Modelling Cities as a collection of TeraSystems - Computational challenges in Multi-Agent Approach

YesAgent-based modeling techniques are ideal for modeling massive complex systems such as insect colonies or biological cellular systems and even cities. However these models themselves are extremely complex to code, test, simulate and analyze. This paper discusses the challenges in using agent-based models to model complete cities as a complex system. In this paper we argue that Cities are actually a collection of various complex models which are themselves massive multiple systems, each of millions of agents, working together to form one system consisting of an order of a billion agents of different types - such as people, communities and technologies interacting together. Because of the agent numbers and complexity challenges, the present day hardware architectures are unable to cope with the simulations and processing of these models. To accommodate these issues, this paper proposes a Tera (to denote the order of millions)-modeling framework, which utilizes current technologies of Cloud computing and Big data processing, for modeling a city, by allowing infinite resources and complex interactions. This paper also lays the case for bringing together research communities for interdisciplinary research to build a complete reliable model of a city

Simulating budget system in the agent model of the Russian Federation spatial development

In this paper we present methods, data and algorithms for simulating budget system in the model of the Russian Federation spatial development. We show the place of this task in methodology of our research and give a brief overview of the background results. We determine revenues and expenditures of the budgets and funds on the basis of federal laws, Budget and Tax codes of the Russian Federation. For validation of the budget system algorithms in the model we conduct retrospective modeling for the federal budget and the budget of Belgorod region in 201

Simulating heterogeneous behaviours in complex systems on GPUs

Agent Based Modelling (ABM) is an approach for modelling dynamic systems and studying complex and emergent behaviour. ABMs have been widely applied in diverse disciplines including biology, economics, and social sciences. The scalability of ABM simulations is typically limited due to the computationally expensive nature of simulating a large number of individuals. As such, large scale ABM simulations are excellent candidates to apply parallel computing approaches such as Graphics Processing Units (GPUs). In this paper, we present an extension to the FLAME GPU 1 [1] framework which addresses the divergence problem, i.e. the challenge of executing the behaviour of non-homogeneous individuals on vectorised GPU processors. We do this by describing a modelling methodology which exposes inherent parallelism within the model which is exploited by novel additions to the software permitting higher levels of concurrent simulation execution. Moreover, we demonstrate how this extension can be applied to realistic cellular level tissue model by benchmarking the model to demonstrate a measured speedup of over 4x

AGENT-BASED MODEL AND SIMULATIONS OF THE MANAGEMENT OF PORTS: THE IMPORT PROCESSES AT THE PORT OF GENOVA

This thesis addressed to use of Agent-Based Modelling (ABM) for the development and implementation of the import process of goods in a port that is suitably applied to help, plan, structure the development of the port model. The main goal of the modelling and implementation was to reach to fruition as more organized, fast and efficient complex logistics network, through development policies. To this purpose, I developed an agent-based model (ABM) of a port that is populated by the real main actors (stakeholders) whose are involved in the port activities such as maritime, customs, financial police etc. The model of the port simulates the actual port processes, i.e. acceptance of the goods, sending them, controlling of the legality, or not to import goods, the transportation planning etc. Agent-based models (ABMs) are being used in modelling in economies as complex systems that is a relatively recent approach in economies [62]. It has increasingly been attracting many scholars belonging to several sub-fields, becoming both a complement and a substitute for more traditional economic-modeling methodologies [62]. We can mention that ABMs are considered as a valid and effective competitor of standard Dynamic Stochastic General Equilibrium (DSGE) models in macroeconomics [26]. The main advantages of using ABMs arrive two main additional values if we compare it with its equivalent systems. ABM provides more descriptive richness, as they characterize ecologies of agents, locally interacting through non-obvious network structures, learning using incomplete information, and competing within imperfect markets. Second, the modeler developing an ABM has typically more flexibility in both input and output validation of its model [34]. Ports have an integral role of our economy, they are strategic places of exchange, and especially over the last few decades and with the phenomenon of globalization, the ports are a reality in continuous movement and growth. Therefore, they are operating places of extreme complexity, especially in their logistics functions of transport management. The thesis discusses the business process is implemented for developing a computer supported management tool to handle the port activities flow. The tool is designed for the integration in a virtual infrastructure that allows an advanced operational management of port traffics. By modelling the time documentation according to the specification of the Genoa case, the business case of the port of Genoa is tested. Results show that the mechanism implemented simulates the actual process. Moreover some bottleneck are discovered, such as delays to the handling of the containers and queues formation due to missing documentation or documentation with errors or not ready

Advances in the Agent-based Modeling of Economic and Social Behavior

In this review we discuss advances in the agent-based modeling of economic and social systems. We show the state of the art of the heuristic design of agents and how behavioral economics and laboratory experiments have improved the modeling of agent behavior. We further discuss how economic networks and social systems can be modeled and we discuss novel methodology and data sources. Lastly, we present an overview of estimation techniques to calibrate and validate agent-based models and show avenues for future research

Complex networks in audit:A data-driven modelling approach

In this thesis, we introduce data-driven audit methods using a network-based approach. Utilizing data from over 300 companies, it transforms transaction data into a network format, providing auditors with a clear overview of a company's financial structure. Chapter 2 details the financial statements network, designed for straightforward interpretation by auditors. This network effectively represents the company's financial structure, aiding in developing universal data-driven audit methods. Chapter 3's analysis reveals that the financial account nodes' degree distribution typically follows a heavy-tail distribution. Moreover, we found only minor variations in network statistics across industries. These findings help establish baseline expectations for network statistics, facilitating risk assessment. Chapter 4 addresses the complexity of these networks, proposing a method to simplify them into a more understandable high-level structure for auditors. Chapter 5 explores a similarity measure to compare financial structures, helping auditors identify deviations in a client's financial network compared to peers or historical data. Deviations could signal increased audit risks. In summary, we pioneer data-driven audit methods using financial statement networks, providing new insights and tools for auditors and paving the way for more efficient and effective audit processes

Examining the entrepreneurial intention of university students: the role of entrepreneurial education and attitudes

Treball Final de Grau en Administració d'Empreses. Codi: AE1049. Curs 2017-2018Purpose: The main objective of this work is to study the influence the entrepreneurial intent of university students, and to observe to what extent students’ attitudes and entrepreneurial education affect their decision to start up a business. Specifically, in this work, the role of education in entrepreneurship and certain socio-demographic aspects such as work experience, gender and training specialty will be analysed, exploring how they influence entrepreneurial intent. In the study of entrepreneurial intent, we will analyse three attitudinal backgrounds, such as the attitude of the person with respect to starting a business, subjective norms and the perceived behavioural control. Design/Methodology: To answer the three research questions posed, we developed a questionnaire aimed at students of the Jaume I University. This study has an exploratory nature, so we selected a sample of levels of two training fields, together with a control group formed by the participants of the Explorer Program of the Santander Bank Entrepreneurship. Findings: The results reveal that the most influential backgrounds in the entrepreneurial intention of the students are the personal attitudes and the perceived behavioural control with respect to entrepreneurship. We also found that education in entrepreneurship is associated with high levels of personal attitude, behavioural control and the intention to launch a business. Finally, the results indicate that students with work experience have higher levels of entrepreneurial intention. It is also observed that the training specialty influences the entrepreneurial intent, and, in contrast, gender does not seem to influence the intention to start up a business

Agent-Based Macroeconomics

Dawid H, Delli Gatti D. Agent-Based Macroeconomics. Universität Bielefeld Working Papers in Economics and Management. Vol 02-2018. Bielefeld: Bielefeld University, Department of Business Administration and Economics; 2018.This chapter surveys work dedicated to macroeconomic analysis using an agent- based modeling approach. After a short review of the origins and general characteristics of this approach a systemic comparison of the structure and modeling assumptions of a set of important (families of) agent-based macroeconomic models is provided. The comparison highlights substantial similarities between the different models, thereby identifying what could be considered an emerging common core of macroeconomic agent-based modeling. In the second part of the chapter agent-based macroeconomic research in different domains of economic policy is reviewed