Hofstede’s cultured negotiating agents

Tese de mestrado, Ciência Cognitiva, Universidade de Lisboa, Faculdade de Psicologia, Faculdade de Ciências, Faculdade de Letras, Faculdade de Medicina, 2011Hofstede and colleagues cultured negotiating agents simulation produced realistic behavior by incorporating Hofstede's dimensional model of culture in the agent's negotiation protocol and overall behavior. Given such a promising model to generate actual human-like behavior in artificial agents, and the lack of sound and well accepted replication methodologies, we tried to remake the original simulation and highlight the roadblocks encountered during the process. Along the way, we present the Hofstede‟s dimensional model of culture and its integration in the cultured agents social simulation. Some suggestions are made in order to avoid such obstacles. New results showed a relational equivalence.A simulação de Hofstede e colegas de agents culturais que negoceiam entre si produziu comportamentos realistas através da incorporação do modelo dimensional da cultura de Hofstede no protocolo de negociação dos agentes, e no seu comportamento em geral. Dado o potencial que tal modelo apresenta para gerar comportamentos humanos verdadeiros em agentes artificiais, assim como a falta de metodologias de replicação padrão e sólidas, tentámos replicar a simulação original e ilustrámos as dificuldades com que nos deparámos durante o processo. Apresentamos também o modelo dimensional da cultura de Hofstede e a sua integração numa simulação social de agentes culturais. Hofstede e colaboradores (2010a) definem cultura como um fenómeno que é específico de um grupo e não de um indivíduo; sistemas partilhados de valores, transmitidos desde tenra idade através da educação e do exemplo; estável ao longo de várias gerações apesar de alterações substantivas no ambiente e na tecnologia. Este modelo dimensional da cultura tem-se revelado fiável a nível de replicações e validações ao longo do tempo. Fazemos também algumas sugestões no sentido de evitar tais dificuldades na re-engenharia necessária à replicação do trabalho de Hostede, tais como usar práticas de Engenharia de Software e publicar resultados das simulações detalhados e de fácil acesso. Os novos resultados, da replicação, mostram uma equivalência relacional (qualitativa) em relação aos resultados originais e fornecem um bom pronúncio quanto ao potencial deste modelo cultural ser aplicado em vários cenários que não apenas o de comércio

Norm Generation in Multi-Agent Systems

Projecte realitzat en col.laboració amb el Departament de Matemàtica Aplicada i Anàlisi de l'Universitat de Barcelon

Towards intelligent transport systems: geospatial ontological framework and agent simulation

In an Intelligent Transport System (ITS) environment, the communication component is of high significance as it supports interactions between vehicles and the roadside infrastructure. Existing studies focus on the physical capability and capacity of the communication technologies, but the equally important development of suitable and efficient semantic content for transmission has received notably less attention. Using an ontology is one promising approach for context modelling in ubiquitous computing environments. In the transport domain, an ontology can be used both for context modelling and semantic contents for vehicular communications. This research explores the development of an ontological framework implementing a geosemantic messaging model to support vehicle-to-vehicle communications. To develop an ontology model, two scenarios (an ambulance situation and a breakdown on the motorway) are constructed to describe specific situations using short-range communication in an ITS environment. In the scenarios, spatiotemporal relations and semantic relations among vehicles and road facilities are extracted and defined as classes, objects, and properties/relations in the ontology model. For the ontology model, some functions and query templates are also developed to update vehicles’ movements and to provide some logical procedures that vehicles need to follow in emergency situations. To measure the effects of the vehicular communication based on the ontology model, an agent-based approach is adopted to dynamically simulate the moving vehicles and their communications following the scenarios. The simulation results demonstrate that the ontology model can support vehicular communications to update each vehicle’s context model and assist its decision-making process to resolve the emergency situations. The results also show the effect of vehicular communications on the efficiency trends of traffic in emergency situations, where some vehicles have a communication device, and others do not. The efficiency trends, based on the percentage of vehicles having a communication device, can be useful to set a transition period plan for implanting communication devices onto vehicles and the infrastructure. The geospatial ontological framework and agent simulation may contribute to increase the intelligence of ITS by supporting data-level and application-level implementation of autonomous vehicle agents to share knowledge in local contexts. This work can be easily extended to support more complex interactions amongst vehicles and the infrastructure

Estimation of missing prices in real-estate market agent-based simulations with machine learning and dimensionality reduction methods

The opacity of real-estate market involves some challenges in their agent-based simulation. While some real-estate Web sites provide the prices of a great amount of houses publicly, the prices of the rest are not available. The estimation of these prices is necessary for simulating their evolution from a complete initial set of houses. Additionally, this estimation could also be useful for other purposes such as appraising houses, letting buyers know which are the best offered prices (i.e., the lowest ones compared to the appraisals) and recommending the buyers to set an initial price. This work proposes combining dimensionality reduction methods with machine learning techniques to obtain the estimated prices. In particular, this work analyzes the use of nonnegative factorization, recursive feature elimination and feature selection with a variance threshold, as dimensionality reduction methods. It compares the application of linear regression, support vector regression, the k-nearest neighbors and a multilayer perceptron neural network, as machine learning techniques. This work has applied a tenfold cross-validation for comparing the estimations and errors and assessing the improvement over a basic estimator commonly used in the beginning of simulations. The developed software and the used dataset are freely available from a data research repository for the sake of reproducibility and the support to other researchers

High Speed Simulation Analytics

Simulation, especially Discrete-event simulation (DES) and Agent-based simulation (ABS), is widely used in industry to support decision making. It is used to create predictive models or Digital Twins of systems used to analyse what-if scenarios, perform sensitivity analytics on data and decisions and even to optimise the impact of decisions. Simulation-based Analytics, or just Simulation Analytics, therefore has a major role to play in Industry 4.0. However, a major issue in Simulation Analytics is speed. Extensive, continuous experimentation demanded by Industry 4.0 can take a significant time, especially if many replications are required. This is compounded by detailed models as these can take a long time to simulate. Distributed Simulation (DS) techniques use multiple computers to either speed up the simulation of a single model by splitting it across the computers and/or to speed up experimentation by running experiments across multiple computers in parallel. This chapter discusses how DS and Simulation Analytics, as well as concepts from contemporary e-Science, can be combined to contribute to the speed problem by creating a new approach called High Speed Simulation Analytics. We present a vision of High Speed Simulation Analytics to show how this might be integrated with the future of Industry 4.0

Modelling Urban Growth: Towards an Agent Based Microeconomic Approach to Urban Dynamics and Spatial Policy Simulation

Urban growth, urban sprawl if uncoordinated and dispersed, can be considered one of the most important policy agendas in modern urban regions. While no single policy option or remedy exists, understanding the urban growth system is the first step towards sustainable urban growth futures. Spatially explicit and dynamic urban growth models provide valuable simulations that encapsulate essential knowledge in planning and policy making such as how and where urban growth can occur and what the driving forces of such changes are. Over the past two decades, cellular automata (CA) models have proven to be an effective modelling approach to the study of complex urban growth systems. More recently Agent Based Modelling (ABM) has developed to yield a useful framework for understanding complex urban systems and this provides an arena for exploring the possible outcome states of various policy actions. Yet most research efforts of this sort adopt physical and heuristic approaches which tend to neglect socio-economic dynamics which is critical in shaping urban form and its transformation. This thesis aims to develop an agent based urban simulation model which has a more rigid theoretical explanation of agent behaviour than most such models hitherto. However, before developing such an agent based model, this study first conducted a series of experimental simulations with two well-known generic CA based urban models, SLEUTH and Metronamica, in order to better understand the complexity of designing and applying this class of urban models. Although CA and ABM are two distinctive modelling approaches, they share certain fundamentals concerning the complexity of systems and thus the empirical simulations with widely used CA models provide useful insights for the development of a new dedicated agent based urban growth model. For this purpose, each CA model is calibrated to the study area of the Seoul Metropolitan Area, Korea. The research then moves towards developing an agent based model based on microeconomic foundations. Utility maximising residential location choices made by households are modelled as the main impetus for urban growth through agglomeration and sprawl. Furthermore, based on such urban dynamics, alternative planning policy options such as greenbelts and public transportation are simulated so that their impacts can be clarified and assessed. In this way, the model is also able to examine how planning policies alter the economic utility of households and redirect market-led urban development. These results confirm the unique value of such modelling approaches. Yet, new research challenges such as the estimation of model parameters and the use of such models in planning support continue to dominate this field and in conclusion, we identify future research directions which build on these challenge

High Speed Simulation Analytics

Simulation, especially Discrete-event simulation (DES) and Agent-based simulation (ABS), is widely used in industry to support decision making. It is used to create predictive models or Digital Twins of systems used to analyse what-if scenarios, perform sensitivity analytics on data and decisions and even to optimise the impact of decisions. Simulation-based Analytics, or just Simulation Analytics, therefore has a major role to play in Industry 4.0. However, a major issue in Simulation Analytics is speed. Extensive, continuous experimentation demanded by Industry 4.0 can take a significant time, especially if many replications are required. This is compounded by detailed models as these can take a long time to simulate. Distributed Simulation (DS) techniques use multiple computers to either speed up the simulation of a single model by splitting it across the computers and/or to speed up experimentation by running experiments across multiple computers in parallel. This chapter discusses how DS and Simulation Analytics, as well as concepts from contemporary e-Science, can be combined to contribute to the speed problem by creating a new approach called High Speed Simulation Analytics. We present a vision of High Speed Simulation Analytics to show how this might be integrated with the future of Industry 4.0

Land Use Change and Economic Opportunity in Amazonia: An Agent-based Model

Economic changes such as rising açaí prices and the availability of off-farm employment are transforming the landscape of the Amazonian várzea, subject to decision-making at the farming household level. Land use change results from complex human-environment interactions which can be addressed by an agent-based model. An agent-based model is a simulation model composed of autonomous interacting entities known as agents, built from the bottom-up. Coupled with cellular automata, which forms the agents’ environment, agent-based models are becoming an important tool of land use science, complementing traditional methods of induction and deduction. The decision-making methods employed by agent-based models in recent years have included optimization, imitation, heuristics, classifier systems and genetic algorithms, among others, but multiple methods have rarely been comparatively analyzed. A modular agent-based model is designed to allow the researcher to substitute alternative decision-making methods. For a smallholder farming community in Marajó Island near Ponta de Pedras, Pará, Brazil, 21 households are simulated over a 40-year period. In three major scenarios of increasing complexity, these households first face an environment where goods sell at a constant price throughout the simulated period and there are no outside employment opportunities. This is followed by a scenario of variable prices based on empirical data. The third scenario combines variable prices with limited employment opportunities, creating multi-sited households as members emigrate. In each scenario, populations of optimizing agents and heuristic agents are analyzed in parallel. While optimizing agents allocate land cells to maximize revenue using linear programming, fast and frugal heuristic agents use decision trees to quickly pare down feasible solutions and probabilistically select between alternatives weighted by expected revenue. Using distributed computing, the model is run through several parameter sweeps and results are recorded to a cenral database. Land use trajectories and sensitivity analyses highlight the relative biases of each decision-making method and illustrate cases where alternative methods lead to significantly divergent outcomes. A hybrid approach is recommended, employing alternative decision-making methods in parallel to illustrate inefficiencies exogenous and endogenous to the decision-maker, or allowing agents to select among multiple methods to mitigate bias and best represent their real-world analogues

The use of computer science practices and methods for developing social simulations to stimulate changes in travellers’ mode choice

In this thesis, Computer Science practices and methods including Software Engineering and Artificial Intelligence techniques are explored to incorporate Human Factors and Psychology knowledge in a structured way into agent-based models to model modal shift in a social system. Observations of peoples’ behaviours in social systems regarding choice-making suggest that they tend to have preferences among the available alternatives in many situations. Experts in the domain of Psychology have been interested in the relationships that exist between the psychological processes (factors) and peoples’ behaviours. Human Factors’ experts are concerned with, among other things, the study of factors and development of tools that improve users’ experiences. The findings from the literature suggest that the two groups have been working from the perspective of their domains without much collaboration. Also, no known framework or methodology offers the required collaborative modelling support and techniques to model people’s emotion as they traverse the system. The aim of this thesis is, therefore, to provide modelling techniques that better support the use of Human Factors and Psychology knowledge in understanding factors that influence travellers’ decision-making in travel mode choice so as to stimulate changes in their behaviours. The support also provides collaboration among relevant stakeholders to work on modal shift project in the transport system. The method adopted in carrying out the research reported in this thesis is informed by the descriptive, developmental, and exploratory nature of the objectives of the research. Our novel methodology which includes a framework is named MOdal SHift (MOSH) methodology. Its development process involves the use of design principles that include encapsulation, data abstraction, inheritance, and polymorphism in defining and integrating the Human Factors and Psychology practices into the methodology. The structures and behaviours of the system components are described and documented using the Unified Modelling Language (UML) as a standard specification language to promote uniform communication among a group of experts. The decision variable decomposition module and techniques for deriving travellers’ emotions that correspond to their context involved the use of the Fuzzy sets system. The methodology contains guides that include the process map diagram showing the major stages in the methodology as well as the step-by-step development guidelines. To verify and to validate the methodology, two case studies in the transport domain are selected. The first case study aims at demonstrating the use of the framework included in the methodology for policy formulation. The second case study has the goal of demonstrating the use of the methodology for understanding individuals’ abilities to satisfy travel requirements. Data Science methods including both supervised and unsupervised learning algorithms are applied at relevant stages of the case studies. The reflection from the cases investigated with the MOSH methodology reveals its novelty in modelling interdependencies among the transport system’s constraints and in modelling travellers’ emotional state as they traverse the transport system’s environment. In addition, the adoption of the standard specification language in the design of the methodology provides the means for easy communication and transfer of knowledge among stakeholders. The use of Software Engineering tools and methods in conjunction with the agent-based modelling paradigm in the MOSH methodology design and development phases promotes the separation of concerns for the interrelated and non-linear levels of organisation within a sociotechnical system. It also promotes extensibility of various aspect of the methodology as a result of the independence among the components and makes reusability of relevant aspects possible when there are needs to use the same functionality in a new project. The agent-based modelling paradigm provides opportunities for investigating the interactions among the agents and the environment as well as providing insights into the various complex interrelated behaviours