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Title: Learning Deep Mean Field Games for Modeling Large Population Behavior

Paper content:
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space.
A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions.
We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP.
This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning.
Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population.
Nothing takes place in the world whose meaning is not that of some maximum or minimum.(Leonhard
Euler)Major global events shaped by large populations in social media, such as the Arab Spring, the Black Lives Matter movement, and the fake news controversy during the 2016 U.S. presidential election, provide significant impetus for devising new models that account for macroscopic population behavior resulting from the aggregate decisions and actions taken by all individuals BID14 BID1 BID30 . Just as physical
systems behave according to the principle of least action, to which Euler's statement alludes, population behavior consists of individual actions that may be optimal with respect to some objective. The increasing usage
of social media in modern societies lends plausibility to this hypothesis BID27 , since the availability of information enables individuals to plan and act based on their observations of the global population state. For example, a population
's behavior directly affects the ranking of a set of trending topics on social media, represented by the global population distribution over topics, while each user's observation of this global state influences their choice of the next topic in which to participate, thereby contributing to future population behavior (Twitter, 2017) . In general, this feedback
may be present in any system where the distribution of a large population over a state space is observable (or partially observable) by each individual, whose behavior policy generates actions given such observations. This motivates multiple criteria
for a model of population behavior that is learnable from real data:can be specialized to many settings: optimal production rate of exhaustible resources such as oil among many producers BID13 ; optimizing between conformity to popular opinion and consistency with one's initial position in opinion networks BID2 ; and the transition between competing technologies with economy of scale BID19 . Representing agents as a distribution
means that MFG is scalable to arbitrary population sizes, enabling it to simulate real-world phenomenon such as the Mexican wave in stadiums BID13 .As the model detailed in Section 3 will
show, MFG naturally addresses the modeling criteria in our problem context while overcoming limitations of alternative predictive methods. For example, time series analysis builds
predictive models from data, but these models are incapable of representing any motivation (i.e. reward) that may produce a population's behavior policy. Alternatively, methods that employ the underlying
population network structure have assumed that nodes are only influenced by a local neighborhood, do not account for a global state, and may face difficulty in explaining events as the result of any implicit optimization. BID8 BID7 . MFG is unique as a descriptive model
whose solution
tells us how a system naturally behaves according to its underlying optimal control policy. This observation enables us to draw a connection with
the framework of Markov decision processes (MDP) and reinforcement learning (RL) BID31 . The crucial difference from a traditional MDP viewpoint
is that we frame the problem as MFG model inference via MDP policy optimization: we use the MFG model to describe natural system behavior by solving an associated MDP, without imposing any control on the system. MFG offers a computationally tractable framework for adapting
inverse reinforcement learning (IRL) methods BID25 BID35 BID9 , with flexible neural networks as function approximators, to learn complex reward functions that may explain behavior of arbitrarily large populations. In the other direction, RL enables us to devise a data-driven
method for solving an MFG model of a real-world system for temporal prediction. While research on the theory of MFG has progressed rapidly in
recent years, with some examples of numerical simulation of synthetic toy problems, there is a conspicuous absence of scalable methods for empirical validation BID19 BID0 BID2 . Therefore, while we show how MFG is well-suited for the specific
problem of modeling population behavior, we also demonstrate a general data-driven approach to MFG inference via a synthesis of MFG and MDP.Our main contributions are the following. We propose a data-driven approach to learn an MFG model along with
its reward function, showing that research in MFG need not be confined to toy problems with artificial reward functions. Specifically, we derive a discrete time graph-state MFG from general
MFG and provide detailed interpretation in a real-world setting (Section 3). Then we prove that a special case can be reduced to an MDP and show
that finding an optimal policy and reward function in the MDP is equivalent to inference of the MFG model (Section 4). Using our approach, we empirically validate an MFG model of a population
's activity distribution on social media, achieving significantly better predictive performance compared to baselines (Section 5). Our synthesis of MFG with MDP has potential to open new research directions
for both fields.
We have motivated and demonstrated a data-driven method to solve a mean field game model of population evolution, by proving a connection to Markov decision processes and building on methods in reinforcement learning.
Our method is scalable to arbitrarily large populations, because the MFG framework represents population density rather than individual agents, while the representations are linear in the number of MFG states and quadratic in the transition matrix.
Our experiments on real data show that MFG is a powerful framework for learning a reward and policy that can predict trajectories of a real world population more accurately than alternatives.
Even with a simple policy parameterization designed via some domain knowledge, our method attained superior performance on test data.
It motivates exploration of flexible neural networks for more complex applications.An interesting extension is to develop an efficient method for solving the discrete time MFG in a more general setting, where the reward at each state i is coupled to the full population transition matrix.
Our work also opens the path to a variety of real-world applications, such as a synthesis of MFG with models of social networks at the level of individual connections to construct a more complete model of social dynamics, and mean field models of interdependent systems that may display complex interactions via coupling through global states and reward functions.

assistant: text: The authors deal with inference in models of collective behavior by using inverse reinforcement learning to learn the reward functions of agents in the model.