“The war on rationality: a personal reflection” by Gerd Gigerenzer (Max Planck Institute for Human Development, Berlin, Germany), with references to the work of Daniel Kahneman and Amos Tversky, involves leading academic institutions in the field of social and psychological sciences. The research explores the tension between models of logical rationality and alternative programs grounded in heuristics and environmental contexts. The overall theme concerns the nature of human rationality in economics, cognitive psychology, and public policy, analyzing critiques, normative interpretations, cognitive effects, and perspectives on uncertainty and decision-making complexity.
Logical Rationality Between the Cold War and Economic Models
During the Cold War years, the idea of logical rationality became firmly established as a foundation for interpreting and prescribing human behavior in strategic situations. Economics and the social sciences adopted the paradigm of expected utility maximization, enriched by internal consistency axioms and the use of Bayesian probability as a tool to handle linear decisions and known contexts. This approach was motivated by the desire to preserve the global order from the risk of war-related disasters, introducing the ambition to predict and constrain the choices of rulers and populations. However, it was a vision confined to small, well-defined, and easily calculable worlds.
When the scientific community, starting in the 1970s, put this approach to the test, a new perspective emerged: the idea that people systematically made mistakes. Critics interpreted these findings as evidence that human beings had a limited capacity for logical reasoning. A famous 1974 study on the use of heuristics and biases in decision-making received over 15,000 citations, eclipsing earlier works that had depicted individuals as good intuitive statisticians, and demonstrating how media attention influenced the debate. This phenomenon led to viewing logical rationality not merely as a useful model but as a universal norm, overlooking its limited validity in uncertain situations. It thus became necessary to ask what really makes a decision-making strategy effective when one lacks complete data or infinite time for calculating optimal solutions.
From Apparent Irrationality to Research on Cognitive Biases
The emergence of the so-called “heuristics-and-biases” program led to interpreting deviations from logical standards as signs of intrinsic cognitive defects. Portraying the human mind as a fallible entity prone to distorted judgments supported the idea of a psychology of irrationality. Certain institutions, governments, and businesses capitalized on this notion to justify paternalistic interventions, arguing that the public was unable to manage risks, probabilities, and complex choices appropriately.
However, a critical examination of these findings revealed no concrete evidence linking such biases to real material damage or tangible harm. There is no proof, for instance, that violating logical axioms systematically leads to economic losses or worse health outcomes. Moreover, many alleged distortions did not replicate in different experimental contexts. Careful analysis shows that participants in the initial tests often had no opportunity to learn from experience or to interact with real-world problems; instead, they were exposed to hypothetical, short-term questions. This methodological shift—from active exploration of randomness to the mere abstract questionnaire—ended up generating distorted impressions. This demonstrates how so-called irrationality depends on context, the time allocated, and the nature of the information. When individuals are given the space to understand frequencies, sets of cases, and situations in which they can experiment with data, the human ability to reason coherently improves markedly.
Ecological Rationality and Heuristics as Adaptive Tools
Subsequently, new currents emerged that rejected the interpretation of these deviations as fallacies, proposing the notion of ecological rationality. This approach, also inspired by the ideas of Herbert Simon, values the use of simple, algorithmically defined heuristics to make functional decisions in uncertain and complex environments. These strategies do not aim for abstract optimality but seek sufficiently good results within limited time frames, exploiting the very structure of the context.
Reducing information and simplifying are not defects, but tools for adapting to concrete problems. In some circumstances, having too much data and relying on complex mathematical models does not improve accuracy, whereas simple heuristics can prove more robust. Analyses have shown that in unstable environments, characterized by sudden changes, ecological strategies can sometimes outperform sophisticated algorithms. The adoption of methods such as fast and frugal decision trees, applied in finance or healthcare, provides a tangible example. In one physician training case, just one hour of intuitive instruction enabled nearly everyone to correctly interpret diagnostic results that had previously been misunderstood. This indicates that targeted education and awareness of the environmental context can bridge the gap between logical ideals and realistic decision-making practices.
Practical Dilemmas Between Nudging, Real-World Context, and Overcoming Abstract Models
As the debate continued, some scholars proposed improving people’s choices by intervening in their “choice architecture,” i.e., organizing the context to encourage what were considered better behaviors. This idea, known as nudging, aimed to achieve positive outcomes simply by modifying default options or suggesting choices deemed more advantageous. Yet, when re-examining the data—considering that studies with positive results are more likely to be published than those with null or negative outcomes (publication bias)—it emerged that the benefits of nudging were often more limited than initially believed.
For example, changing default settings for organ donations did not always produce the actual increase in transplants that one might have expected. In many cases, the intervention targeted symptoms rather than addressing the structural causes hindering more effective decisions. At this stage, the discussion moves beyond the clash between models of logical rationality and systematic errors, acknowledging that the human mind is not a perfect probability-calculating machine, but rather an adaptive system capable of using intelligent shortcuts when needed. This view highlights the difference between rigid, abstract models and the complexity of real life, where uncertainty is not resolved simply with calculations but requires flexibility, experience, and an understanding of the context.
Conclusions
Overall, reflecting on the debate among logical rationality, cognitive biases, and ecological heuristics offers a strategically significant perspective for managers and entrepreneurs who navigate dynamic and unpredictable global markets every day. Compared to the current state of the art—where advanced data analysis tools seek to simulate omniscience and optimality—ecological rationality suggests focusing on flexibility and adaptability rather than the pursuit of mathematical perfection.
Using simple heuristics is not a return to the past, but a recognition of the necessity to operate under conditions where not all variables are known and where speed of action is decisive. This stands in contrast to approaches that emphasize paternalistic control of behavior through invisible nudges, predictive technologies, or static incentive structures. Entrepreneurs and managers, facing the challenges of digitalization and economic complexity, can draw on the insights of ecological rationality to combine quantitative analyses with robust intuitions, develop more effective training systems, and leverage the tacit knowledge of their teams.
In a world without definitive certainties, true foresight is not about trying to tame uncertainty with unassailable formulas, but about learning to navigate it with flexible and comprehensible strategies. Attending context, continuous learning, and selecting cognitive tools suited to the contingent reality offers fertile ground for the development of more aware, pragmatic, and complexity-sensitive corporate policies and decision-making. Taken together, these reflections can represent a mature approach to future challenges, standing apart from simplistic dogmas and opening the way to a deeper understanding of rational action under real conditions.
Comments