[Paper Review] Impact of Artificial Intelligence on Economic Theory

Tshilidzi Marwala|arXiv (Cornell University)|Jul 1, 2015

Computability, Logic, AI Algorithms8 references3 citations

TL;DR

This paper examines how artificial intelligence (AI) transforms core economic theories—bounded rationality, efficient market hypothesis, and prospect theory—by analyzing AI's role in decision-making under uncertainty, market efficiency, and risk perception. It finds that AI enhances predictive accuracy and behavioral modeling, challenging traditional assumptions of rationality and market efficiency.

ABSTRACT

Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.

Motivation & Objective

• To analyze the impact of artificial intelligence on foundational economic theories.
• To investigate how AI challenges or complements traditional models of bounded rationality.
• To assess AI's influence on the efficient market hypothesis and market efficiency.
• To evaluate AI's role in refining prospect theory and behavioral economic modeling.
• To identify shifts in economic reasoning due to AI-driven decision-making processes.

Proposed method

• The study employs a theoretical and conceptual analysis of AI applications in economic decision-making.
• It examines AI techniques such as machine learning and neural networks in modeling human behavior under uncertainty.
• The paper compares traditional economic models with AI-enhanced models in predicting market outcomes.
• It evaluates AI's ability to simulate bounded rationality through adaptive learning algorithms.
• The analysis incorporates AI-based risk assessment tools to test predictions under prospect theory.
• The methodology relies on qualitative synthesis of AI applications in finance and behavioral economics.

Experimental results

Research questions

• RQ1 How does artificial intelligence alter the assumptions of bounded rationality in economic decision-making?
• RQ2 To what extent does AI challenge or support the efficient market hypothesis?
• RQ3 In what ways does AI improve the predictive power of prospect theory in behavioral economics?
• RQ4 How do AI-driven models compare to traditional economic models in simulating market behavior?
• RQ5 What are the implications of AI for the rationality and consistency of economic agents?

Key findings

• AI enhances the modeling of bounded rationality by simulating adaptive, learning-based decision-making processes.
• AI challenges the efficient market hypothesis by identifying persistent market anomalies and inefficiencies.
• AI improves the accuracy of prospect theory predictions through data-driven risk and utility estimation.
• Machine learning models outperform traditional econometric models in forecasting behavioral responses to risk.
• AI enables more realistic representations of human behavior by incorporating cognitive biases and heuristics.
• The integration of AI into economic theory reveals limitations in classical models under conditions of uncertainty and complexity.