Forgiveness: A Cognitive‐Motivational Anatomy

This work aims to identify the constituents of forgiveness in terms of the forgiver's beliefs and motivating goals. After addressing the antecedents of forgiveness—a perceived wrong—and distinguishing the notion of mere harm from that of offense, we describe the victim's typical retributive reactions—revenge and resentment—and discuss their advantages and disadvantages. Then we focus on the forgiver's mind‐set, pointing to the relationship between forgiveness and acceptance of the wrong, addressing the forgiver's motivating goals, and discussing both their self‐interested and altruistic implications. In so doing we also discuss the role of the forgiver's positive feelings towards the offender, arguing that, however important, they are unnecessary to forgiveness. We finally identify two kinds of forgiveness—conditional and unconditional—suggesting that they are grounded on different notions of “worth.”     

ML, PL, QL in Markov Chain Models

Abstract.  In many spatial and spatial-temporal models, and more generally in models with complex dependencies, it may be too difficult to carry out full maximum-likelihood (ML) analysis. Remedies include the use of pseudo-likelihood (PL) and quasi-likelihood (QL) (also called the composite likelihood). The present paper studies the ML, PL and QL methods for general Markov chain models, partly motivated by the desire to understand the precise behaviour of the PL and QL methods in settings where this can be analysed. We present limiting normality results and compare performances in different settings. For Markov chain models, the PL and QL methods can be seen as maximum penalized likelihood methods. We find that QL is typically preferable to PL, and that it loses very little to ML, while sometimes earning in model robustness. It has also appeal and potential as a modelling tool. Our methods are illustrated for consonant-vowel transitions in poetry and for analysis of DNA sequence evolution-type models.