporating both opinion evolution and trust evolution is developed. Under the guidance of consensus mechanism, an improved SNGDM approach with LDAs is presented. Finally, we demonstrate our proposed approach through a case study of sustainable supplier selection in a circular economy. Comparative analysis and sensitive analysis verify our approach’s effectiveness.

Trust evolution, institutional constraints, and land trusteeship decisions among Chinese farmers

Trust evolution, institutional constraints, and land trusteeship decisions among Chinese farmers

Anthropomorphic Design and Self-Reported Behavioral Trust: The Case of a Virtual Assistant in a Highly Automated Car

The latest advances in car automation present new challenges in vehicle–driver interactions. Indeed, acceptance and adoption of high levels of automation (when full control of the driving task is given to the automated system) are conditioned by human factors such as user trust. In this work, we study the impact of anthropomorphic design on user trust in the context of a highly automated car. A virtual assistant was designed using two levels of anthropomorphic design: “voice-only” and “voice with visual appearance”. The visual appearance was a three-dimensional model, integrated as a hologram in the cockpit of a driving simulator. In a driving simulator study, we compared the three interfaces: two versions of the virtual assistant interface and the baseline interface with no anthropomorphic attributes. We measured trust versus perceived anthropomorphism. We also studied the evolution of trust throughout a range of driving scenarios. We finally analyzed participants’ reaction time to takeover request events. We found a significant correlation between perceived anthropomorphism and trust. However, the three interfaces tested did not significantly differentiate in terms of perceived anthropomorphism while trust converged over time across all our measurements. Finally, we found that the anthropomorphic assistant positively impacts reaction time for one takeover request scenario. We discuss methodological issues and implication for design and further research.

Understanding the evolution of trust in a participatory health research partnership: A qualitative study.

Advancements in evaluating the impact of participatory health research (PHR) have been made through comprehensive models like the community-based participatory research (CBPR) conceptual model, which provides a useful framework for exploring how context and partnership processes can influence health research design and interventions. However, challenges in operationalising aspects of the model limit our understanding and evaluation of the PHR process. Trust is frequently identified as an important component of the CBPR model, which supports the development of key partnership outcomes, such as partnership synergy. However, trust continues to be limited to a binary view (as present or absent), which is problematic given its inherently dynamic and temporal nature. The aim of this qualitative study is to understand the evolution of trust in the national public and patient involvement (PPI) network in Ireland. Participants from the PPI network (n = 15/21) completed a semistructured interview discussing the evolution of trust by reviewing four social network maps derived from a previous longitudinal study. Following Braun and Clarke, we used reflexive thematic analysis, to iteratively develop, analyse and interpret our mediated reflection of the data. Participants described the evolution of trust as a function of three contextual factors: (1) the set-up and organisation of the network, (2) how people work togetherand (3) reflection on the process and outcomes. Their descriptions across these themes seemed to vary depending on partnership type with National Partners and Site Leads having more opportunities to demonstrate trust (e.g., via leadership roles or more resources), compared to Local. Thus, visibility and the opportunity to be visible, depending on the set-up and organisation of the network and how people work together, seemingly play an important role in the evolution of trust over time. Based on these findings, we provide important questions for reflection across themes that could be considered for future PHR partnerships. Given that the opportunity and visibility to build and maintain trust over time may not be equally available to all partners, it is important to find ways to invest in and commit to equitable relationships as the key to the success (i.e., longevity) of partnerships. We reflect on/offer important implications for those engaging in PHR partnerships and those who fund such research. A Research Advisory Group comprising four research partners (representing academic, serviceand community organisations) from the PPI Ignite Network provided input and approval for the research objectives of this study as well as previously published work informing this study. Informal consultation occurred with members of this group to discuss findings from this study, assisting with the way findings are presented and described, to be accessible for diverse audiences. Two Research Advisory Group members were involved in the interpretation of the results, and one is a co-author of this manuscript (Zoe Hughes).

The scientific structure and evolution of trust within performance-oriented teams research: A citation network analysis and critical review.

The scientific structure and evolution of trust within performance-oriented teams research: A citation network analysis and critical review.

On the transmission of guilt aversion and the evolution of trust

This paper studies the evolution of trust and trustworthiness by modeling the intergenerational transmission of guilt aversion. The results depend both on features of strategic interaction and on parental transmission. We show that if there is complete information of opponents' traits, independent of parenting style, the share of high-guilt agents in society weakly increases over time, and trust and trustworthiness are maximized. Moreover, when traits are not observable, different levels of guilt always coexist, and trust and trustworthiness might also increase when parents have imperfect empathy in the transmission of traits or if there is homophily in society.