{"id":53469,"date":"2025-08-20T08:42:27","date_gmt":"2025-08-20T12:42:27","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=53469"},"modified":"2025-11-10T15:41:27","modified_gmt":"2025-11-10T20:41:27","slug":"ams-weekly-seminar-ronnie-sicar","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-ronnie-sicar\/","title":{"rendered":"AMS Weekly Seminar | Ronnie Sicar"},"content":{"rendered":"

Location: <\/strong>Krieger 205<\/p>\n

When:<\/strong> November 13th at 1:30 p.m.<\/p>\n

Title: <\/strong>Mean Field Games of Stochastic Intensity Control<\/span><\/p>\n

Abstract: <\/strong>We discuss some mean field games of stochastic intensity control with applications to ticket pricing, income inequality and cryptocurrency mining.\u00a0 For the first problem, one way to capture both the elastic and stochastic reaction of purchases to price is through a model where sellers control the intensity of a counting process, representing the number of sales thus far. The intensity describes the probabilistic likelihood of a sale, and is a decreasing function of the price a seller sets. A classical model for ticket pricing, which assumes a single seller and infinite time horizon, is by Gallego and van Ryzin (1994) and it has been widely utilized by airlines, for instance. Extending to more realistic settings where there are multiple sellers, with finite inventories, in competition over a finite time horizon is more complicated both mathematically and computationally. We discuss a dynamic mean field game of this type, and some numerical and existence results.<\/span><\/p>\n

Zoom link:<\/strong> https:\/\/wse.zoom.us\/j\/93600407710?pwd=JBL8VsObRxX6MkhdjAUxCadqJDoZrZ.1<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Location: Krieger 205 When: November 13th at 1:30 p.m. Title: Mean Field Games of Stochastic Intensity Control Abstract: We discuss some mean field games of stochastic intensity control with 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