Risk-Robust Incentive Mechanism for Disaster Response: A Stochastic Simulation Based on Expected Utility Theory and Multi-Dimensional Risk Vivian Karim Ladesi1 , Dedi Purwana2, Herlitah Herlitah3, Rajendran Narayanasamy4, and Sonia Knerbachi5
1 Department of Port Management and Maritime Logistics, Faculty of Engineering, Universitas Negeri Jakarta, Indonesia
2 Postgraduate Program, Universitas Negeri Jakarta, Indonesia
3 Department of Economic Education, Faculty of Economics and Business, Universitas Negeri Jakarta, Indonesia
4 Senior Lecturer at Malaysia University of Science and Technology, Malaysia
5 Researcher at the University of Bejaia, Algeria
Abstract
Humanitarian logistics in archipelagic countries such as Indonesia face complex, multi-dimensional risks, including operational disruptions, coordination failures, and reputational challenges. This study develops a risk-robust incentive mechanism for disaster response using expected utility theory with risk premium adjustments. A principal-agent model is formulated in which the disaster management agency (BNPB) designs incentive contracts for humanitarian actors, such as NGOs and logistics providers, who hold private information regarding their capacity and effort. The model incorporates multi-dimensional risks into both the principal^s social welfare function and the agents^ utility functions, including empirically derived risk aversion parameters. Monte Carlo simulation is applied to 10,000 hypothetical disaster scenarios with varying risk conditions to illustrate the mechanism^s application. The study emphasizes theoretical and methodological contributions rather than numerical outcomes. Results indicate that the proposed mechanism improves fulfillment rates and reduces response time compared to risk-neutral approaches, particularly under high uncertainty. Sensitivity analysis further shows that accounting for correlated risks and ambiguity aversion enhances robustness. These findings offer practical insights for designing incentive-aligned and risk-aware humanitarian logistics systems in disaster-prone regions.
