Question: The Expected utility hypothesis was initiated by which Swiss mathematician in 1738?

Context articles: Exponential utility
Expected value of sample information
Expected utility hypothesis
Von Neumann–Morgenstern utility theorem
Subjective expected utility
Generalized expected utility
Q-learning
Daniel Bernoulli
David Schmeidler
Rank-dependent expected utility

In economics and finance, exponential utility refers to a specific form of the utility function, used in some contexts because of its convenience when risk (sometimes referred to as uncertainty) is present, in which case expected utility is maximized.
 Formally, exponential utility is given by:
In decision theory, the expected value of sample information (EVSI) is the expected increase in utility that a decision-maker could obtain from gaining access to a sample of additional observations before making a decision.
 The additional information obtained from the sample may allow them to make a more informed, and thus better, decision, thus resulting in an increase in expected utility.
 EVSI attempts to estimate what this improvement would be before seeing actual sample data; hence, EVSI is a form of what is known as "preposterior analysis".
In economics, game theory, and decision theory the expected utility hypothesis, concerning people's preferences with regard to choices that have uncertain outcomes (gambles), states that if specific axioms are satisfied, the subjective value associated with an individual's gamble is the statistical expectation of that individual's valuations of the outcomes of that gamble.
 Initiated by Daniel Bernoulli in 1738, this hypothesis has proved useful to explain some popular choices that seem to contradict the expected value criterion (which takes into account only the sizes of the payouts and the probabilities of occurrence), such as occur in the contexts of gambling and insurance.
 Until the mid-twentieth century, the standard term for the expected utility was the moral expectation, contrasted with "mathematical expectation" for the expected value.
In decision theory, the von Neumann-Morgenstern utility theorem shows that, under certain axioms of rational behavior, a decision-maker faced with risky (probabilistic) outcomes of different choices will behave as if he is maximizing the expected value of some function defined over the potential outcomes at some specified point in the future.
 This function is known as the von Neumann-Morgenstern utility function.
 The theorem is the basis for expected utility theory.
In decision theory, subjective expected utility is the attractiveness of an economic opportunity as perceived by a decision-maker in the presence of risk.
 Characterizing the behavior of decision-makers as using subjective expected utility was promoted and axiomatized by L. J. Savage in 1954 following previous work by Ramsey and von Neumann.
 The theory of subjective expected utility combines two subjective concepts: first, a personal utility function, and second a personal probability distribution (usually based on Bayesian probability theory).
Generalized expected utility is a decision-making metric based on any of a variety of theories that attempt to resolve some discrepancies between expected utility theory and empirical observations, concerning choice under risky (probabilistic) circumstances.
Q-learning is a model-free reinforcement learning technique.
 Specifically, "Q"-learning can be used to find an optimal action-selection policy for any given (finite) Markov decision process (MDP).
 It works by learning an action-value function that ultimately gives the expected utility of taking a given action in a given state and following the optimal policy thereafter.
 A policy is a rule that the agent follows in selecting actions, given the state it is in.
 When such an action-value function is learned, the optimal policy can be constructed by simply selecting the action with the highest value in each state.
 One of the strengths of "Q"-learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment.
 Additionally, "Q"-learning can handle problems with stochastic transitions and rewards, without requiring any adaptations.
 It has been proven that for any finite MDP, "Q"-learning eventually finds an optimal policy, in the sense that the expected value of the total reward return over all successive steps, starting from the current state, is the maximum achievable.
Daniel Bernoulli FRS ( ; ] ; 8 February 1700 – 17 March 1782) was a Swiss mathematician and physicist and was one of the many prominent mathematicians in the Bernoulli family.
 He is particularly remembered for his applications of mathematics to mechanics, especially fluid mechanics, and for his pioneering work in probability and statistics.
 His name is commemorated in the Bernoulli's principle, a particular example of the conservation of energy, which describes the mathematics of the mechanism underlying the operation of two important technologies of the 20th century: the carburetor and the airplane wing.
David Schmeidler (born 1939) is an Israeli mathematician and economic theorist with important contributions in the theory of individual decision making under uncertainty (decision theory).
 He currently holds professorships at Ohio State University and Tel Aviv University.
 He received his PhD in Mathematics from the Hebrew University of Jerusalem under the supervision of Robert Aumann.
 His most influential works include the theories of Choquet expected utility and maxmin expected utility (the latter jointly with Itzhak Gilboa).
 Both these theories explain individual attitudes towards ambiguity that are consistent with the Ellsberg paradox.
The rank-dependent expected utility model (originally called anticipated utility) is a generalized expected utility model of choice under uncertainty, designed to explain the behaviour observed in the Allais paradox, as well as for the observation that many people both purchase lottery tickets (implying risk-loving preferences) and insure against losses (implying risk aversion).