Cooperation and Competition when Bidding for Complex Projects: Centralized and Decentralized Perspectives

To successfully complete a complex project, be it a construction of an airport or of a backbone IT system, agents (companies or individuals) must form a team having required competences and resources. A team can be formed either by the project issuer based on individual agents' offers (centralized formation); or by the agents themselves (decentralized formation) bidding for a project as a consortium---in that case many feasible teams compete for the contract. We investigate rational strategies of the agents (what salary should they ask? with whom should they team up?). We propose concepts to characterize the stability of the winning teams and study their computational complexity

Offshoring in a Knowledge Economy

How does the formation of cross-country teams affect the organization of work and the structure of wages? To study this question we propose a theory of the assignment of heterogeneous agents into hierarchical teams, where less skilled agents specialize in production and more skilled agents specialize in problem solving. We first analyze the properties of the competitive equilibrium of the model in a closed economy, and show that the model has a unique and efficient solution. We then study the equilibrium of two-country model (North and South), where countries differ in their distributions of ability, and in which agents in different countries can join together in teams. We refer to this type of integration as globalization. Globalization leads to better matches for all southern workers but only for the best northern workers. As a result, we show that globalization increases wage inequality in the South but not necessarily in the North. We also study how globalization affects the size distribution of firms and the patterns of consumption and trade in the global economy.

Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior

Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs.\ individual behavior has a strong impact on the ability of evolved teams of artificial agents to exhibit teamwork in challenging tasks. However, such research only made use of single-objective evolution. In contrast, when a multiobjective evolutionary algorithm is used, populations can be subject to individual-level objectives, team-level objectives, or combinations of the two. This paper explores the performance of cooperatively coevolved teams of agents controlled by artificial neural networks subject to these types of objectives. Specifically, predator agents are evolved to capture scripted prey agents in a torus-shaped grid world. Because of the tension between individual and team behaviors, multiple modes of behavior can be useful, and thus the effect of modular neural networks is also explored. Results demonstrate that fitness rewarding individual behavior is superior to fitness rewarding team behavior, despite being applied to a cooperative task. However, the use of networks with multiple modules allows predators to discover intelligent behavior, regardless of which type of objectives are used

Coalitional Matchings

A coalitional matching is a two-sided matching problem in which agents on each side of the market may form coalitions such as student groups and research teams who - when matched - form universities. We assume that each researcher has preferences over the research teams he would like to work in and over the student groups he would like to teach to. Correspondingly, each student has preferences over the groups of students he wants to study with and over the teams of researchers he would like to learn from. In this setup, we examine how the existence of core stable partitions on the distinct market sides, the restriction of agents’ preferences over groups to strict orderings, and the extent to which individual preferences respect common rankings shape the existence of core stable coalitional matchings.Coalitions, Common Rankings, Core, Stability, Totally Balanced Games, Two-Sided Matchings

On the Positive Effects of Overcon fident Self-Perception in Teams

In this paper, we study the individual payoff effects of overconfident self-perception in teams. In particular, we demonstrate that the welfare of an overconfident agent in a team of one rational and one overconfident agent or a team of two overconfident agents can be higher than that of the members of a team of two rational agents. This result holds irrespective of the assumption about the agents' awareness of their colleague's bias. Moreover, we show that an overcondent agent is always better of when he is unaware of a potential bias of his colleague