Empirical Insights on Information, Incentives, and Uncertainty in Complex Decision Environments
(A2026-132735)

AUTHORS

Daniela Schmitt, Nova School of Business and Economics; Vladimir Pavlov, University College London; Aurelie Lemmens, Rotterdam School of Management (RSM) Erasmus University Rotterdam; Rodrigo Belo, Nova School of Business and Economics

ABSTRACT

This Special Session presents four empirical papers that investigate how information, incentives, and uncertainty structure decision-making in complex environments. Together, the contributions show how actors interpret signals, respond to incentive mechanisms, and allocate attention under risk or ambiguity. By drawing on structural modeling, causal inference, and machine learning, the papers collectively advance understanding of the mechanisms that shape engagement, strategic behavior, and economic outcomes. The session highlights both methodological rigor and substantive insight, offering implications for academic research as well as managerial and policy decision-making. Balancing Equity and Efficiency in Retail License Allocation by Vladimir Pavlov, studies the equity–efficiency trade-off in cannabis license allocation. Using a structural model of demand paired with auction-equilibrium simulations, it evaluates how bid-preference mechanisms for minority applicants affect revenue and representation. The findings show that auction-based allocations can outperform lotteries while revealing persistent gaps relative to first-best outcomes, offering guidance for designing fair and effective allocation mechanisms in settings with downstream competition. Retention Through Relevance: Addressing Exit Motives through Targeting Policies by Arno De Caigny, Aurélie Lemmens, Kristof Coussement, and Koen De Boek, examines how firms can tailor retention interventions to predicted exit motives. Drawing on multi-year churn data, field experimentation, and causal machine learning, the study demonstrates that motive-aligned interventions significantly improve retention rates relative to generic policies. The work underscores the importance of modeling behavioral heterogeneity and provides a scalable framework for proactive retention design. Inflating Views: The Effect of Paid Views on Music Consumption by May Truong and Rodrigo Belo, analyzes the impact of purchased views on YouTube performance. Leveraging a platform policy change that makes paid views identifiable and a dataset of K-pop videos, the authors show that purchased views generate short-term increases in organic views, digital chart success, and physical album sales. The results contribute to broader discussions on digital signals, social influence, and the economic consequences of artificially inflated metrics. The Role of Surprise and Suspense for Digital News Providers by Ron Berman and Daniela Schmitt, explores how uncertainty in sports events shapes demand for digital journalism. By linking betting-spread-based measures of suspense and surprise to article-level readership while correcting for editorial selection, the study demonstrates how unpredictable events influence traffic patterns and the economic value of coverage.