Debtholder Monitoring Incentives and Bank Earnings Opacity

We exploit exogenous legislative changes that alter the priority structure of different classes of debt to study how debtholder monitoring incentives affect bank earnings opacity. We present novel evidence that exposing nondepositors to greater losses in bankruptcy reduces earnings opacity, especially for banks with larger shares of nondeposit funding, listed banks, and independent banks. The reduction in earnings opacity is driven by a lower propensity to overstate earnings and is more pronounced among larger banks and in banks with more real estate loan exposure. Our findings highlight the importance of creditors’ monitoring incentives in improving the quality of information disclosure.

A Genetic Algorithm to Goal Programming Model for Crop Production With Interval Data Uncertainty

This chapter presents how Genetic Algorithm (GA) is effectively employed to Goal Programming (GP) formulation of an agricultural planning problem having interval model parameters and a set of chance constraints for optimal production of seasonal crops in uncertain environment. In model formulation, the planned-interval goals associated with objectives of the problem are converted into their equivalent two-objective deterministic goals. The chance constraints are also converted into their deterministic equivalents to solve the problem by using GP methodology. In goal achievement function, minimization of deviational variables associated with model goals is evaluated on the basis of priorities by employing a GA scheme to reach optimal decision. In the decision process, sensitivity analysis with variations of priority structure of goals is performed, and then the notion of Euclidean distance function is used to identify the priority structure under which optimal production of crops can be obtained in the decision environment. A case example is considered to demonstrate the approach.

Is Attention a Non-Propositional Attitude?

The first part of this chapter argues that many forms of attention and attention-entailing mental episodes, such as looking at something, watching something, listening to something, or tactually feeling something, are paradigmatic examples of non-propositional intentional episodes. In addition, attention cannot be reduced to any other (propositional or non-propositional) mental episodes. But is attention a non-propositional attitude? The second part of the chapter argues that it is not. In order to account for attention and its apparently non-propositional character we should reject a certain atomistic model of our mental life and move towards a more holistic conception. I question the assumption that a subject’s mental life should be thought of as a causally connected collection of mental attitudes. This “building-block” model of the mind does not fit the case of attention. Instead, a subject’s mental life can be partitioned along many, equally appropriate dimensions. In a slogan: mentality has priority structure, in addition to attitudinal structure.

Dependent-Chance Goal Programming for Water Resources Management under Uncertainty

Without sufficient data, consulting experts is a good way to quantify unknown parameters in water resources management which will result in human uncertainty. The aim of this paper is to introduce a new tool-uncertainty theory to deal with such uncertainty which is treated as uncertain variable with uncertainty distribution. And a dependent-chance goal programming (DCGP) model is provided for water resources management under such circumstance. In the model uncertain measure is used to measure possibility that an event will occur which is maximized by minimizing the deviation (positive or negative deviation) from target of objective event under a given priority structure. In the end, the developed model is applied to a numerical example to illustrate the effectiveness of the model. The result obtained contributes to the desired water-allocation schemes for decision-markers.