On 28th November OxTalks will move to the new Halo platform and will become 'Oxford Events' (full details are available on the Staff Gateway).
There will be an OxTalks freeze beginning on Friday 14th November. This means you will need to publish any of your known events to OxTalks by then as there will be no facility to publish or edit events in that fortnight. During the freeze, all events will be migrated to the new Oxford Events site. It will still be possible to view events on OxTalks during this time.
If you have any questions, please contact halo@digital.ox.ac.uk
This paper introduces a new simulation algorithm for evaluating the log-likelihood and score functions associated with a class of supermodular complete information discrete games. The algorithm allows for payoff function estimation in games with large numbers of players and/or
many binary actions per player (e.g., games with tens of thousands of strategic binary actions). In such cases the likelihood of the observed pure strategy combination may be (i) very small and (ii)
a high dimensional integral with a complex integration region. Direct numerical integration, as well as accept-reject Monte Carlo integration, are computationally impractical in such settings. In contrast, our method allows for accurate likelihood simulation with modest numbers of simulation draws. Use cases include simulated maximum likelihood (SML) parameter estimation in models of technology adoption, peer effects, and strategic network formation.
