Converging evidence

Decisions—whether made by individuals or groups—often involve estimating quantities, a process that is subject to anchoring bias (Tversky and Kahneman in Science 185: 1124–1131, 1974). Differences in susceptibility to anchoring bias between individuals and groups have been recently explored with the result that groups appear less biased than individuals (Meub and Proeger in Theor Decis 85:117–150, 2018). However, existing studies treat groups monolithically without taking into account their network structure—the pattern of relationships among members. The present paper investigates the effects of group social network structure on anchoring bias. Using a structured survey instrument, we gathered data on competence-based trust relationships among 264 students enrolled in a university degree program. An anchoring experiment was conducted in which some of the students made estimates as individuals, while others did so in groups of different structures. The findings provide initial evidence of differences in bias levels across variously structured groups as well as relative to individuals. Groups with highly centralized trust networks (where a single person owned everyone’s trust) showed more anchoring bias than dense groups (where everyone trusted everyone else) and sparse groups (where no one trusted any other member of the group) showed more bias than dense groups. In addition, despite previous research demonstrating groups are less susceptible than individuals to anchoring bias, this study shows a higher presence of bias in both our centralized groups and sparse groups when compared to individuals, suggesting that group structure might moderate the mitigating effect of groups on anchoring bias. The research has implications for organizational behavior and social network literature. Specifically, this study contributes to the debate on anchoring bias for group decisions by highlighting the significant role of social network structure. At the same time, it contributes to the literature on network structure and performance by providing initial evidence of how network structure affects anchoring bias susceptibility. Moreover, our study contributes to management practice by alerting managers to the dangers of centralized networks, suggesting that competence-based trust plays a vital role in the resistance to anchoring bias.

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Research on growth of innovations introduced to the market has gradually shifted its focus from aggregate-level diffusion to exploring how growth is influenced by a given social network structure's characteristics. In this paper, we critically review this branch of literature. We argue that the growth of an innovation in a social network is shaped by the network's structure. Borrowing from the field of industrial organization in economics, which defines itself as the study of the effect of market structure on market performance, we describe this new wave of research on growth of innovations as the effect of social network structure on innovation performance. Hence, social network structural characteristics should be incorporated into research on new product growth as well as into managerial marketing decisions such as targeting and new product seeding.We review how social network structure influences innovations' market performance. Specifically, we discuss (1) a networks' global characteristics, namely average degree, degree distribution, clustering, and degree assortativity; (2) dyadic characteristics, or the relationships between pairs of network members, namely tie strength and embeddedness; (3) intrinsic individual characteristics, namely opinion leadership and susceptibility; and (4) location-based individual characteristics, namely the degree centrality, closeness centrality, and betweenness centrality of an individual network member.Overall, we find that growth is particularly effective in networks that demonstrate the “3 Cs”: cohesion (strong mutual influence among its members), connectedness (high number of ties), and conciseness (low redundancy). We identify gaps in current knowledge, discuss the implications on managerial decision making, and suggest topics for future research.

Effects of Social Network Structure on the Diffusion and Adoption of Agricultural Technology: Evidence from Rural Ethiopia

Why are individuals altruistic to their friends? Theory suggests that individual, relationship and network factors will all influence the levels of altruism; but to date, the effects of social network structure have received relatively little attention. The present study uses a novel correlational design to test the prediction that an individual will be more altruistic to friends who are well-connected to the individual's other friends. The result shows that, as predicted, even when controlling for a range of individual and relationship factors, the network factor (number of connections) makes a significant contribution to altruism, thus showing that individuals are more likely to be altruistic to better-connected members of their social networks. The implications of incorporating network structure into studies of altruism are discussed.

Supportive social networks are important to the post-traumatic response process. However, the effects of social network structure may be distinct from the perceived function of those networks. The present study examined the relative importance of role diversity and perceived strength of social support in mitigating post-traumatic stress disorder (PTSD). Data were drawn from respondents who report lifetime potentially traumatic events in the National Epidemiologic Survey on Alcohol and Related Conditions (N = 31,650). The Social Network Index (SNI) was used to measure the diversity of social connections. The Interpersonal Support Evaluation List (ISEL-12) was used to measure the perceived availability of social support within the network. Odds of current PTSD were compared among individuals representing four dichotomous types of social support: high diversity/high perceived strength, high diversity/low perceived strength, low diversity/high perceived strength, and low diversity/low perceived strength to examine which type of support is more protective against PTSD. Unadjusted odds of PTSD were 1.59 (95 % CI 1.39-1.82) for those with low versus high perceived support strength, and 1.10 (0.94-1.28) among those with non-diverse versus diverse social networks. Compared to the reference group (high diversity/high perceived strength), the adjusted odds of current PTSD were higher for two groups: low diversity/low perceived strength (OR = 1.62; 1.33-1.99), and low diversity/high perceived strength (OR = 1.57; 1.3-1.91). The high diversity/low perceived strength group had no greater odds of PTSD (OR = 1.02; 0.81-1.28). The diversity of a social network is potentially more protective against PTSD than the perception of strong social support. This suggests that programs, which engage individuals in social groups and activities may effectively attenuate the risk of PTSD. A better understanding of how these networks operate with respect to PTSD prevention and mitigation holds promise for improving psychiatric health.

This study examines the effects of social network structure of intermediaries in health care, namely referring physicians, upon the specialty treatment choices of patients in the United States. The social network of a referring physician is identified by the patient‐sharing pattern in Medicare claims data, and the following three measures are employed as key explanatory variables: (1) number of physicians connected (adjusted degree); (2) tightness of the network (clustering coefficient); and (3) influence of individual physicians in the network (eigenvector centrality). The results of discrete‐choice demand models suggest that if patients are referred by a physician who is a more important player in their social networks (i.e. eigenvector centrality is higher), the patient has a higher chance of choosing a surgeon of better quality.

Occupant behavior in buildings can contribute significantly to building energy demand and consumption. As a result, occupant behavior interventions to promote sustainability are becoming more widespread. Due to the expense in applying interventions, researchers have begun using computer simulations to analyze potential outcomes and better understand how complex systems can affect intervention success, in particular the effect of social network structure. In previous literature, studies have only evaluated social network effects using static social networks which are far from reality. Therefore, in this study we evaluate how a behavior intervention, here a comparative feedback system, is affected as social networks evolve over time using agent-based modeling. Results indicate that static social networks are much less volatile in their behavior and tend to have more convergent behavior relative to dynamic social networks. This implies that for normative interventions, dynamic networks have increased uncertainty in intervention outcome compared to static networks.

Individuals show consistent between-individual behavioural variation when they interact with conspecifics or heterospecifics. Such patterns might underlie emergent group-specific behavioural patterns and between-group behavioural differences. However, little is known about (i) how social and non-social drivers (external drivers) shape group-level social structures and (ii) whether animal groups show consistent between-group differences in social structure after accounting for external drivers. We used automated tracking to quantify daily social interactions and association networks in 12 colonies of zebra finches (Taeniopygia guttata). We quantified the effects of five external drivers (group size, group composition, ecological factors, physical environments and methodological differences) on daily interaction and association networks and tested whether colonies expressed consistent differences in day-to-day network structure after controlling for these drivers. Overall, we found that external drivers contribute significantly to network structure. However, even after accounting for the contribution of external drivers, there remained significant support for consistent between-group differences in both interaction (repeatability R: up to 0.493) and association (repeatability R: up to 0.736) network structures. Our study demonstrates how group-level differences in social behaviour can be partitioned into different drivers of variation, with consistent contributions from both social and non-social factors.

Considering the bottleneck of energy efficiency and reliability in the pervasive and personalized wearable healthcare management based on Human Body Communication (HBC), an all-digital extraordinary HBC coordinator is proposed, as a configurable hub in the baseband system for Body Area Network (BAN). The unique HBC coordinator in a BAN is capable of enhancing the control, and programming of the network system, as the core of the BAN and gateway between body sensor nodes and the devices outside of body. With emphasis on power consumption and high data rate, The implementation of network management unit in the HBC coordinator, equipped with a dynamic lookup table, not only keeps memory for the nodes paired before, but also makes improvements in retransmission mechanism in multipoint communication, and can be initialized whenever you want. Thereby the HBC coordinator chip has been typed out and achieves miniaturized physical size of 0.8mm2 in 0.13-um standard CMOS technology. It periodically monitors the data transmission with 15 nodes with less than 126.75uW and the operation is successfully demonstrated on our test system board.

Human Body Communication (HBC) is the medium to communicate transfer the health informatics by using human body tissue. The Body Area Network (BAN) requires promising physical layer solution. The human centric nature of HBC offers an innovative method to transfer the health care data, transmission requires low interference and reliable data link. HBC electrodes play a role of wireless antennas in other communication standards. In this paper, health monitoring device is designed, the health data collected from wearable sensors (worn on the body) and transmit it through human body to receiver device. The healthiness information is communicated via human to machine contact. By using HBC based wearable health monitoring system is utilizes propose restrains and signal convey distinctiveness can be calculated and evaluated in this system. Finally, calculation of reactance of capacitance among transmitter and receiver electrodes can be done successfully. In this paper is important issues related to HBC data transmission such as signal propagation model, communication performance, and experimental considerations is conducted and necessary results are presented.

Connected smart wearable devices are becoming increasingly popular with the advent of cheap, miniaturized, ultra-low-power computing and communication. Human Body Communication (HBC) is emerging as an alternative to Wireless Body Area Network (WBAN) for communication among these devices, as it provides higher energy-efficiency and security. One of the biggest bottleneck of HBC is the interference picked up due to the human body antenna effect, with Signal to Interference Ratio often worse than -20dB. An interference robust integrating dual data rate (DDR) receiver is introduced which can adapt itself to changing interference conditions and provide high interference rejection by Pulse Width Modulation of integration clock, thus dynamically changing its duty cycle. The theory, architecture of the receiver is developed along with the adaptation algorithm to train the receiver to find the optimum duty cycle of operation. System-level simulations show >20 dB of rejection even in presence of variable interference frequencies.

Little is known about the psychosocial care of older people presenting to the emergency department (ED), or whether their psychosocial well-being during and after an ED admission can be enhanced. People over the age of 65 years experiencing psychosocial distress and mental health concerns have higher rates of ED admission than those without. As part of a larger mixed-methods study investigating the relationships between older people's psychosocial well-being and emergency care, this study aimed to explore the experiences of older people in ED and their influence on patient psychosocial well-being. Participants aged 65 years or older receiving care in a large Australian public hospital ED were invited to participate in a telephone interview soon after discharge. Interviews were audio recorded and transcribed. Transcripts were analysed thematically. Eleven people (five women) aged 68-87 years participated in semi-structured interviews. Analyses revealed three overarching themes: 'interpersonal interactions', 'quality of care' and 'physical environment'. The theme 'interpersonal interactions' had two subthemes: 'communication' and 'human contact'. The three subthemes of 'quality of care' were 'appropriate care', 'psychological care' and 'unmet needs'. Physical environment referred to participant impressions of the ED setting. We found that staff sensitivity in their interactions with patients and their efforts to promote patients' physical comfort and protect their privacy influenced the psychosocial well-being of older adults in the ED. Based on the findings, we present a set of recommendations for enhancing the psychosocial care of older adults during ED admission.

Human Body Communication (HBC) has recently emerged as an alternative to wireless radio wave communication for connecting resource constrained devices around the body. HBC utilizes the human body as the communication medium between the devices and provides higher energy efficiency and enhanced security enabling applications such as remote health monitoring, biometric authentication etc. As these applications require data transfer in small bursts, it is not necessary to keep the higher power consuming main receiver awake all the time. This paper carries out architectural and circuit level analysis considering HBC channel characteristics to find the optimum wake-up receiver architecture. This leads to the proposal of a narrowband HBC wake-up receiver which operates at sub-nW power. Design trade-offs for choosing the architecture (narrowband vs broadband) and system level parameters (frequency of operation, modulation technique) has been discussed by utilizing the appropriate human body channel models. Simulation results using TSMC 65nm technology shows 694 pW operation even under channel loss conditions as high as 80dB, potentially enabling applications with energy harvesting.

The social network structure of a group arises from the patterns of association of its constituent members, which in turn originates from behaviors and preferences of those individuals. Consequently, an individual’s position in their social environment is commonly related to their own attributes. However, most studies that report such relationships are limited to single or very small numbers of groups. I consider one set of attributes suggested to influence network structure, namely personality type, and observed 15 groups of captive meerkats Suricata suricatta. Thus, I could assess whether the relationship between individual attributes and patterns of association and network position persisted across groups. Principal component analysis of behavior, within the 15 groups, revealed two personality dimensions: friendliness and aggressiveness. For a subset of five groups, only friendliness was consistent over 1 year. I found little evidence that they were universally good predictors of social network structure or individual positions within networks. Individuals with high friendliness scores were more central in networks of foraging competitions. There was no evidence that meerkats preferentially associated with or avoid others based on each of their personality scores. Alternatively, and contrary to much-published work, it may not be possible to generalize relationships between individual attributes and network position or overall network structure. This may be because social environments emerge from a complex interplay between individual attributes and social interactions, and thus, the role of these elements along with the physical environment in which they live in shaping network structures and personality is challenging to disentangle. Animal personalities and animal social network structures are increasingly recognized as significant components in animal behavior and welfare. Here, I explore personality in the context of social networks in different captive groups of meerkats. These types of studies are uncommon probably because collecting detailed interaction data from multiple known individuals in multiple groups is time-consuming and because groups are often highly variable in composition and ecological context. When I did consider the social networks and individual attributes of members of 15 groups, I found little evidence that an individual’s personality scores consistently explained either their network position or patterns of association across all groups. Individuals in the wild, with different personality profiles, may adjust their network assortment to balance their costs and benefits and shift their environmental pressures. In a captive environment, such characteristics can vary and therefore social associations may be expressed differently from their wild counterparts.