In today's terrorism-prone and security-focused world, evacuation emergencies, drills, and false alarms are becoming more and more common. Compliance to an evacuation order made by an authority in case of emergency can play a key role in the outcome of an emergency. In case an evacuee experiences repeated emergency scenarios which may be a false alarm (e.g., an evacuation drill, a false bomb threat, etc.) or an actual threat, the Aesop's cry wolf effect (repeated false alarms decrease order compliance) can severely affect his/her likelihood to evacuate. To analyse this key unsolved issue of evacuation research, a game-theoretic approach is proposed. Game theory is used to explore mutual best responses of an evacuee and an authority. In the proposed model the authority obtains a signal of whether there is a threat or not and decides whether to order an evacuation or not. The evacuee, after receiving an evacuation order, subsequently decides whether to stay or leave based on posterior beliefs that have been updated in response to the authority's action. Best-responses are derived and Sequential equilibrium and Perfect Bayesian Equilibrium are used as solution concepts (refining equilibria with the intuitive criterion). Model results highlight the benefits of announced evacuation drills and suggest that improving the accuracy of threat detection can prevent large inefficiencies associated with the cry wolf effect. dynamics during evacuation [2][3][4], which aims at representing human movement and associated behaviour in case of different scenarios and threats [6,7], or models of authority's recommendations [8].Human behaviour in evacuation scenarios has also been investigated using Virtual Reality tools, as they allow the investigation of individual and group decision making during evacuation [9][10][11][12]. Experimental work has been performed in order to validate such models and tools, including the study of different types of emergent behaviours related to evacuation dynamics [13][14][15][16][17].The main limitations of these tools is that they consider evacuation scenarios in isolation and they address only one of the two parties involved in an evacuation, either (i) the decision making of the authority (i.e. their orders/instructions) or (ii) the actions of the evacuees. In contrast, currently there is no framework available which is able to comprehensively consider optimal decision making strategies for an authority and an evacuee at the same time in case of several repeated emergency evacuation threats. Many tragedies have demonstrated the dilemmas that decision makers may face when dealing with emergency situations. For instance, in relation to the Costa Concordia disaster, the Italian court trial is evaluating the behaviour of captain Schettino who allegedly did not order the evacuation of the ship on time [18]. Apart from the Captain's negligence or a simply incorrect assessment of the situation, the analysis of the trade-offs between the cost of a useless evacuation (i.e., the risk of "ruining" the holid...

This study considers evolutionary games with non-uniformly random matching when interaction occurs in groups of n ≥ 2 individuals using pure strategies from a finite strategy set. In such models, groups with different compositions of individuals generally co-exist and the reproductive success (fitness) of a specific strategy varies with the frequencies of different group types. These frequencies crucially depend on the matching process. For arbitrary matching processes (called matching rules), we study Nash equilibrium and ESS in the associated population game and show that several results that are known to hold for population games under uniform random matching carry through to our setting. In our most novel contribution, we derive results on the efficiency of the Nash equilibria of population games and show that for any (fixed) payoff structure, there always exists some matching rule leading to average fitness maximization. Finally, we provide a series of applications to commonly studied normal-form games. Previous versions of the paper have been circulated under the title "Evolutionary Games with Group Selection." We would like to thank

Understanding the ``fit'' of models meant to predict binary outcomes has been a long-standing problem. We propose a novel metric---the InterModel Vigorish (IMV)---for quantifying the value of change in predictive accuracy between two systems in the case of a binary outcome. The metric is based on an analogy to well-characterized physical systems with tractable probabilities. We first translate a baseline prediction of some binary outcomes into a statement about a canonical system---weighted coins---by equating the entropy of the two systems. We then use the weighted coin for a baseline prediction to establish a fair bet. For a second predictive system that we want to gauge relative to the baseline, we use the notion of expected winnings from a single-blind bet wherein the second weighted coin has replaced the first (the opposing player being blinded to this replacement). The resulting quantity has a scale that is both generally applicable and not dependent on the magnitude of the baseline prediction; moreover, it is always a statement about the change in fit relative to some baseline (which can simply be the prevalence) whereas other metrics (e.g., AUC) are stand-alone measures that need to be further manipulated to yield indices related to differences in fit across models. We illustrate the properties of this metric in simulations, and the value of it in empirical applications related to health, political affiliation, and item responses. We also reconsider results from the recent Fragile Families Challenge using the IMV metric.

Assortative mechanisms can overcome tragedies of the commons that otherwise result in dilemma situations. Assortativity criteria include various forms of kin selection, greenbeard genes, and reciprocal behaviors, usually presuming an exogenously fixed matching mechanism. Here, we endogenize the matching process with the aim of investigating how assortativity itself, jointly with cooperation, is driven by evolution. Our main finding is that full-or-null assortativities turn out to be long-run stable in most cases, independent of the relative speeds of both processes. The exact incentive structure of the underlying social dilemma matters crucially. The resulting social loss is evaluated for general classes of dilemma games, thus quantifying to what extent the tragedy of the commons may be endogenously overcome.

Assortative mechanisms can overcome tragedies of the commons that otherwise result in dilemma situations. Assortativity criteria include various forms of kin selection, greenbeard genes, and reciprocal behaviors, usually presuming an exogenously fixed matching mechanism. Here, we endogenize the matching process with the aim of investigating how assortativity itself, jointly with cooperation, is driven by evolution. Our main finding is that full-or-null assortativities turn out to be long-run stable in most cases, independent of the relative speeds of both processes. The exact incentive structure of the underlying social dilemma matters crucially. The resulting social loss is evaluated for general classes of dilemma games, thus quantifying to what extent the tragedy of the commons may be endogenously overcome.