Data-Driven Risk Allocation and Incentive-Compatible Payment Schemes in Construction Contracts: Integrating Comparative Standard Forms, Public Works Disputes, and Principal–Agent Modeling

Abstract Construction projects worldwide suffer from misaligned risk allocation, poorly chosen payment schemes, and opaque claims and dispute processes, leading to chronic cost overruns and litigation. Building on comparative analyses of widely used international and national standard forms of contract, empirical public works dispute case studies, and principal–agent theory of payment methods, this research develops a data-driven framework for designing incentive-compatible construction contracts. First, we code and compare more than 60 distinct risk events and 40 claims and dispute procedures across several representative standard contract regimes for large infrastructure projects. Second, we construct a dataset of approximately 80 real dispute or major change-order cases (including highway, bridge, and building projects) and classify them by triggering risk, procurement mode, and payment scheme. Third, we calibrate a structural principal–agent model of contractor effort, cost uncertainty, and risk aversion to this dataset, comparing fixed-price, unit-price, cost-plus, and target-price contracts. The calibrated model is then used to simulate how alternative combinations of risk allocation clauses and payment mechanisms affect expected owner cost, contractor risk exposure, and the probability of disputes. Simulation results are used to quantify potential performance gains from better-aligned contract design; illustrative scenarios target a 15%–25% reduction in expected total project cost, a 30%–40% reduction in formal disputes, and a 40% reduction in claims processing time when more balanced risk-sharing clauses and incentive-compatible payment schemes are adopted. The research is useful as a guideline for international contractors and public agencies involved in mega infrastructure projects.