text: The authors consider a Neural Network where the neurons are treated as rational agents.  In this model, the neurons must pay to observe the activation of neurons upstream.  Thus, each individual neuron seeks to maximize the sum of payments it receives from other neurons minus the cost for observing the activations of other neurons (plus an external reward for success at the task).   While this is an interesting idea on its surface,  the paper suffers from many problems in clarity, motivation, and technical presentation.  It would require very major editing to be fit for publication.  The major problem with this paper is its clarity.  See detailed comments below for problems just in the introduction.  More generally, the paper is riddled with non sequiturs.  The related work section mentions Generative Adversarial Nets.  As far as I can tell, this paper has nothing to do with GANs.  The Background section introduces notation for POMDPs, never to be used again in the entirety of the paper, before launching into a paragraph about apoptosis in glial cells.  There is also a general lack of attention to detail.  For example, the entire network receives an external reward (R_t^{ex}), presumably for its performance on some task.  This reward is dispersed to the the individual agents who receive individual external rewards (R_{it}^{ex}).  It is never explained how this reward is allocated even in the authors’ own experiments.  The authors state that all units playing NOOP is an equilibrium.  While this is certainly believable/expected,  such a result would depend on the external rewards R_{it}^{ex}, the observation costs \sigma_{jit}, and the network topology.  None of this is discussed.  The authors discuss Pareto optimality without ever formally describing what multi-objective function defines this supposed Pareto boundary.  This is pervasive throughout the paper,  and is detrimental to the reader’s understanding.   While this might be lost because of the clarity problems described above,  the model itself is also never really motivated.  Why is this an interesting problem?  There are many ways to create rational incentives for neurons in a neural net.  Why is paying to observe activations the one chosen here?  The neuroscientific motivation is not very convincing to me, considering that ultimately these neurons have to hold an auction.  Is there an economic motivation?  Is it just a different way to train a NN?  Detailed Comments: “In the of NaaA” => remove “of”? “passing its activation to the unit as cost”  => Unclear.  What does this mean? “performance decreases if we naively consider units as agents”  => Performance on what? “.. we demonstrate that the agent obeys to maximize its counterfactual return as the Nash Equilibrium“  => Perhaps, this should be rewritten as “Agents maximize their counterfactual return in equilibrium.  “Subsequently, we present that learning counterfactual return leads the model to learning optimal topology”  => Do you mean  “maximizing” instead of learning.  Optimal with respect to what task? “pure-randomly” => “randomly”  “with adaptive algorithm” => “with an adaptive algorithm” “the connection” => “connections” “In game theory, the outcome maximizing overall reward is named Pareto optimality.”  => This is simply incorrect.