Lab Home | Phone | Search | ||||||||
|
||||||||
This is the second of several lectures presenting the fundamentals of how game theoreticians view the world together with extensions reflecting recent experimental results, and reflecting more of a physics / machine learning view of the world. In this lecture, I will quickly review the quantal response equilibrium (QRE) QRE and level K models of human behavior from the first lecture. I will then show how to combine Level K satisficing models with Bayes nets, to predict behavior in multi-stage games. I will illustrate this hybrid model by using it to predict the behavior of pilots in near mid-air collisions. I will then introduce unstructured bargaining. In contrast to the case with concepts like Nash equilibrium, in unstructured bargaining the modeler knows the possible outcomes of the player interactions, and the utilities they assign to those outcomes, but does not know the strategy spaces of the players.
I will end by using the unstructured bargaining analysis to predict how a capitalist society at a QRE of a game would collectively decide to change the parameters of that game. This can be used to compare the performance of capitalist, socialist, and anarchist societies. |