I'm sending an abstract for a full paper to AAAI. These are the criteria "Significance of the problem

Excellent: The social impact problem considered by this paper is significant and has not been adequately addressed by the AI community.
Good: This paper represents a new take on a significant social impact problem that has been considered in the AI community before.
Fair: The social impact problem considered by this paper has some significance and this paper represents a new take on the problem.
Poor: This paper’s contribution was elsewhere: it follows up on an existing problem formulation or introduces a new problem with limited immediate potential for social impact.
Engagement with literature

Excellent: Shows an excellent understanding of other literature on the problem, including that outside computer science.
Good: Shows a strong understanding of other literature on the problem, perhaps focusing on various subtopics or on the CS literature.
Fair: shows a moderate understanding of other literature on the topic, but does not engage in depth.
Poor: Does not engage sufficiently with other literature on the topic.
Novelty of approach

Excellent: Introduces a new model, data gathering technique, algorithm, and/or data analysis technique.
Good: Substantially improves upon an existing model, data gathering technique, algorithm, and/or data analysis technique.
Fair: Makes a moderate improvement to an existing model, data gathering technique, algorithm, and/or data analysis technique.
Poor: This paper’s contribution was elsewhere: it employs existing models, data gathering techniques, algorithms, and/or data analysis techniques (e.g., the paper presents a new experimental design and evaluation procedure).
Justification of approach

Excellent: Thoroughly and convincingly justifies the approach taken, explaining strengths and weaknesses as compared to other alternatives.
Good: The justification of the approach is convincing overall, but could have been more thorough and/or alternatives could have been considered in more detail.
Fair: The justification of the approach is relatively convincing, but has weaknesses.
Poor: The justification of the approach is flawed and/or not convincing.
Quality of evaluation

Excellent: Evaluation was exemplary: data described the real world and was analyzed thoroughly.
Good: Evaluation was convincing: datasets were realistic; analysis was solid.
Fair: Evaluation was adequate, but had significant flaws: datasets were unrealistic and/or analysis was insufficient.
Poor: Evaluation was unconvincing.
Facilitation of follow-up work

Excellent: Excellent facilitation of follow-up work: open-source code; public datasets; and a very clear description of how to use these elements in practice.
Good: Strong facilitation of follow-up work: some elements are shared publicly (data, code, or a running system) and little effort would be required to replicate the results or apply them to a new domain.
Fair: Adequate facilitation of follow-up work: moderate effort would be required to replicate the results or apply them to a new domain.
Poor: Weak facilitation of follow-up work: considerable effort would be required to replicate the results or apply them to a new domain.
Scope and promise for social impact

Excellent: Likelihood of social impact is extremely high: the paper’s ideas are already being used in practice or could be immediately.
Good: Likelihood of social impact is high: relatively little effort would be required to put this paper’s ideas into practice, at least for a pilot study.
Fair: Likelihood of social impact is moderate: this paper gets us closer to its goal, but considerably more work would be required before the paper’s ideas could be implemented in practice.
Poor: Likelihood of social impact is low: the ideas proposed in this paper are unlikely to make a significant impact on the proposed problem.". Please provide a feedback on "Social media platforms provide valuable opportunities for
users to gather information, interact with friends, and en-
joy entertainment. However, their addictive potential presents
significant challenges, including overuse and negative psy-
chological and behavioral impacts.
This study explores strategies to mitigate compulsive social
media usage while preserving its benefits and ensuring eco-
nomic sustainability, focusing on recommenders that promote
a balanced usage. Therefore, we analyze user behaviors aris-
ing from interaction with the environment, offering insights
for next-generation social media recommenders that priori-
tize well-being.
Building on reinforcement learning (RL)-based computa-
tional frameworks for addiction modelling, our study intro-
duces
• a recommender system that adapts to user preferences,
considering non-stationary and non-Markovian dynamics
• differentiated state representations for users and recom-
menders to capture nuanced interactions
• distinct usage conditions (light and heavy use)
Specifically, unlike previous approaches that optimize for
engagement at the cost of well-being, we propose a dual-
recommender system, where the first recommender module
maximizes user engagement, while the other subtly guides
users toward balanced usage, discouraging prolonged ses-
sions to promote well-being.
We simulate 200 agents interacting with the environment over
100,000 time steps, considering different recommender and
agent parameters. Simulations demonstrate that: 1) a signif-
icant role is played by the recommender systems in shaping
addiction tendencies or fostering healthier engagement 2) our
framework is effective in reducing addiction levels while pro-
moting balanced usage and platform sustainability"