Crowds, not Drones: Modeling Human Factors in Interactive Crowdsourcing

International audienceIn this vision paper, we propose SmartCrowd, an intelligent and adaptive crowdsourcing framework. Contrary to existing crowdsourcing systems, where the process of hiring workers (crowd), learning their skills, and evaluating the accuracy of tasks they perform are fragmented, siloed, and often ad-hoc, SmartCrowd foresees a paradigm shift in that process, considering unpredictability of human nature, namely human factors. SmartCrowd offers opportunities in making crowdsourcing intelligent through iterative interaction with the workers, and adaptively learning and improving the underlying processes. Both existing (majority of which do not require longer engagement from volatile and mostly non-recurrent workers) and next generation crowdsourcing applications (which require longer engagement from the crowd) stand to benefit from SmartCrowd. We outline the opportunities in SmartCrowd, and discuss the challenges and directions, that can potentially revolutionize the existing crowdsourcing landscape

Crowds, not Drones: Modeling Human Factors in Interactive Crowdsourcing

International audienceIn this vision paper, we propose SmartCrowd, an intelligent and adaptive crowdsourcing framework. Contrary to existing crowdsourcing systems, where the process of hiring workers (crowd), learning their skills, and evaluating the accuracy of tasks they perform are fragmented, siloed, and often ad-hoc, SmartCrowd foresees a paradigm shift in that process, considering unpredictability of human nature, namely human factors. SmartCrowd offers opportunities in making crowdsourcing intelligent through iterative interaction with the workers, and adaptively learning and improving the underlying processes. Both existing (majority of which do not require longer engagement from volatile and mostly non-recurrent workers) and next generation crowdsourcing applications (which require longer engagement from the crowd) stand to benefit from SmartCrowd. We outline the opportunities in SmartCrowd, and discuss the challenges and directions, that can potentially revolutionize the existing crowdsourcing landscape

What prize is right? How to learn the optimal structure for crowdsourcing contests

In crowdsourcing, one effective method for encouraging par-ticipants to perform tasks is to run contests where participants compete against each other for rewards. However, there are numerous ways to implement such contests in specific projects. They could vary in their structure (e.g., performance evaluation and the number of prizes) and parameters (e.g., the maximum number of participants and the amount of prize money). Additionally, with a given budget and a time limit, choosing incentives (i.e., contest structures with specific parameter values) that maximise the overall utility is not trivial, as their respective effectiveness in a specific project is usually unknown a priori. Thus, in this paper, we propose a novel algorithm, BOIS (Bayesian-optimisation-based incentive selection), to learn the optimal structure and tune its parameters effectively. In detail, the learning and tuning problems are solved simultaneously by using online learning in combination with Bayesian optimisation. The results of our extensive simulations show that the performance of our algorithm is up to 85% of the optimal and up to 63% better than state-of-the-art benchmarks

Behavioral Mechanism Design: Optimal Contests for Simple Agents

Incentives are more likely to elicit desired outcomes when they are designed based on accurate models of agents' strategic behavior. A growing literature, however, suggests that people do not quite behave like standard economic agents in a variety of environments, both online and offline. What consequences might such differences have for the optimal design of mechanisms in these environments? In this paper, we explore this question in the context of optimal contest design for simple agents---agents who strategically reason about whether or not to participate in a system, but not about the input they provide to it. Specifically, consider a contest where $n$ potential contestants with types $(q_i,c_i)$ each choose between participating and producing a submission of quality $q_i$ at cost $c_i$, versus not participating at all, to maximize their utilities. How should a principal distribute a total prize $V$ amongst the $n$ ranks to maximize some increasing function of the qualities of elicited submissions in a contest with such simple agents? We first solve the optimal contest design problem for settings with homogenous participation costs $c_i = c$. Here, the optimal contest is always a simple contest, awarding equal prizes to the top $j^*$ contestants for a suitable choice of $j^*$. (In comparable models with strategic effort choices, the optimal contest is either a winner-take-all contest or awards possibly unequal prizes, depending on the curvature of agents' effort cost functions.) We next address the general case with heterogeneous costs where agents' types are inherently two-dimensional, significantly complicating equilibrium analysis. Our main result here is that the winner-take-all contest is a 3-approximation of the optimal contest when the principal's objective is to maximize the quality of the best elicited contribution.Comment: This is the full version of a paper in the ACM Conference on Economics and Computation (ACM-EC), 201

Informational Substitutes

We propose definitions of substitutes and complements for pieces of information ("signals") in the context of a decision or optimization problem, with game-theoretic and algorithmic applications. In a game-theoretic context, substitutes capture diminishing marginal value of information to a rational decision maker. We use the definitions to address the question of how and when information is aggregated in prediction markets. Substitutes characterize "best-possible" equilibria with immediate information aggregation, while complements characterize "worst-possible", delayed aggregation. Game-theoretic applications also include settings such as crowdsourcing contests and Q\&A forums. In an algorithmic context, where substitutes capture diminishing marginal improvement of information to an optimization problem, substitutes imply efficient approximation algorithms for a very general class of (adaptive) information acquisition problems. In tandem with these broad applications, we examine the structure and design of informational substitutes and complements. They have equivalent, intuitive definitions from disparate perspectives: submodularity, geometry, and information theory. We also consider the design of scoring rules or optimization problems so as to encourage substitutability or complementarity, with positive and negative results. Taken as a whole, the results give some evidence that, in parallel with substitutable items, informational substitutes play a natural conceptual and formal role in game theory and algorithms.Comment: Full version of FOCS 2016 paper. Single-column, 61 pages (48 main text, 13 references and appendix

Modeling, enacting, and integrating custom crowdsourcing processes

Crowdsourcing (CS) is the outsourcing of a unit of work to a crowd of people via an open call for contributions. Thanks to the availability of online CS platforms, such as Amazon Mechanical Turk or CrowdFlower, the practice has experienced a tremendous growth over the past few years and demonstrated its viability in a variety of fields, such as data collection and analysis or human computation. Yet it is also increasingly struggling with the inherent limitations of these platforms: each platform has its own logic of how to crowdsource work (e.g., marketplace or contest), there is only very little support for structured work (work that requires the coordination of multiple tasks), and it is hard to integrate crowdsourced tasks into stateof-the-art business process management (BPM) or information systems. We attack these three shortcomings by (1) developing a flexible CS platform (we call it Crowd Computer, or CC) that allows one to program custom CS logics for individual and structured tasks, (2) devising a BPMN-based modeling language that allows one to program CC intuitively, (3) equipping the language with a dedicated visual editor, and (4) implementing CC on top of standard BPM technology that can easily be integrated into existing software and processes. We demonstrate the effectiveness of the approach with a case study on the crowd-based mining of mashup model patterns