This paper investigates economic behavior under uncertainty in hybrid markets populated by human and artificial intelligence (AI) agents. The integration of autonomous algorithmic systems into market processes introduces heterogeneity, bounded rationality, and systemic feedback. Traditional equilibrium models fail to capture emergent phenomena and non-linear dynamics in such environments. We propose a framework grounded in complex adaptive systems theory, combining agent-based computational economics (ACE) with causal economic machine learning (CEML) to analyze decision-making, learning, and trust dynamics. We develop formal characterizations of market stability, welfare, and systemic risk and discuss policy implications under endogenous uncertainty and algorithmic interaction.

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