Behavioral Study of Dashboard Mechanisms

TL;DR

This study investigates how different dashboard visualizations influence bidder behavior and inference accuracy in auction settings, revealing that utility-based dashboards improve bidding but still face systematic biases affecting auction outcomes.

Contribution

It introduces behavioral experiments comparing dashboard designs and demonstrates the importance of modeling behavioral responses for accurate preference inference in auctions.

Findings

Utility visualizations reduce cognitive load and improve bidding.

Bidders tend to under-shade bids despite better dashboards.

Behavioral modeling enhances inference accuracy significantly.

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.