We consider crowdsourcing problems where the users are asked to provide evaluations for items; the user evaluations are then used directly, or aggregated into a consensus value. Lacking an incentive scheme, users have no motive in making effort in completing the evaluations, providing inaccurate answers instead. We propose incentive schemes that are truthful and cheap: truthful as the optimal user behavior consists in providing accurate evaluations, and cheap because the truthfulness is achieved with little overhead cost. We consider both discrete evaluation tasks, where an evaluation can be done either correctly, or incorrectly, with no degrees of approximation in between, and quantitative evaluation tasks, where evaluations are real numbers, and the error is measured as distance from the correct value. For both types of tasks, we propose hierarchical incentive schemes that can be effected with a small amount of additional evaluations, and that scale to arbitrarily large crowd sizes: they have the property that the strength of the incentive does not weaken with increasing hierarchy depth. Interestingly, we show that for these schemes to work, the only requisite is that workers know their place in the hierarchy in advance.
The work in @cite_14 studies the problem of incentives for truthfulness in a setting where persons vote other persons for a position. The analysis derives a randomized approximation technique to obtain the higher voted persons. The technique is strategyproof, that is, voters (which are also candidates) cannot game the system for their own benefit. The setting of this analysis is significantly different from ours, as the limiting assumption is that the sets of voters and votees are identical. Also, the study focuses on obtaining the top- @math voted items, while in our setting we do not necessarily rank items.
@cite_14
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Typical voting rules do not work well in settings with many candidates. If there are just several hundred candidates, then even a simple task such as choosing a top candidate becomes impractical. Motivated by the hope of developing group consensus mechanisms over the internet, where the numbers of candidates could easily number in the thousands, we study an urn-based voting rule where each participant acts as a voter and a candidate. We prove that when participants lie in a one-dimensional space, this voting protocol finds a @math approximation of the Condorcet winner with high probability while only requiring an expected @math comparisons on average per voter. Moreover, this voting protocol is shown to have a quasi-truthful Nash equilibrium: namely, a Nash equilibrium exists which may not be truthful, but produces a winner with the same probability distribution as that of the truthful strategy. We consider approval voting elections in which each voter votes for a (possibly empty) set of candidates and the outcome consists of a set of k candidates for some parameter k, e.g., committee elections. We are interested in the min-imax approval voting rule in which the outcome represents a compromise among the voters, in the sense that the maximum distance between the preference of any voter and the outcome is as small as possible. This voting rule has two main drawbacks. First, computing an outcome that minimizes the maximum distance is computationally hard. Furthermore, any algorithm that always returns such an outcome provides incentives to voters to misreport their true preferences. In order to circumvent these drawbacks, we consider approximation algorithms, i.e., algorithms that produce an outcome that approximates the minimax distance for any given instance. Such algorithms can be considered as alternative voting rules. We present a polynomial-time 2-approximation algorithm that uses a natural linear programming relaxation for the underlying optimization problem and deterministically rounds the fractional solution in order to compute the outcome; this result improves upon the previously best known algorithm that has an approximation ratio of 3. We are furthermore interested in approximation algorithms that are resistant to manipulation by (coalitions of) voters, i.e., algorithms that do not motivate voters to misreport their true preferences in order to improve their distance from the outcome. We complement previous results in the literature with new upper and lower bounds on strategyproof and group-strategyproof algorithms. Plurality voting is perhaps the most commonly used way to aggregate the preferences of multiple voters. The purpose of this paper is to provide a comprehensive study of people's voting behaviour in various online settings under the Plurality rule. Our empirical methodology consisted of a voting game in which participants vote for a single candidate out of a given set. We implemented voting games that replicate two common real-world voting scenarios: In the first, a single voter votes once after seeing a large pre-election poll. In the second game, several voters play simultaneously, and change their vote as the game progresses, as in small committees. The winning candidate in each game (and hence the subject's payment) is determined using the plurality rule. For each of these settings we generated hundreds of game instances, varying conditions such as the number of voters and their preferences. We show that people can be classified into at least three groups, two of which are not engaged in any strategic behavior. The third and largest group tends to select the natural "default" action when there is no clear strategic alternative. When an active strategic decision can be made that improves their immediate payoff, people usually choose that strategic alternative. Our study has insight for multi-agent system designers in uncovering patterns that provide reasonable predictions of voters' behaviors, which may facilitate the design of agents that support people or act autonomously in voting systems. We theoretically and experimentally study voter behavior in a setting characterized by plurality rule and mandatory voting, where voters choose from three options. We are interested in the occurrence of strategic voting in an environment where Condorcet cycles may occur. In particular, we focus on how information about the distribution of preferences affects strategic behavior. We also vary the relative importance of the second preferred option to investigate how this affects the strategic vote. Quantal response equilibrium analysis is used to analyze the game and proves to be a good predictor for the experimental data. Our results indeed show that strategic voting arises, the extent of which depends on (i) the availability of information; (ii) the relative importance of the intermediate candidate; (iii) the electorate's relative support for one's preferred candidate; and (iv) the relative position of the plurality-supported candidate in a voter's preference ordering. Our results show that information serves as a coordination device where strategic voting does not harm the plurality-preferred candidate's chances of winning.