MSc Thesis Defense: Nisa Defne Aksu, MODELING THE EFFECTS OF MARKET NEWS AND AI-EMULATING ADVISORS ON FAIRNESS AND FINANCIAL INCLUSION IN DEFI CREDIT AND TRADING SYSTEMS, Date & Time: June 17, 2026 – 1:00 PM, Place: FENS G025
MODELING THE EFFECTS OF MARKET NEWS AND AI-EMULATING ADVISORS ON FAIRNESS AND FINANCIAL INCLUSION IN DEFI CREDIT AND TRADING SYSTEMS
Nisa Defne Aksu
Computer Science & Engineering, MSc Thesis, June 2026
Thesis Jury
Prof. Selim Saffet Balcısoy (Thesis Advisor)
Prof. Galina Andreeva
Asst. Prof. Dilara Keküllüoğlu
Date & Time: June 17, 2026 – 1:00 PM
Place: FENS G025
Zoom Link: https://sabanciuniv.
Meeting ID: 590 502 4125
Keywords : Decentralized Finance (DeFi), Agent-Based Simulation (ABS), Behavioral Clustering, Algorithmic Fairness, LLM-like Advisory Agents
Abstract
Millions of users with various behavioral profiles have been onboarded via decentralized finance (DeFi) protocols, but little is known about the fairness implications of AI-mediated financial advising in these environments. This thesis examines whether large language model (LLM)-like advisory agents in two DeFi contexts — credit and token trading — produce systematically uneven financial outcomes through news sentiment propagation. A dual agent-based simulation framework is implemented using real on-chain data from 18,901 MakerDAO vault records and 2,405,340 ERC-20 addresses. Each simulation deploys 500 heterogeneous agents across three advisor types — fear-based, neutral, and optimistic — and nine empirically derived news sentiment clusters over 20 cycles. The primary finding is that advisor effects are systematically heterogeneous across user behavioral profiles — concentrated 77 times more strongly among moderate-activity users than high-volume users, a result not predetermined by simulation design. A secondary finding is that fear-based advisors produce higher credit scores and portfolio values under declining market conditions, reflecting alignment between cautious guidance and DeFi protocol incentive structures rather than universal advisor superiority. Fairness audit reveals that standard demographic parity and disparate impact criteria are met, yet the equal opportunity analysis of variance (ANOVA) fails at p = 0.0018, indicating that access-based metrics can pass while outcome-distribution audits detect significant disparities. These findings support an expanded fairness auditing framework that evaluates not only credit access but how advisory systems systematically shape user financial trajectories over time.