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Purpose – This chapter addresses the nature, formalization, and neural bases of (affective) social ties and discusses the relevance of ties for health economics. A social tie is defined as an affective weight attached by an individual to the well-being of another individual (‘utility interdependence’). Ties can be positive or negative, and symmetric or asymmetric between individuals. Characteristic of a social tie, as conceived of here, is that it develops over time under the influence of interaction, in contrast with a trait like altruism. Moreover, a tie is not related to strategic behavior such as reputation formation but seen as generated by affective responses.

Methodology/approach – A formalization is presented together with some supportive evidence from behavioral experiments. This is followed by a discussion of related psychological constructs and the presentation of suggestive existing neural findings. To help prepare the grounds for a model-based neural analysis some speculations on the neural networks involved are provided, together with suggestions for future research.

Findings – Social ties are not only found to be important from an economic viewpoint, it is also shown that they can be modeled and related to neural substrates.

Originality/value of the chapter – By providing an overview of the economic research on social ties and connecting it with the broader behavioral and neuroeconomics literature, the chapter may contribute to the development of a neuroeconomics of social ties.

The chapter briefly reviews the eight volumes in my Legend series – organizational buying behavior, consumer behavior, product and new product management, marketing strategy, market segmentation, global marketing, marketing research and modeling, and the future of marketing. In addition, the chapter highlights the three driving forces of much of my research: (a) the real world challenges facing corporations and organizations, (b) the search for new methodological developments, and (c) the continuous challenge of the prevailing marketing concepts and approaches. The chapter concludes with some reflections on the evolution of marketing in the past five decades and my wish list for the discipline and my future activities.

One of the most important issues facing a firm involved in direct marketing is the selection of addresses from a mailing list. When the parameters of the model describing consumers' reaction to a mailing are known, addresses for a future mailing can be selected in a profit-maximizing way. Usually, these parameters are unknown and have to be estimated. These estimates are used to rank the potential addressees and to select the best targets.

Several methods for this selection process have been proposed in the recent literature. All of these methods consider the estimation and selection step separately. Since estimation uncertainty is neglected these methods lead to a suboptimal decision rule and hence not to optimal profits. We derive an optimal Bayes decision rule that follows from the firm's profit function and which explicitly takes estimation uncertainty into account. We show that the integral resulting from the Bayes decision rule can be either approximated through a normal posterior, or numerically evaluated by a Laplace approximation or by Markov chain Monte Carlo integration. An empirical example shows that indeed higher profits result.