This chapter complements Chapter 4 of Government Accountability: Australian Administrative Law, third edition. The chapter contains several case extracts on the issue of judicial review of delegated legislation. However, judicial review cases provide only a small part of the picture of the practical and substantive issues surrounding delegated legislation. The other material in this chapter fills in the picture. An extract from the Australian Law Reform Commission’s report, Traditional Rights and Freedoms, highlights the problems with over¬reliance on delegated, rather than primary, legislation. A case study of delegated legislation being disallowed has been included to show how the process of parliamentary scrutiny works.

This chapter complements Chapter 11 of Government Accountability: Australian Administrative Law, third edition. It introduces grounds of review under statutory schemes of judicial review.

Describes the rationale for, and approach to, regulation of the rail industry. Considers the effects of restructuring, horizontal and vertical separation policies, and the experience of Ramsey pricing

This chapter complements Chapter 13 of Government Accountability: Australian Administrative Law, third edition. The cases extracted in this chapter illustrate the operation of limits on power that can be discerned from the statute conferring the power: misconceiving the nature or scope of the power, jurisdictional facts (both objective and subjective), procedural error, improper delegation, and mandatory and prohibited considerations. The cases in this chapter are worth studying both for their exposition of legal principles, and for the application of those principles to the facts. O’Reilly v Commissioners of the State Bank of Victoria, for example, is not only the leading Australian case on improper delegation, but also an excellent example of the reasoning process used to determine whether delegation is permissible in a particular situation. With one exception, the cases in this chapter are decisions of the High Court of Australia. The exception is Liversidge v Anderson – which is included because Lord Atkin’s dissenting judgment is one of the most celebrated administrative law judgments.

Chapter 2: Linearly independent lists of vectors that span a vector space are of special importance. They provide a bridge between the abstract world of vector spaces and the concrete world of matrices. They permit us to define the dimension of a vector space and motivate the concept of matrix similarity.

Given the assumption that a loss random variable has a certain parametric distribution, the empirical analysis of the properties of the loss requires the parameters to be estimated. In this chapter, we review the theory of parametric estimation, including the properties of an estimator and the concepts of point estimation, interval estimation, unbiasedness, consistency and efficiency. Apart from the parametric approach, we may also estimate the distribution functions and the probability (density) functions of the loss random variables directly without assuming a certain parametric form.

Exact solutions for infinite Ising systems are rare, specific in terms of the interactions allowed, and limited to one and two dimensions. To study a wider range of models we must resort to various approximation techniques. One of the simplest and most comprehensive of these is the mean-field approximation, the subject of this chapter. Some versions of this approximation rely on a self-consistent requirement, and in this respect the mean-field method for the Ising model is similar to a number of other self-consistent approximation methods in physics, including the Hartree–Fock approximation for atomic and molecular orbitals, the BCS theory of superconductivity, and the relaxation method for determining electric potentials. We will also introduce a somewhat different mean-field approach, the Landau–Ginzburg approximation, which is based on a series expansion of the free energy. One of the drawbacks of all of the mean-field theories, however, is that they predict the same mean-field critical exponents, which, unfortunately, are at odds with the results of exact solutions and experiments.

Macro-causal analysis pivots between exploring patterns of change and exploring causal patterns. It employs a bounded conception of history, keeping causal factors constant, but expands the length of causal chains to explore how their temporal order affects causal outcomes. It analyzes this causal order in terms of elements of physical time: timing, sequencing, tempo, and duration. In paying attention to these elements, it in effect unfreezes physical time, which defines the existing linear notions of causality. Macro-causal analysis relaxes these linearity assumptions by expanding the analysis from what Pierson called short–short to short–long, long–short, and long–long explanations. It thus recognizes that theories rest on temporal assumptions and that understanding those assumptions invites exploring backgrounded causal factors that can help update theories. This udpating process is abduction, which ultimately makes hypotheses more testworthy. Besides elongating causal chains, this type of analysis also elongates outcomes by paying attention to a range of near-miss outcomse that are frequently overlooked but provide often important new inductive insights.

Ratemaking refers to the determination of the premium rates to cover the potential loss payments incurred under an insurance policy. In addition to the losses, the premium should also cover all the expenses as well as the profit margin. As past losses are used to project future losses, care must be taken to adjust for potential increases in the lost costs. There are two methods to determine the premium rates: the loss cost method and the loss ratio method.

Chapter 1: In this chapter, we provide formal definitions of real and complex vector spaces, and many examples. Among the important concepts introduced are linear combinations, span, linear independence, and linear dependence.

Having discussed models for claim frequency and claim severity separately, we now turn our attention to modeling the aggregate loss of a block of insurance policies. Much of the time we shall use the terms aggregate loss and aggregate claim interchangeably, although we recognize the difference between them as discussed in the last chapter. There are two major approaches in modeling aggregate loss: the individual risk model and the collective risk model.