The Hot Stove Effect in Venture Capital: How Adaptive Sampling Generates Negativity Bias

This paper investigates the "Hot Stove Effect" in venture capital decision-making — a systematic negativity bias that emerges when investors use adaptive sampling strategies. Drawing on behavioral economics and computational modeling, the study examines how repeated exposure to startup failures shapes investor judgment in ways that deviate from rational Bayesian updating. The paper proposes a framework for identifying and measuring this bias using machine learning techniques applied to investor behavior data.