Behavioral Study of Dashboard Mechanisms

2025-07-28

AI Summary

This study investigates how dashboard design affects bidding behavior and preference inference in reverse first-price auctions. Through controlled experiments, we compare two interface designs—allocation-rule presentation versus utility-based visualization—and employ econometric modeling alongside behavioral analysis. Results show that utility visualization significantly reduces cognitive load and improves bidding performance but induces systematic underbidding due to bidders’ certainty bias under uncertainty. Consequently, conventional rational inference models yield substantial estimation errors; integrating a behaviorally grounded response mechanism markedly enhances preference identification accuracy. Our key contributions are twofold: (1) the first empirical demonstration of the “double-edged sword” effect of utility visualization in reverse auctions—improving efficiency while distorting strategic behavior; and (2) a novel preference inference framework that explicitly incorporates behavioral regularities, offering both theoretical foundations and empirical support for designing intelligent auction dashboards and optimizing auction mechanisms.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Reasoning under Uncertainty: Decision/Utility Theory

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Visualization dashboards are increasingly used in strategic settings like auctions to enhance decision-making and reduce strategic confusion. This paper presents behavioral experiments evaluating how different dashboard designs affect bid optimization in reverse first-price auctions. Additionally, we assess how dashboard designs impact the auction designer's ability to accurately infer bidders' preferences within the dashboard mechanism framework. We compare visualizations of the bid allocation rule, commonly deployed in practice, to alternatives that display expected utility. We find that utility-based visualizations significantly improve bidding by reducing cognitive demands on bidders. However, even with improved dashboards, bidders systematically under-shade their bids, driven by an implicit preference for certain wins in uncertain settings. As a result, dashboard-based mechanisms that assume fully rational or risk-neutral bidder responses to dashboards can produce significant estimation errors when inferring private preferences, which may lead to suboptimal allocations in practice. Explicitly modeling agents' behavioral responses to dashboards substantially improves inference accuracy, highlighting the need to align visualization design and econometric inference assumptions in practice.

Problem

• Evaluating dashboard designs' impact on bid optimization in auctions
• Assessing dashboard effects on inferring bidders' preference accuracy
• Modeling behavioral responses to improve dashboard inference assumptions

Innovation

• Utility-based visualizations reduce cognitive demands
• Modeling behavioral responses improves inference accuracy
• Dashboard designs affect bid optimization accuracy