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In this chapter we first give a short overview of Learning to Forecast (LtF) experiments, thereby focusing on the differences between markets with positive and negative expectations feedback. Subsequently, we discuss how the results of these experiments can be used to predict behavior for more complicated market environments that exhibit both types of feedback. In particular, we will consider the case where a futures market is connected with a spot market.

Text data pertaining to peoples’ careers have proliferated in the past few decades. Due to the digitization of job search, recruitment, and the development of HR systems, it is relatively easy to access and obtain large datasets containing information about jobs or other work-related information at the micro (individual), meso (institutional), and macro (regional, national and global) levels, or some combination thereof. Examples of text data that may be used to study careers include (auto)biographies, résumés, posts in professional social networking sites, online job boards, public surveys, interview transcripts, personal diary entries, and even academic publications. Of particular interest are job vacancies, as aside from education and job experience, they also contain information about individuals’ roles, responsibilities, knowledge, skills, and abilities, which comes with the promise of adding specificity and context to the career domain, which has come to be dominated by reductionist and generalist approaches to operationalizing key constructs. Online forums and social media also provide data relevant to the study of careers since employees use these platforms to voice their ongoing opinions and sentiments about their past and present employers.

While earlier evolutionary thinking in economics interpreted selection as a competitive process driven by scarcity, akin to Charles Darwin’s idea of struggle for survival, modern evolutionary economics recognizes, as Darwin had noted, that selection also operates at the group level. By contrast, the idea of sexual selection, which Darwin used to explain why traits that are wasteful from the point of view of survival selection may nonetheless evolve, has thus far been overlooked. The chapter demonstrates how, within the group selection context of the firm, the causal logic of sexual selection is relevant, and why this provides a good reason to reject Milton Friedman’s claim that market efficiency implies that firms maximize profits. Specifically, it is likely that the bottom-up process of mutual preference selection between firms and their employees results in organizational cultures that overshoot their functionality in terms of efficiency in product markets.

We discuss the organization of an experimental economics laboratory, and the use of experimental software tools. We distinguish between “software tools” like z-Tree, oTree, PHP and JavaScript and “programs,” which are the actual programs (scripts) containing the logic of the experiment. First, we review the broader topic of laboratory organization and the (re-)use of programs (and protocols), focusing on the need for clear rules, the centrality of a logbook, and the availability of a central repository for programs and protocols. Then we review criteria to select software tools, discussing technical criteria like adaptability, and scalability, but also non-technical criteria like the usefulness of certain programming skills for PhDs leaving academia. Finally, we apply these criteria on three of the available software tools: O-Tree, z-Tree and standard internet programming tools, like PHP and JavaScript.