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      Mitigating Herding in Hierarchical Crowdsourcing Networks

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          Abstract

          Hierarchical crowdsourcing networks (HCNs) provide a useful mechanism for social mobilization. However, spontaneous evolution of the complex resource allocation dynamics can lead to undesirable herding behaviours in which a small group of reputable workers are overloaded while leaving other workers idle. Existing herding control mechanisms designed for typical crowdsourcing systems are not effective in HCNs. In order to bridge this gap, we investigate the herding dynamics in HCNs and propose a Lyapunov optimization based decision support approach - the Reputation-aware Task Sub-delegation approach with dynamic worker effort Pricing (RTS-P) - with objective functions aiming to achieve superlinear time-averaged collective productivity in an HCN. By considering the workers’ current reputation, workload, eagerness to work, and trust relationships, RTS-P provides a systematic approach to mitigate herding by helping workers make joint decisions on task sub-delegation, task acceptance, and effort pricing in a distributed manner. It is an individual-level decision support approach which results in the emergence of productive and robust collective patterns in HCNs. High resolution simulations demonstrate that RTS-P mitigates herding more effectively than state-of-the-art approaches.

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          Experimental evidence for the influence of group size on cultural complexity.

          The remarkable ecological and demographic success of humanity is largely attributed to our capacity for cumulative culture. The accumulation of beneficial cultural innovations across generations is puzzling because transmission events are generally imperfect, although there is large variance in fidelity. Events of perfect cultural transmission and innovations should be more frequent in a large population. As a consequence, a large population size may be a prerequisite for the evolution of cultural complexity, although anthropological studies have produced mixed results and empirical evidence is lacking. Here we use a dual-task computer game to show that cultural evolution strongly depends on population size, as players in larger groups maintained higher cultural complexity. We found that when group size increases, cultural knowledge is less deteriorated, improvements to existing cultural traits are more frequent, and cultural trait diversity is maintained more often. Our results demonstrate how changes in group size can generate both adaptive cultural evolution and maladaptive losses of culturally acquired skills. As humans live in habitats for which they are ill-suited without specific cultural adaptations, it suggests that, in our evolutionary past, group-size reduction may have exposed human societies to significant risks, including societal collapse.
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            Crowd science user contribution patterns and their implications.

            Scientific research performed with the involvement of the broader public (the crowd) attracts increasing attention from scientists and policy makers. A key premise is that project organizers may be able to draw on underused human resources to advance research at relatively low cost. Despite a growing number of examples, systematic research on the effort contributions volunteers are willing to make to crowd science projects is lacking. Analyzing data on seven different projects, we quantify the financial value volunteers can bring by comparing their unpaid contributions with counterfactual costs in traditional or online labor markets. The volume of total contributions is substantial, although some projects are much more successful in attracting effort than others. Moreover, contributions received by projects are very uneven across time--a tendency toward declining activity is interrupted by spikes typically resulting from outreach efforts or media attention. Analyzing user-level data, we find that most contributors participate only once and with little effort, leaving a relatively small share of users who return responsible for most of the work. Although top contributor status is earned primarily through higher levels of effort, top contributors also tend to work faster. This speed advantage develops over multiple sessions, suggesting that it reflects learning rather than inherent differences in skills. Our findings inform recent discussions about potential benefits from crowd science, suggest that involving the crowd may be more effective for some kinds of projects than others, provide guidance for project managers, and raise important questions for future research.
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              Experimental study of the behavioural mechanisms underlying self-organization in human crowds

              In animal societies as well as in human crowds, many observed collective behaviours result from self-organized processes based on local interactions among individuals. However, models of crowd dynamics are still lacking a systematic individual-level experimental verification, and the local mechanisms underlying the formation of collective patterns are not yet known in detail. We have conducted a set of well-controlled experiments with pedestrians performing simple avoidance tasks in order to determine the laws ruling their behaviour during interactions. The analysis of the large trajectory dataset was used to compute a behavioural map that describes the average change of the direction and speed of a pedestrian for various interaction distances and angles. The experimental results reveal features of the decision process when pedestrians choose the side on which they evade, and show a side preference that is amplified by mutual interactions. The predictions of a binary interaction model based on the above findings were then compared to bidirectional flows of people recorded in a crowded street. Simulations generate two asymmetric lanes with opposite directions of motion, in quantitative agreement with our empirical observations. The knowledge of pedestrian behavioural laws is an important step ahead in the understanding of the underlying dynamics of crowd behaviour and allows for reliable predictions of collective pedestrian movements under natural conditions.
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                Author and article information

                Contributors
                han.yu@ntu.edu.sg
                ascymiao@ntu.edu.sg
                yqchen@ict.ac.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 December 2016
                5 December 2016
                2016
                : 6
                Affiliations
                [1 ]ISNI 0000 0001 2224 0361, GRID grid.59025.3b, Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), , Nanyang Technological University,, ; Singapore, Singapore
                [2 ]ISNI 0000 0001 2224 0361, GRID grid.59025.3b, School of Computer Science and Engineering, , Nanyang Technological University, ; Singapore, Singapore
                [3 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Department of Electrical and Computer Engineering, , The University of British Columbia, ; Vancouver, BC Canada
                [4 ]ISNI 0000000119573309, GRID grid.9227.e, Institute of Computing Technology, , Chinese Academy of Sciences, ; Beijing, China
                [5 ]ISNI 0000 0001 2184 9220, GRID grid.266683.f, School of Computer Science, , University of Massachusetts Amherst, ; Amherst, MA USA
                [6 ]ISNI 0000 0004 1937 1450, GRID grid.24515.37, Department of Computer Science and Engineering, , Hong Kong University of Science and Technology, ; Hong Kong, China
                Article
                11
                10.1038/s41598-016-0011-6
                5431372
                28442714
                d9c194c0-b7e6-4b04-aee3-c17edfa45dc5
                © The Author(s) 2016

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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