Current Projects

“Flexible Information Acquisition in Large Coordination Games” [online appendix]
Working Paper 2018:30, Department of Economics, Lund University, 2018 (Revised May 2020). [New version!]
Main idea: I theoretically study how large populations or rationally inattentive individuals behave in the presence of fundamental and coordination motives. Without assuming a normal prior for the fundamental, I characterize the class of equilibria in which players use continuous strategies. I demonstrate that small departures from normality can lead to distributions of equilibrium actions that differ significantly from those of Gaussian models.

“Expertise Disclosure in Markets for Credence Goods”

Joint with Maria Kozlovskaya and Matteo Foschi
Main idea: We theoretically study how heterogeneously knowledgeable buyers can optimally disclose their knowledge in order to avoid being offered unnecessary services by expert sellers. In equilibrium, it is frequently not optimal for buyers to disclose their level of knowledge, and this–when sellers are unable to distinguish “feigned ignorance” from a genuine lack of expertise–may completely eliminate seller exploitation in pooling equilibria.

“Discontinuous and Continuous Stochastic Choice and Coordination in the Lab” [preliminary draft]
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.

“Preferences as Heuristics” (early stage)
Joint with Erik Mohlin
Main idea: We aim to explain observed behavioral patterns of lab experiment participants by proposing a new mechanism for preference evolution. We propose that individuals’ inability to have precise information about their environment can lead to the development of social preferences as a heuristic to compensate for lack of precision.


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.

Old Project

“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.