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Applications of Agent-based Models (ABM) to analyse finance growth in a sustainable manner over a long-term period

  • Host institution: Kaunas University of Technology, Lithuania (KUT)

  • Starting month: M6

  • Duration: 36 months

  • Pillar 1: Sustainable finance (University of Naples Federico II, 4 ECTs), Work Package 5

  • Work Packages: WP5, WP6, WP7, WP8

Objectives

Agent-based systems are computer models that simulate the behaviours and interactions of autonomous agents, either as individuals or in groups, in order to gain a deeper understanding of how a system behaves and what factors influence its outcomes. In agent-based modelling, a system is represented as a collection of autonomous decision-making units, or agents (ABM). Each agent evaluates its own situation and makes decisions according to a set of rules. Agents are capable of a variety of appropriate behaviours for the system they represent. ABM has been utilised in numerous financial investigations. The literature contains few ABM studies that model economies and markets while assuming the industry's adoption of sustainable finance

Expected Results

This study aims to use agent-based models to simulate different market scenarios in which industry agents take sustainable actions. Long-term financial growth will be analysed, and the findings will aid in the development and modification of industry policies and strategies. A public repository containing a library of the developed agent-based models is another anticipated outcome. The Work Package will place a strong emphasis on disseminating and the anticipated outcomes. Several channels, including peer-reviewed articles in high-impact journals, research talks at national and international conferences, and use case presentations at industry workshops, will be utilised to accomplish this objective.

Planned Secondments

  • Royalton Partners (ROY). Dr. Michael Althof, M21, 18 months, research on crypto assets for prototype and user acceptance

  • Athena Research and Innovation Center (ARC). Prof. Dr. Ioannis Emiris, M39, 6 months, applied industry-research, using large-scale computing infrastructure to implement the theory

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Planned Timetable

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