The organisers would like to thank all speakers and other participants for a great workshop! We hope you have enjoyed it is much as we have.
The organizing committee
Denny Borsboom, Department of Psychology, University of Amsterdam
Maarten Speekenbrink, Department of Psychology, University of Amsterdam
Ingmar Visser, Department of Psychology, University of Amsterdam
Eric-Jan Wagenmakers, Department of Psychology, University of Amsterdam
Lourens Waldorp, Department of Psychology, University of Amsterdam
Background
When researchers draw conclusions from a limited set of data, they generally use a model to differentiate the replicable, structural information from the non-replicable, idiosyncratic information. The quality of inference thus relates directly to the quality of the model: An appropriate model will capture a lot of the structure and will at the same time treat idiosyncratic information as ‘noise’, thereby maximizing the probability of correct inference. Usually a variety of different models can be applied to the same data set, each model capturing part of the structural information and part of the idiosyncratic information. How should we then determine the relative quality of the various candidate models? How should we base inference on these candidate models? These are the kinds of questions that the field of model selection aims to answer.
Over the last five years, the role of model selection in inference, in model development, and in model testing has received an increasing amount of attention in the literature (e.g., Myung, Forster, & Browne, 2000; Pitt, Kim, & Myung, 2003; Pitt, Myung, & Zhang, 2002). As the objective of model selection is a very general one, namely to optimize the quality of inference, model selection has become a multi-disciplinary endeavor, being applied in diverse fields such as biology, economics, and psychology, and receiving theoretical input from fields such as the philosophy of science, statistics, and general methodology. The present conference will provide a state of the art overview of advances in this methodological field.
References
Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition.
Myung, I. J., Forster, M. R., & Browne, M. W. (Eds.). (2000). Model selection [Special issue].
Pitt, M. A., Kim, W., & Myung, I. J. (2003). Flexibility versus generalizability in model selection.