AbstractThis study discusses the evolutionary nature of knowledge acquisition at micro and macro levels, and in particular when the process involves an artificial agent's interpretative devices. In order to accomplish this, we propose using an individual learning model (or inner‐world reconstruction model) that in our view overcomes neoclassic epistemological holdups and may increase the predictive power of computational economics, by letting an artificial agent's knowledge evolve by itself, irrespective of globally specified goals or individual motives of behavior; using simultaneous (or parallel) genetic algorithms (GA) to evolve a single agent's learning strategy, each GA with different general specifications, in a multiagent setting. © 2006 Wiley Periodicals, Inc. Complexity 11: 12–19, 2006