Agent-based simulation of open source evolution

We present an agent-based simulation model developed to study how size, complexity and effort relate to each other in the development of open source software (OSS). In the model, many developer agents generate, extend, and re-factor code modules independently and in parallel. This accords with empirical observations of OSS development. To our knowledge, this is the first model of OSS evolution that includes the complexity of software modules as a limiting factor in productivity, the fitness of the software to its requirements, and the motivation of developers. Validation of the model was done by comparing the simulated results against four measures of software evolution (system size, proportion of highly complex modules, level of complexity control work, and distribution of changes) for four large OSS systems. The simulated results resembled the observed data, except for system size: three of the OSS systems showed alternating patterns of super-linear and sub-linear growth, while the simulations produced only super-linear growth. However, the fidelity of the model for the other measures suggests that developer motivation and the limiting effect of complexity on productivity have a significant effect on the development of OSS systems and should be considered in any model of OSS development

Agent-based simulation of a financial market

This paper introduces an agent-based artificial financial market in which heterogeneous agents trade one single asset through a realistic trading mechanism for price formation. Agents are initially endowed with a finite amount of cash and a given finite portfolio of assets. There is no money-creation process; the total available cash is conserved in time. In each period, agents make random buy and sell decisions that are constrained by available resources, subject to clustering, and dependent on the volatility of previous periods. The model herein proposed is able to reproduce the leptokurtic shape of the probability density of log price returns and the clustering of volatility. Implemented using extreme programming and object-oriented technology, the simulator is a flexible computational experimental facility that can find applications in both academic and industrial research projects.Comment: 11 pages, 3 EPS figures, LaTEX. To be published in Physica A (Proceedings of the NATO Advanced Research Workshop on Application of Physics in Economic Modelling, Prague 8-10 February 2001

An agent-based simulation framework for complex systems

In this abstract we present a new approach to the simulation of complex systems as biological interaction networks, chemical reactions, ecosystems, etc. It aims at overcoming previously proposed analytical approaches that, because of several computational challenges, could not handle systems of realistic com- plexity. The proposed model is based on a set of agents interacting through a shared environment. Each agent functions independently from the others, and its be- havior is driven only by its current status and the "content" of the surrounding environment. The environment is the only "data repository" and does not store the value of variables, but only their presence and concentration. Each agent performs 3 main functions: 1. it samples the environment at random locations 2. based on the distribution of the sampled data and a proper Transfer Func- tion, it computes the rate at which the output values are generated 3. it writes the output "products" at random locations. The environment is modeled as a Really Random Access Memory (R2AM). Data is written and sampled at random memory locations. Each memory location represent an atomic sample (a molecule, a chemical compound, a protein, an ion, . . . ). Presence and concentration of these samples are what constitutes the environment data set. The environment can be sensitive to external stimuli (e.g., pH, Temperature, ...) and can include topological information to allow its partitioning (e.g. between nucleus and cytoplasm in a cell) and the modeling of sample "movements" within the environment. The proposed approach is easily scalable in both complexity and computa- tional costs. Each module could implement a very simple object as a single chemical reaction or a very complex process as a gene translation into a pro- tein. At the same time, from the hardware point of view, the complexity of the objects implementing a single agent can range from a single software process to a dedicated computer or hardware platfor

Agent-based Simulation of Open Source Software Evolution

We present an agent-based simulation model of open source software (OSS). To our knowledge, this is the first model of OSS evolution that includes four significant factors: productivity limited by the complexity of software modules, the software's fitness for purpose, the motivation of developers, and the role of users in defining requirements. The model was evaluated by comparing the simulated results against four measures of software evolution (system size, proportion of highly complex modules, level of complexity control work, and distribution of changes) for four large OSS systems. The simulated results resembled all the observed data, including alternating periods of growth and stagnation. The fidelity of the model suggests that the factors included here have significant effects on the evolution of OSS systems

LUNES: Agent-based Simulation of P2P Systems (Extended Version)

We present LUNES, an agent-based Large Unstructured NEtwork Simulator, which allows to simulate complex networks composed of a high number of nodes. LUNES is modular, since it splits the three phases of network topology creation, protocol simulation and performance evaluation. This permits to easily integrate external software tools into the main software architecture. The simulation of the interaction protocols among network nodes is performed via a simulation middleware that supports both the sequential and the parallel/distributed simulation approaches. In the latter case, a specific mechanism for the communication overhead-reduction is used; this guarantees high levels of performance and scalability. To demonstrate the efficiency of LUNES, we test the simulator with gossip protocols executed on top of networks (representing peer-to-peer overlays), generated with different topologies. Results demonstrate the effectiveness of the proposed approach.Comment: Proceedings of the International Workshop on Modeling and Simulation of Peer-to-Peer Architectures and Systems (MOSPAS 2011). As part of the 2011 International Conference on High Performance Computing and Simulation (HPCS 2011

An Agent Based Simulation of Smart Metering Technology Adoption

Based on the classic behavioural theory “the Theory of Planned Behaviour”, we develop an agent-based model to simulate the diffusion of smart metering technology in the electricity market. We simulate the emergent adoption of smart metering technology under different management strategies and economic regulations. Our research results show that in terms of boosting the take-off of smart meters in the electricity market, choosing the initial users on a random and geographically dispersed basis and encouraging meter competition between energy suppliers can be two very effective strategies. We also observe an “S-curve” diffusion of smart metering technology and a “lock-in” effect in the model. The research results provide us with insights as to effective policies and strategies for the roll-out of smart metering technology in the electricity market

Developing an agent-based simulation model of software evolution

Context In attempt to simulate the factors that affect the software evolution behaviour and possibly predict it, several simulation models have been developed recently. The current system dynamic (SD) simulation model of software evolution process was built based on actor-network theory (ANT) of software evolution by using system dynamic environment, which is not a suitable environment to reflect the complexity of ANT theory. In addition the SD model has not been investigated for its ability to represent the real-world process of software evolution. Objectives This paper aims to re-implements the current SD model to an agent-based simulation environment ‘Repast’ and checks the behaviour of the new model compared to the existing SD model. It also aims to investigate the ability of the new Repast model to represent the real-world process of software evolution. Methods a new agent-based simulation model is developed based on the current SD model's specifications and then tests similar to the previous model tests are conducted in order to perform a comparative evaluation between of these two results. In addition an investigation is carried out through an interview with an expert in software development area to investigate the model's ability to represent real-world process of software evolution. Results The Repast model shows more stable behaviour compared with the SD model. Results also found that the evolution health of the software can be calibrated quantitatively and that the new Repast model does have the ability to represent real-world processes of software evolution. Conclusion It is concluded that by applying a more suitable simulation environment (agent-based) to represent ANT theory of software evolution, that this new simulation model will show more stable bahaviour compared with the previous SD model; And it will also shows the ability to represent (at least quantatively) the real-world aspect of software evolution.Peer reviewedFinal Accepted Versio

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.Flocking Behaviour; Hierarchical Leadership; Agent-Based Simulation; Social Dynamics

An Agent-Based Simulation of Rental Housing Markets

We simulate a closed rental housing market with search and matching frictions, in which both landlord and tenant agents are imperfectly informed. Homogeneous landlords set rents to maximise revenue, using information on the market to estimate the relationship between posted rent and time-on-the-market (TOM). Tenants, heterogeneous in income, engage in undirected search accepting residences based on their idiosyncratic tastes for housing and a disagreement point derived from information on the distribution of offers. The steady state to which the simulation evolves shows price dispersion, nonzero search times and vacancies.The main results concern the effects of increasing information on either side of the market. When tenants see a greater percentage of the distribution of offers, tenants learn to refuse high rents and so the population rises and tenants' utilities rise as does overall welfare. Conversely, when landlords have less information, their utility can rise as over estimations in best posting rent move the market to higher rents.Real estate; Rental markets; Search; Information; Simulation; Multi-agent systems

An Agent-Based Simulation API for Speculative PDES Runtime Environments

Agent-Based Modeling and Simulation (ABMS) is an effective paradigm to model systems exhibiting complex interactions, also with the goal of studying the emergent behavior of these systems. While ABMS has been effectively used in many disciplines, many successful models are still run only sequentially. Relying on simple and easy-to-use languages such as NetLogo limits the possibility to benefit from more effective runtime paradigms, such as speculative Parallel Discrete Event Simulation (PDES). In this paper, we discuss a semantically-rich API allowing to implement Agent-Based Models in a simple and effective way. We also describe the critical points which should be taken into account to implement this API in a speculative PDES environment, to scale up simulations on distributed massively-parallel clusters. We present an experimental assessment showing how our proposal allows to implement complicated interactions with a reduced complexity, while delivering a non-negligible performance increase