Biased Error Attribution in Multi-Agent Human-AI Systems Under Delayed Feedback

Abstract

Human decision-making is strongly influenced by cognitive biases, particularly under conditions of uncertainty and risk. While prior work has examined bias in single-step decisions with immediate outcomes and in human interaction with a single autonomous agent, comparatively little attention has been paid to decision-making under delayed outcomes involving multiple AI agents, where decisions at each step affect subsequent states. In this work, we study how delayed outcomes shape decision-making and responsibility attribution in a multi-agent human-AI task. Using a controlled game-based experiment, we analyze how participants adjust their behavior following positive and negative outcomes. We observe asymmetric responses to gains and losses, with stronger corrective adjustments after negative outcomes. Importantly, participants often fail to correctly identify the actions that caused failure and misattribute responsibility across AI agents, leading to systematic revisions of decisions that are weakly related to the underlying causes of poor performance. We refer to this phenomenon as a form of attribution bias, manifested as biased error attribution under delayed feedback. Our findings highlight how cognitive biases can be amplified in human-AI systems with delayed outcomes and multiple autonomous agents, underscoring the need for decision-support systems that better support causal understanding and learning over time.

Figures (9)

• Figure 1: The game environment consists of two regions: the blue ally region and red adversary region. Each region contains a carrier at its center that forces must protect while attempting to destroy the opponent's carrier by launching aircraft. Enemies can enter the ally region at any time, requiring strategic resource allocation for both offensive and defensive operations. The ally side has a total of 5 aircraft available and must decide how many to deploy in each region across 7 time steps. The interface includes a clock and chat box in the bottom right where AI suggestions appear and participants enter their decisions, while scores for both forces are displayed in the top right corner.
• Figure 2: Chat interface displaying AI agent suggestions, remaining aircraft count, and current decision step in the game.
• Figure 3: Overview of the experimental procedure.
• Figure 4: Strategy labeling scheme showing overall strategy labels ($S$-0 to $S$-5). Strategies are arranged from risk-seeking to increasingly loss-averse along the horizontal axis, with an additional erratic category ($S$-5) representing qualitatively distinct defensive behavior. Within the balanced strategies, offense deployment timing labels ($O$-1 and $O$-2) are shown along the vertical axis. Colored regions indicate risk-seeking (red), balanced (blue), loss-averse (green), and erratic (brown) strategy categories.
• Figure 5: Histogram of game scores by difficulty level across all games (26 participants, 6 games each). The score distribution for low difficulty is shifted toward positive values, whereas high difficulty is shifted toward negative values.