This work presents some results in the value of information calculations for multi-attribute decision-making under uncertainty. Almost all engineering activities are undertaken in the face of uncertainty and a decision that maximizes a suitably chosen metric is generally selected. It becomes essential to collect information regarding these uncertainties so that better informed decisions can be made. Calculation of the worth of this information (VoI) is a difficult task, particularly when multiple attributes are present and there exists dependence between the random attributes in the same alternative or across different alternatives. In this paper, closed-form expressions and numerical models for the calculation of VoI are presented. Particularly, we derive methods for the general scenario where we have to decide over two or more alternatives, each involving two or more continuous random attributes exhibiting some level of dependence with the others. These reduce or completely eliminate the need for conducting simulations or approximations, both of which tend to be either computationally expensive (such as Monte Carlo), limited in accuracy or both. It also allows us to conduct more involved analyses such as sensitivity analysis on design parameters and the engineer’s preferences in a feasible and even potentially automated way. We also introduce “attribute-wise VoI,” which shows that collecting information on one or more of the attribute(s) makes sense only in specific dependence scenarios and tradeoff relationships between attributes. Calculation methods for value of such information are also provided. We illustrate our models on mobile autonomous system selection decisions. We conclude with a discussion on the avenues for future research into the optimal mix of a system’s intelligence (autonomy), communication, and information gathering.