Our aim is to build a class of macroeconomic models that explicitly incorporate cognitive limitations and behavioral errors. This approach bridges the gap between micro-level behavioral evidence and macroeconomic modeling, offering new insights to understand long-run economic outcomes and allowing us to design effective public policies.
Contrary to the standard argument that small, idiosyncratic errors cancel out in large populations or over time, our core hypothesis is that even modest, consistent mistakes can have significant cumulative effects.
- H1 (Drift Hypothesis): Small, systematic cognitive errors in high-stakes, irreversible life decisions lead to large and persistent welfare losses over the life cycle.
- H2 (Macroeconomic Significance of Errors): Cognitive errors in early, irreversible life choices have economically meaningful effects at the macro level.
- H3 (Collective Compounding of Individual Errors): In collective decision settings (e.g., voting, pension design, public goods), small, systematic errors by many individuals can aggregate and result in persistent macro-level distortions.
Methodology
To evaluate these hypotheses, we will construct a dynamic overlapping-generations (OLG) model incorporating the CMM framework. This model will be empirically calibrated using micro-level data—such as household surveys, education and demographic panels—to estimate the degree of error-proneness and its distribution across the population.
Expected Outcomes
This modeling framework will allow us to quantify the impact of cognitive errors on aggregate outcomes including lifetime welfare, income inequality, and the sustainability of public systems like pensions and education subsidies. It will also assess whether targeted policy interventions can meaningfully mitigate these effects.
Collective Decision Analysis
Beyond individual choices, DRIFT also examines how errors propagate in collective or market-based decision environments. Here, individuals often perceive their influence as negligible, yet correlated mistakes across many agents can produce large-scale distortions.
Project Goals
By modeling how cognitive errors propagate both across time and through interdependent decision environments, DRIFT aims to provide a deeper understanding of how bounded rationality affects economic dynamics. The project will also explore under what conditions institutional design or behavioral interventions (e.g., nudges, defaults, educational tools) can buffer these effects and improve long-term outcomes.
