Modeling Strategic Risk in School Choice: A Case for Transparent Design

School choice algorithms, which assign students to schools based on preferences, are often designed to balance matching efficiency with strategyproofness. In Amsterdam, policymakers introduced a more efficient assignment mechanism in 2019 that permitted preference manipulation. As strategic behavior increased, transparency around the algorithm was reduced—motivated by prior studies showing that additional information about matching methods tends to increase manipulation. Yet transparency is also essential for institutional trust and informed decision-making. We test whether transparency about the consequences of strategic behavior deters or encourages manipulation. We conduct a behavioral experiment embedded in a survey of 140 parents participating in Amsterdam’s 2025 school choice process. Parents selected among truthful, mildly strategic, and aggressively strategic rankings across three scenarios with varying degrees of probability disclosure. While transparency increased the rate of strategic behavior, truthful reporting remained the most common choice under all conditions. To explain these patterns, we estimate a discrete-choice model grounded in Security-Potential/Aspiration (SP/A) theory, which models how parents weigh aspirational goals against the risk of poor outcomes. The model fits observed behavior better than expected utility and prospect theory benchmarks, even though individual covariates are not statistically significant. We find that parents whose children applied to competitive school tracks (e.g., pre-university) were more risk-averse, selecting safer strategies even when aggressive options offered high chances of success. These results challenge the assumption that transparency inherently worsens inequality, and suggest that communicating risk may serve as a viable policy lever for fairness in algorithmic systems.