Using machine learning for communication classification

The present study explores the value of machine learning techniques in the classification of communication content in experiments. Previously human-coded datasets are used to both train and test algorithm-generated models that relate word counts to categories. For various games, the computer models of the classification are able to match out-of-sample the human classification to a considerable extent. The analysis raises hope that the substantial effort going into such studies can be reduced by using computer algorithms for classification. This would enable a quick and replicable analysis of large-scale datasets at reasonable costs and widen the applicability of such approaches. The paper gives an easily accessible technical introduction into the computational method

Last minute policies and the incumbency advantage

This paper models a purely informational mechanism behind the incumbency advantage. In a two-period electoral campaign with two policy issues, a specialized incumbent and an unspecialized, but possibly more competent challenger compete for election by voters who are heterogeneously informed about the state of the world. Due to the asymmetries in government responsibility between candidates, the incumbent's statement may convey information on the relevance of the issues to voters. In equilibrium, the incumbent sometimes strategically releases his statement early and thus signals the importance of his signature issue to the voters. We find that, since the incumbent's positioning on the issue reveals private information which the challenger can use in later statements, the incumbent's incentives to distort the campaign are decreasing in his quality, as previously documented by the empirical literature. The distortions arising in equilibrium are decreasing in the incumbent's effective ability; however, the distortions may be increasing in the incumbent's reputation of expertise on his signature issue

The winner’s curse: conditional reasoning & belief formation

We investigate the relevance of conditional reasoning and belief formation for the occurrence of the winner’s curse with the help of two experimental manipulations. First, we compare results from a very simple common-value auction game with results from a transformed version of this game that does not require any conditioning on future events. In human opponent settings, we observe significant differences in behavior across the two games. Second, we investigate subjects’ behavior when they face naïve computerized opponents and after they have faced them. We find that both strong and weak assistance in belief formation changes subjects’ play significantly in the auction game. Overall, the results suggest that the difficulty of conditioning on future events is at least as important in explaining frequent occurrences of the winner’s curse as is the challenge to form beliefs

Strategic thinking: The influence of the game

In order to assess the extent to which features of a game affect the strategic sophistication of the people involved, this study investigates the relevance of differing objectives (matching/mismatching) and of virtually moving first or second in the “hide and seek” game. In three different treatments, mismatchers and matchers are not found to exhibit significantly different levels of reasoning although level averages and winning probabilities always are in favor of the matchers. Varying the virtual timing of the game has a significant impact on the shape of the level distribution. The analysis relies on intrateam communication, whose coding is shown to be stable and replicable

The nature of social learning: Experimental evidence

In the wide economic literature on social learning, many types of behavior – rational and non-rational – have been proposed. I suggest a level-k model that unifies many of them in one framework. This paper analyzes experimental data that is able to distinguish between levels of reasoning at the individual level. It relies on rich, existing data from Çelen and Kariv (2004) as well as new experimental data that includes written accounts of reasoning from incentivized intra-team communication. Three datasets provide consistent evidence that naïve inference in form of the best response to truthful play is the most common approach to social learning. The empirical type distributions feature heterogeneity similar to other level-k applications

Herding differently : A level-k model of social learning

This paper proposes a behavioral model of social learning that unies various forms of inferential reasoning in one hierarchy of types. Iterated best responses that are based on uninformative level-0 play lead to the following of the private information (level-1), to the following of the majority (level-2), to a differentiated view on predecessors (level-3), etc. I present evidence from three sources that these are the prevalent types of reasoning in social learning: a review of social learning studies, existing data from Celen and Kariv (2004) as well as new experimental data that includes written accounts of reasoning from incentivized intra-team communication

Persuasion: An experimental study of team decision making

This paper studies persuasion within teams and investigates why teams commonly take, by some measures, better decisions than individuals. The analysis is based on data from electronic communication within teams of two players. Thanks to the experimental design, changes of an individual’s decision can be attributed to the content of the team partner’s message. The results for knowledge-related and strategic problems show that individuals’ decisions change upon receiving more informative and sophisticated arguments and remain the same otherwise. This individual behavior is an essential part of the information aggregation in teams and can explain the advantage of teams in decision making and in games

The winner's curse: Conditional reasoning and belief formation

In explaining the winner's curse, recent approaches have focused on one of two cognitive processes: conditional reasoning and belief formation. We provide the first joint experimental analysis of the role of these two obstacles. First, we observe that overbidding decreases significantly between a simple common-value auction and a transformed version of this auction that does not require conditional reasoning. Second, assistance in belief formation leads to comparable behavioral changes in both games. The two effects are of similar magnitude and amplify each other when jointly present. We conclude that the combination and the interaction of the two cognitive processes in auctions lead to relatively low strategic sophistication compared to other domains. The study's focus on games' objective cognitive challenges is potentially useful for improving predictions across games and complements the common focus on behavioral models and their explanatory power

Last minute policies and the incumbency advantage

This paper models a purely informational mechanism behind the incumbency advantage. In a two-period electoral campaign with two policy issues, a specialized incumbent and an unspecialized, but possibly more competent challenger compete for election by voters who are heterogeneously informed about the state of the world. Due to the asymmetries in government responsibility between candidates, the incumbent's statement may convey information on the relevance of the issues to voters. In equilibrium, the incumbent sometimes strategically releases his statement early and thus signals the importance of his signature issue to the voters. We find that, since the incumbent's positioning on the issue reveals private information which the challenger can use in later statements, the incumbent's incentives to distort the campaign are decreasing in his quality, as previously documented by the empirical literature. The distortions arising in equilibrium are decreasing in the incumbent's effective ability; however, the distortions may be increasing in the incumbent's reputation of expertise on his signature issue

Multi-dimensional reasoning in competitive resource allocation games: Evidence from intra-team communication

We experimentally investigate behavior and reasoning in various competitive resource allocation games with large strategy spaces. In the experiment, a team of two players plays as one entity against other teams. Team members communicate with one another before choosing a strategy. We analyze their messages using three different classification approaches and find that the vast majority of players think in terms of dimensions or characteristics of strategies rather than in terms of individual elements of the strategy space. Furthermore, the dimensions’ metric allows linking the reasoning across the different games. Thus, we suggest that multi-dimensional reasoning is a frequently used decision procedure that connects the behavior observed in various resource allocation games