Paper Number
ICIS2025-1450
Paper Type
Short
Abstract
This paper develops a theoretical framework for when and why human–AI ensembles improve decision accuracy. We show that gains arise when at least one agent is better than chance and when complementary errors can be systematically identified, formalized as the agent-complementary information value (ACIV). In multi-round settings, gain from ensembles decompose into conditional VOI terms, highlighting the role of sequential feedback. Building on this theory, we design an online fusion algorithm and test it in simulation to validate our theory.
Recommended Citation
Wang, Shaohui, "Optimizing Business Forecasting through Human-AI Decision Fusion: A Theoretical Framework and Simulation Study" (2025). ICIS 2025 Proceedings. 3.
https://aisel.aisnet.org/icis2025/da_bus/da_bus/3
Optimizing Business Forecasting through Human-AI Decision Fusion: A Theoretical Framework and Simulation Study
This paper develops a theoretical framework for when and why human–AI ensembles improve decision accuracy. We show that gains arise when at least one agent is better than chance and when complementary errors can be systematically identified, formalized as the agent-complementary information value (ACIV). In multi-round settings, gain from ensembles decompose into conditional VOI terms, highlighting the role of sequential feedback. Building on this theory, we design an online fusion algorithm and test it in simulation to validate our theory.
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