Robust Mechanism Design under Behavioral Signaling Deviations

While classic mechanism design assumes rational agents and equilibrium strategies, the literature increasingly acknowledges behavioral deviations (see, e.g., Mishra 2019, Protte et al. 2020, Abdallah et al. 2022). However, the impact of these deviations on signaling equilibria is underexplored. This research proposes designing mechanisms that explicitly model and utilize predictable behavioral biases—like overconfidence or anchoring—in agents’ information processing and signaling. For example, in auction or resource allocation settings, how might mechanisms harness behavioral over-signaling or under-signaling to steer outcomes toward efficiency or fairness? This approach extends traditional mechanism design by embedding behavioral economics insights (Protte et al.; Abdallah et al.) directly into the design of signaling structures, rather than treating deviations as noise. It’s particularly promising because real-world systems (e.g., online platforms, cyber-physical systems) are rife with such deviations, so mechanisms robust to them could yield better practical performance and new theory.

References:

1. TASHAROK: Using Mechanism Design for Enhancing Security Resource Allocation in Interdependent Systems. Mustafa Abdallah, Daniel W. Woods, Parinaz Naghizadeh Ardabili, Issa M. Khalil, T. Cason, S. Sundaram, S. Bagchi (2022). IEEE Symposium on Security and Privacy.
2. Behavioral Economics for Human-in-the-Loop Control Systems Design: Overconfidence and the Hot Hand Fallacy. Marius Protte, R. Fahr, D. Quevedo (2020). IEEE Control Systems.
3. Behavioral Mechanism Design. D. Mishra (2019). The Future of Economic Design.