Perspectives on aid: Accommodating heterogeneity in knowledge management for development

Huysman, M.H. [Promotor]Soekijad, M. [Copromotor

Accommodating for taste and variance heterogeneity in discrete choice

Understanding and accommodating heterogeneity in variance (also referred to as heteroscedasticity) and taste has become a major area of research within discrete choice analysis. Both scale and taste heterogeneity can be specified as continuous or discrete, the latter can be associated with socio economic characteristics (i.e. observed heterogeneity) or it can be derived probabilistically (i.e. unobserved heterogeneity). Within the context of the Mixed Logit models, unobserved heterogeneity can be represented by a continuous function, a discrete mixture or using a combination of both. This paper uses data from two recreational site choice studies (one elicited through stated preference methods and one through revealed preference methods) to compare various model specifications for accommodating both scale and preference heterogeneity. Results show that model fit, welfare estimates and choice predictions are sensitive to the manner in which both types of heterogeneity are accommodated

State-space based mass event-history model I: many decision-making agents with one target

A dynamic decision-making system that includes a mass of indistinguishable agents could manifest impressive heterogeneity. This kind of nonhomogeneity is postulated to result from macroscopic behavioral tactics employed by almost all involved agents. A State-Space Based (SSB) mass event-history model is developed here to explore the potential existence of such macroscopic behaviors. By imposing an unobserved internal state-space variable into the system, each individual's event-history is made into a composition of a common state duration and an individual specific time to action. With the common state modeling of the macroscopic behavior, parametric statistical inferences are derived under the current-status data structure and conditional independence assumptions. Identifiability and computation related problems are also addressed. From the dynamic perspectives of system-wise heterogeneity, this SSB mass event-history model is shown to be very distinct from a random effect model via the Principle Component Analysis (PCA) in a numerical experiment. Real data showing the mass invasion by two species of parasitic nematode into two species of host larvae are also analyzed. The analysis results not only are found coherent in the context of the biology of the nematode as a parasite, but also include new quantitative interpretations.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS189 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Selecting random parameters in discrete choice experiment for environmental valuation: A simulation experiment

This paper examines the various tests commonly used to select random parameters in choice modelling. The most common procedures for selecting random parameters are: the Lagrange Multiplier test as proposed by McFadden and Train (2000), the t-statistic of the deviation of the random parameter and the log-likelihood ratio test. The identification of random parameters in other words the recognition of preference heterogeneity among population is based on the fact that an individual makes a choice depending on her/his: tastes, perceptions and experiences. A simulation experiment was carried out based on a real choice experiment and the results indicated that the power of these three tests depends importantly on the spread and type of the tested parameter distribution.choice experiment, simulation, preference heterogeneity, random parameter logit, tests for selecting random parameters

Revealing additional preference heterogeneity with an extended random parameter logit model: the case of extra virgin olive oil

Methods that account for preference heterogeneity have received a significant amount of attention in recent literature. Most of them have focused on preference heterogeneity around the mean of the random parameters, which has been specified as a function of socio-demographic characteristics. This paper aims at analyzing consumers' preferences towards extra-virgin olive oil in Catalonia using a methodological framework with two novelties over past studies: 1) it accounts for both preference heterogeneity around the mean and the variance; and 2) it considers both socio-demographic characteristics of consumers as well as their attitudinal factors. Estimated coefficients and moments of willingness to pay (WTP) distributions are compared with those obtained from alternative Random Parameter Logit (RPL) models. Results suggest that the proposed framework increases the goodness-of-fit and provides more useful insights for policy analysis. The most important attributes affecting consumers' preferences towards extra virgin olive oil are the price and the product's origin. The consumers perceive the organic olive oil attribute negatively, as they think that it is not worth paying a premium for a product that is healthy in nature.Postprint (published version

Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a latent class approach

There is a growing body of evidence in the non-market valuation literature suggesting that responses to a sequence of discrete choice questions tend to violate the assumptions typically made by analysts regarding independence of responses and stability of preferences. Heuristics such as value learning and strategic misrepresentation have been offered as explanations for these results. While a few studies have tested these heuristics as competing hypotheses, none have investigated the possibility that each explains the response behaviour of a subgroup of the population. In this paper, we make a contribution towards addressing this research gap by presenting an equality-constrained latent class model designed to estimate the proportion of respondents employing each of the proposed heuristics. We demonstrate the model on binary and multinomial choice data sources and find three distinct types of response behaviour. The results suggest that accounting for heterogeneity in response behaviour may be a better way forward than attempting to identify a single heuristic to explain the behaviour of all respondents

Multimodal and nested preference structures in choice-based conjoint analysis: a comparison of bayesian choice models with discrete and continuous representations of heterogeneity

Die Choice-Based Conjoint-Analyse (CBC) ist heutzutage die am weitesten verbreitete Variante der Conjoint-Analyse, einer Klasse von Verfahren zur Messung von Nachfragerpräferenzen. Der Hauptgrund für die zunehmende Dominanz des CBC-Ansatzes in jüngerer Zeit besteht darin, dass hier das tatsächliche Wahlverhalten von Nachfragern sehr realistisch nachgestellt werden kann, indem die Befragten wiederholt ihre bevorzugte Alternative aus einer Menge mehrerer Alternativen (Choice Sets) auswählen. Im Rahmen der CBC-Analyse ist das Multinomiale Logit- (MNL) Modell das am häufigsten verwendete diskrete Wahlmodell. Das MNL-Modell weist jedoch zwei wesentliche Einschränkungen auf: (a) Es impliziert proportionale Substitutionsmuster zwischen den Alternativen, was als Independence of Irrelevant Alternatives- (IIA) Eigenschaft bezeichnet wird, und (b) es berücksichtigt keine Nachfragerheterogenität, da per Definition Teilnutzenwerte für alle Konsumenten als homogen angenommen werden. Seit den 1990er-Jahren werden hierarchisch bayesianische (HB) Modelle für die Teilnutzenwertschätzung in der CBC-Analyse verwendet. Solche HB-Modelle ermöglichen zum einen eine Schätzung individueller Teilnutzenwerte, selbst bei einer beschränkten Datenlage, zum anderen können sie aufgrund der Modellierung von Heterogenität die IIA-Eigenschaft stark abmildern. Der Schwerpunkt der vorliegenden Thesis liegt auf der Verwendung von HB-Modellen mit unterschiedlichen Darstellungen von Nachfragerheterogenität (diskret vs. kontinuierlich) für CBC-Daten sowie außerdem auf einem speziellen HB-Modell, das die IIA-Eigenschaft durch Berücksichtigung von unterschiedlichen Ähnlichkeitsgraden zwischen Teilmengen von Alternativen (Nestern) zusätzlich abschwächt. Insbesondere wird die statistische Performance von einfachen MNL-, Latent Class- (LC) MNL-, HB-MNL-, Mixture-of-Normals- (MoN) MNL-, Dirichlet Process Mixture- (DPM) MNL- und HB-Nested Multinomialen Logit- (NMNL) Modellen (unter experimentell variierenden Bedingungen) hinsichtlich der Recovery von Präferenzstrukturen, der Anpassungsgüte und der Prognosevalidität analysiert. Dazu werden zwei umfangreiche Monte-Carlo-Studien durchgeführt, ferner werden die verschiedenen Modelltypen auf einen empirischen CBC-Datensatz angewandt. In der ersten Monte-Carlo-Studie liegt der Fokus auf dem Vergleich zwischen dem HB-MNL und dem HB-NMNL bei multimodalen und genesteten Präferenzstrukturen. Die Ergebnisse zeigen, dass es keine wesentlichen Unterschiede zwischen beiden Modelltypen hinsichtlich der Anpassungsgüte und insbesondere hinsichtlich der Prognosevalidität gibt. In Bezug auf die Recovery von Präferenzstrukturen schneidet das HB-MNL-Modell zunehmend schlechter ab, wenn die Korrelation in mindestens einem Nest höher ist, während sich das HB-NMNL-Modell erwartungsgemäß an den Grad der Ähnlichkeit zwischen Alternativen anpasst. Die zweite Monte-Carlo-Studie befasst sich mit multimodalen und segmentspezifischen Präferenzstrukturen. Um Unterschiede zwischen den Klassen von Modellen mit unterschiedlichen Darstellungen von Heterogenität herauszuarbeiten, werden hier gezielt die Grade der Heterogenität innerhalb von Segmenten und zwischen Segmenten manipuliert. Unter experimentell variierenden Bedingungen werden die state-of-the-art Ansätze zur Modellierung von Heterogenität (einfaches MNL, LC-MNL, HB-MNL) mit erweiterten Wahlmodellen, die sowohl Nachfragerheterogenität zwischen Segmenten als auch innerhalb von Segmenten abbilden können (MoN-MNL und DPM-MNL), verglichen. Das zentrale Ergebnis dieser Monte-Carlo-Studie ist, dass sich das HB-MNL-Modell, welches eine multivariate Normalverteilung zur Modellierung von Präferenzheterogenität unterstellt, als äußerst robust erweist. Darüber hinaus kristallisiert sich der LC-MNL-Segmentansatz als der beste Ansatz heraus, um die „wahre“ Anzahl von Segmenten zu identifizieren. Abschließend werden die zuvor vorgestellten Wahlmodelle auf einen realen CBC-Datensatz angewandt. Die Ergebnisse zeigen, dass Modelle mit einer kontinuierlichen Darstellung von Heterogenität (HB-MNL, HB-NMNL, MoN-MNL und DPM-MNL) eine bessere Anpassungsgüte und Prognosevalidität aufweisen als Modelle mit einer diskreten Darstellung von Heterogenität (einfaches MNL, LC-MNL). Weiterhin zeigt sich, dass das HB-MNL-Modell für Prognosezwecke sehr gut geeignet ist und im Vergleich zu den anderen (erweiterten) Modellen mindestens ebenso gute, wenn nicht sogar wesentlich bessere Vorhersagen liefert, was für Manager eine zentrale Erkenntnis darstellt.Choice-Based Conjoint (CBC) is nowadays the most widely used variant of conjoint analysis, a class of methods for measuring consumer preferences. The primary reason for the increasing dominance of the CBC approach over the last 35 years is that it closely mimics real choice behavior of consumers by asking respondents repeatedly to choose their preferred alternative from a set of several offered alternatives (choice sets), respectively. Within the framework of CBC analysis, the multinomial logit (MNL) model is the most frequently used discrete choice model. However, the MNL model suffers from two major limitations: (a) it implies proportional substitution rates across alternatives, referred to as the Independence of Irrelevant Alternatives (IIA) property and (b) it does not account for unobserved consumer heterogeneity, as part-worth utilities are assumed to be equal for all respondents by definition. Since the 1990s, Hierarchical Bayesian (HB) models have been used for part-worth utility estimation in CBC analysis. HB models are able to determine part-worth utilities at the individual respondent level even with little individual respondent information on the one hand and, as a result of addressing consumer heterogeneity, can strongly soften the IIA property on the other hand. The focus of the present thesis is on CBC analysis using HB models with different representations of heterogeneity (discrete vs. continuous) as well as using a HB model which mitigates the IIA property to a further extent by allowing for different degrees of similarity between subsets (nests) of alternatives. In particular, we systematically explore the comparative performance of simple MNL, latent class (LC) MNL, HB-MNL, mixture-of-normals (MoN) MNL, Dirichlet Process Mixture (DPM) MNL and HB nested multinomial logit (NMNL) models (under experimentally varying conditions) using statistical criteria for parameter recovery, goodness-of-fit, and predictive accuracy. We conduct two extensive Monte Carlo studies and apply the different types of models to an empirical CBC data set. In the first Monte Carlo study, the focus lies on the comparative performance of the HB-MNL versus the HB-NMNL for multimodal and nested preference structures. Our results show that there seems to be no major differences between both types of models with regard to goodness-of-fit measures and in particular their ability to predict respondents’ choice behavior. Regarding parameter recovery, the HB-MNL model performs increasingly worse when correlation in at least one nest is higher, while the HB-NMNL model adapts to the degree of similarity between alternatives, as expected. The second Monte Carlo study deals with multimodal and segment-specific preference structures. More precisely, to carve out differences between the classes of models with different representations of heterogeneity, we specifically vary the degrees of within-segment and between-segment heterogeneity. We compare state-of-the-art methods to represent heterogeneity (simple MNL, LC-MNL, HB-MNL) and more advanced choice models representing both between-segment and within-segment consumer heterogeneity (MoN-MNL and DPM-MNL) under varying experimental conditions. The core finding from our Monte Carlo study is that the HB-MNL model appears to be highly robust against violations in its assumption of a single multivariate normal distribution of consumer preferences. In addition, the LC-MNL segment solution proves to be the best approach to recover the “true” number of segments. Finally, we apply the previously presented choice models to a real-life CBC data set. The results indicate that models with a continuous representation of heterogeneity (HB-MNL, HB-NMNL, MoN-MNL and DPM-MNL) perform better than models with a discrete representation of heterogeneity (simple MNL, LC-MNL). Further, it turns out that the HB-MNL model works extremely well for predictive purposes and provides at least as good if not considerably better predictions compared to the other (advanced) models, which is an important aspect for managers