Contrast Effects and Approval Voting: An Illustration of a Systematic Violation of the Independence of Irrelevant Alternatives Condition

In approval voting, voters indicate all alternatives acceptable to them, instead of indicating their most preferred alternative. This study sought to examine the hypothesis that, under certain conditions, approval voting systematically violates a rationality condition of social choice: the independence of irrelevant alternatives condition. Given a well-known psychological phenomenon, the contrast effect, it was hypothesized that sets of acceptable alternatives might be changed as a result of changes in unacceptable alternatives. This hypothesis was tested in a sample of undergraduates who were presented with fictional profiles of juvenile delinquents and then were asked whether they should be granted parole. The outcomes of approval voting changed merely as the result of a change in an alternative least preferred by all voters.     

Multicriteria approval: application of approval voting concepts to MCDM problems

Multicriteria decision making (MCDM) methods generally require information that is difficult and expensive to obtain. It is shown in this paper that given an ordering of criteria by importance, and whether each alternative is above the average evaluation of the alternatives for each criterion, considerable information can be extracted concerning the overall rankings of the alternatives. This is done by recognizing the similarity between criteria in MCDM, and voters in social choice theory. Multicriteria approval, proposed in this paper, is an MCDM adaptation of approval voting which can be used to classify decisions into one of several categories, depending on whether, and the manner in which, a superior alternative can be identified. An exhaustive examination of all possible decision situations of tractable size is made to measure the applicability of multicriteria approval, and the approach is demonstrated in an industrial procurement case. In addition to requiring minimal information from a decision maker, multicriteria approval satisfies several desirable criteria for MCDM techniques. © 1998 John Wiley & Sons, Ltd.     

A Market Response Model for Sales Management Decision Making

Abstract

Sales managers are faced with a variety of decisions that require the evaluation of the probable response of the market to feasible decision alternatives. A conceptual model of market response is presented to offer management a framework for understanding the market response relationships in their selling situation and to provide an operational approach for evaluating the potential effect of decision alternatives. An empirical test illustrates the usefulness of the model and suggests several improvements in market response modeling. Future research directions are identified and important management implications are discussed.     

Testing take-the-best in new and changing environments

Take-the-best is a decision-making strategy that chooses between alternatives, by searching the cues representing the alternatives in order of cue validity, and choosing the alternative with the first discriminating cue. Theoretical support for take-the-best comes from the “fast and frugal” approach to modeling cognition, which assumes decision-making strategies need to be fast to cope with a competitive world, and be simple to be robust to uncertainty and environmental change. We contribute to the empirical evaluation of take-the-best in two ways. First, we generate four new environments—involving bridge lengths, hamburger prices, theme park attendances, and US university rankings—supplementing the relatively limited number of naturally cue-based environments previously considered. We find that take-the-best is as accurate as rival decision strategies that use all of the available cues. Secondly, we develop 19 new data sets characterizing the change in cities and their populations in four countries. We find that take-the-best maintains its accuracy and limited search as the environments change, even if cue validities learned in one environment are used to make decisions in another. Once again, we find that take-the-best is as accurate as rival strategies that use all of the cues. We conclude that these new evaluations support the theoretical claims of the accuracy, frugality, and robustness for take-the-best, and that the new data sets provide a valuable resource for the more general study of the relationship between effective decision-making strategies and the environments in which they operate.     

The Epistemic Edge of Majority Voting Over Lottery Voting

I aim to explain why majority voting can be assumed to have an epistemic edge over lottery voting. This would provide support for majority voting as the appropriate decision mechanism for deliberative epistemic accounts of democracy. To argue my point, I first recall the usual arguments for majority voting: maximal decisiveness, fairness as anonymity, and minimal decisiveness. I then show how these arguments are over inclusive as they also support lottery voting. I then present a framework to measure accuracy so as to compare the two decision mechanisms. I go over four arguments for lottery voting and three arguments for majority voting that support their respective accuracy. Lottery voting is then shown to have, compared to majority voting, a decreased probability of discrimination. That is, I argue that with lottery voting it is less probable under conditions of normal politics that if the procedure selects X, X is reasonable. I then provide two case scenarios for each voting mechanism that illustrate my point.     

How the Bayesians got their beliefs (and what those beliefs actually are): comment on Bowers and Davis (2012)

Bowers and Davis (2012) criticize Bayesian modelers for telling "just so" stories about cognition and neuroscience. Their criticisms are weakened by not giving an accurate characterization of the motivation behind Bayesian modeling or the ways in which Bayesian models are used and by not evaluating this theoretical framework against specific alternatives. We address these points by clarifying our beliefs about the goals and status of Bayesian models and by identifying what we view as the unique merits of the Bayesian approach.     

Measuring voting power for dependent voters through causal models

We construct a new measure of voting power that yields reasonable measurements even if the individual votes are not cast independently. Our measure hinges on probabilities of counterfactuals, such as the probability that the outcome of a collective decision would have been yes, had a voter voted yes rather than no as she did in the real world. The probabilities of such counterfactuals are calculated on the basis of causal information, following the approach by Balke and Pearl. Opinion leaders whose votes have causal influence on other voters’ votes can have significantly more voting power under our measure. But the new measure of voting power is also sensitive to the voting rule. We show that our measure can be regarded as an average treatment effect, we provide examples in which it yields intuitively plausible results and we prove that it reduces to Banzhaf voting power in the limiting case of independent and equiprobable votes.     

Modelling and describing human judgement processes: The multiattribute evaluation case

In this article we describe research methods that are used for the study of individual multiattribute evaluation processes. First we explain that a multiattribute evaluation problem involves the evaluation of a set of alternatives, described by their values on a number of alternatives. We discuss a number of evaluation strategies that may be applied to arrive at a conclusion about the attractiveness or suitability of the alternatives, and next introduce two main research paradigms in this area, structural modelling and process tracing. We argue that the techniques developed within these paradigms all have their advantages and disadvantages, and conclude that the most promising technique to detect the true nature of the evaluation strategy used by a judge seems to be the analysis of verbal protocols. At the same time we think it is wise not to rely on just one technique, but to use a multimethod approach to the study of multiattribute evaluation processes whenever that is possible.     

Measuring voting power for dependent voters through causal models

We construct a new measure of voting power that yields reasonable measurements even if the individual votes are not cast independently. Our measure hinges on probabilities of counterfactuals, such as the probability that the outcome of a collective decision would have been yes, had a voter voted yes rather than no as she did in the real world. The probabilities of such counterfactuals are calculated on the basis of causal information, following the approach by Balke and Pearl. Opinion leaders whose votes have causal influence on other voters’ votes can have significantly more voting power under our measure. But the new measure of voting power is also sensitive to the voting rule. We show that our measure can be regarded as an average treatment effect, we provide examples in which it yields intuitively plausible results and we prove that it reduces to Banzhaf voting power in the limiting case of independent and equiprobable votes.

     

多阶段混合增长模型的方法及研究现状

多阶段混合增长模型(PGMM)可对发展过程中的阶段性及群体异质性特征进行分析,在能力发展、行为发展及干预、临床心理等研究领域应用广泛。PGMM可在结构方程模型和随机系数模型框架下定义,通常使用基于EM算法的极大似然估计和基于马尔科夫链蒙特卡洛模拟的贝叶斯推断两种方法进行参数估计。样本量、测量时间点数、潜在类别距离等因素对模型及参数估计有显著影响。未来应加强PGMM与其它增长模型的比较研究;在相同或不同的模型框架下研究数据特征、类别属性等对参数估计方法的影响。     

Limits to decision-making ability in direct multiattribute alternative evaluation

In recent years there has been considerable interest in how decision strategies in choice problems depend on such characteristics as the number of available criteria and the number of available alternatives. Along with multicriteria choice problems, classification tasks are treated where alternatives are classified into several decision (evaluative) classes. We believe that in classification problems one's ability in information processing is limited and depends on task complexity. In this paper we describe two experiments involving categorization of a large number of multiattribute alternatives into two or four evaluative classes. The results demonstrate that the subjects use different decision strategies and that there is a limit in human ability to implement these two tasks.     

An Approach to Multi‐Criteria Decision Problems Under Severe Uncertainty

Solving complex decision problems is a demanding task; it requires determining and evaluating the consequences of decision alternatives. To this end, uncertain factors that can only partly be influenced by the decision makers, and their interdependencies need to be considered. Scenarios focus on this part of the decision problem; they enable a systematic exploration of a multitude of possible future developments that are relevant for the decision including external events and decisions made. Scenarios are particularly useful when the problem is pervaded by severe uncertainties that cannot be quantified. For the evaluation of alternatives, multiple objectives and the potentially diverging preferences of the involved actors need to be respected. Multi‐criteria decision analysis aims at structuring the problem, evaluating the alternatives and supporting decision makers pursuing multiple goals. We propose an approach integrating scenarios and multi‐criteria decision analysis that focuses on the robustness of alternatives in complex, dynamic, uncertain and time‐bound situations. In this integrated framework, the scenarios provide the basis for evaluating a set of alternatives. Ideally, the set of scenarios considered captures all possible future developments. To appropriately explore this set, formal or analytical approaches to scenario construction generate a large number of scenarios. This challenges the decision makers' information‐processing capacity. To support them in managing the richness of information, a two‐fold approach that uses selection and aggregation is presented. By using a selection method, the scenarios that are deemed most relevant are identified, and their evaluations are presented in detail to decision makers. This approach is complemented by an aggregation of scenario evaluations on the basis of the decision makers' preferences. We present two approaches to facilitate the preference elicitation process. Copyright © 2013 John Wiley & Sons, Ltd.     

An Optimized Design of Choice Experiments: A New Approach for Studying Decision Behavior in Choice Task Experiments

In this paper, we present a new approach for the optimal experimental design problem of generating diagnostic choice tasks, where the respondent's decision strategy can be unambiguously deduced from the observed choice. In this new approach, we applied a genetic algorithm that creates a one‐to‐one correspondence between a set of predefined decision strategies and the alternatives of the choice task; it also manipulates the characteristics of the choice tasks. In addition, this new approach takes into account the measurement errors that can occur when the preferences of the decision makers are being measured. The proposed genetic algorithm is capable of generating diagnostic choice tasks even when the search space of possible choice tasks is very large. As proof‐of‐concept, we used this novel approach to generate respondent‐specific choice tasks with either low or high context‐based complexity that we operationalize by the similarity of alternatives and the conflict between alternatives. We find in an experiment that an increase in the similarity of the alternatives and an increase in the number of conflicts within the choice task lead to an increased use of non‐compensatory strategies and a decreased use of compensatory decision strategies. In contrast, the size of the choice tasks, measured by the number of attributes and alternatives, only weakly influences the strategy selection. Copyright © 2014 John Wiley & Sons, Ltd.     

Kant’s Solution to the Euthyphro Dilemma

Are our actions morally good because we approve of them or are they good independently of our approval? Are we projecting moral values onto the world or do we detect values that are already there? For many these questions don’t state a real alternative but a secular variant of the Euthyphro dilemma: If our actions are good because we approve of them moral goodness appears to be arbitrary. If they are good independently of our approval, it is unclear how we come to know their moral quality and how moral knowledge can be motivating. None of these options seems attractive; the source of moral goodness unclear. Despite the growing literature on Kant’s moral epistemology and moral epistemology the question remains open what Kant’s answer to this apparent dilemma is. The Kantian view I attempt to lay out in this paper is supposed to dissolve the secular version of the Euthyphro dilemma. In responding to this dilemma we need to get clear about the source or the origin of our moral knowledge: Voluntary approval or mind-independent moral facts? Projectivism or detectivism? Construction or given? I believe that all these ways of articulating the problem turn out, on closer inspection, to be false alternatives.     

A general approach to testing multifaceted personality constructs.

Some personality characteristics are composed of multiple, distinct subcomponents (e.g., Type A, hardiness, attributional style, self-monitoring). The advantages and disadvantages of 3 typical approaches to testing these constructs are reviewed. An alternative approach based on structural equation modeling is then offered. This approach has many advantages over its alternatives, including the provision of an explicit test of the structure of the multifaceted construct, the simultaneous test of the effects of this general construct and the unique aspects of its subcomponents, and the explicit consideration of measurement error. Although the modeling approach does have limitations, these limitations are equally applicable to all of its alternatives. Indeed, the principal disadvantages of the modeling approach seem to be its statistical complexity and the lack of education regarding its proper use.     

Choice Environment, Market Complexity, and Consumer Behavior: A Theoretical and Empirical Approach for Incorporating Decision Complexity into Models of Consumer Choice

Most empirical models of choice in economics and consumer research assume that the decision maker assesses all alternatives and information in a perfect information-processing sense. The complexity of the choice environment, the ability of the individual to make complex decisions, and the effect of choice context on the decision strategy are generally not considered in statistical model development. One of the reasons for this omission is that theoretical literature on choice complexity and imperfect ability to choose that has developed in psychology and behavioral decision theory (BDT) literatures has not been translated into empirical econometric analysis. Second, the data used in economics and consumer research studies tend to be somewhat different from the data structures used in psychology and BDT literatures. In this paper we outline a theoretical model that simultaneously considers task complexity, effort applied by the consumer, ability to choose, and choice. We then construct a measure of task complexity and incorporate this in an analysis of a number of data series based on the random utility framework. We also examine the performance of our measure of task complexity in a composite data set that allows for increased variability in factors affecting decision context. Our approach provides a mechanism to link research in BDT and econometric modeling of consumer choice. Our findings suggest that task complexity does affect inferences about choice model parameters and that context effects, such as complexity, have a systematic impact on the parameters of econometric models of choice. The modeling approach provides a mechanism for inclusion of results from psychology and BDT in traditional economic models of consumer choice.     

An optimal adjustment procedure to minimize experiment time in decisions with multiple alternatives

Decisions between multiple alternatives typically conform to Hick’s Law: Mean response time increases log-linearly with the number of choice alternatives. We recently demonstrated context effects in Hick’s Law, showing that patterns of response latency and choice accuracy were different for easy versus difficult blocks. The context effect explained previously observed discrepancies in error rate data and provided a new challenge for theoretical accounts of multialternative choice. In the present article, we propose a novel approach to modeling context effects that can be applied to any account that models the speed–accuracy trade-off. The core element of the approach is “optimality” in the way an experimental participant might define it: minimizing the total time spent in the experiment, without making too many errors. We show how this approach can be included in an existing Bayesian model of choice and highlight its ability to fit previous data as well as to predict novel empirical context effects. The model is shown to provide better quantitative fits than a more flexible heuristic account.     

贝叶斯结构方程模型及其研究现状

在心理学研究中结构方程模型(Structural Equation Modeling, SEM)被广泛用于检验潜变量间的因果效应, 其估计方法有频率学方法(如, 极大似然估计)和贝叶斯方法两类。近年来由于贝叶斯统计的流行及其在结构方程建模中易于处理小样本、缺失数据及复杂模型等方面的优势, 贝叶斯结构方程模型发展迅速, 但其在国内心理学领域的应用不足。主要介绍了贝叶斯结构方程模型的方法基础和优良特性, 及几类常用的贝叶斯结构方程模型及其应用现状, 旨在为应用研究者介绍新的研究工具。     

The Federal Face of Voting: Are Elected Officials Held Accountable for the Functions Relevant to Their Office?

Federalism is designed to enhance democratic representation because it gives citizens the opportunity to shape policymaking at multiple levels of government. This design feature is premised on the assumption that individuals make distinctions in the responsibilities that pertain to different levels of government and link these distinctions to their voting decisions. Citizens are expected to sanction politicians for those policy decisions over which their level of government has responsibility. This paper draws on work in both political and social psychology to develop a theoretical framework consistent with the federalist view of democratic representation to explain how people make voting decisions. Individuals who were able to vote in elections at all three levels of government (national, state, and local) in 2002 were surveyed, allowing a full test of the federalist voting model. Findings show that while citizens do make distinctions among levels of government when evaluating issues, they only link these distinctions to their voting decisions if those issue attitudes are highly accessible. Implications for democratic representation and future research are discussed.