4,191 research outputs found
Human Computation and Convergence
Humans are the most effective integrators and producers of information,
directly and through the use of information-processing inventions. As these
inventions become increasingly sophisticated, the substantive role of humans in
processing information will tend toward capabilities that derive from our most
complex cognitive processes, e.g., abstraction, creativity, and applied world
knowledge. Through the advancement of human computation - methods that leverage
the respective strengths of humans and machines in distributed
information-processing systems - formerly discrete processes will combine
synergistically into increasingly integrated and complex information processing
systems. These new, collective systems will exhibit an unprecedented degree of
predictive accuracy in modeling physical and techno-social processes, and may
ultimately coalesce into a single unified predictive organism, with the
capacity to address societies most wicked problems and achieve planetary
homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added
references to page 1 and 3, and corrected typ
Human Computation and Economics
This article is devoted to economical aspects of Human Computation (HC) and
to perspectives of HC in economics. As of economical aspects of HC, it is first
observed that much of what makes HC systems effective is economical in nature
suggesting that complexity being reconsidered as a âHC complexityâ and the conception
of efficient HC systems as a âHC economicsâ. This article also points to the
relevance of HC in the development of standard software and to the importance of
competition in HC systems. As of HC in economics, it is first argued that markets
can be seen as HC systems avant la lettre. Looking more closely at financial markets,
the article then points to a speed differential between transactions and credit
risk awareness that compromises the efficiency of financial markets. Finally, a HCbased
credit risk rating is proposed that, overcoming the afore mentioned speed
differential, holds promise for better functioning financial markets
Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation
A human computation system can be viewed as a distributed system in which the
processors are humans, called workers. Such systems harness the cognitive power
of a group of workers connected to the Internet to execute relatively simple
tasks, whose solutions, once grouped, solve a problem that systems equipped
with only machines could not solve satisfactorily. Examples of such systems are
Amazon Mechanical Turk and the Zooniverse platform. A human computation
application comprises a group of tasks, each of them can be performed by one
worker. Tasks might have dependencies among each other. In this study, we
propose a theoretical framework to analyze such type of application from a
distributed systems point of view. Our framework is established on three
dimensions that represent different perspectives in which human computation
applications can be approached: quality-of-service requirements, design and
management strategies, and human aspects. By using this framework, we review
human computation in the perspective of programmers seeking to improve the
design of human computation applications and managers seeking to increase the
effectiveness of human computation infrastructures in running such
applications. In doing so, besides integrating and organizing what has been
done in this direction, we also put into perspective the fact that the human
aspects of the workers in such systems introduce new challenges in terms of,
for example, task assignment, dependency management, and fault prevention and
tolerance. We discuss how they are related to distributed systems and other
areas of knowledge.Comment: 3 figures, 1 tabl
Finish Them!: Pricing Algorithms for Human Computation
Given a batch of human computation tasks, a commonly ignored aspect is how
the price (i.e., the reward paid to human workers) of these tasks must be set
or varied in order to meet latency or cost constraints. Often, the price is set
up-front and not modified, leading to either a much higher monetary cost than
needed (if the price is set too high), or to a much larger latency than
expected (if the price is set too low). Leveraging a pricing model from prior
work, we develop algorithms to optimally set and then vary price over time in
order to meet a (a) user-specified deadline while minimizing total monetary
cost (b) user-specified monetary budget constraint while minimizing total
elapsed time. We leverage techniques from decision theory (specifically, Markov
Decision Processes) for both these problems, and demonstrate that our
techniques lead to upto 30\% reduction in cost over schemes proposed in prior
work. Furthermore, we develop techniques to speed-up the computation, enabling
users to leverage the price setting algorithms on-the-fly
Evaluating Singleplayer and Multiplayer in Human Computation Games
Human computation games (HCGs) can provide novel solutions to intractable
computational problems, help enable scientific breakthroughs, and provide
datasets for artificial intelligence. However, our knowledge about how to
design and deploy HCGs that appeal to players and solve problems effectively is
incomplete. We present an investigatory HCG based on Super Mario Bros. We used
this game in a human subjects study to investigate how different social
conditions---singleplayer and multiplayer---and scoring
mechanics---collaborative and competitive---affect players' subjective
experiences, accuracy at the task, and the completion rate. In doing so, we
demonstrate a novel design approach for HCGs, and discuss the benefits and
tradeoffs of these mechanics in HCG design.Comment: 10 pages, 4 figures, 2 table
Personalized human computation
Significant effort in machine learning and information retrieval has been devoted to identifying personalized content such as recommendations and search results.
Personalized human computation has the potential to go beyond existing techniques like collaborative filtering to provide personalized results on demand, over personal data, and for complex tasks. This work-in-progress compares two approaches to personalized human computation. In both, users annotate a small set of training examples which are then used by the crowd to annotate unseen items. In the first approach, which we call taste-matching, crowd members are asked to annotate the same set of training examples, and the ratings of similar users on other items are then used to infer personalized ratings. In the second approach, taste-grokking, the crowd is presented with the training examples and asked to use them predict the ratings of the target user on other items
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