In this work we study the individual and macroeconomic impact of heterogeneous expectations and action rules within an agent-based model populated by heterogeneous, interacting firms. Agents have to cope with a complex, evolving economy characterized by deep uncertainty resulting from technical change, imperfect information and coordination hurdles. In these circumstances, we find that neither individual nor macroeconomic dynamics improve when agents replace myopic expectations with less na ??ve learning rules. In fact, more sophisticated recursive least squares (RLS) expectations produce less accurate individual forecasts and also considerably worsen the performance of the economy. Our results suggest that fast and frugal robust heuristics are not a second-best option: rather they are “rational” in macroeconomic environments with heterogeneous, interacting agents. Finally, we experiment with agents that adjust simply to technological shocks, and we show that individual and aggregate performances dramatically degrade.