## 4 Ambiguity and ICT-Information Implementation  
### The Theory of Ambiguity and Ambiguity Aversion  
As highlighted in the previous section, many theories have been developed in literature to explain decision making under uncertainty. Between these, most of the studies consider risk to be the only element of uncertainty in decisions. However, this does not allow to explain some situations as those described by [PERSON] [18]. In his work, [PERSON] focused on un-measurable uncertainties to show important exceptions to the [PERSON] axioms and in particular to the independence axiom. These exceptions impaired the capability of SEU to represent decisions and to highlighted that it is not only risk, but also ambiguity driving decisions. He considered a series of thought experiments, out of which, the most famous is the one involving two urns. There is a transparent urn with observable content of 50 red and 50 black balls and another opaque urn with the same amount of balls, but with unknown ratio between the two colors. While in the transparent urn the DM faces a situation of pure risk, because probabilities are observable and measurable; the bet in the opaque urn is different because such measurement is not possible. To describe the opaque urn's content (hence the probability of a specific ball's color) hundred combinations between red and black balls are possible. This raises uncertainty over which combination is the one really describing the urn's content. In such settings, [PERSON] considered that most of DMs would have preferred to place a bet on the ball's color from a draw in the transparent urn, instead of a draw from the urn with unknown balls' ratio. For a given prize in the bet, he proved that this choice cannot only be driven by a mere difference in subjective probabilities. Rather, the preference to bet on the transparent urn and, in general, the preference for gambles with known probabilities, is driven by a behavioral phenomenon called Ambiguity Aversion (AA). The concept of AA is similar to Risk Aversion (RA), where ambiguity is identified as:  
\"the nature of one's information concerning the relative likelihood of events a quality depending on the amount, type, reliability and 'unanimity' of information, and giving rise to one's degree of 'confidence' in an estimation of relative likelihoods.\" [18].  
Or, in the words of [PERSON] and [PERSON] [50], ambiguity is more clearly defined as \"uncertainty about probability, created by missing information that is relevant and could be known\". Overall, similarly to what [PERSON] did, uncertainty is then characterized by two elements: (i) risk, represented by the share of measurable uncertainty or estimated trough subjective probabilities, and (ii) ambiguity, expressing the degree of confidence over these probability estimations.  
Because ambiguity affects a large share of decisions under uncertainty, there have been several experimental studies showing its relevance in decision making. Results highlight that AA impacts are comparable with, if not higher than, RA [51]. Further, these studies showed that AA is the main preference behavior of DMs under ambiguity, because DMs dislike situations where more than one probability estimation is possible [52]. The presence of AA implies that decisions do not only reveal subjective probabilities, but also relative preferences for expected outcomes. There are situations, such as in the urns' examples (and, as described later, in ICT-information implementation), in which we need to distinguish between the two elements of uncertainty to understand DM's behavior. In these situations, SEU models cannot be applied in a straightforward way but ambiguity-sensitive preferences have to be accounted for. Further, the uncertainty framing proposed allows to model the process of familiarity which occurs as the DM gains experience with a new phenomenon or a new technology, such in the case of ICT.