Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposes a framework for learning Granger causality with the goal of finding interaction rules from trajectory data of multiple animals. The framework has an "augmented behavior model" based on a conceptual behavior model studied in [53]. The experiments are on a range of datasets, from synthetic to real data of mice, birds, and bats. The main baseline of comparison is GVAR [44], which this method compares against for synthetic data and on real mice data. Based on the experiments, the interaction rules found are in the form of attraction, no interaction, or repulsion between different agents.
main_review: 1.  I appreciate that the authors tested on a range of datasets. However, I feel the results method could be more clear. For example, in Figure 2A, the authors say that "our method extracted a significantly larger duration in the same cage than that in the different cages", but the figure shows that the duration of same cage is lower for both attractive and repulsion, so this point is not clear to me. The y-axis between this method and GVAR is also significantly different - can the authors comment on this? Also, what does the "*" mean in Figure 2A? Additionally, the authors mention that the model is interpretable (ex: lines 8, 10), but there are many ways to interpret whether a model is interpretable and I'm not sure where interpretability is demonstrated in the experiments (is it by the duration of interaction & non-interaction?). Could the authors clarify this point? 
2. Overall, I found the methods section difficult to understand. Perhaps a diagram or pseudo-code of the algorithm (ex, as provided by GVAR [44] in Algorithm 1) might enable the authors to more clearly communicate their setup to a broader range of audiences who may not be familiar with this sub-field. 
3.  Line 291: "for application to multi-animal trajectory datasets that can only be measured in small quantities". Why can multi-animal trajectory data only be in small quantities? Some labs are releasing large amounts of trajectory data collected over time. I agree that it is difficult and time-consuming to measure this but I don't agree that these datasets can only be in small quantities. 

Post Rebuttal Update:

I've read the other reviews and the author response, and my current rating is raised to borderline, learning towards accept (assuming the authors will apply all the changes promised in the rebuttal). This is because the authors have clarified some of my points of confusion with the paper (methods as well as experiments). I still feel the paper could be presented more clearly, but the authors have mentioned adding a figure or a pseudo-code of their algorithm, which I feel would improve clarity of the methods section substantially. I agree with reviewer hrQV that if all the amendments in the rebuttal are applied to the paper, the revision would be stronger.


limitations_and_societal_impact: I didn't see where the authors mentioned the limitations of their method - would be great if the authors could point me in the right direction. There is a paragraph in line 411 addressing societal impacts.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The manuscript proposes a hybrid method for learning Granger causality that combines data-driven learning methods and theory-based behavioral models for the goal of modeling multi-agent behavior. 

The main motivation is that current models either do not consider the data generation process behind the observed time series or if they do, they are tailored to specific animal species, thus having generalization limitations.

More specifically, a Self-Explainable Neural Network (SENN) -like model is used where scientific knowledge on multi-agent interactions is integrated. In this regard, key contributions of the proposed method include: a theory-guided loss function to regularize training and a motion and navigation function Experiments on synthetic and realistic data show the effectiveness of the proposed method.
main_review: The proposed method is sound and different design decisions behind are properly motivated. The content of the manuscript is relatively clear and has a good flow.
 
In addition, the proposed method was tested on real-world datasets, where it proved effective at validating observations already made in other fields.
 
My main criticisms with the manuscript are the following:


- C1: The text in l.118-119 gives the impression that models that learn parameters or rules in a purely data-driven manner are inherently not interpretable. I suggest rephrasing this sentence to avoid this confusion since  this this statement is clearly not accurate.
 
 
- C2: The introduction of the manuscript and presentation of the proposed method were properly motivated and detailed. While these are desirable properties for a manuscript, those two parts are quite extended when compared to the experimental part of the manuscript. Relevant aspects of the conducted experiments and results are delegated to the Appendix. For instance, the ablation study in Appendix G shows the effect that the proposed theory-guided regularization $L_{TG}$ and learning navigation $F^k_N$ and motion $F^k_M$ functions (the main contributions of the manuscript) have on performance. This is a critical experiment that should be in the main body of the paper.
Due to delegations like this, the manuscript does not feel as a self contained document.
  
- C3: In l.311-313  it is stated that for the baselines it is not obvious how to handle multi-dimensional time series
In the one hand, the capability of handling multi-dimensional data it is a strength of the your method. I would suggest emphasizing this strength in the manuscript. 
On the other hand, the observation (l.311-313) makes me wonder whether the selected baselines are the most appropriate ones for a fair comparison with respect to the proposed method.

- C4: For the experiments in the real multi-animal trajectory datasets, in l.358-359 it is stated that since there is no ground truth for real-world data, the hyperparameters of the boid simulation dataset are re-used.
I was wondering, how applicable is the re-use of these  parameters when going from a simplistic synthetic setting to a more realistic one? Are there potential side-effects that could be expected? Did you observe any problem, possibly caused by this factor, during the execution of your experiments on real-world data?

- C5: In several parts of the manuscript it is stated that through the proposed method novel biological insights were obtained. I was wondering were these novel insights validated by experts in the respective fields?

**Post Rebuttal Update**
I thank the author for addressing my review on their rebuttal. Having read all the reviews and the feedback provided by the authors, this is my current position with respect to the manuscript:

Overall the authors addressed to a good extent most of the weak points and criticisms raised on my review, which will be included in a revised version of the submitted manuscripts.

I agree with reviewer C9GF on that some parts of the manuscript could use some extra attention in order to improve their presentation.

The ethical reviewers share my opinion on that while the proposed method poses some potential risks, this are not strong enough to reject it. More importantly, these are aspects that could be easily addressed with a proper discussion either in the manuscript or in its appendix.

On the optimistic side, I believe the manuscript has the potential to be better given that all the amendments promised in the rebuttal are applied in the revised version. On the pessimistic side, I am not sure whether it would be possible to include all the possible amendments given the space limitations imposed in the manuscript.

Keeping the previous points, I am in favor of increasing initial rating towards "6: Marginally above the acceptance threshold" but will not push for the acceptance of the manuscript. In this regard, I am more in favor of trying to improve the current weak aspects of the manuscript rather than trying to get it accepted in its current form. (This observation seems to be shared by C9GF)


limitations_and_societal_impact: 
Regarding potential negative applications of the proposed method. The proposed method has a clear negative behind tracking groups of individuals over time, be it in video recordings or via their activities and potential interactions in social networks. In this regards, the manuscript limits itself to provide a very superficial pointer towards fairness issues when operating on top of data from individuals.
While I do not find a potential dual use issue a barrier to stop the publication of a good idea, I do believe that these issues should be at least acknowledged in a proper manner. I would encourage the authors, in the appendix,  to discuss at length potential problems regarding the tracking of collective activities in social networks.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: In this paper, the authors propose a method for learning rules of interaction between individuals from　trajectory data of animals. Their method is based on the Granger causality methodology for time series data but differs from previous studies in that it introduces a new network architecture and a regularization term based on the theory of animal behavior, which enables more accurate learning. The method has been validated using synthetic datasets and then applied to real animal behavior data to gain new knowledge about animal behavior.
main_review: ### Strengths

* In-depth and extensive experiments. After validating the method on two types of synthetic data, the authors apply the method to real animal movement trajectory data. In the real data experiments, the authors first check whether the results of the proposed method are consistent with existing knowledge on mice data, and then apply the method to birds and bats data to gain new knowledge. All of these experiments have a clear purpose and work well to present the advantages and features of the proposed method.

* The datasets used in the experiment. The animal trajectory datasets are collected for this paper, which is rare in the machine learning community. It is interesting to see the new insights derived by the proposed method from such data.

* This paper is well-written; the problems, the backgrounds, and the contributions are clearly stated with appropriate citations. 

### Weaknesses

* Small novelty of the method. It is claimed that the novelty of the method is the introduction of a network architecture and a regularization term based on the theory of animal behavior to the existing Granger causality estimation method, but this in itself is a straightforward combination of previous methods and has little originality. 

* Validation has only been done on datasets where the problem size is relatively small. It would be of great interest to analyze whether it is possible to successfully find Granger causality on larger datasets, what the computation time would be, and whether any other problems would arise.

### Other comments
* Are the discoveries about growing birds and wild bats new discoveries or are they a validation of previous discoveries?  In particular, for wild bats, it would be good to compare and discuss with the contents of [57].

* This paper may possibly be preferred more at data mining conferences such as KDD than at machine learning conferences such as NeurIPS. 
limitations_and_societal_impact: Limitations are not written by the authors, but it is not clear whether the method can be applied to large datasets. There seems to be no negative impact on society.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: The proposed paper introduces a novel approach for learning the Granger causality based on augmented behavioral models. The objective of the discussed method is to extract interaction rules from real-world multi-agent trajectory data with neural networks. The main contributions of this work are a framework for learning Granger causality, methods for efficient and interpretable learning, and the decomposition of motion and navigation. The method is evaluated based on a number of experiments and based on datasets of mice, flies, birds, and bats.   

main_review: Overall, this is an interesting paper that has most things going for it. The proposed framework is technically-sound, the experiments are well thought-out, and the results are promising. Furthermore, the topic the paper addresses is important and I appreciate the interdisciplinary character of this work. However, the paper is somewhat dense and many sections are not easy to comprehend. I have the impression that more care could be taken in providing additional details and explanations across Section 3 and 4. Especially, the motivation for developing interpretable behavior models comes too short and interpretability of the proposed framework is not assessed. Moreover, several sentences show grammar and spelling mistakes that need to be taken care of (examples provided below). So in conclusion, I think this paper could be accepted if proper care is taken in preparing the final version of this work. However, the paper would also benefit from a more thorough revision outside the review cycle from NeurIPS. Therefore, I am not fully too enthusiastic about accepting this work. 

Specific comments:  

- L1: Is 'moving sequences' really the correct meaning here? Or should it be 'movement sequences'? Similar in L407 it doesn't seem 'moving sequence' is the right grammar.

- L27, L83: For the less movement-ecology-versed reader it would be helpful to provide more information about what is meant by 'generative process in biological multi-agent trajectories' or the 'generative process in animal behaviors. 

- L88: What is meant by 'we incorporate the structures into the base model of a CG framework'? Sentences like this are too vague and need to be clarified. What is meant by 'structures'?

- In Sections 3 and 4, I am missing a clearer description of the model and what components are actually being learned. Perhaps an overview figure would help to make this more clear. 

- L70 'an multi layer perceptrons' --> 'a ... perceptron'

- L71 and L72: 'some loss', 'some fixed function'. This sounds too vague and it would be helpful to be more specific here. 

- L103: A clearer description as to why only one time series is considered needs to be provided here. It is also not clear based on the description provided in Line 653 as it is mostly a copy from the main text.

- L105: While I understand the motivation to make the contributions of f_M, f_N, and f_U interpretable -- which is in fact an interesting objective -- it appears somewhat out of context and more details are necessary. As is the paragraph (L103-112) is too difficult to understand. Similar in Line 120. What is the goal for making f_* interpretable? What this goal accomplished?

- L131-132, L134-135: These sentences read too repetitive. 

- In Section 4.1. it is not clear what is actually learned. What is the model architecture that is being trained?

- L346: 'were sensitive the sign as shown' --> rephrase.

-L649: If i am not mistaken u_t is not being referenced anywhere. 
limitations_and_societal_impact: Limitations of the method are not discussed. In the Conclusion the authors suggest that their method could have negative impact, but the discussion remains rather vague. Both should be prepared appropriately prior to publishing this work.
review_title: null

Meta-review: The paper proposes a hybrid method for learning Granger causality that combines data-driven learning methods and theory-based behavioral models for the goal of modeling multi-agent behavior. The paper addresses an important problem and can stimulate further research in this direction.
Initial reviews were split on the technical contribution and also the technique being a combination of existing methods. 
However, after the discussion and rebuttal the reviewers have turned more positive about the ideas presented in the paper. 
The AC echoes the view point in this case that combining existing methods is often challenging and can reveal important scientific insights.
Overall after the discussion all reviewers are in agreement about the value of the contribution and recommend acceptance. 
Decision: Accept (Poster)