28 research outputs found
Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations
Collective, especially group-based, managerial decision making is crucial in
organizations. Using an evolutionary theoretic approach to collective decision
making, agent-based simulations were conducted to investigate how human
collective decision making would be affected by the agents' diversity in
problem understanding and/or behavior in discussion, as well as by their social
network structure. Simulation results indicated that groups with consistent
problem understanding tended to produce higher utility values of ideas and
displayed better decision convergence, but only if there was no group-level
bias in collective problem understanding. Simulation results also indicated the
importance of balance between selection-oriented (i.e., exploitative) and
variation-oriented (i.e., explorative) behaviors in discussion to achieve
quality final decisions. Expanding the group size and introducing non-trivial
social network structure generally improved the quality of ideas at the cost of
decision convergence. Simulations with different social network topologies
revealed collective decision making on small-world networks with high local
clustering tended to achieve highest decision quality more often than on random
or scale-free networks. Implications of this evolutionary theory and simulation
approach for future managerial research on collective, group, and multi-level
decision making are discussed.Comment: 27 pages, 5 figures, 2 tables; accepted for publication in Complexit
Effects of Network Connectivity and Diversity Distribution on Human Collective Ideation
Human collectives, e.g., teams and organizations, increasingly require
participation of members with diverse backgrounds working in networked social
environments. However, little is known about how network structure and the
diversity of member backgrounds would affect collective processes. Here we
conducted three sets of human-subject experiments which involved 617
participants who collaborated anonymously in a collective ideation task on a
custom-made online social network platform. We found that spatially clustered
collectives with clustered background distribution tended to explore more
diverse ideas than in other conditions, whereas collectives with random
background distribution consistently generated ideas with the highest utility.
We also found that higher network connectivity may improve individuals' overall
experience but may not improve the collective performance regarding idea
generation, idea diversity, and final idea quality.Comment: 43 pages, 19 figures, 4 table
Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial
Background
Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy
Article quality and publication impact via levels of analysis incorporation : An illustration with transformational/charismatic leadership
Calls for the inclusion of levels of analysis in theory building and testing have increased over the last 25 years. Through analysis of 539 published articles we assess the prevalence of incorporation of levels of analysis in theory/hypothesis formulation, measurement, data analysis, and subsequent theory–data alignment (i.e., article quality) within charismatic and transformational leadership research. Additionally, we examine the relationship between incorporation of levels of analysis into research and publication source quality, as reflected by journal impact factors or when not available, estimated journal impact factors. When controlling for the level of analysis within all articles, results revealed that increasing the complexity of the level of analysis (i.e., higher than individual level), increased the likelihood that measurement, analysis and alignment of theory and data would be presented at the appropriate levels of analysis. In contrast, for articles with published impact factors, when controlling for the level of analysis, results revealed that increasing the complexity of the level of analysis (i.e., higher than individual level) decreased the likelihood that measurement, analysis and alignment of theory and data would be presented at the appropriate levels of analysis
Visualizing Collective Idea Generation and Innovation Processes in Social Networks
Collective idea generation and innovation processes are complex and dynamic,
involving a large amount of qualitative narrative information that is difficult
to monitor, analyze, and visualize using traditional methods. In this study, we
developed three new visualization methods for collective idea generation and
innovation processes and applied them to data from online social network
experiments. The first visualization is the Idea Cloud, which helps monitor
collective idea posting activity and intuitively tracks idea clustering and
transition. The second visualization is the Idea Geography, which helps
understand how the idea space and its utility landscape are structured and how
collaboration was performed in that space. The third visualization is the Idea
Network, which connects idea dynamics with the social structure of the people
who generated them, displaying how social influence among neighbors may have
affected collaborative activities and where innovative ideas arose and spread
in the social network