Lambros Pechlivanos

Career Concerns in Teams

We investigate how changes in the commitment power of a principal affect cooperation among agents who work in a team. When the principal and her agents are symmetrically uncertain about the agents’ innate abilities, workers have career concerns. Then, unless the principal can commit herself to long‐term wage contracts, an implicit sabotage incentive emerges. Agents become reluctant to help their teammates. Anticipating this risk, and in order to induce the desired level of cooperation, the principal offers more collectively oriented incentive schemes. Temporary workers, though, are not affected by the sabotage effect, and their incentives are more individually oriented.

An intelligent agent negotiation strategy in the electronic marketplace environment

Abstract E-commerce will strongly penetrate the market if coupled with appropriate technologies and mechanisms. Mobile agents may enhance the intelligence and improve the efficiency of systems in the e-marketplace. We propose a dynamic multi-lateral negotiation model and construct an efficient negotiation strategy based on a ranking mechanism that does not require a complicated rationale on behalf of the buyer agents. This strategy can be used to extend the functionality of autonomous intelligent agents, so that they quickly reach to an agreement aiming to maximise their owner’s utility. The framework proposed considers both contract and decision issues, is based on real market conditions, and has been empirically evaluated. Moreover, it is shown that in a linear framework like the one we employ, more elaborate ranking mechanisms do not necessarily improve efficiency.

An efficient negotiation model for the next generation electronic marketplace

E-commerce is expected to dominate the market if coupled with the appropriate technologies and mechanisms. Mobile agents are one of the means that may enhance the intelligence and improve the computational efficiency of systems in the e-marketplace. In this paper, we propose a dynamic multilateral negotiation model that can be used to extend the functionality of autonomous agents, so that they can reach an agreement that maximises their owners utility. The model considers both the contract and decision issues based on the real market conditions and has been empirically evaluated.

Heterogeneity-promoting optimal procurement

When procurement takes place in the presence of horizontally differentiated contractors, the design of the object being procured affects the resulting degree of competition. This paper highlights the interaction between the optimal procurement mechanism and the design choice. Contrary to conventional wisdom, the sponsors design choice, instead of homogenizing the market to generate competition, promotes heterogeneity.

Estimation of the buyer's contract space incorporating learning from experience techniques to the seller's rationale in e-commerce context

Various state-of-the-art technologies are necessary to enhance the efficiency and increase the interest for e-commerce transactions. Mobile agents are one of the means that may enhance the intelligence and improve the effectiveness of systems in the e-marketplace. This paper aims to present the basic elements of the designed dynamic multilateral negotiation model and strategies that do not require a complicated rationale on behalf of the buyer agents. It focuses on the enhancement of the sellers reasoning component by incorporating to the designed negotiation strategies a novel mechanism for the estimation of the mutually acceptable contract region by exploiting relative market data combined with knowledge acquired from previous experience. This technique is used to extend the functionality of autonomous agents, so that they reach to an agreement faster aiming to maximise their owners utility. The framework considers both contract and decision issues, is based on real market conditions, and has been empirically evaluated.

An effective negotiation strategy for simple buyer response in e-commerce environment

Electronic commerce is expected to dominate the market if coupled with the appropriate technologies and mechanisms. Mobile agents are one of the means that may enhance the intelligence and improve the efficiency of systems in the e-marketplace. We propose a dynamic multilateral negotiation model and an efficient negotiation strategy that can be used to extend the functionality of autonomous agents, so that they reach to an agreement aiming to maximise their owners utility. The framework considers contract and decision issues, is based on real market conditions, adopts a light ranking mechanism that does not require a complicated rationale on behalf of the buyer agents, and has been empirically evaluated.

Auctioning Off with a Split Mind: Privatization Under Political Constraints

When privatizing, governments have conflicting objectives, like raising revenues and minimizing induced unemployment. We construct two mechanisms that take into account both criteria: a first-score auction in which bidders bid both in terms of price and retained excess labor, and a first-price auction in which bidders bid only over price but they also commit to keep a predetermined by the government number of employees. When bidders differ in their costs of accommodating excess labor, the resulting competition softens, and governments may optimally want to appear strong against labor redundancies. In the first auction this is done by setting a scoring rule that does not correspond to their genuine preferences, and in the second by announcing a smaller labor requirement. Nonetheless, such policies require strong commitment ability.