Risk associated with project outcomes is usually handled with
probability theory. Although it can be factored into the discount rate (to have uncertainty increasing over time), it is usually considered separately. Particular consideration is often given to agent
risk aversion: preferring a situation with less uncertainty to one with greater uncertainty, even if the latter has a higher
expected return. Uncertainty in CBA parameters can be evaluated with a
sensitivity analysis, which indicates how results respond to parameter changes. A more formal risk analysis may also be undertaken with the
Monte Carlo method. However, even a low parameter of uncertainty does not guarantee the success of a project.
Principle of maximum entropy Suppose that we have sources of uncertainty in a CBA that are best treated with the Monte Carlo method, and the distributions describing uncertainty are all continuous. How do we go about choosing the appropriate distribution to represent the sources of uncertainty? One popular method is to make use of the
principle of maximum entropy, which states that the distribution with the best representation of current knowledge is the one with the largest
entropy – defined for continuous distributions as:H(X) = \mathbb{E}\left[ -\log f(X) \right] = -\int_{\mathcal{S}}f(x)\log f(x) dxwhere \mathcal{S} is the support set of a
probability density function f(x). Suppose that we impose a series of constraints that must be satisfied: • f(x) \geq 0, with equality outside of \mathcal{S} • \int_{\mathcal{S}}f(x) dx =1 • \int_{\mathcal{S}}r_{i}(x)f(x) dx = \alpha_{i}, \quad i = 1,...,m where the last equality is a series of
moment conditions. Maximizing the entropy with these constraints leads to the
functional:J = \max_{f} \; \int_{\mathcal{S}} \left( -f\log f + \lambda_{0}f + \sum_{i=1}^{m}\lambda_{i}r_{i}f\right) dxwhere the \lambda_{i} are
Lagrange multipliers. Maximizing this functional leads to the form of a maximum entropy distribution:f(x) = \exp\left[ \lambda_{0} - 1 + \sum_{i=1}^{m}\lambda_{i}r_{i}(x) \right]There is a direct correspondence between the form of a maximum entropy distribution and the
exponential family. Examples of commonly used continuous maximum entropy distributions in simulations include: •
Uniform distribution • No constraints are imposed over the support set \mathcal{S}\in[a,b] • It is assumed that we have maximum ignorance about the uncertainty •
Exponential distribution • Specified mean \mathbb{E}(X) over the support set \mathcal{S}\in[0,\infty) •
Gamma distribution • Specified mean \mathbb{E}(X) and log mean \mathbb{E}(\log X) over the support set \mathcal{S}\in[0,\infty) • The exponential distribution is a special case •
Normal distribution • Specified mean \mathbb{E}(X) and variance \text{Var}(X) over the support set \mathcal{S}\in(-\infty,\infty) • If we have a specified mean and variance on the log scale, then the
lognormal distribution is the maximum entropy distribution
CBA under US administrations The increased use of CBA in the US regulatory process is often associated with President
Ronald Reagan's administration. Although CBA in US policy-making dates back several decades, Reagan's
Executive Order 12291 mandated its use in the regulatory process. After campaigning on a deregulation platform, he issued the 1981 EO authorizing the
Office of Information and Regulatory Affairs (OIRA) to review agency regulations and requiring federal agencies to produce regulatory impact analyses when the estimated annual impact exceeded $100 million. During the 1980s, academic and institutional critiques of CBA emerged. The three main criticisms were: • That CBA could be used for political goals. Debates on the merits of cost and benefit comparisons can be used to sidestep political or philosophical goals, rules and regulations. • That CBA is inherently anti-regulatory, and therefore a biased tool. The monetization of policy impacts is an inappropriate tool for assessing mortality risks and distributional impacts. • That the length of time necessary to complete CBA can create significant delays, which can impede policy regulation. These criticisms continued under the
Clinton administration during the 1990s. Clinton furthered the anti-regulatory environment with his
Executive Order 12866. The order changed some of Reagan's language, requiring benefits to justify (rather than exceeding) costs and adding "reduction of discrimination or bias" as a benefit to be analyzed. Criticisms of CBA (including uncertainty valuations, discounting future values, and the calculation of risk) were used to argue that it should play no part in the regulatory process. The use of CBA in the regulatory process continued under the Obama administration, along with the debate about its practical and objective value. Some analysts oppose the use of CBA in policy-making, and those in favor of it support improvements in analysis and calculations. == Criticisms ==