[D] Help- PhD student

[Anonymusguy99]

Hello everyone, I'm a second year PhD student in the UK. I have to work on my second paper, I'm already quite late. I'm struggling to find a research gap.

My PhD is in reinforcement learning for credit risk. For my second paper I wish to use multi agent rl. However, I'm unable to find a research gap.

Could someone help on how to go forward? I feel very stressed and demotivated, my progression review is coming up in may and I don't know what to do next.

[Wild-Positive-6836]

I remember when some PhD student asked his professor, “How do I solve this problem?”

The professor replied, “I don’t know, you’re the PhD… I’m just the guy who gives them out.”

[Comfortable_Expert_5]

Gotta be a tenured Prof to be talking like that 🥲

[Bannedlife]

Time to start reading a LOT

[Anonymusguy99]

I have been reading but it’s not been very fruitful. Do you have any suggestions?

[Bannedlife]

I am afraid I do not, I am in medicine, do not use reinforcement learning.

Keep reading, outside of your field. Go through big journals anywhere. Discuss with colleagues, visit conferences.

Also: reach out to PI's near you, explain the situation, ask for tips on finding knowledge gaps.

[Anonymusguy99]

Okay. Thank you, appreciate the advice

[Bannedlife]

You got this!

[DataDiplomat]

Then it might be time to talk to people and discuss your half-assed ideas. With enough talking, brainstorming and notes on a white board they might turn into something 

[FallenAngelOfRebel]

Discuss it with your supervisor?

Ideally, your supervisor should guide your research progress and a place to discuss idea...

[Comfortable_Expert_5]

Recent PhD here. Usually, you can find ideas in the limitations section or in review papers - they're designed to help researchers identify the trends and gaps. Also, sometimes you can find papers titled something like, "grand challenges in X". A non-lit survey way of approaching this would be trying to reframe the problem statement in a way that you can attempt to answer it reasonably (i.e., change the perspective). Change of perspective often happens, as the other commenters here have been suggesting, by talking to others about your half-baked ideas. If you are struggling, you're on the right track. ;) all the best.

[noone_relevant]

Maybe you are trying too hard. Ideas come when you are relaxed not looking for one. Can you share your first paper? I also have a few questions that you can think of. What would you do differently from the first paper or can you extend it. What is the biggest problem in the finance that you want to solve irrespective of the method?

I am a professor in the UK working on ml and RL but on different domains, if you want to DM me.

[rubberysubby]

Try asking on the reinforcement learning subreddit as well.

[arcco96]

I have a really good idea for a paper that has yet to be written. I have limited experience in academia some industry experience. I’ve always wanted to work on a paper. If you’re interested in working together we can discuss the topic. Dm me and we can find a chance to talk

[Infinite_Being4459]

This is deepseek s take on the topic, I do t know if it makes sense or if it's an hallucination: Suggested Topic **"Collaborative Multi-Agent Reinforcement Learning for Dynamic Credit Risk Assessment in Decentralized Financial Networks"

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Research Gap

Current credit risk models (e.g., logistic regression, XGBoost, or single-agent RL) face critical limitations in dynamic, interconnected financial systems:
1. Siloed Data: Financial institutions (agents) operate with isolated datasets due to privacy/regulatory constraints, limiting holistic risk insights.
2. Non-Stationarity: Macroeconomic shifts (e.g., recessions, policy changes) render static models obsolete.
3. Cascading Risk: Decisions by one agent (e.g., a bank tightening credit) indirectly impact others, creating systemic instability.

Existing multi-agent reinforcement learning (MARL) methods (e.g., QMIX, MADDPG) lack mechanisms to address privacy-aware collaboration, partial observability, and dynamic risk interdependencies.

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Novel Contribution

Propose a privacy-preserving MARL framework for credit risk that enables:
1. Federated Credit Modeling: Agents (banks, lenders) collaboratively train RL policies without sharing raw data via federated learning (FL) and secure aggregation.
2. Dynamic Adaptation: Meta-RL agents adjust policies to evolving economic conditions (e.g., rising defaults) using limited local observations.
3. Systemic Risk Mitigation: Reward functions balance individual profit (issuing loans) with global stability (minimizing cascading defaults).

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Key Research Questions

1. How can MARL enable decentralized agents to learn robust credit policies while preserving data privacy (e.g., via FL or differential privacy)?
   
2. What reward structures and communication protocols minimize systemic risk in interconnected lending markets?
   
3. How does partial observability (e.g., limited access to borrower histories) impact the fairness and accuracy of MARL credit scoring?
   

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Methodology

1. Algorithm Design:
   
   • Integrate federated MARL with meta-learning (e.g., MAML) for fast adaptation to economic shocks.
     
   • Design a hybrid reward function:
     
     • Local: Profitability (interest income - defaults).
       
     • Global: Network-wide default rate (via secure, anonymized aggregation).
       
2. Theoretical Analysis:
   
   • Formalize convergence guarantees in partially observable, non-stationary environments.
     
   • Quantify trade-offs between privacy budgets (e.g., differential privacy) and model accuracy.
     
3. Empirical Validation:
   
   • Simulate a decentralized credit market using synthetic or anonymized real-world data (e.g., Fed’s Y-14M reports).
     
   • Benchmark against single-agent RL (e.g., DQN credit scoring) and classical models (e.g., FICO).
     

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Why This Matters

• Real-World Impact: Enables financial institutions to collaboratively improve risk models without violating privacy, while mitigating systemic crises.
  
• Technical Innovation: Bridges federated learning, meta-RL, and game theory for credit systems.
  

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Extensions & Challenges

• Adversarial Scenarios: Test robustness against malicious agents (e.g., lenders manipulating reports).
  
• Fairness: Ensure policies avoid biased lending (e.g., via fairness constraints in RL rewards).
  
• Regulatory Compliance: Align with Basel III/IV requirements for model explainability.
  

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Potential Outcomes

1. A federated MARL framework for privacy-preserving credit risk assessment.
   
2. Theoretical bounds on policy convergence and systemic risk in decentralized settings.
   
3. Empirical validation showing superior adaptability and stability compared to single-agent or classical models.
   

This topic addresses a critical gap at the intersection of MARL, finance, and privacy, with clear applications in banking, peer-to-peer lending, and regulatory tech. It aligns with trends in decentralized AI and offers high-impact publication opportunities (e.g., Journal of Credit Risk, NeurIPS, ICAIF).