The transition to IFRS: disclosures by Portuguese listed companies

In the context of the CESR and of the Portuguese market regulator recommendations regarding the disclosure of the impacts of the transition to IFRS, this paper analyses the content of those disclosures by Portuguese listed companies. We found a high degree of variability among the disclosure either regarding the qualitative (narrative explanations of transition) or quantitative (reconciliations) disclosures. The results show that the objective of comparability, relevance and understandability stated in CESR’s recommendation were not achieved. Regarding accounting changes, the analysis shows that the reported impacts by companies confirmed expectations based on prior de jure studies on major impacts of changing from Portuguese GAAP to IFRS; these major impacts regard the recognition of intangibles, the accounting treatment of goodwill and financial instruments. Finally, Gray’s (1980) “conservatism” index was computed using the reconciliated profits to IFRS reported by companies. This analysis shows that Portuguese standards are more conservative than IFRS. This study is relevant to several parties: to the market regulators and policy makers in predicting the level of compliance with IFRS and calling attention for the importance of enforcement mechanisms; to the preparers, auditors and users in identifying the most problematic areas of implementation of IFRS.International Accounting, Disclosure, IAS/IFRS, Portugal

THE NATURE OF FEEDBACK:HOW DIFFERENT TYPES OF PEER FEEDBACK AFFECT WRITING PERFORMANCE

Although providing feedback is commonly practiced in education, there is general agreement regarding what type of feedback is most helpful and why it is helpful. This study examined the relationship between various types of feedback, potential internal mediators, and the likelihood of implementing feedback. Five main predictions were developed from the feedback literature in writing, specifically regarding feedback features (summarization, identifying problems, providing solutions, localization, explanations, scope, praise, and mitigating language) as they relate to potential causal mediators of problem or solution understand and problem or solution agreement, leading to the final outcome of feedback implementation.To empirically test the proposed feedback model, 1073 feedback segments from writing assessed by peers was analyzed. Feedback was collected using SWoRD, an online peer review system. Each segment was coded for each of the feedback features, implementation, agreement, and understanding. The correlations between the feedback features, levels of mediating variables, and implementation rates revealed several significant relationships. Understanding was the only significant mediator of implementation. Several feedback features were associated with understanding: including solutions, a summary of the performance, and the location of the problem were associated with increased understanding; and explanations to problems were associated with decreased understanding. Implications of these results are discussed

Explanations reconsidered

Edna Ullmann-Margalit .introduced the notion of an invisiblehand explanation (I-H explanation) to the philosophical literature in 1978, and made a distinction between "aggregate" and "functional-evolutionary" (F-E) forms of I -H explanations. The present paper produces a substantially refined analysis of the forms and functions of I-H explanations. Sections (1) and (2) introduce the ideas of I-H and aggregate I-H explanation, respectively. Section (J) argues that no one form of explanation can serve the explanatory fUnctions Ullmann-Margalit attributes to aggregate explanations, and divides those explanatory functions between genetic and "systematic-dispositional" explanations. Section (4) identifies difficulties with the idea of F-E explanation in the social realm, and shows that any I-H explanations fitting the P-E mold would constitute simply a special class of "aggregate" explanation

Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps

With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation. Explaining the behavior of these agents is challenging, as the environments in which they act have large state spaces, and their decision-making can be affected by delayed rewards, making it difficult to analyze their behavior. To address this problem, several approaches have been developed. Some approaches attempt to convey the $\textit{global}$ behavior of the agent, describing the actions it takes in different states. Other approaches devised $\textit{local}$ explanations which provide information regarding the agent's decision-making in a particular state. In this paper, we combine global and local explanation methods, and evaluate their joint and separate contributions, providing (to the best of our knowledge) the first user study of combined local and global explanations for RL agents. Specifically, we augment strategy summaries that extract important trajectories of states from simulations of the agent with saliency maps which show what information the agent attends to. Our results show that the choice of what states to include in the summary (global information) strongly affects people's understanding of agents: participants shown summaries that included important states significantly outperformed participants who were presented with agent behavior in a randomly set of chosen world-states. We find mixed results with respect to augmenting demonstrations with saliency maps (local information), as the addition of saliency maps did not significantly improve performance in most cases. However, we do find some evidence that saliency maps can help users better understand what information the agent relies on in its decision making, suggesting avenues for future work that can further improve explanations of RL agents

The heuristic conception of inference to the best explanation

An influential suggestion about the relationship between Bayesianism and inference to the best explanation holds that IBE functions as a heuristic to approximate Bayesian reasoning. While this view promises to unify Bayesianism and IBE in a very attractive manner, important elements of the view have not yet been spelled out in detail. I present and argue for a heuristic conception of IBE on which IBE serves primarily to locate the most probable available explanatory hypothesis to serve as a working hypothesis in an agent’s further investigations. Along the way, I criticize what I consider to be an overly ambitious conception of the heuristic role of IBE, according to which IBE serves as a guide to absolute probability values. My own conception, by contrast, requires only that IBE can function as a guide to the comparative probability values of available hypotheses. This is shown to be a much more realistic role for IBE given the nature and limitations of the explanatory considerations with which IBE operates