Wisdom of the Crowd Mechanisms

As Web 2.0 facilitates the collection of a vast amount of interactions, a phenomena, known as the wisdom of the crowd, is increasingly enlisted to justify using those interactions as surrogates for expert opinions. This dissertation explores this phenomena through an analysis of the micro elements of two wisdom of the crowd simulations: (1) Hong and Page’s (2004) Diversity trumps ability model and (2) Luan et al.’s (2012) Fast and Frugal simulation. The focus of this study is on the micro elements that contribute to those simulations’ results. This focus leads to the identification of a search mechanism that favors exploitation as a first step followed by exploration as defined by March’s (1991) Exploration/Exploitation simulation. Three new methods for creating a group of experts were developed and were shown to be not only superior to the Top 10 agents but also superior to the more diverse random group of ten agents which consistently outperformed the Top 10 agents in the Hong-Page model. It was also shown that these expert groups were more efficient in incorporating the entire range of heuristics possessed by the universe of agents. The problem spaces were manipulated in various manners and the effect of such manipulations demonstrated. Additionally, group process losses were demonstrated through the simulation of a Hidden Profile scenario in which skills possessed by only one agent were ignored by the group. The effect of the dichotomization rate in the Fast and Frugal paradigm was highlighted and the effect of an alternative dichotomization rate demonstrated along with increasing the number of cues and manipulating the degree of correlation among them. Additionally, a set of perfect cue weights was developed for the Fast and Frugal paradigm and a simulation showed how a single agent executing the paradigm to choose the correct alternative saw its ability deteriorate as the cue weights progressed from the perfect order to all cues being equally weighted while groups of agents experienced increasing accuracy over the same progression

Medical Crowdsourcing: Harnessing the “Wisdom of the Crowd” to Solve Medical Mysteries

Medical crowdsourcing offers hope to patients who suffer from complex health conditions that are difficult to diagnose. Such crowdsourcing platforms empower patients to harness the “wisdom of the crowd” by providing access to a vast pool of diverse medical knowledge. Greater participation in crowdsourcing increases the likelihood of encountering a correct solution. However, more participation also leads to increased “noise,” which makes identifying the most likely solution from a broader pool of recommendations (i.e., diagnostic suggestions) difficult. The challenge for medical crowdsourcing platforms is to increase participation of both patients and solution providers, while simultaneously increasing the efficacy and accuracy of solutions. The primary objectives of this study are: (1) to investigate means to enhance the solution pool by increasing participation of solution providers referred to as “medical detectives” or “detectives,” and (2) to explore ways of selecting the most likely diagnosis from a set of alternative possibilities recommended by medical detectives. Our results suggest that our strategy of using multiple methods for evaluating recommendations by detectives leads to better predictions. Furthermore, cases with higher perceived quality and more negative emotional tones (e.g., sadness, fear, and anger) attract more detectives. Our findings have strong implications for research and practice

Wisdom of the Crowd? Information Aggregation in Representative Democracy

In representative democracy, voters elect candidates who strategically propose policies. In a common value environment with imperfectly informed voters and candidates, we establish that intermediation by candidates can render information aggregation unfeasible even when a large electorate presented with exogenous options would almost always select the correct policy. In fact, the possibility of information aggregation encourages candidates' conformism and stifles the competition among ideas. Neither liberalizing access to candidacy nor introducing additional frictions in voters information guarantees feasible information aggregation. Thus, the political failure we uncover is due to the intermediation by candidates---that is, the nature of representative democracy

Wisdom of the Crowd? Information Aggregation and Electoral Incentives

Elections have long been understood as a mean to encourage candidates to act in voters' interest as well as a way to aggregate dispersed information. This paper juxtaposes these two key features within a unified framework. As in models of electoral control, candidates compete for office by strategically proposing policy platforms. As in models of information aggregation, agents are not always informed about the policy which maximizes the electorate welfare. Candidates face a trade-off between acting in the electorate's best interest and maximizing their chance of being elected. We provide conditions under which electoral institutions encourage candidates' conformism---thereby stifling proper competition among ideas---and render information aggregation unfeasible in equilibrium. In extensions, we highlight that the new political failure we uncover cannot be fully resolved by liberalizing access to candidacy or reducing voter information

Do we really need to catch them all? A new User-guided Social Media Crawling method

With the growing use of popular social media services like Facebook and Twitter it is challenging to collect all content from the networks without access to the core infrastructure or paying for it. Thus, if all content cannot be collected one must consider which data are of most importance. In this work we present a novel User-guided Social Media Crawling method (USMC) that is able to collect data from social media, utilizing the wisdom of the crowd to decide the order in which user generated content should be collected to cover as many user interactions as possible. USMC is validated by crawling 160 public Facebook pages, containing content from 368 million users including 1.3 billion interactions, and it is compared with two other crawling methods. The results show that it is possible to cover approximately 75% of the interactions on a Facebook page by sampling just 20% of its posts, and at the same time reduce the crawling time by 53%. In addition, the social network constructed from the 20% sample contains more than 75% of the users and edges compared to the social network created from all posts, and it has similar degree distribution

Harvesting the wisdom of the crowd: using online ratings to explore care experiences in regions.

Regional population health management (PHM) initiatives need an understanding of regional patient experiences to improve their services. Websites that gather patient ratings have become common and could be a helpful tool in this effort. Therefore, this study explores whether unsolicited online ratings can provide insight into (differences in) patient's experiences at a (regional) population level

Enabling Embodied Analogies in Intelligent Music Systems

The present methodology is aimed at cross-modal machine learning and uses multidisciplinary tools and methods drawn from a broad range of areas and disciplines, including music, systematic musicology, dance, motion capture, human-computer interaction, computational linguistics and audio signal processing. Main tasks include: (1) adapting wisdom-of-the-crowd approaches to embodiment in music and dance performance to create a dataset of music and music lyrics that covers a variety of emotions, (2) applying audio/language-informed machine learning techniques to that dataset to identify automatically the emotional content of the music and the lyrics, and (3) integrating motion capture data from a Vicon system and dancers performing on that music.Comment: 4 page

Rethinking Resource Allocation in Science

US funding agencies alone distribute a yearly total of roughly $65B dollars largely through the process of proposal peer review: scientists compete for project funding by submitting grant proposals which are evaluated by selected panels of peer reviewers. Similar funding systems are in place in most advanced democracies. However, in spite of its venerable history, proposal peer review is increasingly struggling to deal with the increasing mismatch between demand and supply of research funding.Comment: This working paper formed the basis of J. Bollen, Who would you share your funding with. Nature 560, 143 (2018