6,586 research outputs found
Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing
In mobile crowdsourcing (MCS), mobile users accomplish outsourced human
intelligence tasks. MCS requires an appropriate task assignment strategy, since
different workers may have different performance in terms of acceptance rate
and quality. Task assignment is challenging, since a worker's performance (i)
may fluctuate, depending on both the worker's current personal context and the
task context, (ii) is not known a priori, but has to be learned over time.
Moreover, learning context-specific worker performance requires access to
context information, which may not be available at a central entity due to
communication overhead or privacy concerns. Additionally, evaluating worker
performance might require costly quality assessments. In this paper, we propose
a context-aware hierarchical online learning algorithm addressing the problem
of performance maximization in MCS. In our algorithm, a local controller (LC)
in the mobile device of a worker regularly observes the worker's context,
her/his decisions to accept or decline tasks and the quality in completing
tasks. Based on these observations, the LC regularly estimates the worker's
context-specific performance. The mobile crowdsourcing platform (MCSP) then
selects workers based on performance estimates received from the LCs. This
hierarchical approach enables the LCs to learn context-specific worker
performance and it enables the MCSP to select suitable workers. In addition,
our algorithm preserves worker context locally, and it keeps the number of
required quality assessments low. We prove that our algorithm converges to the
optimal task assignment strategy. Moreover, the algorithm outperforms simpler
task assignment strategies in experiments based on synthetic and real data.Comment: 18 pages, 10 figure
Peer Effects and Social Preferences in Voluntary Cooperation
Substantial evidence suggests the behavioral relevance of social preferences and also the importance of social influence effects ("peer effects"). Yet, little is known about how peer effects and social preferences are related. In a three-person gift-exchange experiment we find causal evidence for peer effects in voluntary cooperation: agents' efforts are positively related despite the absence of material payoff interdependencies. We confront this result with major theories of social preferences which predict that efforts are unrelated, or negatively related. Some theories allow for positively-related efforts but cannot explain most observations. Conformism, norm following and considerations of social esteem are candidate explanations.social preferences, voluntary cooperation, peer effects, reflection problem, gift exchange, conformism, social norms, social esteem
Shareholder Wealth Maximization: A Schelling Point
(Excerpt)
Imagine a reality television game show where two contestants begin the game in two different places in New York City. The object of the game is for the two contestants to find each other, but they do not know anything about each other and they have no way of communicating. If they succeed, both contestants win a prize. If they fail, they get nothing. With no ability to explicitly bargain over the meeting, the parties have to make an educated guess about what the other person is most likely to do. Most people, confronted with this sort of tacit coordination game, will attempt the meeting at a major New York City landmark such as the Empire State Building. Absent any other clues as to the optimal equilibrium meeting point, both parties choose a place that is imaginatively unique and intuitive, expecting that the place will also be unique in the otherâs imagination. The Empire State Building stands out not because it is a particularly optimal meeting place, but rather because it is iconic, nearly synonymous with New York City itself. This is called a âfocal point,â or âSchelling point,â after Professor Thomas Schelling.
There are two important observations that arise from the New York City game: first, that people can coordinate without communication and, second, that value-creating outcomes can be achieved despite multiple equilibria and high transaction costs. As to the former, the fact that many more people than would be expected by chance would likely collect the prize illustrates that coordination without communication is possible
Estimating Position Bias without Intrusive Interventions
Presentation bias is one of the key challenges when learning from implicit
feedback in search engines, as it confounds the relevance signal. While it was
recently shown how counterfactual learning-to-rank (LTR) approaches
\cite{Joachims/etal/17a} can provably overcome presentation bias when
observation propensities are known, it remains to show how to effectively
estimate these propensities. In this paper, we propose the first method for
producing consistent propensity estimates without manual relevance judgments,
disruptive interventions, or restrictive relevance modeling assumptions. First,
we show how to harvest a specific type of intervention data from historic
feedback logs of multiple different ranking functions, and show that this data
is sufficient for consistent propensity estimation in the position-based model.
Second, we propose a new extremum estimator that makes effective use of this
data. In an empirical evaluation, we find that the new estimator provides
superior propensity estimates in two real-world systems -- Arxiv Full-text
Search and Google Drive Search. Beyond these two points, we find that the
method is robust to a wide range of settings in simulation studies
Contextual Centrality: Going Beyond Network Structures
Centrality is a fundamental network property which ranks nodes by their
structural importance. However, structural importance may not suffice to
predict successful diffusions in a wide range of applications, such as
word-of-mouth marketing and political campaigns. In particular, nodes with high
structural importance may contribute negatively to the objective of the
diffusion. To address this problem, we propose contextual centrality, which
integrates structural positions, the diffusion process, and, most importantly,
nodal contributions to the objective of the diffusion. We perform an empirical
analysis of the adoption of microfinance in Indian villages and weather
insurance in Chinese villages. Results show that contextual centrality of the
first-informed individuals has higher predictive power towards the eventual
adoption outcomes than other standard centrality measures. Interestingly, when
the product of diffusion rate and the largest eigenvalue is
larger than one and diffusion period is long, contextual centrality linearly
scales with eigenvector centrality. This approximation reveals that contextual
centrality identifies scenarios where a higher diffusion rate of individuals
may negatively influence the cascade payoff. Further simulations on the
synthetic and real-world networks show that contextual centrality has the
advantage of selecting an individual whose local neighborhood generates a high
cascade payoff when . Under this condition, stronger homophily
leads to higher cascade payoff. Our results suggest that contextual centrality
captures more complicated dynamics on networks and has significant implications
for applications, such as information diffusion, viral marketing, and political
campaigns
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Evaluation and Optimal Calibration of Purchase Time Recommendation Services
Price Comparison Sites enable customers to make better â more informed, less costly â buying decisions through providing price information and offering buying advice in the form of prediction services. While these services differ to some extent, they are comparable regarding their prediction target and usually monitor every arbitrarily small price decrease. We use a large data set of daily minimum prices for 272 smartphones consisting of 198,560 daily price movements from a Price Comparison Site to show that the standard prediction setting is not optimal. A custom evaluation framework allows the maximization of the achievable savings by altering the calibration of the forecasting service to monitor changes that exceed a certain threshold. Additionally, we show that time series features calculated in a calibration period can be used to obtain precise out of sample estimates of the saving optimal forecasting setting
- âŠ