Does bounded rationality lead to individual heterogeneity? The impact of the experimentation process and of memory constraints

In this paper we explore the effect of bounded rationality on the convergence of individual behavior toward equilibrium. In the context of a Cournot game with a unique and symmetric Nash equilibrium, firms are modeled as adaptive economic agents through a genetic algorithm. Computational experiments show that (1) there is remarkable heterogeneity across identical but boundedly rational agents; (2) such individual heterogeneity is not simply a consequence of the random elements contained in the genetic algorithm; (3) the more rational agents are in terms of memory abilities and pre-play evaluation of strategies, the less heterogeneous they are in their actions. At the limit case of full rationality, the outcome converges to the standard result of uniform individual behavior.

Adaptive business intelligence for an open negotiation environment

Engineering of negotiation model allows to develop effective heuristic for business intelligence. Digital ecosystems demand open negotiation models. To define in advance effective heuristics is not compliant with the requirement of openness. The new challenge is to develop business intelligence in advance exploiting an adaptive approach. The idea is to learn business strategy once new negotiation model rise in the e-market arena. In this paper we present how recommendation technology may be deployed in an open negotiation environment where the interaction protocol models are not known in advance. The solution we propose is delivered as part of the ONE Platform, open source software that implements a fully distributed open environment for business negotiation

Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasinglypopular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigationaladaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

Adaptive approach heuristics for the generalized assignment problem

The Generalized Assignment Problem consists in assigning a setof tasks to a set of agents with minimum cost. Each agent hasa limited amount of a single resource and each task must beassigned to one and only one agent, requiring a certain amountof the resource of the agent. We present new metaheuristics forthe generalized assignment problem based on hybrid approaches.One metaheuristic is a MAX-MIN Ant System (MMAS), an improvedversion of the Ant System, which was recently proposed byStutzle and Hoos to combinatorial optimization problems, and itcan be seen has an adaptive sampling algorithm that takes inconsideration the experience gathered in earlier iterations ofthe algorithm. Moreover, the latter heuristic is combined withlocal search and tabu search heuristics to improve the search.A greedy randomized adaptive search heuristic (GRASP) is alsoproposed. Several neighborhoods are studied, including one basedon ejection chains that produces good moves withoutincreasing the computational effort. We present computationalresults of the comparative performance, followed by concludingremarks and ideas on future research in generalized assignmentrelated problems.

Consumers' behavior and the Bertrand paradox: an ACE approach

We analyze the classical Bertrand model when consumers exhibit some strategic behavior in deciding from which seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers' behavior in uences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the process behavior. Second, we use nite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the rst approach and still obtain the same basic results. It is suggested that the limitations of the rst approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach.

Selecting negotiation processes with Health Care

We address the question of how a third-party payer (e.g. an insurer) decides what providers to contract with. Three different mechanisms are studied and their properties compared. A first mechanism consists in the third-party payer setting up a bargaining procedure with both providers jointly and simultaneously. A second mechanism envisages the outcome of the same simultaneous bargaining but independently with every provider. Finally, the last mechanism is of different nature. It is the so-called "any willing provider" where the third-party payer announces a contract and every provider freely decides to sign it or not. The main finding is that the decision of the third-party payer depends on the surplus to be shared. When it is relatively high the third-party payer prefers the any willing provider system. When, on the contrary, the surplus is relatively low, the third-party payer will select one of the other two systems accor ing to how bargaining power is distributed.

Negotiation advantages of professional associations in health care

In several instances, third-party payers negotiate prices of health care services with providers. We show that a third-party payer may prefer to deal with a professional association than with the sub-set constituted by the more efficient providers, and then apply the same price to all providers. The reason for it is the increase in the bargaining position of providers. The more efficient providers are also the ones with higher profits in the event of negotiation failure. This allows them to ext act a higher surplus from the third-party payer.

Mobilitat inter-plataforma d'agents software: protocol de migració fragmentada

En aquest projecte s'ha realitzat l'anàlisi, disseny i implementació d'un protocol de migració d'agents software basat en l'enviament del codi dels agents fragmentat en múltiples missatges. Aquest protocol es troba dins d'una arquitectura de migració multi-protocol per a la mobilitat d'agents entre plataformes JADE. Finalment, s'ha realitzat un estudi que compara el rendiment assolit pel protocol i les prestacions que aporta.

MedIGS, un sistema segur basat en agents per al descobriment i obtenció de dades mèdiques distribuïdes: estudi i desenvolupament

En aquest projecte es desenvolupa en totes les seves fases(estudi, anàlisi, disseny, implementació i proves) l'aplicació MedIGS. MedIGS és una aplicació destinada a satisfer algunes de les necesitats actuals del sistema sanitari, la compartició d’informació i la màxima coordinació possible, utilitzant una tecnologia novedosa: els agents, i més concretament, els agents mòbils. Gràcies a aquesta tecnologia aconseguirem una integració segura de dades mèdiques distribuïdes. Està previst fer una prova pilot a Portugal, a partir dels resultats d’aquest projecte.

Construcció modular d'agents basats en components: estudi i desenvolupament

La complexitat de disseny d’agents mòbils creix a mesura que s’incrementen les seves funcionalitats. Aquest projecte proposa enfocar el problema des d’un punt de vista modular. S’ha realitzat un estudi tant dels propis agents com de les parts que ho integren. De la mateixa forma, s’han establert i s'han implementat els mecanismes necessaris per habilitar les comunicacions segures entre agents. Finalment, s’han desenvolupat dos components que ofereixen les funcionalitats de seguiment de l’agent mòbil i la recuperació dels resultats generats. El desenvolupament d’agents basats en components tracta d’aplicar la vella estratègia "divideix i venceràs" a la fase de disseny, reduint, així,la seva gran complexitat.

Proposta de migració segura d'agents amb sol·licitud de classes sota demanda per a la plataforma JADE

Aquest projecte consisteix en la implementació d'una migració d'agents mòbils amb sol·licitud de classes sota demanda de manera segura per a la plataforma JADE. En aquest projecte hem desenvolupat dos protocols de control d'accés i autentificació sobre agents i/o plataformes. Però la línia central de desenvolupament d'aquest projecte radica en la creació d'un protocol de migració sota demanda d'agents. Hem implementat dues versions d'aquest protocol. La primera versió del protocol de migració es centra en la sol·licitud de classes sota demanda i la segona versió, per tal de millorar-ne el rendiment de la primera, emmagatzema les classes que ha demanat sota demanada en una cache de la plataforma.

A class of stochastic games with infinitely many interacting agents related to Glauber dynamics on random graphs

We introduce and study a class of infinite-horizon nonzero-sum non-cooperative stochastic games with infinitely many interacting agents using ideas of statistical mechanics. First we show, in the general case of asymmetric interactions, the existence of a strategy that allows any player to eliminate losses after a finite random time. In the special case of symmetric interactions, we also prove that, as time goes to infinity, the game converges to a Nash equilibrium. Moreover, assuming that all agents adopt the same strategy, using arguments related to those leading to perfect simulation algorithms, spatial mixing and ergodicity are proved. In turn, ergodicity allows us to prove “fixation”, i.e. that players will adopt a constant strategy after a finite time. The resulting dynamics is related to zerotemperature Glauber dynamics on random graphs of possibly infinite volume.

Extensió de l'entorn SMARD per permetre la depuració d'aplicacions basades en agents mòbils

Un dels principals obstacles per l’adopció dels agents mòbils, fins i tot per tasques per les quals s’ha demostrat la seva idoneïtat, és la dificultat de desenvolupar-los. Aquest projecte aporta solucions per ajudar en aquest procés. Més concretament, per provar agents i observar-ne el seu comportament en un entorn de simulació, abans d’executar-los en un escenari real. Hem afegit un depurador i un constructor d’agents mòbils a la interfície gràfica de disseny d’itineraris de l’entorn SMARD. Fent servir afegit aquests nous components, el programador pot dissenyar, construir i fer proves d’un agent mòbil des d’aquesta aplicació.

Contribució a la mobilitat inter-plataforma d'agents mòbils: protocol de migració fragmentada

En aquest projecte s’ha dissenyat un protocol de migració d’agents mòbils per a l’arquitectura IPMA basat en l’enviament dels agents fragmentats en diversos missatges FIPA ACL. Aquest s’ha implementat dins el servei de migració JIPMS per a la plataforma JADE. Finalment s’ha dut a terme un conjunt exhaustiu de tests per avaluar-ne el rendiment i comparar-lo amb altres protocols de migració existents.