Some Concerns Regarding Explanatory Pluralism: The Explanatory Role of Optimality Models

Optimality models are widely used in different parts of biology. Two important questions that have been asked about such models are: are they explanatory and, if so, what type of explanations do they offer? My concern in this paper is with the approach of Rice (2012, 2015) and Irvine (2015), who claim that these models provide non-causal explanations. I argue that there are serious problems with this approach and with the accounts of explanation it is intended to justify. The idea behind this undertaking is to draw attention to an important issue associated with the recent pluralist stance on explanation: the rampant proliferation of theories of explanation. This proliferation supports a pluralist perspective on explanation, and pluralism encourages such a proliferation. But, if we are not careful about how we arrive at and how we justify new accounts of explanation — i.e., if we do not try to avoid the sort of problems discussed in this paper — we may end up trivializing the concept of explanation

CONSUMER’S SATISFACTION - EXPLANATORY MODELS

When the first studies related to consumer satisfaction began to appear in the sixties, nobody could imagine protagonism that it would reach with the course of the time. Nowadays not only private sector companies dedicate part from their resources to the study of the degree of satisfaction of their clients, but satisfaction studies are more and more increasing preoccupation in the state sector, therefore works related to the satisfaction of the patients, the contributors or with the tourist destiny can be found. Firstly, a revision of the different models that have been used to explain customer satisfaction level is presented, using the cognitive and the affective-cognitive models. In the first case, human being is looking as a rational being that can process information about the different attributes of the services to form his personal satisfaction. The most useful model within this category is the expectation disconfirmation model. These kind of models explain satisfaction as a function of the degree and direction of the discrepancy between expectation and perceptions. It has evolved all over time resulting in a lot of different approaches. We have also studied the equity model, in which consumer does a benefit-cost analysis not only its owns but from the rest of people who take part in the transaction. Finally, in the affective-cognitive models, human being is seeing like a complex being that is not solely an information processor but experiences feelings and emotions that also influence in their judgments of satisfaction. Secondly, it has been realized an empirical application in which we have used the main variables in the expectation disconfirmation model: perceptions, expectations and discrepancies to estimate some logit models. The tourists who visit Tenerife are classified as satisfied or unsatisfied. Then, we model the probability of each characteristic using tourist’s scores on some destination attributes. Two samples have been used. The first one was obtained at the time of arriving; the second one has been made when leaving the island. Since tourists are not necessary the same in both samples, a statistic inference process has been made to use all the information available. The best model is obtained when expectations and perceptions are used at the same time, so we obtain a 75% of right classification. To sum up, we have found that perceptions are the main subject for the tourist’s satisfaction, although we can’t forget the importance of expectations to complete the model.

How could a rational analysis model explain?

Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances

Regression games

The solution of a TU cooperative game can be a distribution of the value of the grand coalition, i.e. it can be a distribution of the payo (utility) all the players together achieve. In a regression model, the evaluation of the explanatory variables can be a distribution of the overall t, i.e. the t of the model every regressor variable is involved. Furthermore, we can take regression models as TU cooperative games where the explanatory (regressor) variables are the players. In this paper we introduce the class of regression games, characterize it and apply the Shapley value to evaluating the explanatory variables in regression models. In order to support our approach we consider Young (1985)'s axiomatization of the Shapley value, and conclude that the Shapley value is a reasonable tool to evaluate the explanatory variables of regression models

Conditional Transformation Models

The ultimate goal of regression analysis is to obtain information about the conditional distribution of a response given a set of explanatory variables. This goal is, however, seldom achieved because most established regression models only estimate the conditional mean as a function of the explanatory variables and assume that higher moments are not affected by the regressors. The underlying reason for such a restriction is the assumption of additivity of signal and noise. We propose to relax this common assumption in the framework of transformation models. The novel class of semiparametric regression models proposed herein allows transformation functions to depend on explanatory variables. These transformation functions are estimated by regularised optimisation of scoring rules for probabilistic forecasts, e.g. the continuous ranked probability score. The corresponding estimated conditional distribution functions are consistent. Conditional transformation models are potentially useful for describing possible heteroscedasticity, comparing spatially varying distributions, identifying extreme events, deriving prediction intervals and selecting variables beyond mean regression effects. An empirical investigation based on a heteroscedastic varying coefficient simulation model demonstrates that semiparametric estimation of conditional distribution functions can be more beneficial than kernel-based non-parametric approaches or parametric generalised additive models for location, scale and shape

When Do Probit Residuals Sum to Zero?

Probit residuals need not sum to zero in general. However, if explanatory variables are qualitative the sum can be shown to be zero for many models. Indeed this remains true for binary dependent variable models other than Probit and Logit. Even if some explanatory variables are quantitative, residuals can sum to almost zero more often than might at first seem plausible.

Partially linear models on Riemannian manifolds

In partially linear models the dependence of the response y on (x^T,t) is modeled through the relationship y=\x^T \beta+g(t)+\epsilon where \epsilon is independent of (x^T,t). In this paper, estimators of \beta and g are constructed when the explanatory variables t take values on a Riemannian manifold. Our proposal combine the flexibility of these models with the complex structure of a set of explanatory variables. We prove that the resulting estimator of \beta is asymptotically normal under the suitable conditions. Through a simulation study, we explored the performance of the estimators. Finally, we applied the studied model to an example based on real dataset.Comment: 7 pages, 2 figure

CONSUMER'S SATISFACTION - EXPLANATORY MODELS

When the first studies related to consumer satisfaction began to appear in the sixties, nobody could imagine protagonism that it would reach with the course of the time. Nowadays not only private sector companies dedicate part from their resources to the study of the degree of satisfaction of their clients, but satisfaction studies are more and more increasing preoccupation in the state sector, therefore works related to the satisfaction of the patients, the contributors or with the tourist destiny can be found. Firstly, a revision of the different models that have been used to explain customer satisfaction level is presented, using the cognitive and the affective-cognitive models. In the first case, human being is looking as a rational being that can process information about the different attributes of the services to form his personal satisfaction. The most useful model within this category is the expectation disconfirmation model. These kind of models explain satisfaction as a function of the degree and direction of the discrepancy between expectation and perceptions. It has evolved all over time resulting in a lot of different approaches. We have also studied the equity model, in which consumer does a benefit-cost analysis not only its owns but from the rest of people who take part in the transaction. Finally, in the affective-cognitive models, human being is seeing like a complex being that is not solely an information processor but experiences feelings and emotions that also influence in their judgments of satisfaction. Secondly, it has been realized an empirical application in which we have used the main variables in the expectation disconfirmation model: perceptions, expectations and discrepancies to estimate some logit models. The tourists who visit Tenerife are classified as satisfied or unsatisfied. Then, we model the probability of each characteristic using tourist's scores on some destination attributes. Two samples have been used. The first one was obtained at the time of arriving; the second one has been made when leaving the island. Since tourists are not necessary the same in both samples, a statistic inference process has been made to use all the information available. The best model is obtained when expectations and perceptions are used at the same time, so we obtain a 75% of right classification. To sum up, we have found that perceptions are the main subject for the tourist's satisfaction, although we can't forget the importance of expectations to complete the model