Institution profile

Memorial University of Newfoundland

Academic institutionnorthamerica · ca
Official website
Research library94linked papers
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Selected work

Representative Papers

Optimal sharing, equilibria, and welfare without risk aversion

Jan 06, 2024

This paper addresses the impact of empirically observed heterogeneity in individual risk attitudes—particularly loss-domain risk-seeking—on risk-sharing mechanisms, challenging the conventional assumption of universal risk aversion. Method: We develop a general equilibrium model of risk exchange without presupposing risk aversion, employing inverse-monotonic optimization, rank-dependent utility, and expected utility frameworks to characterize Pareto-optimal allocations, existence of competitive equilibria, and validity conditions for the First and Second Welfare Theorems. Contribution/Results: We provide the first rigorous proof of both welfare theorems under pure risk-seeking preferences. Introducing the “jackpot allocation” concept, we identify a scale-dependent mechanism: jackpot allocation is Pareto optimal for small gains but dominated by proportional allocation for large ones—unifying explanations of the disposition effect and small-stakes gambling. Our results resolve a fundamental tension between behavioral evidence and standard general equilibrium theory.

3 citationsRead paper

Quantum Takes Flight: Two-Stage Resilient Topology Optimization for UAV Networks

Jan 27, 2026

This work addresses the challenge of maintaining reliable connectivity in dynamic unmanned aerial vehicle (UAV) networks, where rapidly changing topologies, fluctuating link quality, and stringent latency constraints render traditional global optimization methods computationally prohibitive and poorly adaptive. To overcome these limitations, the authors propose a two-stage quantum-assisted framework: in the offline phase, high-diversity, high-quality topology candidates are generated in parallel via quantum annealing based on a QUBO formulation; in the online phase, a lightweight classical mechanism selects the optimal topology in real time, balancing efficiency and robustness. This study presents the first application of quantum annealing to UAV network topology optimization, demonstrating a 6.6% improvement in performance retention over static optimal topologies within 30-second dynamic windows, a 5.15% enhancement in objective function value over classical approaches, and a 28.3% increase in solution diversity.

1 citationsRead paper
Recent publications

Latest Papers

Time-Aware Validation of Machine Learning Fuel Consumption Models: Evidence from 1\,Hz Operational Data, CCGS \textit{Sir Wilfrid Laurier}

Aug 17, 2026

This study addresses the issues of temporal leakage and inflated evaluation metrics caused by random data splitting in ship fuel consumption prediction. We propose a time-aware, blocked cross-validation strategy to mitigate these biases. Leveraging a dataset of 3.88 million steady-state records sampled at 1 Hz, we systematically evaluated multiple regression models alongside physics-based baselines. The proposed approach effectively eliminates temporal dependency bias and overcomes validation challenges inherent to high-frequency data. Consequently, this method yields reliable performance indicators that accurately reflect real-world deployment conditions. Ultimately, this work provides a rigorous methodological foundation for the fair assessment and practical engineering application of ship energy efficiency models, ensuring that predictive performance is validated without artifacts from improper temporal partitioning.

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