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Issues in utility modeling and rational decision making
Published in Marc A. Maes, Luc Huyse, Reliability and Optimization of Structural Systems, 2020
In Savage’s (1954) maximum expected utility theory there is in fact no room for someone who talks about reliable probabilities and unreliable probabilities. Every decision maker is expected to express in each probability the best possible state of belief. However, rational informed behavior apparently is difficult when decision makers do not know the rules of the game they are playing. This is reflected clearly by Ellsberg’s paradox, constructed as a criticism of Savage’s axioms by Ellsberg (1961): an example is given in Appendix A, section A4. As summarized in e.g. Camerer and Weber (1992) several approaches have been suggested for the treatment of ambiguity aversion, reaching from a modification of the utility subject to epistemic uncertainty over a non-linear representation of second order probabilities, to the concept of minimum subjective expected utility and non-additive probabilities. Even though such approaches may be applicable to represent a certain behavior of decision makers in given situation it is still highly questionable whether this is relevant – assuming that the decision maker would act rationally when provided with the right information. Furthermore the introduction of such approaches may also give rise to a whole spectrum of inconsistencies in the even larger field of subjective or Bayesian probability.
It won't happen to me
Published in Vicki Bier, Risk in Extreme Environments, 2018
People tend to prefer “known risks” (whose probabilities are known) over equivalent “unknown risks” (whose probabilities are ambiguous), a phenomenon known as “ambiguity aversion” (Camerer & Weber, 1992; Ellsberg, 1961). Ert and Trautmann (2014) studied the effect of experience on ambiguity aversion by letting their subjects sample the “unknown risk.” They found that this sampling experience eliminates the ambiguity aversion tendency, and leads people to exhibit the “it won't happen to me” effect. In a related study on product promotion, Ert, Raz, and Heiman (2016) studied consumer responses to products that are beneficial overall (i.e., whose expected values exceed their costs), but for which the distribution of values is highly skewed (e.g., longshot lotteries or safety-related products), so that they yield large benefits with low probability. In that context, they found that letting consumers experience these products before making a purchase decision was counterproductive, because consumers were too sensitive to the “typical” performance while experiencing the product (in which large benefits were most often not received). Therefore, such personal experience with a product actually lowered consumers' tendency to buy the sampled product despite its advantages.
The impacts of longitudinal separation, efficiency loss, and cruise speed adjustment in robust terminal traffic flow problem under uncertainty
Published in Transportation Letters, 2023
Kam K.H. Ng, Felix T.S. Chan, Yichen Qin, Gangyan Xu
The ambiguity aversion, in economics literature, is referred to as that the decision maker and tends to prefer known risk over unknown risk or uncertainty (Epstein 1999; Gilboa and Schmeidler 1989; Schmeidler 1989). Ben-Tal, Bertsimas, and Brown (2010) first developed the soft robust model for convex optimization under ambiguity aversion. The conservativeness of solutions under the convex risk measure guarantees that the solution quality is against the downside performance in terms of uncertainty in convex optimization (Bertsimas, Nohadani, and Teo 2010). Furthermore, the estimation of unknown parameters in robust optimization usually falls into interval cases. In this regard, the robust solution is deemed to be too conservative but less vulnerable to disruption (De La Vega, Munari, and Morabito 2020). It is not possible to monitor closely when dealing with delay estimations for all approaching flights. The choice of robust optimization methods is subject to the preference and the balance between the levels of disruption and resilience (Aissi, Bazgan, and Vanderpooten 2009). Absolute robustness, robust deviation, and relative deviation are well-known robust optimization methods (Xu et al. 2013). The aim of robust optimization is to neutralize the outcome of uncertainty if wrong decisions create a dramatic failure in operations (Basso 2008; Delavernhe et al. 2020). Ng et al. (2017) proposed a min-max regret approach with regard to hedging the arrival and departure uncertainty under the worst-case scenario in order to develop a robust ASSP schedule for a mix-mode parallel runway operation.
An experimental investigation of newsvendor decisions under ambiguity
Published in International Journal of Production Research, 2021
Abhishek Shinde, Peeyush Mehta, R. K. Amit
Scarf (1958) is one of the early works that characterise inventory problem with partial demand information. In the model, a newsvendor maximises expected profit against the worst possible distribution (min–max approach) of the demand with the known mean and standard deviation (). Scarf's model can be classified under neoclassical models of decision-making under ambiguity that assumes pessimistic decision-makers. The optimal order quantity from the Scarf's formulation is Alfares and Elmorra (2005), Andersson et al. (2013), Gallego and Moon (1993), Kamburowski (2014), Perakis and Roels (2008) and Yue, Chen, and Wang (2006) examine the robustness of Scarf's formula. Han, Du, and Zuluaga (2014) propose that the decision-makers simultaneously exhibit risk aversion and ambiguity aversion. They incorporate the risk aversion preferences in the stocking policy suggested by the Scarf's min–max policy.
Term structures and scenario-based social discount rates under smooth ambiguity
Published in The Engineering Economist, 2021
Dowon Kim, Kyoung-Kuk Kim, Jiwoong Lee
Ambiguity refers to uncertain situations where probabilities are unknown, and a preference for known probabilities over unknown ones is referred to as ambiguity aversion (see Machina & Siniscalchi, 2014; Wakker, 2010 for comprehensive surveys). This can be a problem in terms of robustness because, for instance, a relatively small change in the permanent trend of growth has an immense impact on future consumption when projected over many decades. This weakness becomes more relevant when the time horizon is so long as to be difficult to model uncertainties about economic growth by a single probability distribution. Such difficulty has been widely recognized in the context of climate change. The Intergovernmental Panel on Climate Change (IPCC) presents several growth scenarios until 2100 (Nakićenović et al., 2000), but it clearly states that “probabilities or likelihood are not assigned to individual scenarios.” Hence, in order to be applied in the economics of climate change, the Ramsey rule needs to be extended in a way to deal with such ambiguity.