Agent-based modelling as a foundation for big data

Chen, Shu‐Heng; Venkatachalam, Ragupathy

doi:10.1080/1350178x.2017.1388964

Cited by 24 publications

(15 citation statements)

References 67 publications

(68 reference statements)

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“…Furthermore, agent-based modeling can also help modern economies that have been greatly influenced by the big data phenomenon [7]. By applying computational methods to big data, economists have addressed microeconomic issues in the internet marketplaces, such as pricing and product design.…”

Section: Discussionmentioning

confidence: 99%

On Complex Economic Dynamics: Agent-Based Computational Modeling and Beyond

Chen

Kao

et al. 2018

Complex Systems Modeling and Simulation in Economics and Finance

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This chapter provides a selective overview of the recent progress in the study of complex adaptive systems. A large part of the review is attributed to agentbased computational economics (ACE). In this chapter, we review the frontier of ACE in light of three issues that have long been grappled with, namely financial markets, market processes, and macroeconomics. Regarding financial markets, we show how the research focus has shifted from trading strategies to trading institutions, and from human traders to robot traders; as to market processes, we empathetically point out the role of learning, information, and social networks in shaping market (trading) processes; finally, in relation to macroeconomics, we demonstrate how the competition among firms in innovation can affect the growth pattern. A minor part of the review is attributed to the recent econometric computing, and methodology-related developments which are pertinent to the study of complex adaptive systems. Keywords Financial markets • Complexity thinking • Agent-based computational economics • Trading institutions • Market processes This book is the post-conference publication for the 21st International Conference on Computing in Economics and Finance (CEF 2015) held on June 20-22, 2015 in Taipei. Despite being the largest conference on computational economics for two decades, CEF has never produced any book volume that documents the pathbreaking and exciting developments made in any of its single annual events.

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Section: Discussionmentioning

confidence: 99%

On Complex Economic Dynamics: Agent-Based Computational Modeling and Beyond

Chen

Kao

et al. 2018

Complex Systems Modeling and Simulation in Economics and Finance

Self Cite

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“…A key challenge is how to process these data effectively while providing feedback to the model. The use of supervised, unsupervised, and semi-supervised learning techniques can produce new methods for model calibration, for example, by enabling classification and comparison of key features of the model within a particular observation window or by easing the definition of baselines for predicted behaviours across the model [255,256].…”

Section: Multi-agent Systemsmentioning

confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

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Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.Processes 2020, 8, 312 2 of 29 in a telecommunication system network, nodes (e.g., routers and switches) may suffer over time a degradation in their properties that may lead them to diminish their performance. However, just the daily variation on traffic demand also has an impact on such nodes performance. Unexpected, external intervention such as cyber-attacks or extreme weather conditions may affect the properties, even the topology of a telecommunication system.Intelligent distributed systems are capable of modelling how different parts of the network might work individually and collectively [10]. Ultimately, this is used as support for any decision-making system aiming to achieve better overall system functioning and its consequent performance. Intelligent distributed systems encompass autonomous learning units that can be associated with the nodes of a complex network. These intelligent network nodes are able to act independently and also interact with other nodes to pursue both individual (local) and general system-level targets. The necessary communication between individuals can be represented by links of the complex network connecting their nodes. It is natural to understand such nodes as intelligent agents within a MAS framework. Each agent separately obeying simple rules but working together with other agents makes it possible to approach complex engineering challenges. Intelligent distributed systems have been proposed in a broad range of engineered complex systems [11,12]. Precisely, the review proposed herein focuses only on engineered systems and manufacturing processes, given the wide spectrum of the approached topic. Out of this review fall, then, topics related to social systems analysis such as migration models [13,14] or online social media [15,16]. Similarly, natural systems are out of this review scope. This is the case of computational chemistry [17,18], biology [19,20], and metabolic networks [21,22].Automatic optimisation and control in an engineering system is associated with a near real-time data acquisition and an optimal decision-making. Th...

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“…The agent-based model views the financial market as a complex adaptive system that contains a plurality of heterogeneous agents. Based on bottom-up microscopic modeling methods and advanced computer simulation technologies, an agent-based model can deeply reveal the general laws of the financial market-an area that has garnered considerable research attention (Battiston et al 2016;Chen and Venkatachalam 2017;Lussange et al 2018;Iori and Porter 2018;Cui et al 2020;Zhao et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Cost-benefit analysis of trading strategies in the stock index futures market

et al. 2020

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With the introduction of many derivatives into the capital market, including stock index futures, the trading strategies in financial markets have been gradually enriched. However, there is still no theoretical model that can determine whether these strategies are effective, what the risks are, and how costly the strategies are. We built an agent-based cross-market platform that includes five stocks and one stock index future, and constructed an evaluation system for stock index futures trading strategies. The evaluation system includes four dimensions: effectiveness, risk, occupation of capital, and impact cost. The results show that the informed strategy performs well in all aspects. The risk of the technical strategy is relatively higher than that of the other strategies. Moreover, occupation of capital and impact cost are both higher for the arbitrage strategy. Finally, the wealth of noise traders is almost lost.

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Agent-based modelling as a foundation for big data

Cited by 24 publications

References 67 publications

On Complex Economic Dynamics: Agent-Based Computational Modeling and Beyond

On Complex Economic Dynamics: Agent-Based Computational Modeling and Beyond

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Cost-benefit analysis of trading strategies in the stock index futures market

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