Untrustworthy Behavior Research Articles

Navigating the Social Environment: Linking Motivations, Impressions, and Behaviors Via a Sampling Approach to Trustworthiness.

Our understanding of impression formation comes from experiments constraining participants' control over information sampling. This limits our understanding of how people sample information when forming impressions as well as the effects of self-generated samples on impressions. Our paradigm allows participants to actively collect information samples. This work investigates how people explore the social environment and how sampled information informs resulting impressions. Four experiments tested theoretically-driven predictions regarding information sampling patterns under interested and disinterested information search. Under interested search, participants truncated the sampling of social targets showing early untrustworthy behavior. Under disinterested search, participants sampled extensively and systematically, avoiding small-sample biases. The sampled information, once obtained, accurately determines final impressions. Moreover, we documented a direct link between sampled information and subsequent behaviors (partner selection in trust games, trustworthiness expectations). By investigating sampling-based trustworthiness impressions, the present research informs the origins of trustworthiness and sampling accounts of judgment and decision-making.

Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach

Establishing service-driven IoT systems that are reliable, efficient, and stable requires building trusted IoT environments to reduce catastrophic and unforeseen damages. Hence, building trusted IoT environments is of great importance. However, we cannot assume that every node in wide-area network is aware of every other node, nor can we assume that all nodes are trustworthy and honest. As a result, prior to any collaboration, we need to develop a trust model that can evolve and establish trust relationships between nodes. Our proposed trust model uses subjective logic as a default artificial reasoning over uncertain propositions to collect recommendations from other nodes in the IoT environment. It also manages and maintains existing trust relationships established during direct communications. Furthermore, it resists dishonest nodes that provide inaccurate ratings for malicious reasons. Unlike existing trust models, our trust model is scalable as it leverages a Fog-based hierarchy architecture which allows IoT nodes to report/request the trust values of other nodes. We conducted extensive performance studies, and confirm the efficiency of our proposed trust model. The results show that at an early stage of the simulation time (i.e., within the first 2% of the number of transactions), our trust model accurately captures and anticipates the behavior of nodes. Results further demonstrate that our proposed trust model isolates untrustworthy behavior within the same FCD and prevents untrustworthy nodes from degrading trustworthy nodes’ reputations.

HEXACO Personality Dimensions Do Not Predict Individual Differences in Adolescent Trust Behavior

Adolescence is an important developmental period for both trust behavior and personality maturation, and individual differences in trust decisions may be related to different personality traits. In the current study, a group of adolescents (n = 483, Mage = 13.5, SDage = 0.4) played two counterbalanced conditions of a multi-round trust game. In one condition, the partner displayed trustworthy behavior (the trustworthy condition), while the partner in the other condition played untrustworthy behavior (the untrustworthy condition). Three types of trust behavior were examined: initial trust behavior, the adaptation of trust behavior (trustworthy condition), and the adaptation of trust behavior (untrustworthy condition). Personality was measured using the Brief HEXACO Inventory. We expected the HEXACO personality dimensions of honesty–humility and agreeableness to be positively associated with initial trust behavior, but conscientiousness to be negatively related to initial trust behavior. The examination of the relationship between these dimensions and the adaptation of trust behavior were conducted on an exploratory basis. The investigation of the relationship between the remaining dimensions (emotionality, extraversion, and openness to experience) and the three types of trust behavior were also carried out on an exploratory basis. For each type of trust behavior, a hierarchical multiple regression analysis was undertaken to examine whether the HEXACO personality dimensions were related to trust behavior. Using frequentist analyses, no evidence was found that supported the HEXACO dimensions as significant predictors of the three types of trust behavior. Moreover, additional Bayesian analyses showed evidence that the hypothesized HEXACO dimensions (honesty–humility, agreeableness, and conscientiousness) did not outperform the non-hypothesized HEXACO dimensions (emotionality, extraversion, and openness to experience). The association between personality traits and trust might be less pronounced during adolescence as personality maturates across an individual’s lifespan. Additionally, due to a heightened sensitivity to the environment, contextual cues may affect adolescent decision-making processes, leaving less room for personality-driven behaviors.

A Distributed Power Trading Scheme Based on Blockchain and Artificial Intelligence in Smart Grids

As an emerging hot technology, smart grids (SGs) are being employed in many fields, such as smart homes and smart cities. Moreover, the application of artificial intelligence (AI) in SGs has promoted the development of the power industry. However, as users’ demands for electricity increase, traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities. This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation, electrical security, and other aspects. Accordingly, this study proposes a distributed power trading scheme based on blockchain and AI. To protect the legitimate rights and interests of consumers and producers, credibility is used as an indicator to restrict untrustworthy behavior. Simultaneously, the reliability and communication capabilities of nodes are considered in block verification to improve the transaction confirmation efficiency, and a weighted communication tree construction algorithm is designed to achieve superior data forwarding. Finally, AI sensors are set up in power equipment to detect electricity generation and transmission, which alert users when security hazards occur, such as thunderstorms or typhoons. The experimental results show that the proposed scheme can not only improve the trading security but also reduce system communication delays.

Evolutionary dynamics in networked trust games with diverse investment patterns

Most previous works study the evolution of trust by commonly assuming that investors adopt a deterministic investment strategy. In this work, we propose a mechanism of diverse investment in the trust game model on social networks, where each investor adopts a probabilistic strategy by considering the trustworthiness level in the local group to decide whether to invest or not. Extensive simulation results suggest that the proposed mechanism inhibits the untrustworthy behavior and limits its spread, thus stabilizing the cooperative cluster of investors and trustworthy trustees. Therefore, the trust level and global wealth are greatly enhanced comparing to the traditional setup with homogeneous investment pattern. The strong investment diversity can even eliminate untrustworthiness completely despite the fact that the severe temptation condition is disadvantageous to the evolution of trust. We also investigate the impact of investment diversity on trust game model embedding in different network structures with different initial conditions, where we observe similarly positive evolutionary outcomes. We hope these observations can provide valuable insights into further exploring the improvement of trust in real life.

Evolution of trust in the sharing economy with fixed provider and consumer roles under different host network structures

We present an evolutionary trust game to investigate the formation of trust in sharing economy situations, where participants have a fixed provider or consumer role, and can only choose between trustworthy or untrustworthy behaviour. There are a variety of sharing economy platforms catering for differing goods and services, the properties of which may affect the degree to which these roles are variable for users. To the best of our knowledge, this is the first time the evolution of trust in sharing economy situations with fixed provider and consumer roles is being studied in the literature. Our trust model comprises four player types: trustworthy consumer, trustworthy provider, untrustworthy consumer, and untrustworthy provider. Five scenarios with varying initial population ratios of these player types under different host network structures are systematically investigated. Our results show that, in contrast to previous work that allowed switching roles between providers and consumers, trust declines monotonically as the reward for trustworthy behaviour is reduced, with a critical transition point for inversion of trustworthy/untrustworthy populations. In addition, the initial population of trustworthy providers is shown to significantly affect the point at which trustworthy behaviour most declines, with a high proportion resulting in the persistence of trustworthiness even when the reward for such behaviour is significantly low. Our results also show that, when different host network structures are considered, the average degree of the underlying network is an important factor in determining the level of trustworthiness in the population. Our findings may be of importance for understanding the emergence and maintenance of trust in sharing economy platforms where user roles are completely or predominantly rigid.

Dominance, reward, and affiliation smiles modulate the meaning of uncooperative or untrustworthy behaviour

ABSTRACT We investigated the effects of different types of smiles on the perception of uncooperative or untrustworthy behaviour. In five studies, participants assigned to one group played an economic game with a representative of another group. In an initial round, the representative acted uncooperatively by favouring their group and then displayed a dominance, reward, or affiliation smile. Participants rated the motives of the representative and played a second round of the game with a different member of the same outgroup. Following uncooperative or untrustworthy behaviour, affiliation smiles communicated less positivity and superiority, and a greater desire to both repair the relationship between groups and change the uncooperative decision than reward or dominance smiles. Perceptions of a desire to repair the relationship and to change the decision were associated with trust and cooperation in a subsequent round of the game. Together, these findings show that smiles that are subtly different in their morphology can convey different messages and highlight the importance of these expressions in influencing the perceptions of others’ intentions.

The economic and interpersonal consequences of deflecting direct questions.

Direct, difficult questions (e.g., Do you have other offers? When do you plan on having children?) pose a challenge. Respondents may incur economic costs for honestly revealing information, reputational costs for engaging in deception, and interpersonal costs, including harm to perceptions of trust and liking, for directly declining to answer the question (e.g., I would rather not answer that question.). Across 8 experiments, we explore the relative economic and interpersonal consequences of a fourth approach: deflection, answering a direct question with another question. We describe how individuals infer the respondent's communication motive from their response (e.g., a motive to seek or hide information), and how these inferences influence perceptions of the respondent's trust and likability. We contrast deflection with other types of responses and show that deflection causes significantly less reputational harm than detected deception and causes significantly less interpersonal harm than directly declining to answer a question. In some cases, deflection even yields better interpersonal and economic outcomes than honest disclosures (e.g., deflecting questions about prior acts of untrustworthy behavior). (PsycInfo Database Record (c) 2020 APA, all rights reserved).

An Evolutionary Game Model with Punishment and Protection to Promote Trust in the Sharing Economy

In this paper, we present an evolutionary trust game, taking punishment and protection into consideration, to investigate the formation of trust in the so-called sharing economy from a population perspective. This sharing economy trust model comprises four types of players: a trustworthy provider, an untrustworthy provider, a trustworthy consumer, and an untrustworthy consumer. Punishment in the form of penalty for untrustworthy providers and protection in the form of insurance for consumers are mechanisms adopted to prevent untrustworthy behaviour. Through comprehensive simulation experiments, we evaluate dynamics of the population for different initial population setups and effects of having penalty and insurance in place. Our results show that each player type influences the ‘existence’ and ‘survival’ of other types of players, and untrustworthy players do not necessarily dominate the population even when the temptation to defect (i.e., to be untrustworthy) is high. Additionally, we observe that imposing a heavier penalty or having insurance for all consumers (trustworthy and untrustworthy) can be counterproductive for promoting trustworthiness in the population and increasing the global net wealth. Our findings have important implications for understanding trust in the context of the sharing economy, and for clarifying the usefulness of protection policies within it.

Learning to trust a face: The time course of brain activation during a money game

Evidence shows that human faces can rapidly produce impressions of trust or distrust on the basis of their facial features. However, trust is also built through repeated interactions in which an opposite party acts positively towards the subject in a consistent way. The dynamics of cortical activation of this form of interactively-experienced trust is unclear. The current study therefore investigated the electrophysiological response to trust/distrust, arising through interactions in an investment game. Using an ERP paradigm, participants took part in a money game in which they chose to entrust different amounts to fictitious players. Some of these players were associated with the higher probability of a positive outcome (trustworthy behaviour), others were associated with a higher negative outcome (untrustworthy behaviour), and yet others were neutral. Over the course of the game, a strong central positivity emerged between 450 and 650 ms for trustworthy faces, compared to both neutral and untrustworthy players. This time period thus reflects the window during which the trustworthiness of a face is processed, when based on prior interaction. In addition, by evidencing ERP modifications for trustworthy faces alone, these findings suggest that the “default mode” of processing is initially biased towards distrust.

Drivers of trust and trustworthiness

PurposeThe purpose of this paper is to contribute to the understanding of why people act trustworthily in anonymous non-repeated meetings where trustworthiness benefits the trustor and runs against the trustee’s material self-interest.Design/methodology/approachThe paper uses a survey originally developed by Bicchieri et al. (2011). The survey makes it possible to explore whether trustworthiness has a normative element. Is there a norm of trustworthiness that inflicts punishment for disobedience?FindingsThe participants in the experiment strongly believe that most people will punish untrustworthy behavior, lending support to the idea that trustworthiness is norm driven. The data provide little evidence for a parallel norm of trust.Originality/valueThe theory of repeated games explains how trust can emerge among players in ongoing interactions. But why do people choose to trust others who they do not know in non-ongoing interactions? The results offer an explanation. When trustors are aware that trustworthiness is rooted in norms, they have reason to expect trustees to act trustworthily. Then, it makes sense to trust since trustors will benefit from their trusting.

TRUST AS A BOOSTER

In his book Trust. The Evolutionary Game of Mind and Society, social psychologist Toshio Yamagishi (2011) states that trust can be viewed as a “booster rocket” that provides the necessary push for the take-off from the secure ground of committed relations. This article formalizes this idea with the help of a simple game theoretic model. The article looks at a situation where networks of personalized exchange relationships provide assurance against untrustworthy behaviour but reduce the opportunity to profit from trade in larger markets. Assuming that the anonymous market contains both trustworthy and untrustworthy types, it is demonstrated that mutual trust relations can emerge, even when there is a clear danger of opportunism and the possibility of repeated interaction is ruled out. From a more practical perspective, the model provides an insight into the role trust plays for the decision to transact in networks or markets. It is also demonstrated that networks appear as mixed blessings. Networks reduce the problems arising from incomplete contracts and behavioural risk, but they also restrict individuals’ possibility to reap potential gains produced in larger markets.

The influence of voice pitch on perceptions of trustworthiness across social contexts

Abstract Perceptions of trustworthiness are an important predictor of social outcomes, such as monetary exchanges, criminal sentencing, and the attainment of leadership roles. Higher testosterone levels predict both lower voice pitch and untrustworthy behavior, across economic and mating contexts. Here, we tested the influence of voice pitch on perceptions of trustworthiness across general, economic, and mating-related (mate poaching, infidelity) contexts. We found that the context of trust and the sex of the speaker both changed how voice pitch affected perceived trustworthiness. Listeners were more trusting of higher-pitched female voices in economic and mate poaching contexts, but trusted lower-pitched female voices more in general. Listeners were more trusting of higher-pitched male voices in economic and mating-related contexts, and also tended to perceive higher-pitched male voices as more trustworthy in general. Listeners' attributions of trustworthiness were generally unrelated to perceptions of attractiveness from similarly-pitched voices, indicating that trust-related attributions were independent of preferences for higher- or lower-pitched voices. Furthermore, perceptions of general trustworthiness were associated with perceptions of economic trust, but were not consistently associated with perceptions of mating-related trust. These findings provide evidence that voice pitch alone is sufficient to influence trust-related perceptions, and demonstrates that listeners use voice pitch as a cue to trustworthy behavior.

Influence of indirect information on interpersonal trust despite direct information.

Trust is integral to successful relationships. The development of trust stems from how one person treats others, and there are multiple ways to learn about someone's trust-relevant behavior. The present research captures the development of trust to examine if trust-relevant impressions and behavior are influenced by indirect behavioral information (i.e., descriptions of how a person treated another individual)-even in the presence of substantial direct behavioral information (i.e., self-relevant, first-hand experience with a person). Participants had repeated interpersonal exchanges with a partner who was trustworthy or untrustworthy with participants' money. The present studies vary the frequency with which (Studies 1 & 2), the order in which (Study 3) and the number of people for whom (Study 4) indirect information (i.e., brief vignettes describing trustworthy or untrustworthy behavior) were presented. As predicted, across 4 studies, we observed a robust effect of indirect-information despite the presence of substantial direct information. Even after dozens of interactions in which a partner betrayed (or not), a brief behavioral description of a partner influenced participants' willingness to actually trust the partner with money, memory-based estimates of partner-behavior, and impressions of the partner. These effects were observed even though participants were also sensitive to partners' actual trust behavior, and even when indirect behavioral descriptions were only presented a single time. Impressions were identified as a strong candidate mechanism for the effect of indirect-information on behavior. We discuss implications of the persistence of indirect information for impression formation, relationship development, and future studies of trust. (PsycINFO Database Record

How Do Controls Impact Employee Trust in the Employer?

Do organizational controls facilitate or hinder employees’ trust in their organization? We addressed this question through a mixed‐methods design using three studies. Based on a literature review and an open‐response survey study (Study 1), we developed a theoretical model proposing that organizational control is positively related to employees’ trust in their organization, and that this relationship is mediated by procedural fairness and organizational prestige. This mediated model was tested and supported in a quantitative survey of 582 European managers and professional employees from a range of organizations (Study 2). A complementary, qualitative interview‐based study (Study 3) confirmed that well‐implemented controls facilitate trust in the organization; however, poorly implemented control systems that are inconsistent, overly rigid, or incentivize untrustworthy behavior can undermine trust in the organization. © 2015 Wiley Periodicals, Inc.

A lack of material resources decreases trust propensity

This research tested the idea that a lack of material resources decreases trust propensity because it makes people less able to cope with the negative consequences of others’ untrustworthy behavior...

UNCERTAINTY-BASED TRUST ESTIMATION IN A MULTI-VALUED TRUST ENVIRONMENT

Despite the widespread usage of the evaluation mediums for online services by the clients, there is a requirement for a trust evaluation tool that provides the clients with the degree of trustworthiness of the service providers. Such a tool can provide increased familiarity with unknown third party entities, e.g. service providers, especially when those entities neither project completely trustworthy nor totally untrustworthy behaviour. Indeed, developing some metrics for trust evaluation under uncertainty can come handy, e.g., for customers interested in evaluating the trustworthiness of an unknown service provider throughout queries to other customers of unknown reliability. In this research, we propose an evaluation metric to estimate the degree of trustworthiness of an unknown agent, say aD, through the information acquired through a group of agents who have interacted with agent aD. This group of agents is assumed to have an unknown degree of reliability. In order to tackle the uncertainty associated with the trust of these set of unknown agents, we suggest to use possibility distributions. Later, we introduce a new certainty metric to measure the degree of agreement in the information reported by the group of agents in A on agent aD. Fusion rules are then used to measure an estimation of the agent aD’s degree of trustworthiness. To the best of our knowledge, this is the first work that estimates trust, out of empirical data, subject to some uncertainty, in a discrete multi-valued trust domain. Finally, numerical experiments are presented to validate the proposed tools and metrics.

Computationally modeling interpersonal trust

We present a computational model capable of predicting—above human accuracy—the degree of trust a person has toward their novel partner by observing the trust-related nonverbal cues expressed in their social interaction. We summarize our prior work, in which we identify nonverbal cues that signal untrustworthy behavior and also demonstrate the human mind's readiness to interpret those cues to assess the trustworthiness of a social robot. We demonstrate that domain knowledge gained from our prior work using human-subjects experiments, when incorporated into the feature engineering process, permits a computational model to outperform both human predictions and a baseline model built in naiveté of this domain knowledge. We then present the construction of hidden Markov models to investigate temporal relationships among the trust-related nonverbal cues. By interpreting the resulting learned structure, we observe that models built to emulate different levels of trust exhibit different sequences of nonverbal cues. From this observation, we derived sequence-based temporal features that further improve the accuracy of our computational model. Our multi-step research process presented in this paper combines the strength of experimental manipulation and machine learning to not only design a computational trust model but also to further our understanding of the dynamics of interpersonal trust.

Who is to blame? Oxytocin promotes nonpersonalistic attributions in response to a trust betrayal

Recent research revealed that the neuropeptide Oxytocin (OT) increases and maintains trustful behavior, even towards interaction partners that have proven to be untrustworthy. However, the cognitive mechanisms behind this effect are unclear. In the present paper, we propose that OT might boost trust through the link between angry rumination and the use of nonpersonalistic and personalistic attributions. Nonpersonalistic attributions put the blame for the betrayal on the perpetrator's situation, whereas personalistic attributions blame his dispositions for the event. We predict that OT changes attribution processes in favor of nonpersonalistic ones and thereby boosts subsequent trust. Participants played a classic trust game in which the opponent systematically betrayed their trust. As predicted, OT strengthened the relationship between angry rumination about the event and nonpersonalistic attribution of the opponents’ behavior and weakened the link between angry rumination and personalistic attribution. Critically, nonpersonalistic attribution also mediated the interactive effect of OT and angry rumination on how strongly investments were reduced in the remaining rounds of the trust game. In summary, the present findings suggest that one underlying cognitive mechanism behind OT-induced trust might relate to how negative emotions evoked by a breach of trust influence the subsequent attributional analysis: OT seems to augment trust by fostering the interpretation of untrustworthy behavior as caused by non-personal factors.

Specific neural correlates of successful learning and adaptation during social exchanges

Cooperation and betrayal are universal features of social interactions, and knowing who to trust is vital in human society. Previous studies have identified brain regions engaged by decision making during social encounters, but the mechanisms supporting modification of future behaviour by utilizing social experience are not well characterized. Using functional magnetic resonance imaging (fMRI), we show that cooperation and betrayal during social exchanges elicit specific patterns of neural activity associated with future behaviour. Unanticipated cooperation leads to greater behavioural adaptation than unexpected betrayal, and is signalled by specific neural responses in the striatum and midbrain. Neural responses to betrayal and willingness to trust novel partners both decrease as the number of individuals encountered during repeated social encounters increases. We propose that, as social groups increase in size, uncooperative or untrustworthy behaviour becomes progressively less surprising, with cooperation becoming increasingly important as a stimulus for social learning. Effects on reputation of non-trusting decisions may also act to drive pro-social behaviour. Our findings characterize the dynamic neural processes underlying social adaptation, and suggest that the brain is optimized to cooperate with trustworthy partners, rather than avoiding those who might betray us.