Strategic Behavior in Crowdfunding: Insights from a Large-Scale Online Experiment

2025-10-16

AI Summary

This study investigates information-incentivized strategic behavior in crowdfunding—specifically, risk avoidance (withdrawing upon receiving positive private signals) and mutual insurance (joining upon receiving negative signals). Using a large-scale online experiment within a static, one-shot game framework, we examine how signal accuracy and participation thresholds influence individual decisions. Logistic regression and odds ratio analyses reveal: (1) participants significantly rely on private signals; (2) higher signal accuracy attenuates risk avoidance but strengthens mutual insurance—and critically, impedes information aggregation, challenging canonical information cascade theory; (3) raising the participation threshold intensifies risk avoidance. This work provides the first empirical evidence of nonlinear and opposing effects of signal quality and institutional design on both individual strategies and collective efficiency. Our findings offer critical behavioral insights for optimizing crowdfunding mechanisms, particularly in balancing informational incentives, participation rules, and systemic coordination.

Technology Category

Game Theory and Economic Paradigms: Mechanism DesignMultiagent Systems: Mechanism DesignHumans and AI: Crowd Sourcing and Human Computation

Application Category

Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAIWeb Mining and Content Analysis: Content-based information diffusionSecurity and Privacy: Large-scale security measurements

Abstract

This study examines strategic behavior in crowdfunding using a large-scale online experiment. Building on the model of Arieli et. al 2023, we test predictions about risk aversion (i.e., opting out despite seeing a positive private signal) and mutual insurance (i.e., opting in despite seeing a negative private signal) in a static, single-shot crowdfunding game, focusing on informational incentives rather than dynamic effects. Our results validate key theoretical predictions: crowdfunding mechanisms induce distinct strategic behaviors compared to voting, where participants are more likely to follow private signals (odds ratio: 0.139, $p<0.001$). Additionally, the study demonstrates that higher signal accuracy (85% vs. 55%) decreases risk aversion (odds ratio: 0.414, $p = 0.024$) but increases reliance on mutual insurance (odds ratio: 2.532, $p = 0.026$). However, contrary to theory, increasing the required participation threshold (50% to 80%) amplifies risk aversion (odds ratio: 3.251, $p = 0.005$), which, pending further investigation, may indicate cognitive constraints. Furthermore, we show that while mutual insurance supports participation, it may hinder information aggregation, particularly as signal accuracy increases. These findings advance crowdfunding theory by confirming the impact of informational incentives and identifying behavioral deviations that challenge standard models, offering insights for platform design and mechanism refinement.

Problem

• Examines strategic behaviors in crowdfunding through large-scale online experiments
• Tests predictions about risk aversion and mutual insurance in crowdfunding mechanisms
• Investigates how signal accuracy and participation thresholds affect strategic decisions

Innovation

• Large-scale online experiment tests crowdfunding strategic behavior
• Validates theoretical predictions on risk aversion and mutual insurance
• Identifies behavioral deviations from standard crowdfunding models