The Impossibility of Transparent and Decentralized DeFi Trading & Assortment Optimization for Patient-Provider Matching

Hanna Halaburda (NYU) - 60 min

The Impossibility of Transparent and Decentralized DeFi Trading
Decentralized finance (DeFi) aspires to replace traditional intermediaries with transparent, decentralized markets. Yet we show that transparency itself creates a paradox: by exposing pending trades, it enables others to profit from informed orders, undermining incentives for information acquisition. Traders respond by seeking protection from specialized execution agents, who compete for order flow but face costs of securing transaction inclusion. This competition generates a feedback loop in which higher inclusion probabilities attract more order flow, reinforcing dominance. Paradoxically, greater transparency—by raising the value of protection—can increase the rents and market power of a leading intermediary. Our analysis highlights a general force: in decentralized markets, radical transparency can endogenously recreate the very centralization it was meant to eliminate.

Naveen Raman (CMU) - 30 min

Assortment Optimization for Patient-Provider Matching
Rising provider turnover results in frequently needing to rematch patients with available providers. However, the rematching process is cumbersome for both patients and health systems, resulting in labor-intensive and ad hoc reassignments. We propose a novel patient-provider matching approach to address this issue by offering patients limited provider menus. The goal is to maximize match quality across the system while preserving patient choice. We frame this as a novel variant of assortment optimization, where patient-specific provider menus are offered upfront, and patients respond in a random sequence to make their selections. This hybrid offline-online setting is understudied in previous literature and captures system dynamics across various domains. We first demonstrate that a greedy baseline policy--which offers all providers to all patients--can maximize the match rate but lead to low-quality matches. Based on this, we construct a set of policies and demonstrate that the best policy depends on problem specifics, such as a patient's willingness to match and the ratio of patients to providers. Our analysis reveals a tradeoff between menu size and system-wide match quality, highlighting the value of balancing patient choice with centralized planning.