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Inflow neglect: Forecasting failures after stocks run out
Megan E Weber1, Stephen A Spiller1, Hal E Hershfield1
1Anderson School of Management, University of California, Los Angeles.
None:
People frequently encounter dynamic systems that involve inflows, outflows, and accumulated stocks-whether within their own households (e.g., financial accounts, stocks of food or supplies) or in larger institutional settings (e.g., manufacturing inventory, government benefit accounts). In this research, we introduce a novel stock-flow reasoning error, inflow neglect, and argue that this error can lead to important misperceptions regarding future outflows. To study this reasoning, we first focus on the United States' Social Security trust funds, whose impending depletion generates significant attention due to implications for American retirees. In Experiments 1-3, we show participants information about the trust funds over time that focus on the stock (i.e., balance) or flows (i.e., tax revenue and benefits payments), finding that those who see flows presentations are significantly less likely to expect benefits to cease completely after depletion (i.e., hold zero-outflow beliefs). In Experiments 4a and 4b, we show that prompting participants to reflect on the continuity of inflows (i.e., by reminding them that they expect payroll taxes to continue) significantly reduces inflow neglect and zero-outflow beliefs. Experiment 5 replicates these results in a separate domain, illustrating the generalizability of inflow neglect and underscoring the efficacy of presentations and targeted questions that emphasize the flows. This research contributes both theoretically and practically, advancing the literature on stock-flow reasoning and highlighting how communications about particular components of dynamic systems may contribute to-or be used to remedy-misconceptions that outflows will cease after depletion. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.