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Abstract. In contrast to customers of bricks and mortar stores, users of online selling environments are not supported by human sales experts. In such situations recommender applications help to identify the products and/or services that fit the user's wishes and needs. In order to successfully apply recommendation technologies we have to develop an in-depth understanding of decision strategies of users. These decision strategies are explained in different models of human decision making. In this paper we provide an overview of selected models and discuss their importance for recommender system development. Furthermore, we provide an outlook on future research issues.

The worldwide distribution of honeybees and their fast propagation to new areas rests on their ability to keep up optimal ‘tropical conditions’ in their brood nest both in the cold and in the heat. Honeybee colonies behave like ‘superorganisms’ where individuals work together to promote reproduction of the colony. Social cooperation has developed strongly in thermal homeostasis, which guarantees a fast and constant development of the brood. We here report on the cooperation of individuals in reaction to environmental variation to achieve thermal constancy of 34–36 °C. The measurement of body temperature together with bee density and in-hive microclimate showed that behaviours for hive heating or cooling are strongly interlaced and differ in their start values. When environmental temperature changes, heat production is adjusted both by regulation of bee density due to migration activity and by the degree of endothermy. Overheating of the brood is prevented by cooling with water droplets and increased fanning, which start already at moderate temperatures where heat production and bee density are still at an increased level. This interlaced change and onset of different thermoregulatory behaviours guarantees a graded adaptation of individual behaviour to stabilise the temperature of the brood.

Requirements engineering (RE) is considered as one of the most critical phases in the software life-cycle, and poorly implemented RE processes are among the major risks for project failure. Stakeholders are often faced with the challenge that the complexity of information outstrips their capability to survey it and to decide about which requirements should be taken into account. Additionally, preferences regarding a set of requirements are typically not known beforehand but constructed within the scope of a decision making process. In this paper we introduce a simple application scenario and discuss recommendation and decision technologies which can be exploited for proactively supporting stakeholders in their decision making.

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