A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

Moral Structure Falls Out of General Event Structure

The notion of agency has been explored within research in moral psychology and, quite separately, within research in linguistics. Moral psychologists have suggested that agency attributions play a role in moral judgments, while linguists have argued that agency attributions play a role in syntactic intuitions. To explore the connection between these two lines of research, we report the results of an experiment in which we manipulate syntactic cues for agency and show a corresponding impact on moral judgments. This result suggests that the two effects observed previously — in morality and in syntax — might each be a reflection of a more general capacity to understand event structure

Sharing or gambling? On risk attitudes in social contexts

This paper investigates experimentally whether risk attitudes are stable across social contexts. In particular, it focuses on situations where some resource (for instance, a position, decision power, a bonus) has to be allocated between two parties: the decision maker can either opt for sharing the resource or for using a random device that allocates the entire prize to one of the two parties. By varying the relative situation of the decision maker with respect to the other party, we show that risk attitude is strongly affected by social contexts: participants in the experiment seem to be relatively risk seeking when they possess a relatively weaker position than the other party and risk averse when the opposite is true. Our main average results seem to be driven by the behavior of around a quarter of subjects whose choices appear to be fully determined by social comparisons. Various interpretations of the behavior are provided linking our results to preferences under risk with a social reference point and on status-seeking preferences

The Impact of Motivational Factors on Daily Fantasy Sports Participation

Since the passing of the Unlawful Internet Gambling Act (UIGEA) in 2006, the fantasy sports world has had a tumultuous decade. Shortly after the passing of UIGEA, daily fantasy sports became marketable, and saw several years of tremendous growth. However, recent legal issues have clouded the industry, and lawmakers have questioned whether daily fantasy sports indeed fall under the exception granted by UIGEA as a “game of skill”, or whether the games are illegal gambling. This study is meant to look at what motivates fantasy sports participants, especially through this time of turmoil in the industry. It specifically looks at how players’ competitive, knowledge-seeking, or social tendencies affect their participation habits. Additionally, this thesis explores the effect that marketing messaging that portrays a daily fantasy sports website as a perfect place to satisfy these individual traits has on player participation. Finally, this study investigates the role that a sense of fairness plays in shaping players’ perceptions of these websites. To study these things, we began by looking at previous studies that focus on competition, knowledge, and social factors, both in the fantasy sports field and elsewhere, and creating a literature review. Following the literature review, an experiment was created, which tested these three scales as well as the perception of fairness, using a fictitious fantasy sports site, Fantasyland. The results obtained from this experiment indicate that those with competitive or social dispositions are the most likely to try fantasy sports. Additionally, it was found that those who are socially motivated are more likely to recommend a fantasy sports site to friends or strangers. Lastly, the perception of fairness did not have a direct effect of participants, although in one case, it did positively impact an individual’s willingness to try a daily sports website. These results and their implications as well as future research directions are outlined in the concluding discussion section

What If? The Art of Scenario Thinking for Nonprofits

Gives an overview of scenario thinking customized for a nonprofit audience. Outlines the basic phases of scenario development, and provides examples and advice for putting the process into practice. Includes an annotated bibliography of select readings

Network-constrained models of liberalized electricity markets: the devil is in the details

Numerical models for electricity markets are frequently used to inform and support decisions. How robust are the results? Three research groups used the same, realistic data set for generators, demand and transmission network as input for their numerical models. The results coincide when predicting competitive market results. In the strategic case in which large generators can exercise market power, the predicted prices differed significantly. The results are highly sensitive to assumptions about market design, timing of the market and assumptions about constraints on the rationality of generators. Given the same assumptions the results coincide. We provide a checklist for users to understand the implications of different modelling assumptions