Trust and trustworthiness are of great importance in social and human systems, especially when considering managerial and economic decision-making. In this paper, we investigate the emergent dynamics of an evolutionary game-theoretic model-the N-player evolutionary trust game-consisting of three types of players: 1) an investor; 2) a trustee who is trustworthy; and 3) a trustee who is untrustworthy. Here, we limit the interactions between players to local neighborhoods defined by a specific spatial topology or social network. Players are able to adjust their game-playing strategies using an evolutionary update rule based on the payoffs obtained by their neighbors. Through comprehensive simulation experiments, we find that trust can be promoted when players interact on a social network despite a substantial number of untrustworthy individuals in the initial population. These results differ from findings reported for an unstructured population of the same game, where the existence of a single untrustworthy individual would eliminate trust completely. We compare the dynamics of the model with different social network densities and structures (e.g., from regular lattices to scale-free and random networks). We observe that the levels of trust vary under different network structures, and the level is correlated with how “difficult” the game is. When game conditions are easy (i.e., low temptation to defect and/or almost no initial untrustworthy trustees), homogeneous networks with higher densities can promote higher levels of trust. However, when the game becomes harder, heterogeneous social networks with lower densities are able to promote higher levels of trust and global net wealth.

“Bee pollen” is pollen collected from flowers by honey bees. It is used by the bees to nourish themselves, mainly by providing royal jelly and brood food, but it is also used for human nutrition. For the latter purpose, it is collected at the hive entrance as pellets that the bees bring to the hive. Bee pollen has diverse bioactivities, and thus has been used as a health food, and even as medication in some countries. In this paper, we provide standard methods for carrying out research on bee pollen. First, we introduce a method for the production and storage of bee pollen which assures quality of the product. Routine methods are then provided for the identification of the pollen’s floral sources, and determination of the more important quality criteria such as water content and content of proteins, carbohydrates, fatty acids, vitamins, alkaloids, phenolic and polyphenolic compounds. Finally, methods are described for the determination of some important bioactivities of bee pollen such as its antioxidant, anti-inflammatory, antimicrobial and antimutagenic properties. Métodos estándar Para la investigación del polen El "polen de abeja" es el polen recogido de las flores por las abejas melíferas. El polen de abeja es utilizado para nutrir a las propias abejas, principalmente para proporcionar jalea real y alimento para las crías, pero también se utiliza para la nutrición humana. Para este último fin, se recoge en la entrada de la colmena en forma de gránulos que las abejas llevan a la colmena. El polen de abeja tiene diversas bioactividades, por lo que se hautilizado como alimento para la salud, e incluso como medicamento en algunos países. En este artículo, proporcionamos métodos estándar para llevar a cabo investigaciones sobre el polen de abeja. En primer lugar, presentamos un método de producción y almacenamiento de polen de abeja que garantiza la calidad del producto. A continuación, se ofrecen métodos de rutina para la identificación de las fuentes florales del polen y la determinación de los criterios de calidad más importantes, como el contenido de agua y de proteínas, carbohidratos, ácidos grasos, vitaminas, alcaloides y compuestos fenólicos y polifenólicos. Por último, se describen métodos para la determinación de algunas bioactividades importantes del polen de abeja, como sus propiedades antioxidantes, antiinflamatorias, antimicrobianas y antimutagénicas.

Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics.Peer ReviewedPostprint (author’s final draft

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimanned assembly line balancing under uncertain demand, and presents a new robust mixed-integer linear programming model to minimize the production and penalty costs simultaneously. In addition, a reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed to tackle the problem. The algorithm includes a priority-based solution representation and a new task-worker-sequence decoding that considers robustness processing and idle time reductions. Five crossover and three mutation operators are proposed. The $Q$ -learning-based strategy determines the crossover and mutation operator at each iteration to effectively obtain Pareto sets of solutions. Finally, a time-based probability-adaptive strategy is designed to effectively coordinate the crossover and mutation operators. The experimental study, based on 269 benchmark instances, demonstrates that the proposal outperforms 11 competitive MOEAs and a previous single-objective approach to the problem. The managerial insights from the results as well as the limitations of the algorithm are also highlighted.