Discrete choice models with unobserved heterogeneity are commonly used Econometric models for dynamic Economic behavior which have been adopted in practice to predict behavior of individuals and firms from schooling and job choices to strategic decisions in market competition. These models feature optimizing agents who choose among a finite set of options in a sequence of periods and receive choice-specific payoffs that depend on both variables that are observed by the agent and recorded in the data and variables that are only observed by the agent but not recorded in the data. Existing work in Econometrics assumes that optimizing agents are fully rational and requires finding a functional fixed point to find the optimal policy. We show that in an important class of discrete choice models the value function is globally concave in the policy. That means that simple algorithms that do not require fixed point computation, such as the policy gradient algorithm, globally converge to the optimal policy. This finding can both be used to relax behavioral assumption regarding the optimizing agents and to facilitate Econometric analysis of dynamic behavior. In particular, we demonstrate significant computational advantages in using a simple implementation policy gradient algorithm over existing "nested fixed point" algorithms used in Econometrics.
This paper deals with a certain class of models, known as discrete choice models. These models are popular in econometrics, and aim at modelling the complex behavioural patterns of individuals or firms. Entities in these models are typically modelled as rational agents, that behave optimally for reaching their goal of maximizing a certain objective function such as maximizing expected cumulative discounted payoff over a fixed period.
This class of models, modelled as a MDP with choice-specific heterogeneity, is challenging as not all the payoffs received by the agents is externally observable. One solution in this case is finding a balance condition and a functional fixed point to find the optimal policy (closely related to value function iteration), and this is apparently the key idea behind ‘nested fixed point’ methods used in Econometrics.
The paper proposes an alternative. First it identifies a subclass of discrete choice models (essentially MDP with stochastic rewards) where the value function is globally concave in the policy. The consequence of this observation is that a direct method, such as the policy gradient that circumvents explicitly estimating the value function, can (at least in principle) converge to the optimal policy without calculating a fixed point. The authors illustrate computational advantages of this direct approach. Moreover, the generality of the policy gradient method enables the relaxation of extra assumptions regarding the behaviour of agents while facilitating a wider applicability/econometric analysis. 
The key contribution claimed by the paper is the observation that in the class of dynamic discrete choice models with unobserved heterogeneity, the value function is globally concave in the policy. This enables using computationally efficient policy gradient algorithms with convergence guarantees for this class of problems. The authors also claim that the simplicity of policy gradient makes it also a viable model for understanding economic behaviour in econometric analysis, and more broadly for social sciences.
The paper deals with Discrete choice models with unobserved heterogeneity as a special class of MDP’s and is relevant to ICLR. However, the writing style is quite technical and terse -- while I could appreciate the rigour, the authors develop the basic material until page 5 -- Notation is also somewhat non-standard at places (alternating using delta or sigma for a policy and pi for thresholds of an exponential softmax distribution) and makes it harder to see the additional structure from generic MDPs more familiar in RL. I suspect that a reader more familiar with the relevant econometric, marketing and finance literature could follow the model description more easily. 
There are a number of assumptions in the paper, especially 2.4 and 2.5 that relate to the monotonicity and ordering of the states. These assumptions seem to be important in subsequent developments for showing the concavity but they seem to be coming from out of the blue. Unfortunately the authors do not provide any intuition/discussion -- an example problem with these properties would make these assumptions more concrete. I was hoping to find such an example in the empirical application however this section does not make the necessary connections with the theoretical development. There are not even references to basic claims 
done in the abstract, for example, I am not able to find an illustration of ‘significant computational advantages in using a simple implementation policy gradient algorithm over existing “nested fixed point” algorithms used in Econometrics’. The lack of any conclusions makes it also hard for me to appreciate the contributions.
Minor:
Abstract:  
.. Existing work in Econometrics assumes that optimizing agents are fully rational and requires finding a functional fixed point to find the optimal policy. …
Ambiguous sentence: Existing work in Econometrics [...] requires finding a functional fixed point to find the optimal policy.
Theorem 3.1 and elsewhere  Freéchet =>  Fréchet