Sequential equilibrium in monotone games: theory-based analysis of experimental data

A monotone game is an extensive-form game with complete information, simultaneous moves and an irreversibility structure on strategies. It captures a variety of situations in which players make partial commitments and allows us to characterize conditions under which equilibria result in socially desirable outcomes. However, since the game has many equilibrium outcomes, the theory lacks predictive power. To produce stronger predictions, one can restrict attention to the set of sequential equilibria, or Markov equilibria, or symmetric equilibria, or pure-strategy equilibria. This paper explores the relationship between equilibrium behavior in a class of monotone games, namely voluntary contribution games, and the behavior of human subjects in an experimental setting. We find evidence of both pure- and mixed-strategy equilibria and several key features of the symmetric Markov perfect equilibrium (SMPE) in the data. To judge how well the SMPE fits the data, we estimate a model of Quantal Response Equilibrium (QRE) (McKelvey and Palfrey 1995, 1998) and find that the decision rules of the QRE model are qualitatively very similar to the empirical choice probabilities

Consistency and heterogeneity of individual behavior under uncertainty

By using graphical representations of simple portfolio choice problems, we generate a very rich data set to study behavior under uncertainty at the level of the individual subject. We test the data for consistency with the maximization hypothesis, and we estimate preferences using a two-parameter utility function based on Faruk Gul (1991). This specification provides a good interpretation of the data at the individual level and can account for the highly heterogeneous behaviors observed in the laboratory. The parameter estimates jointly describe attitudes toward risk and allow us to characterize the distribution of risk preferences in the population

Network architecture, salience and coordination

This paper reports the results of an experimental investigation of monotone games with imperfect information. Players are located at the nodes of a network and observe the actions of other players only if they are connected by the network. These games have many sequential equilibria; nonetheless, the behavior of subjects in the laboratory is predictable. The network architecture makes some strategies salient and this in turn makes the subjects’ behavior predictable and facilitates coordination on efficient outcomes. In some cases, modal behavior corresponds to equilibrium strategies

Global and Feature Based Gender Classification of Faces: A Comparison of Human Performance and Computational Models

Original paper can be found at: http://eproceedings.worldscinet.com/9789812701886/9789812701886_0036.html Copyright World Scientific Publishing Company. http://dx.doi.org/10.1142/9789812701886_0036Most computational models for gender classification use global information (the full face image) giving equal weight to the whole face area irrespective of the importance of the internal features. Here, we use a global and feature based representation of face images that includes both global and featural information. We use dimensionality reduction techniques and a support vector machine classifier and show that this method performs better than either global or feature based representations alone.Peer reviewe

A neural network model of visual object recognition impairment after brain damage

Dysfunction of the visual object recognition system in humans is briefly discussed and a basic connectionist model of visual object recognition is introduced. Experimentation in which two variants of this model are lesioned is undertaken. The results suggest that the well documented phenomenon of superordinate preservation is model independent. Differential category specific recognition deficits are also observed in this model, however these are sensitive to each particular variant