Today enormous data storage capacities and computational power in the e-big data era have created unforeseen opportunities for big data hoarding corporations to reap hidden benefits from individuals' information sharing, which occurs bit by bit in small tranches over time. Behavioral economics describes human decision-making fallibility over time but has—to this day—not covered the problem of individuals' decisions to share information about themselves in tranches on social media and big data administrators being able to reap a benefit from putting data together over time and reflecting the individual's information in relation to big data of others.
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Utility theory is concerned with people’s choices and decisions based on preferences and values (Fishburn, 1968). Representing satisfaction experienced, utility is derived from the self-attributed worth and goodness of an option compared to other options. Standard neo-classical economic theory describes utility as a set of internally-consistent assumptions about options in the wish to maximize utility (Fishburn, 1968). Utility theory has leveraged as one of the most dominant theories in economics as an underpinning of rational choice and game theory. Utility is usually revealed in people’s willingness to pay different money amounts for different options, leading to the concept of revealed preferences (Samuelson, 1937).
Whereas economic utility studies primarily focus on prescriptive approaches to guide how people should behave to maximize their well-being (Arrow, 1951, 1958; Majumdar, 1958; Simon, 1959); decision sciences started capturing how people actually decide regarding choices in an uncertain world and over time (Becker & McClintock, 1967; Edwards, 1954, 1961; Luce & Suppes, 1965). Expected utility theory introduces a first temporal discussion of expectations of utility rather than the actual utility derived from a choice (Alchian, 1953; Marschak, 1950; Strotz, 1953). Von Neumann and Morgenstern (1953) introduced that outcomes of choices are not known with certainty but have probabilities of occurrence, which weighted linear combination allows inferences about the overall utility derived over time.
Since the end of the 1970ies, a wide range of psychological, economic and sociological laboratory and field experiments proved human beings deviating from rational choices as standard neo-classical profit maximization axioms to fail to explain how human actually behave (Kahneman & Thaler, 1991). Human beings were shown to use heuristics in the day-to-day decision making as mental short cuts that enable to cope with information overload in a complex world (Bazerman & Tenbrunsel, 2011; Kahneman & Tversky, 1979; Thaler & Sunstein, 2008).
As one of the most recent developments in utility theory studies, behavioral economics find human utility choices biased (Bowles, 2004; Camerer, Loewenstein & Rabin, 2004; Ebert & Prelec, 2007; Kahneman, 2011; Okada & Hoch, 2004; Putnam, 2002; Sen, 1971, 1993, 1995, 1997, 2002a; Zauberman, Kim, Malkoc & Bettman, 2009) by heuristics (Kahneman, Slovic & Tversky, 1982; Simon, 1979), analogical thinking (Colinsky, 1996; Gentner, 2002), and minimized effort (Allport, 1979; Shah & Oppenheimer, 2008).