Greek, Latin, and the Indian Civil Service

This is a slightly revised version of an article first published in the Cambridge Classical Journal, vol. 51 (2005), pp. 35-71.

A conceptual framework for service system interaction

There is a growing research concern on how service ecosystems form and interact. This research thus aims to explore the service ecosystem formation and interaction as well as its underlying nature of value co-creation. This work develops an initial conceptual framework for assessing service system interactions that includes the various stages of value co-creation within ecosystem context. How the conceptual framework will further be developed and future plan are also presented.

Mapping evidence gaps in anti-corruption: assessing the state of the operationally relevant evidence on donors' actions and approaches to reducing corruption

This paper charts the current evidence on effectiveness of different anti-corruption reforms, and identifies significant evidence gaps. Despite a substantial amount of literature on corruption, this review found very few studies focusing on anti-corruption reforms, and even fewer that credibly assess issues of effectiveness and impact. The evidence was strong for only two types of interventions: public financial management (PFM) reforms and supreme audit institutions (SAIs). For PFM, the evidence in general showed positive results, whereas the effectiveness was mixed for SAIs. No strong evidence indicates that any of the interventions pursued have been ineffective, but there is fair evidence that anti-corruption authorities, civil service reforms and the use of corruption conditionality in aid allocation decisions in general have not been effective. The paper advocates more operationally-relevant research and rigorous evaluations to build up the missing evidence base, particularly in conflict-afflicted states, in regards to the private sector, and on the interactions and interdependencies between different anti-corruption interventions.

Conditioning and preconditioning of the optimal state estimation problem

Optimal state estimation is a method that requires minimising a weighted, nonlinear, least-squares objective function in order to obtain the best estimate of the current state of a dynamical system. Often the minimisation is non-trivial due to the large scale of the problem, the relative sparsity of the observations and the nonlinearity of the objective function. To simplify the problem the solution is often found via a sequence of linearised objective functions. The condition number of the Hessian of the linearised problem is an important indicator of the convergence rate of the minimisation and the expected accuracy of the solution. In the standard formulation the convergence is slow, indicating an ill-conditioned objective function. A transformation to different variables is often used to ameliorate the conditioning of the Hessian by changing, or preconditioning, the Hessian. There is only sparse information in the literature for describing the causes of ill-conditioning of the optimal state estimation problem and explaining the effect of preconditioning on the condition number. This paper derives descriptive theoretical bounds on the condition number of both the unpreconditioned and preconditioned system in order to better understand the conditioning of the problem. We use these bounds to explain why the standard objective function is often ill-conditioned and why a standard preconditioning reduces the condition number. We also use the bounds on the preconditioned Hessian to understand the main factors that affect the conditioning of the system. We illustrate the results with simple numerical experiments.