Abstract
The success of a human computation system depends critically on the humans in the system actually behaving, or acting, as necessary for the system to function effectively. Since users have their own costs and benefits from participation, they will undertake desirable actions only if properly incentivized to do so: Indeed, while there are a vast number of human computation systems on the Web, the extent of participation and quality of contribution varies widely across systems. How can a game-theoretic approach help understand why, and provide guidance on designing systems that incentivize high participation and effort from contributors?
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Notes
- 1.
Such as furthering science in a Citizen Science project
- 2.
The motivations of contributors in human computation are, naturally, closely related to those for user-generated content; some of the literature on which is discussed in Jian and MacKie-Mason (2012).
- 3.
(Arising from a range of motivations including possibly other-regarding, or ‘altruistic’, preferences)
- 4.
This particular problem is addressed in a model stylized for online crowdsourcing (contests, as well as crowdsourced content as in Q&A forums), in Ghosh and McAfee (2012).
- 5.
Albeit not all systems; peer-grading in online education being a prominent example
- 6.
A number of different equilibrium concepts exist to predict how strategic agents will behave under a given mechanism; see, for instance, Nisan et al. (2007).
- 7.
- 8.
(By choosing which answers to display, and how often or prominently to display them)
- 9.
- 10.
- 11.
- 12.
A Nash equilibrium is a set of strategies, one for each player, such that no player can benefit by deviating from her strategy given the strategy choices of other players; see, for instance, Nisan et al. (2007).
- 13.
A strategy is strictly dominated if there is another strategy that always leads to larger payoffs regardless of other players’ choices, i.e., for all possible strategies of other players.
- 14.
Such as in Duolingo and peer-grading systems
- 15.
Note that these objects can also be the producers themselves, rather than only the content produced, as might be the case when constructing rankings of users based on their contributions in some online community.
- 16.
An example of such a situation, outside of the context of human computation or the Internet, is the election of the pope in the papal conclave.
- 17.
For readers familiar with the voting literature, this setting is a special case of approval voting where the set of voters coincides with the set of options.
- 18.
That is, the set of winners obtains at least 1∕4 as many votes as the k most popular agents
- 19.
For a broad set of general guidelines on incentivizing participation and engagement in online communities, see Kraut et al. (2012).
- 20.
An equilibrium here consists of some level of participation and some level of effort from participants, such that no participant can benefit from either dropping out or choosing to exert a different level of effort, and no non-participant would prefer to participate.
- 21.
For preliminary work on social norms for reputation, see Ho et al. (2012).
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Ghosh, A. (2013). Game Theory and Incentives in Human Computation Systems. In: Michelucci, P. (eds) Handbook of Human Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8806-4_58
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