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

Incentives and Efficiency in Uncertain Collaborative Environments

We consider collaborative systems where users make contributions across multiple available projects and are rewarded for their contributions in individual projects according to a local sharing of the value produced. This serves as a model of online social computing systems such as online Q&A forums and of credit sharing in scientific co-authorship settings. We show that the maximum feasible produced value can be well approximated by simple local sharing rules where users are approximately rewarded in proportion to their marginal contributions and that this holds even under incomplete information about the player's abilities and effort constraints. For natural instances we show almost 95% optimality at equilibrium. When players incur a cost for their effort, we identify a threshold phenomenon: the efficiency is a constant fraction of the optimal when the cost is strictly convex and decreases with the number of players if the cost is linear

Learning by volunteer computing, thinking and gaming: What and how are volunteers learning by participating in Virtual Citizen Science?

Citizen Science (CS) refers to a form of research collaboration that engages volunteers without formal scientific training in contributing to empirical scientific projects. Virtual Citizen Science (VCS) projects engage participants in online tasks. VCS has demonstrated its usefulness for research, however little is known about its learning potential for volunteers. This paper reports on research exploring the learning outcomes and processes in VCS. In order to identify different kinds of learning, 32 exploratory interviews of volunteers were conducted in three different VCS projects. We found six main learning outcomes related to different participants' activities in the project. Volunteers learn on four dimensions that are directly related to the scope of the VCS project: they learn at the task/game level, acquire pattern recognition skills, on-topic content knowledge, and improve their scientific literacy. Thanks to indirect opportunities of VCS projects, volunteers learn on two additional dimensions: off topic knowledge and skills, and personal development. Activities through which volunteers learn can be categorized in two levels: at a micro (task/game) level that is direct participation to the task, and at a macro level, i.e. use of project documentation, personal research on the Internet, and practicing specific roles in project communities. Both types are influenced by interactions with others in chat or forums. Most learning happens to be informal, unstructured and social. Volunteers do not only learn from others by interacting with scientists and their peers, but also by working for others: they gain knowledge, new status and skills by acting as active participants, moderators, editors, translators, community managers, etc. in a project community. This research highlights these informal and social aspects in adult learning and science education and also stresses the importance for learning through the indirect opportunities provided by the project: the main one being the opportunity to participate and progress in a project community, according to one's tastes and skills

e-Learning research: emerging issues?

e-Learning research is an expanding and diversifying field of study. Specialist research units and departments proliferate. Postgraduate courses recruit well in the UK and overseas, with an increasing focus on critical and research-based aspects of the field, as well as the more obvious professional development requirements. Following this years launch of a National e-Learning Research Centre, it is timely to debate what the field of study should be prioritising for the future. This discussion piece suggests that the focus should fall on questions that are both clear and tractable for researchers, and likely to have a real impact on learners and practitioners. Suggested questions are based on early findings from a series of JISC-funded projects on e-learning and pedagogy

Reconsidering online reputation systems

Social and socioeconomic interactions and transactions often require trust. In digital spaces, the main approach to facilitating trust has effectively been to try to reduce or even remove the need for it through the implementation of reputation systems. These generate metrics based on digital data such as ratings and reviews submitted by users, interaction histories, and so on, that are intended to label individuals as more or less reliable or trustworthy in a particular interaction context. We suggest that conventional approaches to the design of such systems are rooted in a capitalist, competitive paradigm, relying on methodological individualism, and that the reputation technologies themselves thus embody and enact this paradigm in whatever space they operate in. We question whether the politics, ethics and philosophy that contribute to this paradigm align with those of some of the contexts in which reputation systems are now being used, and suggest that alternative approaches to the establishment of trust and reputation in digital spaces need to be considered for alternative contexts

The Art of Knowledge Exchange: A Results-Focused Planning Guide for Development Practitioners

Designing and implementing knowledge exchange initiatives can be a big undertaking. This guide takes the guesswork out of the process by breaking it down into simple steps and providing tools to help you play a more effective role as knowledge connector and learning facilitator