THE GOLEM LEGEND AND THE ENIGMA OF FACEBOOK

We are easily misguided as to the true nature of Facebook, and tend to treat it simply as a powerful technological instrument in the service of human intentions. We can, however, gain a better picture of it through recourse to the Jewish tradition of the golem, an image of human beings, created by them in a re-enactment of their own creation by God. It turns into a magic servant in modernity with an inherent dynamic running between its human and its subhuman characteristics. This dynamic is the main cause behind its becoming uncontrollable. In like manner, what is subhuman in Facebook serves its masters and functions under their total control, but also empowers Facebook's increasingly human operation, an algorithm-based capability which raises growing doubts about what counts as human. Facebook implies the crisis of humanity which coincides with the “death of God,” that is, the obsolescence of the idea of a divine creator.     

How much self-presentation behavior do applicants from the United Arab Emirates exhibit?

Applicants from different cultures vary in their self-presentation behavior during job interviews. This study investigates self-presentation behavior in the United Arab Emirates (UAE), the second largest economy in the Arab world. Specifically, it examines self-presentation behavior of applicants from the UAE and compares it to the behavior of American, European, and Chinese applicants from previous studies. The randomized response technique was used to gather self-presentation prevalence data of 111 UAE applicants regarding self-presentation behavior in their last job interview. Prevalence rates were lower than those from the United States and from China but higher than those from Iceland and from Switzerland. Results indicate that though UAE culture values modesty, UAE applicants still engage in distinct self-presentation behavior.     

Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology

A theoretical structure for multiattribute decision making is presented, based on a dynamical system for interactions in a neural network incorporating affective and rational variables. This enables modeling of problems that elude two prevailing economic decision theories: subjective expected utility theory and prospect theory. The network is unlike some that fit economic data by choosing optimal weights or coefficients within a predetermined mathematical framework. Rather, the framework itself is based on principles used elsewhere to model many other cognitive and behavioral data, in a manner approximating how humans perform behavioral functions. Different, interconnected modules within the network encode (a) attributes of objects among which choices are made, (b) object categories, (c) and goals of the decision maker. An example is utilized to simulate the actual consumer choice between old and new versions of Coca-Cola. Potential applications are also discussed to market decisions involving negotiations between participants, such as international petroleum traders.     

The decision to check in multialternative choices and limited sensitivity to default

Given the rapid proliferation of smartphone applications and data aggregation websites, in many situations people can use decision aids to guide their choices. For example, they may consider whether to use a navigation device to check the fastest route or whether to use a price comparison website to find the cheapest deal. In what circumstances will subjects use a costly comparison decision aid (which I refer to as “checking”) to choose for them? In six studies, I investigate the impact of the number of available alternatives and checking's attractiveness on the decision to check. While at first increasing the attractiveness of checking led to higher checking rates, a further increase in the number of available alternatives (and thus checking's attractiveness) did not increase the checking rate. Surprisingly, even when checking had a 40% higher expected value compared with not checking, the observed checking rate was below 45%, contrary to risk and ambiguity aversion predictions. Furthermore, labeling the checking alternative as the default had no impact on its choice rate. I find large individual differences in decisions to check. Surprisingly, subjects' initial decisions had high predictive power over their subsequent checking rates, even after 100 trials with full feedback. I propose two simple learning models that capture well the aggregated results.