182 research outputs found
Sequential Voting Promotes Collective Discovery in Social Recommendation Systems
One goal of online social recommendation systems is to harness the wisdom of
crowds in order to identify high quality content. Yet the sequential voting
mechanisms that are commonly used by these systems are at odds with existing
theoretical and empirical literature on optimal aggregation. This literature
suggests that sequential voting will promote herding---the tendency for
individuals to copy the decisions of others around them---and hence lead to
suboptimal content recommendation. Is there a problem with our practice, or a
problem with our theory? Previous attempts at answering this question have been
limited by a lack of objective measurements of content quality. Quality is
typically defined endogenously as the popularity of content in absence of
social influence. The flaw of this metric is its presupposition that the
preferences of the crowd are aligned with underlying quality. Domains in which
content quality can be defined exogenously and measured objectively are thus
needed in order to better assess the design choices of social recommendation
systems. In this work, we look to the domain of education, where content
quality can be measured via how well students are able to learn from the
material presented to them. Through a behavioral experiment involving a
simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we
show that sequential voting systems can surface better content than systems
that elicit independent votes.Comment: To be published in the 10th International AAAI Conference on Web and
Social Media (ICWSM) 201
In memoriam Ernst Kullmann, 1931 - 1996
Der Beitrag umfasst neben dem Nachruf auf den Arachnologen Ernst Kullmann von Bertrand Krafft auch Bemerkungen von Peter Jäger und eine umfangreiche Liste der Veröffentlichungen des Verstorbenen
Modeling Human Ad Hoc Coordination
Whether in groups of humans or groups of computer agents, collaboration is
most effective between individuals who have the ability to coordinate on a
joint strategy for collective action. However, in general a rational actor will
only intend to coordinate if that actor believes the other group members have
the same intention. This circular dependence makes rational coordination
difficult in uncertain environments if communication between actors is
unreliable and no prior agreements have been made. An important normative
question with regard to coordination in these ad hoc settings is therefore how
one can come to believe that other actors will coordinate, and with regard to
systems involving humans, an important empirical question is how humans arrive
at these expectations. We introduce an exact algorithm for computing the
infinitely recursive hierarchy of graded beliefs required for rational
coordination in uncertain environments, and we introduce a novel mechanism for
multiagent coordination that uses it. Our algorithm is valid in any environment
with a finite state space, and extensions to certain countably infinite state
spaces are likely possible. We test our mechanism for multiagent coordination
as a model for human decisions in a simple coordination game using existing
experimental data. We then explore via simulations whether modeling humans in
this way may improve human-agent collaboration.Comment: AAAI 201
Modeling human ad hoc coordination
Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore how one can come to believe that other actors will coordinate, and with regard to systems involving humans, an important empirical question is how humans arrive at these expectations. We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. Our algorithm is valid in any environment with a finite state space, and extensions to certain countably infinite state spaces are likely possible. We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-Agent collaboration
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