Abstract
We examine markets in which companies are allowed to obfuscate prices and customers are forced to rely on their direct experience and signals they receive from social networks to make purchasing decisions. We compare interventions by public regulators that impose constraints on the amount of price obfuscation with those that augment customers’ cognitive capacities in order to determine which class of policies enhances social welfare the most in such a setting. We implement the strategic behavior of companies by a recursive simulation of n-th order rationality and extend the Experience Weighted Attractions framework to incorporate information from social networks for adaptive customers. Therefore, we search for market designs that are robust with respect to bounded rationality of companies and customers.
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Latek, M., Kaminski, B. (2009). Social Learning and Pricing Obfuscation. In: Hernández, C., Posada, M., López-Paredes, A. (eds) Artificial Economics. Lecture Notes in Economics and Mathematical Systems, vol 631. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02956-1_9
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DOI: https://doi.org/10.1007/978-3-642-02956-1_9
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