Agent based simulation to optimise emergency departments

Nowadays, many of the health care systems are large and complex environments and quite dynamic, specifically Emergency Departments, EDs. It is opened and working 24 hours per day throughout the year with limited resources, whereas it is overcrowded. Thus, is mandatory to simulate EDs to improve qualitatively and quantitatively their performance. This improvement can be achieved modelling and simulating EDs using Agent-Based Model, ABM and optimising many different staff scenarios. This work optimises the staff configuration of an ED. In order to do optimisation, objective functions to minimise or maximise have to be set. One of those objective functions is to find the best or optimum staff configuration that minimise patient waiting time. The staff configuration comprises: doctors, triage nurses, and admissions, the amount and sort of them. Staff configuration is a combinatorial problem, that can take a lot of time to be solved. HPC is used to run the experiments, and encouraging results were obtained. However, even with the basic ED used in this work the search space is very large, thus, when the problem size increases, it is going to need more resources of processing in order to obtain results in an acceptable time.

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paperexplores the consistency of some of its standard techniques. We focus in particular on prevailingwholesale electricity trading simulation methods. We include different supply and demandrepresentations and propose the Experience-Weighted Attractions method to include severalbehavioural algorithms. We compare the results across assumptions and to economic theorypredictions. The match is good under best-response and reinforcement learning but not underfictitious play. The simulations perform well under flat and upward-slopping supply bidding,and also for plausible demand elasticity assumptions. Learning is influenced by the number ofbids per plant and the initial conditions. The overall conclusion is that agent-based simulationassumptions are far from innocuous. We link their performance to underlying features, andidentify those that are better suited to model wholesale electricity markets.

Spatial behavior in groups: an agent-based approach

We present an agent-based model with the aim of studying how macro-level dynamics of spatial distances among interacting individuals in a closed space emerge from micro-level dyadic and local interactions. Our agents moved on a lattice (referred to as a room) using a model implemented in a computer program called P-Space in order to minimize their dissatisfaction, defined as a function of the discrepancy between the real distance and the ideal, or desired, distance between agents. Ideal distances evolved in accordance with the agent's personal and social space, which changed throughout the dynamics of the interactions among the agents. In the first set of simulations we studied the effects of the parameters of the function that generated ideal distances, and in a second set we explored how group macrolevel behavior depended on model parameters and other variables. We learned that certain parameter values yielded consistent patterns in the agents' personal and social spaces, which in turn led to avoidance and approaching behaviors in the agents. We also found that the spatial behavior of the group of agents as a whole was influenced by the values of the model parameters, as well as by other variables such as the number of agents. Our work demonstrates that the bottom-up approach is a useful way of explaining macro-level spatial behavior. The proposed model is also shown to be a powerful tool for simulating the spatial behavior of groups of interacting individuals.

ACACIA: an agent-based program for simulating behavior to reach long-term goals

We present ACACIA, an agent-based program implemented in Java StarLogo 2.0 that simulates a two-dimensional microworld populated by agents, obstacles and goals. Our program simulates how agents can reach long-term goals by following sensorial-motor couplings (SMCs) that control how the agents interact with their environment and other agents through a process of local categorization. Thus, while acting in accordance with this set of SMCs, the agents reach their goals through the emergence of global behaviors. This agent-based simulation program would allow us to understand some psychological processes such as planning behavior from the point of view that the complexity of these processes is the result of agent-environment interaction.

Flocking behaviour: Agent-based simulation and hierarchical leadership.

We have studied how leaders emerge in a group as a consequence of interactions among its members. We propose that leaders can emerge as a consequence of a self-organized process based on local rules of dyadic interactions among individuals. Flocks are an example of self-organized behaviour in a group and properties similar to those observed in flocks might also explain some of the dynamics and organization of human groups. We developed an agent-based model that generated flocks in a virtual world and implemented it in a multi-agent simulation computer program that computed indices at each time step of the simulation to quantify the degree to which a group moved in a coordinated way (index of flocking behaviour) and the degree to which specific individuals led the group (index of hierarchical leadership). We ran several series of simulations in order to test our model and determine how these indices behaved under specific agent and world conditions. We identified the agent, world property, and model parameters that made stable, compact flocks emerge, and explored possible environmental properties that predicted the probability of becoming a leader.

Emergent patterns of social organization in captive Cercocebus torquatus: Testing the GrooFiWorld agent-based model

We empirically applied the GrooFiWorld agent-based model (Puga-González et al. 2009) in a group of captive mangabeys (Cercocebus torquatus). We analysed several measurements related to aggression and affiliative patterns. The group adopted a combination of despotic and egalitarian behaviours resulting from the behavioural flexibility observed in the Cercopithecinae subfamily. Our study also demonstrates that the GrooFiWorld agent-based model can be extended to other members of the Cercopithecinae subfamily generating parsimonious hypotheses related to the social organization.

Modeling the Emergence of Social Structure from a Phylogenetic Point of View

Based on provious (Hemelrijk 1998; Puga-González, Hildenbrant & Hemelrijk 2009), we have developed an agent-based model and software, called A-KinGDom, which allows us to simulate the emergence of the social structure in a group of non-human primates. The model includes dominance and affiliative interactions and incorporate s two main innovations (preliminary dominance interactions and a kinship factor), which allow us to define four different attack and affiliative strategies. In accordance with these strategies, we compared the data obtained under four simulation conditions with the results obtained in a provious study (Dolado & Beltran 2012) involving empirical observations of a captive group of mangabeys (Cercocebus torquatus)

Creació i transmissió de coneixement als clústers empresarials: un model de simulació

Projecte de recerca elaborat a partir d’una estada a l’Snider Entrepreneurial Research Center de la Wharton School de la University of Pennsilvanya y, EUA entre juliol i desembre del 2007. L’objectiu d’aquest projecte és estudiar la relació entre les estratègies de gestió del coneixement i les tecnologies de la informació i la comunicació (TIC) en l’evolució de les poblacions d’organitzacions i els seus efectes en els patrons industrials d’aglomeració espacial. Per a això s’adopta una aproximació fonamentada en la utilització d'un model basats en agents per a obtenir hipòtesis significatives i provables sobre l’evolució de les poblacions d’organitzacions al si de clústers geogràfics. El model de simulació incorpora les perspectives i supòsits d’un marc conceptual, l’Espai de la Informació o I-Space. Això permet una conceptualització basada en la informació de l’entorn econòmic que té en compte les seves dimensions espacials i temporals. Mitjançant els paràmetres del model es dóna la possibilitat d’assignar estratègies específiques de gestió del coneixement als diversos agents i de localitzar-los en una posició de l’espai físic. La simulació mostra com l'adopció d'estratègies diverses pel que fa a la gestió del coneixement influeix en l'evolució de les organitzacions i de la seva localització espacial, i que aquesta evolució es veu modificada pel desenvolupament de les TIC. A través de la modelització de dos casos ben coneguts de clústers geogràfics d’alta tecnologia, com són Silicon Valley a Califòrnia i la Route 128 als voltants de Boston, s’estudia la interrelació entre les estratègies de gestió del coneixement adoptades per les empreses i la seva tria de localització espacial, i també com això és afectat per l’evolució de les tecnologies de la informació i de la comunicació (TIC). Els resultats obtinguts generen una sèrie d’hipòtesis de rica potencialitat sobre l’impacte del desenvolupament de les TIC en la dinàmica d’aquests clusters geogràfics. Concretament, es troba que la estructuració del coneixement i l’aglomeració espacial co-evolucionen i que aquesta coevolució es veu significativament alterada pel desenvolupament de les TIC.

Organisational adaptation of multi-agent systems in a peer-to-peer scenario

Organisations in Multi-Agent Systems (MAS) have proven to be successful in regulating agent societies. Nevertheless, changes in agents' behaviour or in the dynamics of the environment may lead to a poor fulfilment of the system's purposes, and so the entire organisation needs to be adapted. In this paper we focus on endowing the organisation with adaptation capabilities, instead of expecting agents to be capable of adapting the organisation by themselves. We regard this organisational adaptation as an assisting service provided by what we call the Assistance Layer. Our generic Two Level Assisted MAS Architecture (2-LAMA) incorporates such a layer. We empirically evaluate this approach by means of an agent-based simulator we have developed for the P2P sharing network domain. This simulator implements 2-LAMA architecture and supports the comparison between different adaptation methods, as well as, with the standard BitTorrent protocol. In particular, we present two alternatives to perform norm adaptation and one method to adapt agents'relationships. The results show improved performance and demonstrate that the cost of introducing an additional layer in charge of the system's adaptation is lower than its benefits.

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.

Plataforma per a la simulació de xarxes de sensors

Aquest projecte descriu una plataforma de simulació per a xarxes de sensors des de la perspectiva dels sistemes multi-agents. La plataforma s'ha dissenyat per facilitar la simulació de diferents aplicacions concretes de xarxes de sensors. A més, s'ha entregat com a artefacte del projecte IEA (Institucions Electròniques Autònomes, TIN2006-15662-C02-0) de l'IIIACSIC. Dins l'entorn de l'IEA, aquesta és l'eina que aporta les capacitats de simulació per donar suport al disseny d'algorismes adaptatius per a xarxes de sensors.

Agents mòbils en situacions d'emergència: gestió dinàmica de serveis

Aquest projecte consisteix en el disseny i desenvolupament d'una arquitectura de serveis sota el paradigma dels agents inteligents. El propòsit d'ADASMI (Architecture for Dynamic Agent Service Management and Interaction) és permetre la gestió i utilització de serveis per altres agents. L'arquitectura s'ha implementat utilitzant la plataforma d'agents de JADE i es pot utilitzar amb qualsevol altra plataforma que compleixi els estàndards d'IEEE FIPA. A més, és prou flexible com per adaptar-se en entorns dinàmics, com per exemple les xarxes ad-hoc en situacions d'emergència.

Simulación de departamentos de urgencias empleando un modelo basado en agentes

Los servicios de salud son sistemas muy complejos, pero de alta importancia, especialmente en algunos momentos críticos, en todo el mundo. Los departamentos de urgencias pueden ser una de las áreas más dinámicas y cambiables de todos los servicios de salud y a la vez más vulnerables a dichos cambios. La mejora de esos departamentos se puede considerar uno de los grandes retos que tiene cualquier administrador de un hospital, y la simulación provee una manera de examinar este sistema tan complejo sin poner en peligro los pacientes que son atendidos. El objetivo de este trabajo ha sido el modelado de un departamento de urgencias y el desarrollo de un simulador que implementa este modelo con la finalidad de explorar el comportamiento y las características de dicho servicio de urgencias. El uso del simulador ofrece la posibilidad de visualizar el comportamiento del modelo con diferentes parámetros y servirá como núcleo de un sistema de ayuda a la toma de decisiones que pueda ser usado en departamentos de urgencias. El modelo se ha desarrollado con técnicas de modelado basado en agentes (ABM) que permiten crear modelos funcionalmente más próximos a la realidad que los modelos de colas o de dinámicas de sistemas, al permitir la inclusión de la singularidad que implica el modelado a nivel de las personas. Los agentes del modelo presentado, descritos internamente como máquinas de estados, representan a todo el personal del departamento de urgencias y los pacientes que usan este servicio. Un análisis del modelo a través de su implementación en el simulador muestra que el sistema se comporta de manera semejante a un departamento de urgencias real.

Statistical mechanics of multi-edge networks

Statistical properties of binary complex networks are well understood and recently many attempts have been made to extend this knowledge to weighted ones. There are, however, subtle yet important considerations to be made regarding the nature of the weights used in this generalization. Weights can be either continuous or discrete magnitudes, and in the latter case, they can additionally have undistinguishable or distinguishable nature. This fact has not been addressed in the literature insofar and has deep implications on the network statistics. In this work we face this problem introducing multiedge networks as graphs where multiple (distinguishable) connections between nodes are considered. We develop a statistical mechanics framework where it is possible to get information about the most relevant observables given a large spectrum of linear and nonlinear constraints including those depending both on the number of multiedges per link and their binary projection. The latter case is particularly interesting as we show that binary projections can be understood from multiedge processes. The implications of these results are important as many real-agent-based problems mapped onto graphs require this treatment for a proper characterization of their collective behavior.

An index for quantifying flocking behavior

One of the classic research topics in adaptive behavior is the collective displacement of groups of organisms such as flocks of birds, schools of fish, herds of mammals and crowds of people. However, most agent-based simulations of group behavior do not provide a quantitative index for determining the point at which the flock emerges. We have developed an index of the aggregation of moving individuals in a flock and have provided an example of how it can be used to quantify the degree to which a group of moving individuals actually forms a flock.