Fulfilment of the Maastricht Inflation Criterion by the Czech Republic: Potential Costs and Policy Options

The purpose of this paper is twofold: firstly, to identify and quantify the potential costs to the Czech economy should fulfilment of the Maastricht inflation criterion (MIC) require disinflation; and secondly, to discuss and suggest policies geared towards minimising the costs related to meeting the MIC. We assume that the real appreciation of the koruna will be about 1.5% during the reference period. Three disinflation simulations are derived from this assumption. The results show that a decline in inflation by 0.5 p.p., 1 p.p. and 1.5 p.p. leads to a cumulative loss of output reaching about 0.5%, 1% and 1.6% respectively of annual potential GDP over a period of four years. The time restriction imposed on the simulations implies that the shorter the time to reach a given lower level of inflation, the higher the initial increase in the interest rate and the more aggressive the policy rule needed. The simulation results and the likely application of the monetary convergence criteria are relevant to the discussion of policy options. We argue that due to the asymmetry of the Maastricht exchange rate criterion (MERC), allowing for nominal appreciation rather than depreciation, fulfilment of the MIC should be superior. Also, we suggest that the main task for the CNB will be to focus on reaching a level of inflation consistent with the presumed level of the MIC sufficiently early before the reference period. This may require a downward adjustment of the CNB’s inflation point target and an extension of the current policy horizon.Disinflation, EMU entry, euro adoption, Maastricht inflation criterion, policy options, real exchange rate appreciation, output loss.

The Future of Human-Artificial Intelligence Nexus and its Environmental Costs

The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The gratuitous generalisation capability refers to a discrepancy between the cognitive demands of a task to be accomplished and the performance (accuracy) of a used ML/AI model. If the latter exceeds the former because the model was optimised to achieve the best possible accuracy, it becomes inefficient and its operation harmful to the environment. The future dominated by the non-anthropic induction describes a use of ML/AI so all-pervasive that most of the inductive inferences become furnished by ML/AI generalisations. The paper argues that the present debate deserves an expansion connecting the environmental costs of research and ineffective ML/AI uses (the issue of gratuitous generalisation capability) with the (near) future marked by the all-pervasive Human-Artificial Intelligence Nexus

Elimination of Bias in Introspection: Methodological Advances, Refinements, and Recommendations

Building on past constructive criticism, the present study provides further methodological development focused on the elimination of bias that may occur during first-person observation. First, various sources of errors that may accompany introspection are distinguished based on previous critical literature. Four main errors are classified, namely attentional, attributional, conceptual, and expressional error. Furthermore, methodological recommendations for the possible elimination of these errors have been determined based on the analysis and focused excerpting of introspective scientific literature. The following groups of methodological recommendations were determined: 1) a better focusing of the subject’s attention to their mental processes, 2) providing suitable stimuli, and 3) the sharing of introspective experience between subjects. Furthermore, the potential of adjustments in introspective research designs for eliminating attentional, attributional, conceptual, and expressional error is discussed