Influence of Losing Multi-dimensional Information in an Agent-Based Model

This agent-based study investigates the effect of losing information on market performance of agents in a marketplace with various quality requirements. It refines an existing model on multi-dimensional information diffusion among agents in a network. The agents need to align their supply with available markets, the quality criteria of which must match the agents’ information. Turnover (information entering and leaving the system) had a significant effect in the old model. Information items became obsolete based on age, causing a risk for the agents to lose valuable information. In the refined model presented here, an information item may become obsolete based on two additional aspects: (1) whether it is ‘in use’ for meeting the agent’s current market criteria, and (2) its value, reflecting its owner’s experience or skill with the information item. The research questions concern the influence of these two aspects on model outcomes. Two key parameters are value-threshold, below which items are candidate for disposal, and keep-chance, indicating the probability that in-use items are not disposed of. Both simulation runs and a local sensitivity analysis were performed. Simulation results show that value-threshold is a more influential parameter than keep-chance. An interesting pattern suggesting a tipping point was observed: with increasing value-threshold, agents initially reach higher quality, but then the quality diminishes again. This pattern is consistently observed for the majority of parameter settings. An explanation is that agents with only high-valued information cannot afford to lose anything. The sensitivity analysis adds insight to where keep-chance and value-threshold are most influential, and where other parameters are responsible for observed outputs. The sensitivity analysis does not provide any further insight in why the observed tipping point occurs. The paper also aims to highlight methodological issues with respect to refining an existing model in such a way that results of successive model versions are still comparable, and observed differences can be attributed to newly introduced changes.