362,595 research outputs found
Sequential Deliberation in Collective Decision-Making: The Case of the FOMC
A process of deliberation, in which policymakers exchange information prior to formal voting procedures, precedes almost every collective decision. Yet, beyond scarce evidence coming from field and laboratory experiments, few studies have analyzed the role played by sequential deliberation in policy-relevant decision-making bodies. To fill this gap, I estimate an empirical model of policy-making that incorporates social learning via deliberation.
In the model, committee members speak in sequence, allowing them to weight their own information and biases against recommendations made by others. The empirical model is
structurally estimated using historical transcripts from the Federal Open Market Committee (FOMC), which is the body in charge of implementing monetary policy in the United States. I find the process of deliberation significantly changes members’ behavior: a typical FOMC member would modify her policy recommendation in 36% of the meetings after listening to previous speakers, with respect to the scenario where members exclusively follow their private
information. Counterfactual simulations show modest gains of modifying the order of speech on the quality of the committee’s policy choices. Incorporating sequential learning explains the pattern of individual recommendations and collective choices extremely well and improves the fit over behavioral models that ignore deliberation
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition
We present a unified framework for understanding human social behaviors in
raw image sequences. Our model jointly detects multiple individuals, infers
their social actions, and estimates the collective actions with a single
feed-forward pass through a neural network. We propose a single architecture
that does not rely on external detection algorithms but rather is trained
end-to-end to generate dense proposal maps that are refined via a novel
inference scheme. The temporal consistency is handled via a person-level
matching Recurrent Neural Network. The complete model takes as input a sequence
of frames and outputs detections along with the estimates of individual actions
and collective activities. We demonstrate state-of-the-art performance of our
algorithm on multiple publicly available benchmarks
Fostering collective intelligence education
New educational models are necessary to update learning environments to the digitally shared communication and information. Collective intelligence is an emerging field that already has a significant impact in many areas and will have great implications in education, not only from the side of new methodologies but also as a challenge for education. This paper proposes an approach to a collective intelligence model of teaching using Internet to combine two strategies: idea management and real time assessment in the class. A digital tool named Fabricius has been created supporting these two elements to foster the collaboration and engagement of students in the learning process. As a result of the research we propose a list of KPI trying to measure individual and collective performance. We are conscious that this is just a first approach to define which aspects of a class following a course can be qualified and quantified.Postprint (published version
Incentive and stability in the Rock-Paper-Scissors game: an experimental investigation
In a two-person Rock-Paper-Scissors (RPS) game, if we set a loss worth
nothing and a tie worth 1, and the payoff of winning (the incentive a) as a
variable, this game is called as generalized RPS game. The generalized RPS game
is a representative mathematical model to illustrate the game dynamics,
appearing widely in textbook. However, how actual motions in these games depend
on the incentive has never been reported quantitatively. Using the data from 7
games with different incentives, including 84 groups of 6 subjects playing the
game in 300-round, with random-pair tournaments and local information recorded,
we find that, both on social and individual level, the actual motions are
changing continuously with the incentive. More expressively, some
representative findings are, (1) in social collective strategy transit views,
the forward transition vector field is more and more centripetal as the
stability of the system increasing; (2) In the individual behavior of strategy
transit view, there exists a phase transformation as the stability of the
systems increasing, and the phase transformation point being near the standard
RPS; (3) Conditional response behaviors are structurally changing accompanied
by the controlled incentive. As a whole, the best response behavior increases
and the win-stay lose-shift (WSLS) behavior declines with the incentive.
Further, the outcome of win, tie, and lose influence the best response behavior
and WSLS behavior. Both as the best response behavior, the win-stay behavior
declines with the incentive while the lose-left-shift behavior increase with
the incentive. And both as the WSLS behavior, the lose-left-shift behavior
increase with the incentive, but the lose-right-shift behaviors declines with
the incentive. We hope to learn which one in tens of learning models can
interpret the empirical observation above.Comment: 19 pages, 14 figures, Keywords: experimental economics, conditional
response, best response, win-stay-lose-shift, evolutionary game theory,
behavior economic
Using pattern languages to mediate theory–praxis conversations in design for networked learning
Educational design for networked learning is becoming more complex but also more inclusive, with teachers and learners playing more active roles in the design of tasks and of the learning environment. This paper connects emerging research on the use of design patterns and pattern languages with a conception of educational design as a conversation between theory and praxis. We illustrate the argument by drawing on recent empirical research and literature reviews from the field of networked learning
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