Sequential Voting Promotes Collective Discovery in Social Recommendation Systems

One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.Comment: To be published in the 10th International AAAI Conference on Web and Social Media (ICWSM) 201

Modeling Human Ad Hoc Coordination

Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore how one can come to believe that other actors will coordinate, and with regard to systems involving humans, an important empirical question is how humans arrive at these expectations. We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. Our algorithm is valid in any environment with a finite state space, and extensions to certain countably infinite state spaces are likely possible. We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-agent collaboration.Comment: AAAI 201

Successful Protein Extraction from Over-Fixed and Long-Term Stored Formalin-Fixed Tissues

One of the major breakthroughs in molecular pathology during the last decade was the successful extraction of full-length proteins from formalin-fixed and paraffin-embedded (FFPE) clinical tissues. However, only limited data are available for the protein extraction efficiency of over-fixed tissues and FFPE blocks that had been stored for more than 15 years in pathology archives. In this study we evaluated the protein extraction efficiency of FFPE tissues which had been formalin-fixed for up to 144 hours and tissue blocks that were stored for 20 years, comparing an established and a new commercial buffer system. Although there is a decrease in protein yield with increasing fixation time, the new buffer system allows a protein recovery of 66% from 144 hours fixed tissues compared to tissues that were fixed for 6 hours. Using the established extraction procedure, less than 50% protein recovery was seen. Similarly, the protein extraction efficiency decreases with longer storage times of the paraffin blocks. Comparing the two buffer systems, we found that 50% more proteins can be extracted from FFPE blocks that were stored for 20 years when the new buffer system is used. Taken together, our data show that the new buffer system is superior compared to the established one. Because tissue fixation times vary in the routine clinical setting and pathology archives contain billions of FFPE tissues blocks, our data are highly relevant for research, diagnosis, and treatment of disease

Visualization of Abscess Formation in a Murine Thigh Infection Model of Staphylococcus aureus by 19F-Magnetic Resonance Imaging (MRI)

Background: During the last years, 19 F-MRI and perfluorocarbon nanoemulsion (PFC) emerged as a powerful contrast agent based MRI methodology to track cells and to visualize inflammation. We applied this new modality to visualize deep tissue abscesses during acute and chronic phase of inflammation caused by Staphylococcus aureus infection. Methodology and Principal Findings: In this study, a murine thigh infection model was used to induce abscess formation and PFC or CLIO (cross linked ironoxides) was administered during acute or chronic phase of inflammation. 24 h after inoculation, the contrast agent accumulation was imaged at the site of infection by MRI. Measurements revealed a strong accumulation of PFC at the abscess rim at acute and chronic phase of infection. The pattern was similar to CLIO accumulation at chronic phase and formed a hollow sphere around the edema area. Histology revealed strong influx of neutrophils at the site of infection and to a smaller extend macrophages during acute phase and strong influx of macrophages at chronic phase of inflammation. Conclusion and Significance: We introduce 19 F-MRI in combination with PFC nanoemulsions as a new platform to visualize abscess formation in a murine thigh infection model of S. aureus. The possibility to track immune cells in vivo by this modality offers new opportunities to investigate host immune response, the efficacy of antibacterial therapies and th

The Solar Orbiter Science Activity Plan: translating solar and heliospheric physics questions into action

Solar Orbiter is the first space mission observing the solar plasma both in situ and remotely, from a close distance, in and out of the ecliptic. The ultimate goal is to understand how the Sun produces and controls the heliosphere, filling the Solar System and driving the planetary environments. With six remote-sensing and four in-situ instrument suites, the coordination and planning of the operations are essential to address the following four top-level science questions: (1) What drives the solar wind and where does the coronal magnetic field originate?; (2) How do solar transients drive heliospheric variability?; (3) How do solar eruptions produce energetic particle radiation that fills the heliosphere?; (4) How does the solar dynamo work and drive connections between the Sun and the heliosphere? Maximising the mission’s science return requires considering the characteristics of each orbit, including the relative position of the spacecraft to Earth (affecting downlink rates), trajectory events (such as gravitational assist manoeuvres), and the phase of the solar activity cycle. Furthermore, since each orbit’s science telemetry will be downloaded over the course of the following orbit, science operations must be planned at mission level, rather than at the level of individual orbits. It is important to explore the way in which those science questions are translated into an actual plan of observations that fits into the mission, thus ensuring that no opportunities are missed. First, the overarching goals are broken down into specific, answerable questions along with the required observations and the so-called Science Activity Plan (SAP) is developed to achieve this. The SAP groups objectives that require similar observations into Solar Orbiter Observing Plans, resulting in a strategic, top-level view of the optimal opportunities for science observations during the mission lifetime. This allows for all four mission goals to be addressed. In this paper, we introduce Solar Orbiter’s SAP through a series of examples and the strategy being followed

A rational choice framework for collective behavior

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 137-146).As the world becomes increasingly digitally mediated, people can more and more easily form groups, teams, and communities around shared interests and goals. Yet there is a constant struggle across forms of social organization to maintain stability and coherency in the face of disparate individual experiences and agendas. When are collectives able to function and thrive despite these challenges? In this thesis I propose a theoretical framework for reasoning about collective intelligence--the ability of people to accomplish their shared goals together. A simple result from the literature on multiagent systems suggests that strong general collective intelligence in the form of "rational group agency" arises from three conditions: aligned utilities, accurate shared beliefs, and coordinated actions. However, achieving these conditions can be difficult, as evidenced by impossibility results related to each condition from the literature on social choice, belief aggregation, and distributed systems. The theoretical framework I propose serves as a point of inspiration to study how human groups address these difficulties. To this end, I develop computational models of facets of human collective intelligence, and test these models in specific case studies. The models I introduce suggest distributed Bayesian inference as a framework for understanding shared belief formation, and also show that people can overcome other difficult computational challenges associated with achieving rational group agency, including balancing the group "exploration versus exploitation dilemma" for information gathering and inferring levels of "common p-belief" to coordinate actions.by Peter M. Krafft.Ph. D

Bots as Virtual Confederates: Design and Ethics

The use of bots as virtual confederates in online field experiments holds extreme promise as a new methodological tool in computational social science. However, this potential tool comes with inherent ethical challenges. Informed consent can be difficult to obtain in many cases, and the use of confederates necessarily implies the use of deception. In this work we outline a design space for bots as virtual confederates, and we propose a set of guidelines for meeting the status quo for ethical experimentation. We draw upon examples from prior work in the CSCW community and the broader social science literature for illustration. While a handful of prior researchers have used bots in online experimentation, our work is meant to inspire future work in this area and raise awareness of the associated ethical issues