Sequential Pre-Marital Investment Games: Implications for Unemployment

Agents on the same side of a two-sided matching market (such as the marriage or labor market) compete with each other by making self-enhancing investments to improve their worth in the eyes of potential partners. Because these expenditures generally occur prior to matching, this activity has come to be known in recent literature (Peters, 2007) as pre-marital investment. This paper builds on that literature by considering the case of sequential pre-marital investment, analyzing a matching game in which one side of the market invests first, followed by the other. Interpreting the first group of agents as workers and the other group as firms, the paper provides a new perspective on the incentive structure that is inherent in labor markets. It also demonstrates that a positive rate of unemployment can exist even in the absence of matching frictions. Policy implications follow, as the prevailing set of equilibria can be altered by restricting entry into the workforce, providing unemployment insurance, or subsidizing pre-marital investment.

Computer Simulation of Crowd Dynamics and Destructive Crowd Behavior

The social processes that lead to destructive behavior in celebratory crowds can be studied through an agent-based computer simulation. Riots are an increasingly common outcome of sports celebrations, and pose the potential for harm to participants, bystanders, property, and the reputation of the groups with whom participants are associated. Rioting cannot necessarily be attributed to the negative emotions of individuals, such as anger, rage, frustration and despair. For instance, the celebratory behavior (e.g., chanting, cheering, singing) during UConn’s “Spring Weekend” and after the 2004 NCAA Championships resulted in several small fires and overturned cars. Further, not every individual in the area of a riot engages in violence, and those who do, do not do so continuously. Instead, small groups carry out the majority of violent acts in relatively short-lived episodes. Agent-based computer simulations are an ideal method for modeling complex group-level social phenomena, such as celebratory gatherings and riots, which emerge from the interaction of relatively “simple” individuals. By making simple assumptions about individuals’ decision-making and behaviors and allowing actors to affect one another, behavioral patterns emerge that cannot be predicted by the characteristics of individuals. The computer simulation developed here models celebratory riot behavior by repeatedly evaluating a single algorithm for each individual, the inputs of which are affected by the characteristics of nearby actors. Specifically, the simulation assumes that (a) actors possess 1 of 5 distinct social identities (group memberships), (b) actors will congregate with actors who possess the same identity, (c) the degree of social cohesion generated in the social context determines the stability of relationships within groups, and (d) actors’ level of aggression is affected by the aggression of other group members. Not only does this simulation provide a systematic investigation of the effects of the initial distribution of aggression, social identification, and cohesiveness on riot outcomes, but also an analytic tool others may use to investigate, visualize and predict how various individual characteristics affect emergent crowd behavior.

A Qualitative Study of the Assistive Technology Decision-Making Process in Four Pennsylvania School Districts

The purpose of this study was to gain an understanding of the Assistive Technology decision making process at four regional school districts in Pennsylvania. A qualitative case study research method involving the triangulation of data sources was implemented to collect and analyze data. Through an analysis of the data, three major topics emerged that will be addressed in the body of this paper: (a) the procedure for determining assistive technology needs and the dynamics of the decision-making process, b) the cohesiveness of Special Education and General Education programs, and c) major concerns that impact the delivery of assistive technology services.