Reforms to the means tests in England for state-financed long-term care were planned for implementation in 2025. They included a lifetime limit (cap) on how much an individual must contribute to their care, with the state meeting subsequent care costs. We present projections of the costs and distributional impacts of these reforms for older people, using two linked simulation models which draw on a wide range of data. We project that by 2038 public spending on long-term care for older people in England would be about 14% higher than without the reforms. While the main direct beneficiaries of the lifetime cap would have been the better off who currently receive no state help with their care costs, the reforms also treated capital assets more generously than the current system, helping people with more modest incomes and wealth. When analysing the impacts of the reforms it is therefore important to consider the whole reform package. Our results depend on a range of assumptions, and the impacts of the reforms would be sensitive to the levels of the cap and other reformed parameters of the means test on implementation.

Artificial neural networks are increasingly used for geophysical modeling to extract complex nonlinear patterns from geospatial data. However, it is difficult to understand how networks make predictions, limiting trust in the model, debugging capacity, and physical insights. EXplainable Artificial Intelligence (XAI) techniques expose how models make predictions, but XAI results may be influenced by correlated features. Geospatial data typically exhibit substantial autocorrelation. With correlated input features, learning methods can produce many networks that achieve very similar performance (e.g., arising from different initializations). Since the networks capture different relationships, their attributions can vary. Correlated features may also cause inaccurate attributions because XAI methods typically evaluate isolated features, whereas networks learn multifeature patterns. Few studies have quantitatively analyzed the influence of correlated features on XAI attributions. We use a benchmark framework of synthetic data with increasingly strong correlation, for which the ground truth attribution is known. For each dataset, we train multiple networks and compare XAI-derived attributions to the ground truth. We show that correlation may dramatically increase the variance of the derived attributions, and investigate the cause of the high variance: is it because different trained networks learn highly different functions or because XAI methods become less faithful in the presence of correlation? Finally, we show XAI applied to superpixels, instead of single grid cells, substantially decreases attribution variance. Our study is the first to quantify the effects of strong correlation on XAI, to investigate the reasons that underlie these effects, and to offer a promising way to address them.

The examples above suggest governmental paralysis in the face of a pervasive corruption which impacts severely upon every important policy and institution in the republic. The President takes initiatives but hesitates fatally to follow through, and compromises his own reputation and effectiveness in the process. Most government ministries, for whom tactical funds and off-budget budgets are the stuff of life, do not even have a reform plan in front of them which would permit them to parlay their own essential income-boosting black funds (civil service pay being universally inadequate) against budgetary reform and a big boost to salaries.

Indonesia since the fall of Suharto in 1998 under reformasi remains subject to powerful tendencies for disintegrasi—both province-based “separatism” and general socio-political decay. These tendencies are greatly aggravated by the failure of democratically elected presidents and parliaments to effectively tackle endemic corruption or reform the armed forces, which continue to enjoy near-total immunity as a major practitioner, guarantor and enforcer of corrupt business practice and extortion. This article notes the activism of civil society and liberal media on the corruption issue and the commendable new array of anti-corruption institutions. But it argues that reform efforts have been virtually nullified by broad collusion of Indonesia's political, bureaucratic, military and business elites in sustaining—while “democratizing” and decentralizing—the system of corruption inherited from Suharto. The reform effort is now subject to political stasis or gridlock induced by money politics. Some reformers believe that politics in Indonesia has been effectively replaced by “transactions”. In arguing that “KKN” “ruins everything”—people's well-being, investment prospects, government budgets and development planning, democratic politics, the justice sector (including its corruption-fighting capacities), military professionalism, the environment and more—the author is serious. The article suggests that real change must await new social and political constellations and struggles initiated outside the parliamentary arena.