In this paper, we provide a simple game-theoretic model of an online question and answer forum. We focus on factual questions in which user responses aggregate while a question remains open. Each user has a unique piece of information and can decide when to report this information. The asker prefers to receive information sooner rather than later, and will stop the process when satisfied with the cumulative value of the posted information. We consider two distinct cases: a complements case, in which each successive piece of information is worth more to the asker than the previous one; and a substitutes case, in which each successive piece of information is worth less than the previous one. A best-answer scoring rule is adopted to model Yahoo! Answers, and is effective for substitutes information, where it isolates an equilibrium in which all users respond in the first round. But we find that this rule is ineffective for complements information, isolating instead an equilibrium in which all users respond in the final round. In addressing this, we demonstrate that an approval-voting scoring rule and a proportional-share scoring rule can enable the most efficient equilibrium with complements information, under certain conditions, by providing incentives for early responders as well as the user who submits the final answer.

The paradigm of “human computation ” seeks to harness human abilities to solve computational problems or otherwise perform distributed work that is beyond the scope of current AI technologies. One aspect of human computation has become known as “games with a purpose ” and seeks to elicit useful computational work in fun (typically) multi-player games. Human computation also encompasses distributed work (or “peer production”) systems such as Wikipedia and Question and Answer forums. In this short paper, we survey existing game-theoretic models for various human computation designs, and outline research challenges in advancing a theory that can enable better design. 1.

Abstract—Creating descriptive labels for pictures is an important task with applications in image retrieval, Web accessibility and computer vision. Automatic creation of such labels is difficult, especially for pictures of artworks. “Games With A Purpose” strive to create image descriptions by harnessing the intelligence of human players. Existing implementations are highly successful in terms of player count and number of collected labels, but hardly create comprehensive tag sets containing both general and specific labels. We propose Karido, a new image labeling game designed to collect more diverse tags. This paper describes the design and implementation of the game, along with an evaluation based on data collected during a trial deployment. The game is compared to an existing image labeling game using the same database of images. Results of this evaluation indicate that Karido collects more diverse labels and is fun to play.

ARTigo is a gaming platform with two goals: The development of a semantic search engine for art works and a study on art work reception. Even though ARTigo quite successfully achieves its goals, it is so far still deficient at collecting “deep semantics ” tags. This article first discusses why this is the case, then proposes two approaches we call “squaring ” and “scripting ” to collecting deep semantic tags that both consist in deploying the ESP Game in unconventional manners. Finally, the relevance of squaring and scripting for other GWAPs than the ESP Game is discussed. ARTigo: GWAPs for Art History Comparing and interpreting art works is at the core of art history whatever flavour of this many sided discipline is considered. To this aim, databases of art work photographs are very useful but, unfortunately, in most cases still difficult to fully exploit. Indeed, at best art work databases contain a few stereotypical descriptions such as the art work’s title, date, and artist that do not help much in discovering art works one does not already know. Since art works descriptions useful for similarity or semantic search are still out of the reach of image processing algorithms, art work database curators therefore rely on the tedious, expensive and not always bias-free indexing of art works by students and low paid manpower. As an alternative approach to indexing the art work of a database of more than 30.000 art works, we developed ARTigo. 1 ARTigo is a gaming platform offering so far an ESP Game [8] that, so as to collect tags ’ frequencies useful in our context does not prevent entering tags already assigned and a game with a purpose [7, 5] we developed called Karido [6] so as to collect more specific tags than those the ESP Game tends to collects [9, 2], thus complementing the ESP Game. Thus, Karido has the same objektives as Polarity [4]. ARTigo is rather successful: Within 4 years, we collected 4.03 million tags 0.855 million of which (21%) have been validated, that is, suggested by at least two players acting independently from each other. Furthermore, ARTigo succeeded in attracting an active community of 14.169 registered users and additional 26.428 unregistered users of which on every day at least 73 play 1

Recent developments in many domains have highlighted the complexity and interconnectedness of our economic systems. Over the past decade, the growth of global computing and information networks has enabled new kinds of markets — for on-line commerce, search advertising, and other applications — that are growing rapidly in societal importance, but in which there are

the degree of Doctor of Philosophy. Human computation is the study of systems where humans perform a major part of the computation or are an integral part of the overall computational process. The ESP Game, for example, is a human computation system that maps images to tags, by engaging humans to play a game in which they are rewarded each time they agree on a description for an image. It was shown that these so-called Games with a Purpose are a reliable way to quickly collect millions of accurate image descriptors, which can then used to index images and facilitate search. However, most existing human computation systems operate without any machine intervention. Likewise, very few supervised learning systems are taking advantage of these powerful new platforms to elicit help from human teachers. It is therefore largely unknown what more a human computation system can achieve with machines in the loop. This thesis is centered around the problem of attribute learning – using the joint effort of human game players and machine learning algorithms to determine that a piece of music is “soothing”, that the bird in an image “has a red beak”, or that Ernest Hemingway is an “Nobel Prize winning author”. In particular, our work focuses on two aspects of the problem – how to acquire attributes and attribute values from human computers using incentive-compatible game mechanisms, and what active learning strategies to employ for attribute and attribute value acquisition.

This article describes a novel approach to linguistic field research consisting in exploiting the self-regulation of a market for collecting data on language use. The market is conceived as an output-agreement game with a purpose called Borsa Parole. The agreement can be traded with by the players what makes it adjustable. Borsa Parole has been conceived and is deployed for a linguistic study on the divergence of Italian dialects and vernaculars.

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