Group Fairness and Game Theory

In this paper I extend Matthew Rabins model of fairness equilibria (1993) to groups of individuals. This allow me to introduce three aspects from reality that are absent in game theory: i) individuals discriminate in favor of members of their own groups, ii) individuals like individuals that not only are kind to them, but are kind to other individuals, specially individuals of their own groups, and iii) individuals discrimate in favor of members of groups they like. I define a new equilibrium that takes in consideration this emotions, what I call group fairness equilibrium. Rabin defines the mutual-max outcomes for a single game as outcomes where each player maximize the other players material mayoffs and the mutual-min outcomes as outcomes where each player minimize the other players material payoffs. Some basic results of my model are that a combination of strict Nash equilibrium in several games, will always be a group fairness equibrium for large values of the material payoffs, and that any outcome that is either strictly mutual-max for both games or strictly mutual-min for both games is a group fairness equilibrium for large values of the material payoffs.

Habit Formation and Oligopolistic Competition

In this paper I introduce competition in the habit formation literature by extending the idea of habit formation to the characteristics of the products. I model a two-period game in which two rms can enter a market and compete with each other, and individuals’ favorite characteristics in the second period are the characteristics of the product they consumed in the rst period. I nd that if two rms enter the market, they do it sequentially. That is, one rm enters in the rst period and attracts individuals’ preferences to the characteristics of its product, while the other rm enters in the second period and competes for the individuals that have grown to prefer the characteristics of the original product. However, the second rm’s product is similar to the original one, but not exactly the same. The model also applies to habit formation for different markets with characteristics in common. For example, sweetness is a common characteristic of sodas and ice-cream, and consumption of a product in one market affects the preferences for products in the other market. I nd that new rms produce products with similar characteristics not only to a product that has entered the same market, but to products that have entered other markets with characteristics in common. I apply my model to durable goods and nd a new explanation for fashion: rms can take advantage of habit formation by showing their products in the media to generate demand for new durable goods.

Front propagation in an exclusion one-dimensional reactive dynamics

We consider an exclusion process representing a reactive dynamics of a pulled front on the integer lattice, describing the dynamics of first class $X$ particles moving as a simple symmetric exclusion process, and static second class $Y$ particles. When an $X$ particle jumps to a site with a $Y$ particle, their position is intechanged and the $Y$ particle becomes an $X$ one. Initially, there is an arbitrary configuration of $X$ particles at sites $..., -1,0$, and $Y$ particles only at sites $1,2,...$, with a product Bernoulli law of parameter $\rho,0<\rho<1$. We prove a law of large numbers and a central limit theorem for the front defined by the right-most visited site of the $X$ particles at time $t$. These results corroborate Monte-Carlo simulations performed in a similar context. We also prove that the law of the $X$ particles as seen from the front converges to a unique invariant measure. The proofs use regeneration times: we present a direct way to define them within this context.Comment: 19 page

Alcohol Myopia and Risk Taking

The aim of this paper is to develop a model that explains how the consumption of some additive substances a¤ects an individual?s choice between risky alternatives. We do this by assuming that some additives substances, speci?cally alcohol, increase individual?s present bias. As individuals that consume alcohol show greater preference for the present and less for the future, they would ?nd risky choices with rewards in the present and costs in the future more attractive. Theferore, an individual that wouldn´t have accepted a lottery may do so after consuming alcohol and he regret his decision after the alcohol in his blood is eliminated. We analyze the e¤ect of two taxes in discouraging a risky activity: a tax on the consumption of alcohol and a tax (or penalty) if the future costs of the lottery are realized.habit-formation, risk taking, alcohol consumption

Torture and Its Definition in International Law—An Interdisciplinary Approach, by Metin Başoğlu

Torture and Its Definition in International Law—An Interdisciplinary Approach was edited by Metin Başoğlu, and written by him and another sixteen experts in the medicolegal aspects of torture and cruel, inhuman, or degrading treatment or punishment (CIDT/P). The book has 506 pages and 16 chapters, which are organised into four parts: “Behavioral Science Perspectives”; “International Law Perspectives”; “Enhanced Interrogation Techniques: Definitional Issues”; and “Discussion and Conclusions”. The book is for health, legal and human rights professionals, beyond just those just working with victims of torture&nbsp;and CIDT/P, and is of interest to those who work with victims of other violent crimes, such as child abuse, interpersonal abuse, and forced displacement. The book raises many important questions

Current Trends in Simheuristics: from smart transportation to agent-based simheuristics

Simheuristics extend metaheuristics by adding a simulation layer that allows the optimization component to deal efficiently with scenarios under uncertainty. This presentation reviews both initial as well as recent applications of simheuristics, mainly in the area of logistics and transportation. We also discuss a novel agent-based simheuristic (ABSH) approach that combines simheuristic and multi-agent systems to efficiently solve stochastic combinatorial optimization problems. The presentation is based on papers [1], [2], and [3], which have been already accepted in the prestigious Winter Simulation Conference.Peer ReviewedPostprint (published version