“Flexible Information Acquisition in Large Coordination Games” [online appendix]
Working Paper 2018:30, Department of Economics, Lund University, 2018 (Revised July 2019; submitted).
Main idea: I study how large populations process and use information about economic fundamentals when players are also driven by coordination motives and are rationally inattentive. I characterize the class of equilibria in which players use continuous strategies without assuming a normal prior for the fundamental. I show that if the prior is not normal, equilibria are not tractable. Despite that, I demonstrate that there are new insights to be gained from the study of non-normal priors.
“Signaling Expertise” (manuscript in preparation; draft available upon request)
Joint with Maria Kozlovskaya and Matteo Foschi
This paper models strategic information transmission between buyers in need of a service and an expert seller who can provide it. Buyers are heterogeneously informed about their needs and so the seller can try to offer them unnecessary services. We assume better-informed buyer types can differentiate themselves from worse-informed types by providing verifiable evidence of their expertise. We show that, in equilibrium, there can be strong incentives to hide one’s expertise. By selectively hiding their knowledge, partially-informed buyers can even completely protect themselves from seller fraud. Additionally, more-informed buyers can protect less-informed buyers from fraud by concealing their knowledge.
“Discontinuous and Continuous Stochastic Choice and Coordination in the Lab” (preliminary title; draft available upon request)
Joint with Maxim Goryunov
Main idea: We introduce a novel experimental design to study the implications of different information structures for agents’ behavior in the lab. We apply the design to a coordination game of incomplete information.
The Cry Wolf Effect in Evacuation: A Game-Theoretic Approach, Physica A 526, 120890, 2019. [pre-print] [.bib]
Joint with Enrico Ronchi and Erik Mohlin
Main idea: We build a game-theoretic model to analyse strategic interactions in an evacuation setting. We show that if the Authority cannot accurately and confidently detect threats, then this can lead to the Authority ordering evacuations too often. As a response, Evacuees only partially comply to ordered evacuations, leading to a situation reminiscent of Aesop’s story “The Boy who Cried Wolf.”
“Evolutionary Games and Matching Rules”, International Journal of Game Theory 47(3), 707-735, 2018. [Old WP] [.bib]
Joint with Martin Kaae Jensen
Main idea: We introduce a formalism (called a matching rule) that succinctly captures any kind of non-uniformly random matching for any symmetric normal-form game in an evolutionary setting. We examine how matching affects equilibrium efficiency and show that evolutionary optima can be implemented as Nash equilibria if an appropriate matching rule is chosen.
“Assortativity Evolving from Social Dilemmas”, Journal of Theoretical Biology 395, 194-203, 2016. [pre-print] [.bib]
Joint with Heinrich H Nax
Main idea: We study populations receiving fitness by playing 2-player, 2-strategy “social dilemma” games in an evolutionary setting. The assortativity of the matching process is endogenous as individuals “vote” for more or less assortativity. We assess the extent to which the populations can overcome the tragedy of the commons.
“Can social group-formation norms influence behavior? An experimental Study” [Slides](please view slides in presentation mode)
We investigate experimentally the impact of different group formation norms expressed by constant-index-of-assortativity matching rules. We implement a random matching rule as well as an assortative matching rule in a 12-player Hawk-Dove game setting. We test whether the different matching rule implementation affects participant behavior. Our findings suggest that increased assortativity induces lower aggression levels which is consistent with theoretical predictions. More than that, we get evidence of slow convergence towards equilibrium behavior. We also computationally evaluate the predictions of several learning models through simulations.