The Full Strategy Minority Game (FSMG) is an instance of the Minority Game (MG) which includes a single copy of every potential agent. In this work, we explicitly solve the FSMG thanks to certain symmetries of this game. Furthermore, by considering the MG as a statistical sample of the FSMG, we compute approximated values of the key variable {\sigma}2/N in the symmetric phase for different versions of the MG. As another application we prove that our results can be easily modified in order to handle certain kind of initial biased strategies scores, in particular when the bias is introduced at the agents' level. We also show that the FSMG verifies a strict period two dynamics (i.e., period two dynamics satisfied with probability 1) giving, to the best of our knowledge, the first example of an instance of the MG for which this feature can be analytically proved. Thanks to this property, it is possible to compute in a simple way the probability that a general instance of the MG breaks the period two dynamics for the first time in a given simulation.Comment: To appear in Physica

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We calculate the standard deviation of (N 1 − N 0 ), the difference of the number of agents choosing between the two alternatives of the minority game. Our approach is based on two approximations: we use the whole set of possible strategies, rather than only those distributed between the agents involved in a game; moreover, we assume that a period-two dynamics discussed by previous authors is appropriate within the range of validity of our work. With these appproximations we introduce a set of states of the system, and are able to replace time averages by ensemble averages over these states. Our results show a very good agreement with simulations results for most part of the informationally efficient phase.

We partially modify the rules of the Minority Game (MG) by introducing some degree of local information in the game, which is only available for some agents, called the interacting agents. Our work shows that, for small values of the new parameter of the model (the fraction of interacting agents), there is an improvement of the use of the resources with respect to the MG, while as this number grows the response of the system changes, and ends up behaving worst than the usual MG.

In this work we analyze the language competition problem by using an interacting agent-based model which interpolates the classical Schelling and Voter models. Briefly, an agent may change its place of residence or his language when he is surrounded by more individuals of the other kind than the ones he can tolerate. We analyze this dynamic process in terms of the free space to move in, the pressure to change the language, and the propensity to change location. We identify the different regimes and the relationship with the language competition problem.

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This work presents a new method for assisting in the identification process of missing persons in several contexts, such as enforced disappearances. We apply a Bayesian technique to incorporate non-genetic variables in the construction of prior information. in that way, we can learn from the already-solved cases of a particular mass event of death, and use that information to guide the search among remaining victims. this paper describes a particular application to the proposed method to the identification of human remains of the so-called disappeared during the last dictatorship in Argentina, which lasted from 1976 until 1983. Potential applications of the techniques presented hereby, however, are much wider. the central idea of our work is to take advantage of the already-solved cases within a certain event to use the gathered knowledge to assist in the investigation process, enabling the construction of prioritized rankings of victims that could correspond to each certain unidentified human remains.The process of identification that guides searches in contexts such as disaster victim identification (DVI), missing person identification (MPI), migration and other situations of violence (OSV) requires the collection of background information from different sources (e.g. legal courts documents, testimonies from survivors, witnesses and families of the missing) 1 . The identification process is essential not only for the sake of Justice and for humanitarian reasons 2 but also to offer answers to victims' families and friends 3-6 . The process of identification usually includes both, (i) the construction of hypotheses of identity from the analysis of such background information that needs to be evaluated at a later stage through genetic evidence, and (ii) the validation of the information gathered from a genetic DNA-led process through the comparison of the ante-mortem and post-mortem information. It is our aim in this paper to describe a general method which could contribute to the investigation process by taking advantage of the already-solved cases of a particular mass death event, to use that elicited knowledge for guiding new searches of related unidentified human remains (UHR). Whenever a pattern does exist within the already-solved cases, the method presented here allows us to make predictions in the identification process of the cases still unsolved, and it also makes it possible to minimize any bias from the researcher. Predictions are understood as the act of prioritizing some individuals over others to be more likely related to certain UHR within the same event. The available information is: (i) information regarding the context of the mass death event, such as date and place in which the event has occurred and the total number of victims, (ii) a database with information of reported victims who are potential candidates to correspond with a set of UHR. That database also includes non-genetic variables amenable to be modeled mathematically in the search for patterns, and (iii) information of the set of alr...