AI Performance x Economic Value: Inductivising Philosophy in Cognitive Systems

TL;DR

Artificial Intelligence is at a crossroads where logic and computation meet the messy, value-driven world of human economics. This paper explores how AI can evolve from mere symbolic manipulation to truly "intelligent" systems by grounding its cognitive processes in economic utility, human interaction, and evolutionary learning.

Background: Beyond the Ghost in the Machine

In the early days of AI, intelligence was often treated as a disembodied process—physical symbol systems (PSS) operating in a vacuum. However, as the field matured, the gap between "knowing that" (propositional knowledge) and "knowing how" (practical action) became a bottleneck. The author argues that for AI to fulfill its original promise, it must bridge this gap by incorporating the "Inductive Philosophy" of economic cognition.

Problem & Motivation: The Disembodiment Trap

Why do intelligent programs often fail in real-world scenarios?

1. Symbol Grounding: Symbols in a computer don't have intrinsic meaning unless they are connected to the physical or economic world.
2. Computational Costs: Real-world reasoning isn't free. Thinking, perceiving, and acting require "costly resources," a concept often ignored by early AI researchers but central to economic theory.
3. Static Logic: Traditional AI struggles with the dynamic, unpredictable nature of human preferences and socio-economic shifts.

Methodology: The Interactionist Alternative

The core insight of this paper is the move toward Interactionism. Instead of just "encoding" the world into a database, the system should learn through interaction.

1. The Economic Nexus

The author suggests using the Expected Value of Computation (EVC). This means the AI must decide, in real-time, whether the effort of more data processing is worth the potential economic gain. This mimics human survival and financial strategies.

2. Human-Centered Design

Drawing from the "UTOPIA" project and "Participatory Design," the methodology emphasizes that users shouldn't just be "operators" but part of the feedback loop.

The figure above illustrates a "Plan Selection Problem Solver" using Classification, a prime example of how domain knowledge (Human Capital) is structured to solve asset acquisition problems.

Experiments & Results: AI in the Real Market

The paper cites several "real-world" success stories where this integrated approach has outperformed traditional systems:

• Financial Services: Impactful expert systems like Credex use qualitative risk interval scales to assess credit. By mimicking cognitive psychology, these systems lower mortality expenses and improve pricing accuracy.
• E-Commerce: Agent-based systems that handle fuzzy logic allow for personalized product recommendations that adapt to buyer feedback autonomously.
• Genetic Algorithms: The use of evolutionary "crossover and mutation" algorithms to generate trading strategies shows that AI can find "new equilibrium structures" that traditional economic models miss.

Critical Analysis & Conclusion: The Ethical Horizon

The author leaves us with a provocative takeaway: AI is not just a technological tool; it is a manifestation of human values.

Takeaway

True intelligence emerges when neuronal interaction (Connectionism) meets socio-economic utility. The "third intelligence revolution" is not about faster chips, but about better integration between the strata of biology, commerce, and philosophy.

Limitations

• Responsibility: If a grounded AI makes a mistake (e.g., a false medical diagnosis), who is Liable? The physician, the programmer, or the system itself?
• Bias: Learning modules are prone to systemic bias, especially in behavioral finance.

Future Work

The next step for AI is the development of "socially sustainable" systems that prioritize "Rawlsian Justice"—ensuring that the technological gain is distributed equitably throughout the human-machine network.