Mathematical functions designed similar to emotional concepts in order to provide tools for emotion-based choice are investigated. Emotions have been treated as a disturbing factor to logical assumptions and decisions. Now they are rather treated as a necessity and a requirement for agents in complex or not fully graspable situations with complicated or concurrent goals. The software of SOCIAL, the 'Simulation Of Consciousness In Artificial Life' was developed to provide a research universe for virtual agents named BUGs. The emotionbased models of Affect Logic, Subsumption and Fungus Eater were compared against 2 randomizing benchmark approaches. To compare success of various routines, agents equipped with decision making strategies designed similar to these theoretical approaches were tested in predator-prey simulations with limited energy supply. The quantitative aspect of survival was measured through experiments. Experiments include three kinds of simulations: Simulations where all emotional routines compete inside BUGs of the same type, simulations where all types of BUGs are equipped with the same routine, and simulations with a mixture of type and emotional routine. The evolutionary concepts of selection and mutation were utilized to allow the BUGs to adapt their decision making strategy to the current simulation. For simulations run within SOCIAL, the memory based randomizing benchmark approach outperforms all sophisticated routines. Complex models of emotional choice seem to be not beneficial to virtual agents, but a burden instead. This is probably not a general result but limited to the specific experiment setups tested.
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