To achieveour result, westartfrom theapproximatelinear programming(ALP) approach where the value function is approximated using the feature vectors.

LP-based techniques struggling to scale favorably in large instances.

D () has lower space complexity than the original problem or can be minimized more efficiently.

We introduce and study the problem of detecting whether an agent is updating their prior beliefs given new evidence in an optimal way that is Bayesian, or whether they are biased towards their own prior. In our model, biased agents form posterior beliefs that are a convex combination of their prior and the Bayesian posterior, where the more biased an agent is, the closer their posterior is to the prior. Since we often cannot observe the agent's beliefs directly, we take an approach inspired by information design . Specifically, we measure an agent's bias by designing a signaling scheme and observing the actions the agent takes in response to different signals, assuming that the agent maximizes their own expected utility. Our goal is to detect bias with a minimum number of signals. Our main results include a characterization of scenarios where a single signal suffices and a computationally efficient algorithm to compute optimal signaling schemes.

They iteratively modify and resolve linear programs to conduct a depth-first search from any node in the search tree. Existing divers rely on generic decision rules that fail to exploit structural commonality between similar problem instances that often arise in practice.

Neural Information Processing Systems http://nips.cc/

Recently, Kleiman and Page [ 2019 ] used the classical U-Statistic to extend the definition to multiclass settings that alleviates such common pitfalls.

Neural Information Processing Systems http://nips.cc/