Introduction
Modern artificial intelligence is not just a technology, but a new logic of power. The central figure of this order is the rational agent—a system that transforms data streams into goal-oriented decisions. This article analyzes how an engineering approach to rationality is reshaping the economy, the risks it poses to society, and why the selection of algorithmic goals is the most critical political decision of our time.
Russell, Norvig, and the Engineering of Market Power
According to Stuart Russell and Peter Norvig, a rational agent is a system that perceives stimuli from its environment and performs actions that optimize expected utility. In the AI paradigm, rationality ceases to be an economic metaphor and becomes a precise technical specification. Whoever designs the agent's objective function decides the direction of social process automation.
Elżbieta Mączyńska points here to the paradox of the Fourth Industrial Revolution: despite ubiquitous automation, we are increasingly overworked. If algorithms optimize only for GDP growth or profit, human and planetary well-being becomes merely a variable in an equation, leading to social and ecological regression.
From Atoms to Structures: Modeling and Heuristics
World modeling in AI is evolving from atomic representations (indivisible points), through factored ones (vectors of variables), to structural representations (objects and relations). While the ontological richness of first-order logic allows for describing the complexities of debt or ownership, it leads to an aporia of inference—the richer the language, the greater the risk of a combinatorial explosion.
The solution lies in heuristics and Hierarchical Task Network (HTN) planning. These allow agents to operate within finite time by decomposing tasks. However, these "intelligent shortcuts" often ignore side effects, such as team morale or system stability, which in business translates into a radically consistent but narrow logic of profit.
Bayesian Networks and Global Regulatory Models
In a world of uncertainty, AI employs probabilistic reasoning and Bayesian networks, turning facts into fields of possibility. This enables diagnostic inference, which is crucial in credit risk assessment. Globally, three strategies are emerging: the USA focuses on capital and productivity, Arab countries on state modernization, and the European Union on normative regulations (the AI Act).
A logical experiment with board-level theses reveals a fundamental tension: rationality does not imply transparency. AI systems can operate optimally while remaining "black boxes," creating the temptation to legitimize opaque decisions under the banner of a superior algorithmic logic inaccessible to the human mind.
Infrastructure Asymmetry and the Four Aporias
Traditional information asymmetry is transforming into analytical infrastructure asymmetry. Entities equipped with advanced agents see the market in higher resolution, consolidating their dominance. Acemoglu and Stiglitz warn: AI could affect 60% of jobs, threatening wage stagnation and deepening inequality.
In social practice, four aporias of algorithmic rationality emerge: 1. The transformation of political problems into technical ones. 2. A lack of understanding regarding decisions (silent rationality). 3. Goodhart’s Law (when a measure becomes a target, it ceases to be a good measure). 4. The blurring of responsibility between designers and code. We face a choice between algorithmic feudalism (corporate power) and a republic of public agents (AI as a common good).
Summary: Strategy and Civilizational Choice
Boards must treat rationality as a design norm, not just a financial one. Strategic prudence is key: precisely defining objective functions, maintaining a human veto, and understanding model assumptions. A rational agent will do what it is supposed to do, given what it knows—the question is whether we know what we truly want from it.
In an era where algorithms measure our aspirations, we face a choice: will we allow them to reduce the richness of relationships to cold calculation, or will we program them with a desire for justice? The answer is not written in code, but in our decisions regarding the shape of the common good. Can we create an agent that embodies our noblest ideals?
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