Agent-based simulation of lock-in dynamics in a duopoly

Lock-in is observed in real world markets of experience goods; experience goods are goods whose characteristics are difficult to determine in advance, but ascertained upon consumption. We create an agent-based simulation of consumers choosing between two experience goods available in a virtual market. We model consumers in a grid representing the spatial network of the consumers. Utilising simple assumptions, including identical distributions of product experience and consumers having a degree of follower tendency, we explore the dynamics of the model through simulations. We conduct simulations to create a lock-in before testing several hypotheses upon how to break an existing lock-in; these include the effect of advertising and free give-away. Our experiments show that the key to successfully breaking a lock-in required the creation of regions in a consumer population. Regions arise due to the degree of local conformity between agents within the regions, which spread throughout the population when a mildly superior competitor was available. These regions may be likened to a niche in a market, which gains in popularity to transition into the mainstream.

An agent approach to improving radio frequency identification enabled Returnable Transport Equipment

Returnable transport equipment (RTE) such as pallets form an integral part of the supply chain and poor management leads to costly losses. Companies often address this matter by outsourcing the management of RTE to logistics service providers (LSPs). LSPs are faced with the task to provide logistical expertise to reduce RTE related waste, whilst differentiating their own services to remain competitive. In the current challenging economic climate, the role of the LSP to deliver innovative ways to achieve competitive advantage has never been so important. It is reported that radio frequency identification (RFID) application to RTE enables LSPs such as DHL to gain competitive advantage and offer clients improvements such as loss reduction, process efficiency improvement and effective security. However, the increased visibility and functionality of RFID enabled RTE requires further investigation in regards to decision‐making. The distributed nature of the RTE network favours a decentralised decision‐making format. Agents are an effective way to represent objects from the bottom‐up, capturing the behaviour and enabling localised decision‐making. Therefore, an agent based system is proposed to represent the RTE network and utilise the visibility and data gathered from RFID tags. Two types of agents are developed in order to represent the trucks and RTE, which have bespoke rules and algorithms in order to facilitate negotiations. The aim is to create schedules, which integrate RTE pick‐ups as the trucks go back to the depot. The findings assert that: - agent based modelling provides an autonomous tool, which is effective in modelling RFID enabled RTE in a decentralised utilising the real‐time data facility. ‐ the RFID enabled RTE model developed enables autonomous agent interaction, which leads to a feasible schedule integrating both forward and reverse flows for each RTE batch. ‐ the RTE agent scheduling algorithm developed promotes the utilisation of RTE by including an automatic return flow for each batch of RTE, whilst considering the fleet costs andutilisation rates. ‐ the research conducted contributes an agent based platform, which LSPs can use in order to assess the most appropriate strategies to implement for RTE network improvement for each of their clients.

Making motes intelligent:an agent-based approach to wireless sensor networks

In-Motes is a mobile agent middleware that generates an intelligent framework for deploying applications in Wireless Sensor Networks (WSNs). In-Motes is based on the injection of mobile agents into the network that can migrate or clone following specific rules and performing application specific tasks. By doing so, each mote is given a certain degree of perception, cognition and control, forming the basis for its intelligence. Our middleware incorporates technologies such as Linda-like tuplespaces and federated system architecture in order to obtain a high degree of collaboration and coordination for the agent society. A set of behavioral rules inspired by a community of bacterial strains is also generated as the means for robustness of the WSN. In this paper, we present In-Motes and provide a detailed evaluation of its implementation for MICA2 motes.

Agent-based Anomalies Monitoring in Distributed Systems

In this paper an agent-based approach for anomalies monitoring in distributed systems such as computer networks, or Grid systems is proposed. This approach envisages on-line and off-line monitoring in order to analyze users’ activity. On-line monitoring is carried in real time, and is used to predict user actions. Off-line monitoring is done after the user has ended his work, and is based on the analysis of statistical information obtained during user’s work. In both cases neural networks are used in order to predict user actions and to distinguish normal and anomalous user behavior.

The challenges of representing human behaviour in simulation models:some case examples from supply chain, evacuation modelling and rail disruption

As more of the economy moves from traditional manufacturing to the service sector, the nature of work is becoming less tangible and thus, the representation of human behaviour in models is becoming more important. Representing human behaviour and decision making in models is challenging, both in terms of capturing the essence of the processes, and also the way that those behaviours and decisions are or can be represented in the models themselves. In order to advance understanding in this area, a useful first step is to evaluate and start to classify the various types of behaviour and decision making that are required to be modelled. This talk will attempt to set out and provide an initial classification of the different types of behaviour and decision making that a modeller might want to represent in a model. Then, it will be useful to start to assess the main methods of simulation in terms of their capability in representing these various aspects. The three main simulation methods, System Dynamics, Agent Based Modelling and Discrete Event Simulation all achieve this to varying degrees. There is some evidence that all three methods can, within limits, represent the key aspects of the system being modelled. The three simulation approaches are then assessed for their suitability in modelling these various aspects. Illustration of behavioural modelling will be provided from cases in supply chain management, evacuation modelling and rail disruption.

NLSE-based modelling of a random distributed feedback fiber laser

Random distributed feedback (DFB) fiber lasers have attracted a great attention since first demonstration [1]. Despite big advance in practical laser systems, random DFB fiber laser spectral properties are far away to be understood or even numerically modelled. Up to date, only generation power could be calculated and optimized numerically [1,2] or analytically [3] within the power balance model. However, spectral and statistical properties of random DFB fiber laser can not be found in this way. Here we present first numerical modelling of the random DFB fiber laser, including its spectral and statistical properties, using NLSE-based model. © 2013 IEEE.

Comprehensive secondary pyrolysis in fluidized-bed fast pyrolysis of biomass, a fluid dynamics based modelling effort

Homogenous secondary pyrolysis is category of reactions following the primary pyrolysis and presumed important for fast pyrolysis. For the comprehensive chemistry and fluid dynamics, a probability density functional (PDF) approach is used; with a kinetic scheme comprising 134 species and 4169 reactions being implemented. With aid of acceleration techniques, most importantly Dimension Reduction, Chemistry Agglomeration and In-situ Tabulation (ISAT), a solution within reasonable time was obtained. More work is required; however, a solution for levoglucosan (C6H10O5) being fed through the inlet with fluidizing gas at 500 °C, has been obtained. 88.6% of the levoglucosan remained non-decomposed, and 19 different decomposition product species were found above 0.01% by weight. A homogenous secondary pyrolysis scheme proposed can thus be implemented in a CFD environment and acceleration techniques can speed-up the calculation for application in engineering settings.

Implementing an agent-based model with a spatial visual display in discrete-event simulation software

There has been an increasing interest in the use of agent-based simulation and some discussion of the relative merits of this approach as compared to discrete-event simulation. There are differing views on whether an agent-based simulation offers capabilities that discrete-event cannot provide or whether all agent-based applications can at least in theory be undertaken using a discrete-event approach. This paper presents a simple agent-based NetLogo model and corresponding discrete-event versions implemented in the widely used ARENA software. The two versions of the discrete-event model presented use a traditional process flow approach normally adopted in discrete-event simulation software and also an agent-based approach to the model build. In addition a real-time spatial visual display facility is provided using a spreadsheet platform controlled by VBA code embedded within the ARENA model. Initial findings from this investigation are that discrete-event simulation can indeed be used to implement agent-based models and with suitable integration elements such as VBA provide the spatial displays associated with agent-based software.

An agent-based model of team coordination and performance

This research is based on the premises that teams can be designed to optimize its performance, and appropriate team coordination is a significant factor to team outcome performance. Contingency theory argues that the effectiveness of a team depends on the right fit of the team design factors to the particular job at hand. Therefore, organizations need computational tools capable of predict the performance of different configurations of teams. This research created an agent-based model of teams called the Team Coordination Model (TCM). The TCM estimates the coordination load and performance of a team, based on its composition, coordination mechanisms, and job’s structural characteristics. The TCM can be used to determine the team’s design characteristics that most likely lead the team to achieve optimal performance. The TCM is implemented as an agent-based discrete-event simulation application built using JAVA and Cybele Pro agent architecture. The model implements the effect of individual team design factors on team processes, but the resulting performance emerges from the behavior of the agents. These team member agents use decision making, and explicit and implicit mechanisms to coordinate the job. The model validation included the comparison of the TCM’s results with statistics from a real team and with the results predicted by the team performance literature. An illustrative 26-1 fractional factorial experimental design demonstrates the application of the simulation model to the design of a team. The results from the ANOVA analysis have been used to recommend the combination of levels of the experimental factors that optimize the completion time for a team that runs sailboats races. This research main contribution to the team modeling literature is a model capable of simulating teams working on complex job environments. The TCM implements a stochastic job structure model capable of capturing some of the complexity not capture by current models. In a stochastic job structure, the tasks required to complete the job change during the team execution of the job. This research proposed three new types of dependencies between tasks required to model a job as a stochastic structure. These dependencies are conditional sequential, single-conditional sequential, and the merge dependencies.

Agent-Based Knowledge Discovery

Postprint

Distributed Learning: An Agent-Based Approach to Data-Mining

Peer reviewed

Uses of agent-based modeling for health communication: the TELL ME case study

Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals’ protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.

Individual-based modelling of the development and transport of a Karenia mikimotoi bloom on the North-West European continental shelf

In 2006, a large and prolonged bloom of the dinoflagellate Karenia mikimotoi occurred in Scottish coastal waters, causing extensive mortalities of benthic organisms including annelids and molluscs and some species of fish ( Davidson et al., 2009). A coupled hydrodynamic-algal transport model was developed to track the progression of the bloom around the Scottish coast during June–September 2006 and hence investigate the processes controlling the bloom dynamics. Within this individual-based model, cells were capable of growth, mortality and phototaxis and were transported by physical processes of advection and turbulent diffusion, using current velocities extracted from operational simulations of the MRCS ocean circulation model of the North-west European continental shelf. Vertical and horizontal turbulent diffusion of cells are treated using a random walk approach. Comparison of model output with remotely sensed chlorophyll concentrations and cell counts from coastal monitoring stations indicated that it was necessary to include multiple spatially distinct seed populations of K. mikimotoi at separate locations on the shelf edge to capture the qualitative pattern of bloom transport and development. We interpret this as indicating that the source population was being transported northwards by the Hebridean slope current from where colonies of K. mikimotoi were injected onto the continental shelf by eddies or other transient exchange processes. The model was used to investigate the effects on simulated K. mikimotoi transport and dispersal of: (1) the distribution of the initial seed population; (2) algal growth and mortality; (3) water temperature; (4) the vertical movement of particles by diurnal migration and eddy diffusion; (5) the relative role of the shelf edge and coastal currents; (6) the role of wind forcing. The numerical experiments emphasized the requirement for a physiologically based biological model and indicated that improved modelling of future blooms will potentially benefit from better parameterisation of temperature dependence of both growth and mortality and finer spatial and temporal hydrodynamic resolution.

Individual-based modelling of the development and transport of a Karenia mikimotoi bloom on the North-West European continental shelf

In 2006, a large and prolonged bloom of the dinoflagellate Karenia mikimotoi occurred in Scottish coastal waters, causing extensive mortalities of benthic organisms including annelids and molluscs and some species of fish ( Davidson et al., 2009). A coupled hydrodynamic-algal transport model was developed to track the progression of the bloom around the Scottish coast during June–September 2006 and hence investigate the processes controlling the bloom dynamics. Within this individual-based model, cells were capable of growth, mortality and phototaxis and were transported by physical processes of advection and turbulent diffusion, using current velocities extracted from operational simulations of the MRCS ocean circulation model of the North-west European continental shelf. Vertical and horizontal turbulent diffusion of cells are treated using a random walk approach. Comparison of model output with remotely sensed chlorophyll concentrations and cell counts from coastal monitoring stations indicated that it was necessary to include multiple spatially distinct seed populations of K. mikimotoi at separate locations on the shelf edge to capture the qualitative pattern of bloom transport and development. We interpret this as indicating that the source population was being transported northwards by the Hebridean slope current from where colonies of K. mikimotoi were injected onto the continental shelf by eddies or other transient exchange processes. The model was used to investigate the effects on simulated K. mikimotoi transport and dispersal of: (1) the distribution of the initial seed population; (2) algal growth and mortality; (3) water temperature; (4) the vertical movement of particles by diurnal migration and eddy diffusion; (5) the relative role of the shelf edge and coastal currents; (6) the role of wind forcing. The numerical experiments emphasized the requirement for a physiologically based biological model and indicated that improved modelling of future blooms will potentially benefit from better parameterisation of temperature dependence of both growth and mortality and finer spatial and temporal hydrodynamic resolution.

Uses of Agent-Based Modeling for Health Communication: the TELL ME Case Study

Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals' protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.