Performance pay, group selection and group performance

Within a laboratory experiment we investigate a principal-agent game in which agents may, first, self-select into a group task (GT) or an individual task (IT) and, second, choose work effort. In their choices of task and effort the agents have to consider pay contracts for both tasks as offered by the principal. The rational solution of the game implies that contract design may not induce agents to select GT and provide positive effort in GT. Furthermore it predicts equal behavior of agents with different productivities. In contrast, considerations of trust, reciprocity and cooperation – the social-emotional model of behavior – suggest that contract design can influence the agents’ willingness to join groups and provide effort. We analyze the data by applying a two-step regression model (multinomial logit and tobit) and find that counter to the rational solution, contract design does influence both, task selection and effort choice. The principal can increase participation in work groups and can positively influence group performance. Larger payment increases the share of socially motivated agents in work groups. The selection effect is larger than the motivation effect

Ms Pac-Man versus Ghost Team CEC 2011 competition

Games provide an ideal test bed for computational intelligence and significant progress has been made in recent years, most notably in games such as Go, where the level of play is now competitive with expert human play on smaller boards. Recently, a significantly more complex class of games has received increasing attention: real-time video games. These games pose many new challenges, including strict time constraints, simultaneous moves and open-endedness. Unlike in traditional board games, computational play is generally unable to compete with human players. One driving force in improving the overall performance of artificial intelligence players are game competitions where practitioners may evaluate and compare their methods against those submitted by others and possibly human players as well. In this paper we introduce a new competition based on the popular arcade video game Ms Pac-Man: Ms Pac-Man versus Ghost Team. The competition, to be held at the Congress on Evolutionary Computation 2011 for the first time, allows participants to develop controllers for either the Ms Pac-Man agent or for the Ghost Team and unlike previous Ms Pac-Man competitions that relied on screen capture, the players now interface directly with the game engine. In this paper we introduce the competition, including a review of previous work as well as a discussion of several aspects regarding the setting up of the game competition itself. © 2011 IEEE

Job Mix, Performance Pay, and Matching Outcomes: Contracting with Multiple Heterogeneous Agents

We examine the problem of designing performance contracts with multiple agents when principals must compete for quality teams from a heterogeneous pool of agents. The trade-off principals face between good recruiting and good team performance provides micro foundations for agents to form stable matches, and for initially identical principals to adopt different organizational schemes. The equilibrium pattern of team formation exhibits two distinct, and inversely related, forms of assortative matching. We find that a greater share of principals offering diverse performance incentives across teammates (extensive margin), leads to a lesser degree of heterogeneity in abilities within teams on average (intensive margin). We apply the model to firm behavior to examine the mix of jobs offered and the degree of performance pay in a general equilibrium environment. At the aggregate level, increases in the supply of high-skilled workers leads to a polarization of jobs offered, i.e. relatively greater use of high- and low- skill occupations, consistent with changing labor demands in recent history. Moreover, skill accumulation among the labor force induces more firms to offer a steep set of performance contracts.Multi-Agent Contracting, Matching, Job Design

Mean-Field-Type Games in Engineering

A mean-field-type game is a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile but also the joint distributions of state-action pairs. This article presents some engineering applications of mean-field-type games including road traffic networks, multi-level building evacuation, millimeter wave wireless communications, distributed power networks, virus spread over networks, virtual machine resource management in cloud networks, synchronization of oscillators, energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201