Agent-Based Computational Economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.

Collaborative Models for Supply Networks Coordination and Healthcare Consolidation

This work discusses the collaboration framework among different members of two complex systems: supply networks and consolidated healthcare systems. Although existing literature advocates the notion of strategic partnership/cooperation in both supply networks and healthcare systems, there is a dearth of studies quantitatively analyzing the scope of cooperation among the members and its benefit on the global performance. Hence, the first part of this dissertation discusses about two-echelon supply networks and studies the coordination of buyers and suppliers for multi-period procurement process. Viewing the issue from the same angel, the second part studies the coordination framework of hospitals for consolidated healthcare service delivery. Realizing the dynamic nature of information flow and the conflicting objectives of members in supply networks, a two-tier coordination mechanism among buyers and suppliers is modeled. The process begins with the intelligent matching of buyers and suppliers based on the similarity of users profiles. Then, a coordination mechanism for long-term agreements among buyers and suppliers is proposed. The proposed mechanism introduces the importance of strategic buyers for suppliers in modeling and decision making process. To enhance the network utilization, we examine a further collaboration among suppliers where cooperation incurs both cost and benefit. Coalitional game theory is utilized to model suppliers\u27 coalition formation. The efficiency of the proposed approaches is evaluated through simulation studies. We then revisit the common issue, the co-existence of partnership and conflict objectives of members, for consolidated healthcare systems and study the coordination of hospitals such that there is a central referral system to facilitate patients transfer. We consider three main players including physicians, hospitals managers, and the referral system. As a consequence, the interaction within these players will shape the coordinating scheme to improve the overall system performance. To come up with the incentive scheme for physicians and aligning hospitals activities, we define a multi-objective mathematical model and obtain optimal transfer pattern. Using optimal solutions as a baseline, a cooperative game between physicians and the central referral system is defined to coordinate decisions toward system optimality. The efficiency of the proposed approach is examined via a case study

Flow-based reputation with uncertainty: Evidence-Based Subjective Logic

The concept of reputation is widely used as a measure of trustworthiness based on ratings from members in a community. The adoption of reputation systems, however, relies on their ability to capture the actual trustworthiness of a target. Several reputation models for aggregating trust information have been proposed in the literature. The choice of model has an impact on the reliability of the aggregated trust information as well as on the procedure used to compute reputations. Two prominent models are flow-based reputation (e.g., EigenTrust, PageRank) and Subjective Logic based reputation. Flow-based models provide an automated method to aggregate trust information, but they are not able to express the level of uncertainty in the information. In contrast, Subjective Logic extends probabilistic models with an explicit notion of uncertainty, but the calculation of reputation depends on the structure of the trust network and often requires information to be discarded. These are severe drawbacks. In this work, we observe that the `opinion discounting' operation in Subjective Logic has a number of basic problems. We resolve these problems by providing a new discounting operator that describes the flow of evidence from one party to another. The adoption of our discounting rule results in a consistent Subjective Logic algebra that is entirely based on the handling of evidence. We show that the new algebra enables the construction of an automated reputation assessment procedure for arbitrary trust networks, where the calculation no longer depends on the structure of the network, and does not need to throw away any information. Thus, we obtain the best of both worlds: flow-based reputation and consistent handling of uncertainties

A Multi-Agent Approach for Provisioning of e-Services in u-Commerce Environments

Purpose: Taking into account the importance of e-commerce and the current applications of AI techniques in this area, this research aims to adequate the design of a multi-agent system for the provisioning of e-services in u-commerce environments. This proposal is centred on the methods of evaluation in a u-e-commerce environment. Design/methodology/approach: The multi-agent systems (MAS) approach is based on an MAS model developed for AmI that has been redesigned to support u-commerce. The use of a recommendation system, previously developed by the research group, is suggested for this MAS. The methodological proposal centres on the evaluation of this type of system. Findings: The evaluation of this type of system is the principal problem of current research. Therefore, this is the main contribution of the paper. Research limitations/implications: The different evaluation methods that are proposed, whether qualitative or quantitative, offer the possibility of measuring the added value that the context can give to the use of e-services in different domains of application. Qualitative evaluation should consider the customer as a central piece in the system. In addition, quantitative methods should objectively evaluate the contribution of context to the application. Practical implications: At present, there is no single method for evaluating the benefits of different u-commerce systems, so a new method needs to be found based on these techniques. Originality/value: The research proposes an MAS designed for u-commerce domains, analyzes the capacity of trust management techniques in this environment, and proposes several evaluation methods to show the benefits of context information in the use of e-services. Several real developments are described to show the different applications of MAS in u-commerce and how evaluation is carried out.This work has been partially supported by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM CONTEXT and DPS2008-07029-C02-02.Publicad

Reputation for complex societies

Reputation, the germ of gossip, is addressed in this chapter as a distributed instrument for social order. In literature, reputation is shown to promote (a) social control in cooperative contexts—like social groups and subgroups—and (b) partner selection in competitive ones, like (e-) markets and industrial districts. Current technology that affects, employs and extends reputation, applied to electronic markets or multi-agent systems, is discussed in light of its theoretical background. In order to compare reputation systems with their original analogue, a social cognitive model of reputation is presented. The application of the model to the theoretical study of norm-abiding behaviour and partner selection are discussed, as well as the refinement and improvement of current reputation technology. The chapter concludes with remarks and ideas for future research.</p