reviews: 
Comments: Motivated by behavioral economics, this work proposes to investigate the combination of bounded rationality and risk aversion to define a regularized game leading to a new risk averse nash equilibrium concept. The main motivation of the paper is to introduce tractable equilibria in all n-player matrix and finite horizon Markov games. Notably, the introduced class of games is ‘tractable’ independently of the game structure while only depending on both levels of bounded rationality and risk aversion. The paper further proposes illustrations to motivate the games from the behavioral economics viewpoint and performs a few simulations to test the algorithms on simple examples. A sample complexity for finite Markov games under the generative model further completes the theoretical results.
Strengths: - Writing is clear overall (I have some concerns for some aspects though, see next section) and the paper is well-organized. The proofs are well exposed in the appendix. 
- Investigating the tractability of games combining bounded rationality and risk aversion is interesting. In particular I find the idea of achieving tractability independently from the payoffs interesting. 
- The paper covers matrix games and their extension to finite horizon Markov games. More importantly the results hold for general sum games.  
- Modelling the behavior of decision making agents and their strategic interaction beyond expected utility theory is interesting on its own.
Weaknesses: - My main concern about this paper is the following question: What exactly makes the introduced games tractable? The paper argues that it is the combination of bounded rationality and risk aversion. I find the focus on tractability rather confusing. 
Risk aversion has its own motivation but it is unclear if it is required here for tractability. To be precise, I think the contribution in this paper is that you can achieve tractability (for computing another solution concept  than NE/CCE relating to a modified game) independently of the knowledge of payoff functions (their bounds I guess). More precisely, I think bounded rationality is already enough to allow for tractability but it should require some assumptions on the payoffs which this paper avoids. Please correct me if I have missed something here. The paper does not exactly present the contribution this way. In particular, I think the following statements would gain in clarity: 

-- l. 111-112: ‘RQE not only incorporates features of human decision-making but are also more amenable to computation than QRE …’ Why? There is no extended discussion about this whereas I believe it is essential to clarify the contributions. 

-- l. 235-238: While risk aversion might not be enough, I think bounded rationality should be enough (e.g. QRE as demonstrated in a number of works) under relevant assumptions on the structure of the game (mainly conditions on the bounded rationality level compared to the magnitude of the utilities/rewards). I find the presentation order confusing here. You start by introducing risk aversion and show its shortcomings as for the tractability of computing equilibria in such risk averse games to motivate the need of something else but equilibria should become tractable only with bounded rationality. 

-- From the title and the abstract (and throughout the paper), the emphasis is on tractability, as if both are actually needed to ‘achieve’ tractability. If this is not the case as bounded rationality is the main enabler, I would maybe highlight less this computational aspect and clarify that the combination brings you the possibility to achieve tractability under a condition on both levels of bounded rationality and risk aversion which is independent of the rewards. How does this condition compare to the one for say QREs? What’s the price to pay and advantages (if any) for RQNEs compared to QREs (beyond the advantage of incorporating risk aversion)? 

- Equilibrium collapse is briefly mentioned in l. 294. I would like to see a more in-depth description of how the results of this paper connect to the prior work regarding this phenomenon. It seems that the extended game you introduce seems to have some zero sum structure under a perhaps more stringent assumption than what you set (\epsilon_1 = \xi_{2,1} and \epsilon_2 = \xi_{1,2}, a condition satisfying your condition), under which J_1 + J_2 + \bar{J}_1 + \bar{J}_2 = 0. This can be seen from setting \lambda = 1/2 in your proof of Theorem 7 p. 21, l. 1133-1146. Is this an observation that connects to prior work about zero sum games and equilibrium collapse? It is also worth mentioning that setting \lambda = 1/2 leads to a condition that is consistent with your extension to the n-player setting (l. 1226-1232). 

- I think the introduction and motivation of (6) and (7) needs some improvement. Could you give more motivation and intuition? 

- The notations $\xi_1^*, \xi_2^*$ are quite confusing, especially that you also use the notations $\tau_1, \tau_2$ in Fig. 1 without defining them in the main part. What do you mean precisely by $\tau_1, \tau_2$ satisfy the conditions of Theorem 2 (l. 328)? How do the \tau s and \xi s relate?I believe one issue in the presentation is that you chose to hide \tau in the definition of the regularizer D without putting it in front of D (as it appears from the appendix). Maybe a solution is just to add the examples to the main part to clarify this and further comment on the relationship between the \xi s and \tau s. 

- Comments about experiments: While the paper focuses on RQREs, experiments do not seem to illustrate the concept. What are the RQREs in this game and why? Are they different from NEs? Why would they be more reasonable? 

- The introduction (and some other parts of the work e.g. l. 772-780), highlights the limitations of CCEs. Then Theorem 2 assumes access to a CCE of the 4-player game (which can be computed with a no-regret algorithm). While this CCE is for the modified (regularized) game and not the original one, why would this alleviate all the limitations that are inherent to CCEs in general and that you highlight in the paper? More specifically, the facts that the set of CCEs can be large, may have support on strictly dominated strategies and that stationary CCEs are PPAD-hard to compute in dynamic general sum games (l. 47-51). For the last point, does it mean that your output is also a nonstationary CCE since you also rely on no-regret algorithms (l. 8 of algorithm 1).   
 
- Dependence on the action space size: a host of theoretical results in the MARL literature have devoted efforts to ‘break’ the ‘curse of multi-agency’ and go from the product of action spaces to the sum. Your sample complexity is exponential in the number of players. While I understand that space might have been constraining, at least a comment is needed here on the theorem. The very last point of the conclusion mentions scalability but this is not clearly delineating the limitations of the result and why. 

- I have several other questions that I would like the authors to address for clarifying their contributions and the technical novelty.

Minor:
- l. 47-48: ‘the set of CCE can be large (introducing an additional problem of equilibrium selection)’: This equilibrium solution problem is already largely relevant for Nash equilibria, I find the argument confusing here. This is even more confusing since you also rely on CCE computation (for a modified game though) in the paper. 
- l. 305-307: H notations do not seem to be used in the main part. 
- l. 377, eq. (9): \tilde P V: the notation is a bit confusing here, I guess it is the usual dynamic programming notation but \tilde{P} \in \Delta_S and V \in R^S is a bit unclear, \tilde{P} is usually a matrix. 
- (24) l. 1045: for the argmax to be unique I guess you need D_i to be strictly convex w.r.t. p_i here. 
- Proposition 1 p. 20: bounded rationality does not seem to play any role in obtaining this result, this is probably worth mentioning.
Recommendation: 8
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 2