Power struggles and gender discrimination in the workplace

This study explores the impact of power struggles on the emergence of gender discrimination within the organizational culture. Utilizing an agent-based model, we simulate power struggles as an asymmetric hawk and dove game where agents may categorize their opponents based on their observable traits to make effective decisions. Our model includes two categories: prestigious education and sex, with prestigious education having higher struggling power. We examine three categorization strategies: fine-grained, regular-grained, and coarse-grained categorization. Our results indicate that fine-grained categorizers gain an advantage when the cost of fighting is low. In contrast, coarse-grained categorizers become more peaceful, leading to an advantage when the cost of fighting is high. Our simulation reveals that although there is no meaningful difference between sexes, different behaviors emerge when fine-categorizing agents dominate

Optimal Categorization

The importance of categorical reasoning in human cognition is well-established in psychology and cognitive science, and it is generally acknowledged that one of the most important functions of categorization is to facilitate prediction. This paper provides a model of optimal categorization. In the beginning of each period a subject observes a two-dimensional object in one dimension and wants to predict the object's value in the other dimension. The subject partitions the space of objects into categories. She has a data base of objects that were observed in both dimensions in the past. The subject determines what category the new object belongs to on the basis of observation of its first dimension. The average value in the second dimension, of objects in this category in the data base, is used as prediction for the object at hand. At the end of each period the second dimension is observed and the observation is stored in the data base. The main result is that the optimal number of categories is determined by a trade-off between (a) decreasing the size of categories in order to enhance category homogeneity, and (b) increasing the size of categories in order to enhance category sample size.Categorization; Priors; Prediction; Similarity-Based Reasoning.

Menu choices with categories

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Departamento de Economia, Programa de Pós-Graduação em Economia, 2017.Este trabalho apresenta uma representação de preferências sobre menus na qual a tomadora de decisão categoriza o conjunto de alternativas. Tendo uma preferência estrita sobre as alternativas, ela escolhe o menu hoje sabendo que amanhã irá escolher a melhor alternativa do menu que pertence a uma categoria escolhida aleatoriamente. A incerteza sobre a relevância de cada categoria no ato de escolha leva a uma preferência por flexibilidade. Serão desenvolvidas representações ordinal e aditiva e será mostrado que aditividade impõe restrições adicionais apenas quando as categorias são disjuntas dois-a-dois.This work presents a representation of preference over menus in which the decision-maker categorizes the set of alternatives. Endowed with a strict preference over the alternatives, she chooses a menu today knowing that tomorrow she will pick the best alternative in the menu that belongs to a randomly drawn category. The uncertainty about the relevance of each category in the act of choice leads to a preference for flexibility. It will be developed ordinal and additive representations and it will be shown that additivity imposes additional restrictions only when the categories are pairwise disjoint

Categorization and Coordination

The use of coarse categories is prevalent in various situations and has been linked to biased economic outcomes, ranging from discrimination against minorities to empirical anomalies in financial markets. In this paper we study economic rationales for categorizing coarsely. We think of the way one categorizes one's past experiences as a model of the world that is used to make predictions about unobservable attributes in new situations. We first show that coarse categorization may be optimal for making predictions in stochastic environments in which an individual has a limited number of past experiences. Building on this result, and this is a key new insight from our paper, we show formally that cases in which people have a motive to coordinate their predictions with others may provide an economic rationale for categorizing coarsely. Our analysis explains the intuition behind this rationale

Rule Rationality

We study the strategic advantages of following rules of thumb that bundle different games together (called rule rationality) when this may be observed by one's opponent. We present a model in which the strategic environment determines which kind of rule rationality is adopted by the players. We apply the model to characterize the induced rules and outcomes in various interesting environments. Finally, we show the close relations between act rationality and “Stackelberg stability” (no player can earn from playing first)

Rule Rationality

We study the strategic advantages of following rules of thumb that bundle different games together (called rule rationality) when this may be observed by one's opponent. We present a model in which the strategic environment determines which kind of rule rationality is adopted by the players. We apply the model to characterize the induced rules and outcomes in various interesting environments. Finally, we show the close relations between act rationality and “Stackelberg stability” (no player can earn from playing first)

The Benefits of Coarse Preferences

We study the strategic advantages of coarsening one’s utility by clustering nearby payoffs together (i.e., classifying them the same way). Our solution concept, coarse-utility equilibrium (CUE) requires that (1) each player maximizes her coarse utility, given the opponent’s strategy, and (2) the classifications form best replies to one another. We characterize CUEs in various games. In particular, we show that there is a qualitative difference between CUEs in which only one of the players clusters payoffs, and those in which all players cluster their payoffs, and that the latter type induce players to treat co-players better than in Nash equilibria in the large class of games with monotone externalities