LJDM Seminars consist of led paper discussions (classic and new
papers), discussions of LJDM members' work, invited external speakers,
and workshops. These focus on judgment and decision making, judgments of likelihood,
reasoning, thinking, problem solving, forecasting, risk perception/communication,
and other related topics. Please contact the organisers Manos Konstantinidis or Neil Bramley to volunteer to host a session or to suggest a speaker. All are
welcome to attend.
Future talks will be advertised in due course
(for an outline see below). To
be automatically updated, you can subscribe to the risk and decision
mailing list. To subscribe, please click "RISK & DECISION LIST". If you have any problems in registration,
please contact David Hardman
. See the bottom of the page
for old schedules.
Unless specified otherwise, all seminars take place on Wednesdays
at 5pm, in Room 313 at the Psychology Department, University College
London (on the corner of Bedford Way, Gordon Square and Torrington
Place, London WC1).Map
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Other seminars at UCL
Forthcoming talks at LJDM
New York University
Causal-knowledge and Information Search During Categorization
This research assessed how causal knowledge influences the order in which classifiers seek information. Undergraduates learned two novel categories. One categoryís features exhibited a common cause network (one feature causes two others), and the other exhibited a common effect network (one feature is caused by two others). One neutral dimension did not take part in any causal relations. Participants chose which of two feature dimensions they would like to see in order to classify an object. Participants preferred to query features involved in two causal relations over those involved in one, which in turn were preferred to those involved in none. In addition, when some features of the to-be-classified item were already known, participants chose to query causally-related dimensions. Existing models of causal-based classification failed to account for these results.
Karin S Moser
University of Roehampton London
Knowledge sharing as social dilemma: Status and feedback moderate expert contributions
Groups and organisations set cooperative goals for their members, yet in reality some team members contribute more than others towards these goals. Experts, in particular, face a social dilemma: From the groupís perspective they should share their knowledge, whereas individually they are better off not sharing their knowledge, because acquiring knowledge is costly. Two experiments tested the hypothesis, derived from indirect reciprocity and competitive altruism theory, that experts contribute more if their status is being recognized. In two experiments with different designs (scenario and virtual team simulation) we manipulated expertise and performance feedback and examined the impact on peopleís contributions in various information-sharing tasks. As predicted, experts contributed more when feedback was individualized and public, thus ensuring status rewards. In contrast, novices contributed more when performance feedback was collective, regardless of whether it was public or private feedback. Implications for theory and practice on knowledge sharing in teams are discussed.
University of Bristol
Bayesian sampling to cluster and explore
I present results from two recent papers in which Bayesian ideas are shown to replicate either desirable or observed behaviour for sequential decision-makers. In the first part of the talk I present and discuss an old and inherently Bayesian idea of Thompson (Biometrika 1933) on how to balance exploration and exploitation in a sequential decision-making problem. When faced with a decision opportunity, a single sample should be drawn from the posterior distribution of the value of each available action, and the action with the highest sampled value should be selected. This ensures that the action with the highest expected value is most likely to be selected, whereas other actions may also be selected, but with probability that decreases with their expected value, and increases with the uncertainty about this value. I will then present a Bayesian clustering model of learning and decision-making suitable for `jumpy but sticky' environments, and show that the model naturally replicates several of the classically paradoxical effects observed in rat decision-making. Joint work with Benedict May and Kevin Lloyd.