How Gerd Gigerenzer might approach Computer Science

The term "Computer Science" itself, I suspect, is a rather grand pronouncement, promising a profound understanding of something so new. Yet, what are these "computers" but elaborate machines designed to process information? And how do humans interact with these machines, or instruct them? It is, at its core, a question of decision-making under conditions that often mimic the uncertainty we face daily.

We are told that these machines, and the science behind them, are built upon logic, upon algorithms of exquisite complexity, aiming for optimal solutions. But this is where we must distinguish. Is this true optimization, or an illusion of certainty? The problems these machines tackle – be it navigating vast networks of knowledge or predicting outcomes – are seldom crystalline. They are rife with unknowns, with incomplete data, with situations that shift faster than any slow, ponderous calculation can accommodate.

Consider the human mind, our own remarkable "computer." We do not, by and large, engage in painstaking, step-by-step calculations for every decision. Instead, we employ a suite of mental tools, heuristics, honed by evolution to be fast and frugal. We recognize patterns, we use simple search rules, we rely on what is "good enough" in a given environment. This is ecological rationality in action.

Perhaps, then, "Computer Science" ought to focus more on *this* intelligence, on designing systems that can operate adaptively, not just rigidly. Can these machines learn to use heuristics? Can they be designed to exploit the structure of their digital environments, just as our minds exploit the structure of the physical world? The pursuit of pure computational power, divorced from the realities of information processing in complex, uncertain domains, strikes me as a…

Imagined perspective — an AI synthesis grounded in Gerd Gigerenzer’s recorded ideas and methods, not a quotation or a statement they actually made.