Insensitivity to social reputation in autism

People act more prosocially when they know they are watched by others, an everyday observation borne out by studies from behavioral economics, social psychology, and cognitive neuroscience. This effect is thought to be mediated by the incentive to improve one's social reputation, a specific and possibly uniquely human motivation that depends on our ability to represent what other people think of us. Here we tested the hypothesis that social reputation effects are selectively impaired in autism, a developmental disorder characterized in part by impairments in reciprocal social interactions but whose underlying cognitive causes remain elusive. When asked to make real charitable donations in the presence or absence of an observer, matched healthy controls donated significantly more in the observer's presence than absence, replicating prior work. By contrast, people with high-functioning autism were not influenced by the presence of an observer at all in this task. However, both groups performed significantly better on a continuous performance task in the presence of an observer, suggesting intact general social facilitation in autism. The results argue that people with autism lack the ability to take into consideration what others think of them and provide further support for specialized neural systems mediating the effects of social reputation

The mechanics of trust: a framework for research and design

With an increasing number of technologies supporting transactions over distance and replacing traditional forms of interaction, designing for trust in mediated interactions has become a key concern for researchers in human computer interaction (HCI). While much of this research focuses on increasing users’ trust, we present a framework that shifts the perspective towards factors that support trustworthy behavior. In a second step, we analyze how the presence of these factors can be signalled. We argue that it is essential to take a systemic perspective for enabling well-placed trust and trustworthy behavior in the long term. For our analysis we draw on relevant research from sociology, economics, and psychology, as well as HCI. We identify contextual properties (motivation based on temporal, social, and institutional embeddedness) and the actor's intrinsic properties (ability, and motivation based on internalized norms and benevolence) that form the basis of trustworthy behavior. Our analysis provides a frame of reference for the design of studies on trust in technology-mediated interactions, as well as a guide for identifying trust requirements in design processes. We demonstrate the application of the framework in three scenarios: call centre interactions, B2C e-commerce, and voice-enabled on-line gaming

Screening, Competition, and Job Design

In recent decades, many firms offered more discretion to their employees, often increasing the productivity of effort but also leaving more opportunities for shirking. These “high-performance work systems” are difficult to understand in terms of standard moral hazard models. We show experimentally that complementarities between high effort discretion, rent-sharing, screening opportunities, and competition are important driving forces behind these new forms of work organization. We document in particular the endogenous emergence of two fundamentally distinct types of employment strategies. Employers either implement a control strategy, which consists of low effort discretion and little or no rent-sharing, or they implement a trust strategy, which stipulates high effort discretion and substantial rent-sharing. If employers cannot screen employees, the control strategy prevails, while the possibility of screening renders the trust strategy profitable. The introduction of competition substantially fosters the trust strategy, reduces market segmentation, and leads to large welfare gains for both employers and employees

Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems

Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing Systems, temporally recruited mobile users can provide agile, fine-grained, and economical sensing labors, however their self-interest cannot guarantee the quality of the sensing data, even when there is a fair return. Therefore, a mechanism is required for the system server to recruit well-behaving users for credible sensing, and to stimulate and reward more contributive users based on sensing truth discovery to further increase credible reporting. In this paper, we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile Crowdsensing Systems, which achieves credibility-driven user recruitment and payback maximization for honest users with quality data. Via theoretical analysis, we demonstrate the correctness of our design. The performance of our scheme is evaluated based on extensive realworld trace-driven simulations. Our evaluation results show that our scheme is proven to be effective in terms of both guaranteeing sensing accuracy and resisting potential cheating behaviors, as demonstrated in practical scenarios, as well as those that are intentionally harsher

Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory

In Internet of Vehicles (IoV), data sharing among vehicles is essential to improve driving safety and enhance vehicular services. To ensure data sharing security and traceability, highefficiency Delegated Proof-of-Stake consensus scheme as a hard security solution is utilized to establish blockchain-enabled IoV (BIoV). However, as miners are selected from miner candidates by stake-based voting, it is difficult to defend against voting collusion between the candidates and compromised high-stake vehicles, which introduces serious security challenges to the BIoV. To address such challenges, we propose a soft security enhancement solution including two stages: (i) miner selection and (ii) block verification. In the first stage, a reputation-based voting scheme for the blockchain is proposed to ensure secure miner selection. This scheme evaluates candidates' reputation by using both historical interactions and recommended opinions from other vehicles. The candidates with high reputation are selected to be active miners and standby miners. In the second stage, to prevent internal collusion among the active miners, a newly generated block is further verified and audited by the standby miners. To incentivize the standby miners to participate in block verification, we formulate interactions between the active miners and the standby miners by using contract theory, which takes block verification security and delay into consideration. Numerical results based on a real-world dataset indicate that our schemes are secure and efficient for data sharing in BIoV.Comment: 12 pages, submitted for possible journal publicatio