Current Research Projects

Examines how individuals make strategic decisions under uncertainty, comparing crowdfunding and simple voting mechanisms. Using a custom experimental platform to analyze how risk, collective influence, and social trust affect participant choices and collective outcomes.

Develops an intelligent traffic control system where each traffic light functions as an autonomous agent learning to make real-time decisions. Applies game-theoretic principles to enable cooperation and competition among agents for optimizing traffic flow and reducing congestion.

Develops a blockchain platform for food security management in Israel, where 23% of the population faces food insecurity. The system provides disaster preparedness simulation and food waste reduction strategies. Key finding: recovering 20% of current food waste could eliminate national food insecurity.

Analyzes how the merger of two major Organ Procurement Organizations affected the organ allocation landscape. Uses OPTN database and Difference-in-Differences methodology to assess whether consolidation enhanced organ-patient matching efficiency or introduced new market distortions.

Develops MARL mechanisms for fair decisions in asymmetric environments. Introduces a novel optimization framework with real-time performance gap measurement and dual update mechanisms that guide agents toward balanced, cooperative strategies. Applications in robotics, resource allocation, and collaborative AI.

Develops a graph-based allocation mechanism for displaced populations that preserves social networks. Proves that minimum graph cut solutions are game-theoretically stable and introduces an efficient Cycle-Based Improvement algorithm. Combines computational efficiency with social sensitivity for humanitarian housing allocation.

Our research examines how strategic decisions are made under conditions of competition and pressure using a controlled experiment. We measure decision speed under different competitive settings and compare it to the ex-post quality and accuracy of the decisions. This allows us to test how the mere presence and varying intensity of competition affect both speed and quality. At a broader level, the study aims to inform a more efficient model for allocating organs for transplantation, in which decisions are made faster—reducing the number of expired organs—while maintaining decision quality and safeguarding transplant recipients. We analyze tailored performance measures for this experiment and seek to propose a theoretical model that predicts the optimal decision framework for this problem.