“Evolutionary Games and Matching Rules”, International Journal of Game Theory 47(3), 707-735, 2018. [.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.
“Flexible Information Acquisition in Large Coordination Games” (submitted)
Working Paper 2018:30, Department of Economics, Lund University, 2018.
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 classes of equilibria in which players use continuous strategies and equilibria without information acquisition without assuming a normal prior for the fundamental. Equilibria where the population-wide average action is an affine function of the fundamental exist only when the fundamental is normally distributed.
“The Cry Wolf Effect in Evacuation: a Game Theoretic Approach” (R&R Physica A)
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.”
Other Work in Progress
In markets for credence goods, better-informed sellers can take advantage of less-informed buyers by providing unnecessary services (overtreatment). This paper considers heterogeneously informed customers who can signal their expertise to an expert. We show how the incentive to signal one’s expertise depends on the type of language available to buyers. We consider the cases of i) no language (where the customer cannot send any message), ii) hard evidence (where the customer can choose to disclose or hide information he has but cannot try to fake expertise), and iii) full language/cheap talk (where all messages can be sent by all types). Our results show that, under ii) and iii), full efficiency (i.e. no overtreatment) can be achieved in pooling equilibria where informed customers choose to conceal all of their information. Under ii), they can also choose to partially reveal their information, in which case the uninformed customers are the only ones who may be overtreated. Interestingly, in all other cases, partially informed customers are at least weakly better off hiding their information.
“Coordination and Information Acquisition: An Experiment”
Joint with Maxim Goryunov
“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.