Strategic Alliances in NetLogo: A Flocking Algorithm with Reinforcement Learning

Sónia Teixeira () and Pedro Campos ()
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Sónia Teixeira: University of Porto, LIAAD - INESC TEC
Pedro Campos: University of Porto, FEP, LIAAD-INESC TEC

Chapter Chapter 12 in Machine Learning Perspectives of Agent-Based Models, 2025, pp 287-306 from Springer

Abstract: Abstract The evolution of markets provides a change in the way organisations act. To improve their competitive performance and stay on the market, organisations often adopt a strategy to establish agreements with other organisations, known as strategic alliances. Several tools, algorithms, and computational systems call upon other sciences as a source of inspiration. In this work we explore flocking behaviour, a paradigm of biology, to analyse the collective intelligence behaviour that emerges from a group of individuals or firms. Inspired by the Cucker and Smale algorithm (C-S), we propose a new version of the flocking algorithm, AllFlock, applied to strategic alliances, considering a learning mechanism. For this new approach, metrics were obtained for the parameters of the C-S algorithm: position, velocity, and influence. The latter uses cooperative games, adapted mechanisms, and methods currently explored in reinforcement learning. We have used Netlogo as the modelling environment. Five parameter configurations were analysed. For each of those configurations, the average number of iterations, the permanence rate of organisations in the alliance, and the average growth of the organisations were computed. The behaviour of the organisations reveals a tendency for convergence, confirming the existence of flocking behaviour.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-73354-3_12

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DOI: 10.1007/978-3-031-73354-3_12

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