Intelligent Agents in Co-Evolving Knowledge Networks

Evangelos Ioannidis, Nikos Varsakelis and Ioannis Antoniou
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Evangelos Ioannidis: Complex Systems Analysis Laboratory (COSAL), Faculty of Sciences, School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Nikos Varsakelis: Complex Systems Analysis Laboratory (COSAL), Faculty of Economic and Political Sciences, School of Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Ioannis Antoniou: Complex Systems Analysis Laboratory (COSAL), Faculty of Sciences, School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Mathematics, 2021, vol. 9, issue 1, 1-17

Abstract: We extend the agent-based models for knowledge diffusion in networks, restricted to random mindless interactions and to “frozen” (static) networks , in order to take into account intelligent agents and network co-evolution . Intelligent agents make decisions under bounded rationality. This is the key distinction of intelligent interacting agents compared to mindless colliding molecules , involved in the usual diffusion mechanism resulting from accidental collisions. The co-evolution of link weights and knowledge levels is modeled at the local microscopic level of “agent-to-agent” interaction . Our network co-evolution model is actually a “ learning mechanism” , where weight updates depend on the previous values of both weights and knowledge levels. The goal of our work is to explore the impact of (a) the intelligence of the agents, modeled by the selection-decision rule for knowledge acquisition, (b) the innovation rate of the agents, (c) the number of “top innovators” and (d) the network size . We find that rational intelligent agents transform the network into a “centralized world” , reducing the entropy of their selections-decisions for knowledge acquisition. In addition, we find that the average knowledge , as well as the “knowledge inequality” , grow exponentially.

Keywords: networks; knowledge diffusion; co-evolution dynamics; agent-based modeling; computer simulations; bounded rationality; intelligent agents; entropy; network centralization; innovation rate (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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