The coordinating power of social norms

A popular empirical technique to measure norms uses coordination games to elicit what subjects in an experiment consider appropriate behavior in a given situation (Krupka and Weber, 2013). The Krupka-Weber method works under the assumption that subjects use their normative expectations to solve the coordination game. However, subjects might use alternative focal points to coordinate, in which case the method may deliver distorted measurements of the social norm. We test the vulnerability of the Krupka-Weber method to the presence of alternative salient focal points in two series of experiments with more than 3000 subjects. We find that the method is robust, especially when there are clear normative expectations about what constitutes appropriate behavior

Inductive probabilistic taxonomy learning using singular value decomposition

Capturing word meaning is one of the challenges of natural language processing (NLP). Formal models of meaning, such as networks of words or concepts, are knowledge repositories used in a variety of applications. To be effectively used, these networks have to be large or, at least, adapted to specific domains. Learning word meaning from texts is then an active area of research. Lexico-syntactic pattern methods are one of the possible solutions. Yet, these models do not use structural properties of target semantic relations, e.g. transitivity, during learning. In this paper, we propose a novel lexico-syntactic pattern probabilistic method for learning taxonomies that explicitly models transitivity and naturally exploits vector space model techniques for reducing space dimensions. We define two probabilistic models: the direct probabilistic model and the induced probabilistic model. The first is directly estimated on observations over text collections. The second uses transitivity on the direct probabilistic model to induce probabilities of derived events. Within our probabilistic model, we also propose a novel way of using singular value decomposition as unsupervised method for feature selection in estimating direct probabilities. We empirically show that the induced probabilistic taxonomy learning model outperforms state-of-the-art probabilistic models and our unsupervised feature selection method improves performance

Tournaments and piece rates revisited: a theoretical and experimental study of output-dependent prize tournaments

Tournaments represent an increasingly important component of organizational compensation systems. While prior research focused on fixed-prize tournaments where the prize to be awarded is set in advance, we introduce ‘output-dependent prizes’ where the tournament prize is endogenously determined by agents’ output—it is high when the output is high and low when the output is low. We show that tournaments with output-dependent prizes outperform fixed-prize tournaments and piece rates. A multi-agent experiment supports the theoretical result

A Survey of Experimental Research on Contests, All-Pay Auctions and Tournaments

Many economic, political and social environments can be described as contests in which agents exert costly efforts while competing over the distribution of a scarce resource. These environments have been studied using Tullock contests, all-pay auctions and rankorder tournaments. This survey provides a review of experimental research on these three canonical contests. First, we review studies investigating the basic structure of contests, including the contest success function, number of players and prizes, spillovers and externalities, heterogeneity, and incomplete information. Second, we discuss dynamic contests and multi-battle contests. Then we review research on sabotage, feedback, bias, collusion, alliances, and contests between groups, as well as real-effort and field experiments. Finally, we discuss applications of contests to the study of legal systems, political competition, war, conflict avoidance, sales, and charities, and suggest directions for future research. (author's abstract

Singular value decomposition for Feature Selection in Taxonomy Learning

In this paper, we propose a novel way to include unsupervised feature selection methods in probabilistic taxonomy learning models. We leverage on the computation of logistic regression to exploit unsupervised feature selection of singular value decomposition (SVD). Experiments show that this way of using SVD for feature selection positively affects performances