## 4 Ambiguity and ICT-Information Implementation
### Ambiguity and ICT-Information  
The capability of the theory developed by [PERSON] to provide a complete picture of the elements shaping uncertainty explains its large adoption in different decision problems [44]. In the majority of decisions, DMs have to cope with a certain level of risk and ambiguity. The latter expresses the degree of confidence the DM puts on the former [53]. This situation is typical with decisions under climate change [54]. Here, probabilistic distributions built with past records are mistrusted to be representative of future climate trends and different models propose different projections. Ambiguity can be found in climate forecasts too. Uncertainty in forecasts is characterized by two elements: (i) the intrinsic variability of climate events, represented by probability distributions and (ii) the uncertainty about the forecast itself [55]. The first uncertainty can be expressed as risk, over which, the second uncertainty emerges because the DM does not know whether the forecast is reliable. This lack of knowledge raise ambiguity because several forecasts could be delivered and the DM is not sure whether the one received is really representing future states. One common way to deal with such complex uncertainty settings in forecasts is through uncertainty folding [56]. It consists in folding ambiguity with risk to obtain a single probability estimate used as input in SEU models. The method is rather simple and appealing. However, it has been shown by [PERSON] and [PERSON] [56] that it fails in representing real decision making, due to important informational losses.  
As with forecasts information, ambiguity rises even with the adoption of new technologies [57]. When a DM is faced with a new technology, he is uncertain on the probability of such technology to be good performing. In this context, even if the DM has expectations or information from the developer, neither of the two probability estimations can be reliably assessed. As a result, in the first stages of a new technology, AA has the potential to strongly limit its diffusion. Only after having gained enough experience with the new technology, prior expectations can be confirmed or rejected and ambiguity solved in a learning process [58]. Most of the applied economic literature considers ambiguity generated by new technologies to be risk, thereby losing precious DM's behavioral insights [59]. If risk and ambiguity are treated as one, risk-averse and ambiguity-averse DMs would behave equally. Both would be less prone in experimenting new technologies or implementing new forecast information, but this does not always happen. A new technology could be risk-reducing but ambiguous, or the opposite, it is possible to gain information reducing ambiguity but not risk [60]. In this complex framework, [PERSON] [61] defined the relation between information value and AA, where information reducing ambiguity is always sought by AA individuals, and the benefits from its implementation rise with the degree of aversion. The same applies with information reducing risk and RA. However, while information solving ambiguity is valued only by AA DMs, if information solving risk completely disclose states of the world, its benefits rise with both AA and RA [61]. These phenomena are extremely important in the context of new technology adoption. If a new technology reduces the variability of outcomes, it is risk-decreasing but, as said before, it might raise ambiguity. Here, RA plays in favor of the technology, while AA might limit its implementation.