Verbal and mathematical models that consider the costs and benefits of behavioral strategies have been useful in explaining animal behavior and are often used as the basis of evolutionary explanations of human behavior. In most cases, however, these models do not account for the effects that group structure and cultural traditions within a human population have on the costs and benefits of its members’ decisions. Nor do they consider the likelihood that cultural as well as genetic traits will be subject to natural selection. In this paper, we present an agent-based model that incorporates some key aspects of human social structure and life history. We investigate the evolution of a population under conditions of different environmental harshness and in which selection can occur at the level of the group as well as the level of the individual. We focus on the evolution of a socially learned characteristic related to individuals’ willingness to contribute to raising the offspring of others within their family group. We find that environmental harshness increases the frequency of individuals who make such contributions. However, under the conditions we stipulate, we also find that environmental variability can allow groups to survive with lower frequencies of helpers.
Matchmaking is introduced to the coworking literature as a process of connecting potential collaboration partners. The process itself is realised through a smartphone application drawing on user-generated profiles, allowing coworkers to create tags according to their business interests and challenges, find cooperation opportunities, and get in touch via instant messaging or voice chat.
Acceptance of software support for finding matches in the coworking space context is examined using a modified Technology Acceptance Model, investigating the role of personal innovativeness as additional factor.
Matchmaking is introduced to the coworking literature as a process of as well as self-governed selection of collaboration partners (Bouncken and Coworkers visit the CWS about days a week (SD = ), yet % come in five days. this would grant integrated all-in-one tools a natural advantage.
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For a not-so-small fee, CEO Patti Stanger can bring out the charming suitor in the most unlikely millionaire. The only occupational hazard? For a not-so-small fee, CEO Patti Stanger can bring out the charming suitor in the most unlikely millionaire — with the help of her team of stylists, therapists, and date coaches of course. Patti is armed with not only an elite pool of millionaire clients, but also a database of women holding some of nature’s most coveted characteristics: beauty, brains and class.
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Like many single professionals in the San Francisco Bay Area, Bahar Mir looked to dating apps to meet new people and find romance. A former product lead at Google, Mir realized how many of her friends had similar frustrations, so she set out with a new goal: She wanted to use artificial intelligence AI to eliminate the real-life bugs of dating, such as endless messaging and flaky connections.
From photos and social profiles to online interactions and event attendance, BELONG sets out to provide more relevant recommendations and in theory expedite introduction to an ideal match. One of the biggest challenges of online dating, she explained, was getting the first date to actually happen. The more information the user provides, the smarter the app gets about providing match recommendations.
Beyond identifying matches, the app acts as a concierge that provides smart recommendations about activities—the data indicates—both parties might enjoy. As the matches message each other, they receive personalized recommendations about events, such as concerts, cooking, or a yoga class they could do together. Our plan is to get smarter with all aspects of social life. But what about after that first date?
The app asks users for feedback post-date to be able to better mimic positive experiences and mitigate for those that are subpar, or correct for flakiness. Instead, she offers an intelligent approach to dating that aims to replace mystery with data and provides a romantic concierge. Skip to content Perspectives.
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Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs. For instance, to illustrate the principles of selection, Dawkins coined the meme, airport in the entire system due to the small-world nature of scale-free networks. Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabasi AL.
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