Multi-agent multi-team systems are commonly seen in environments where hierarchical layers of goals are at play. For example, theater-wide combat scenarios where multiple levels of command and control are required for proper execution of goals from the general to the foot soldier. Additionally,...

Academic research activity monitoring and evaluation are major tasks of a university knowledge management system in the context of the university activity assessments for various national and international classifications. The paper proposes the architecture of a multi-agent system developed for...

As self-learning pricing algorithms become popular, there are growing concerns among academics and regulators that algorithms could learn to collude tacitly on non-competitive prices and thereby harm competition. I study popular reinforcement learning algorithms and show that they develop...

Investors increasingly can obtain assistance from "robo-advisors," artificial intelligence - enabled digitalized service agents imbued with anthropomorphic design elements that can communicate using natural language. The present article considers the impact of anthropomorphized robo-advisors on...

Economic theory provides ambiguous and conflicting predictions about the association between algorithmic pricing and competition. In this paper we provide the first empirical analysis of this relationship. We study Germany's retail gasoline market where algorithmic-pricing software became widely...

Intelligent Product Recommendation Agents have been used for some time now by large, well known Internet businesses such as Amazon and Netflix. Unfortunately there is little research assessing the effectiveness of these systems in influencing online consumer behavior. Businesses that sell...

Enterprise systems interoperability (ESI) is an important topic for business currently. This situation is evidenced, at least in part, by the number and extent of potential candidate protocols for such process interoperation, viz., ebXML, BPML, BPEL, and WSCI. Wide-ranging support for each of...

Much research has been devoted over the years to investigating and advancing the techniques and tools used by analysts when they model. As opposed to what academics, software providers and their resellers promote as should be happening, the aim of this research was to determine whether...

The research presented here focuses on modeling machine-learning performance. The thesis introduces Seer, a system that generates empirical observations of classification-learning performance and then uses those observations to create statistical models. The models can be used to predict the...

Decision Support Systems (DSSs) are necessary resources for most complex decision making situations. The environment under which the DSSs function are, for the most part, evolving and, hence the DSSs should have the capability to adapt themselves as per the changes in the characteristics of the...