Abstract
We study cognitive agents’ success in learning to cross a Cellular Automaton (CA) based highway, for two decision formulas used by the agents’ in their decision-making process. The agents use an “observational social learning” strategy based on the observation of performance of other agents, mimicking what worked for them and avoiding what did not. We investigate how the incorporation of the assessment of outcomes of agents waiting decisions into their decision-making process based only on the assessment of outcomes of their crossing decisions affects the agents’ success in learning to cross the highway. The agents’ success is measured by the numbers of successful, killed and queued agents at simulation end.
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