Vocational choice: A decision making perspective

We propose a model of vocational choice that can be used for analyzing and guiding the decision processes underlying career and job choices. Our model is based on research in behavioral decision making (BDM), in particular the choice goals framework developed by Bettman, Luce, and Payne (1998). The basic model involves two major processes. First, the selection of a decision strategy according to four choice goals: maximizing decision accuracy, minimizing cognitive effort, minimizing negative emotion, and maximizing justifiability of the decision. Second, the construction of situation-specific preferences, which can reflect irrelevant task and context factors such as the evaluation mode. This basic model is extended to account for social influences and the long decision time typical of most career and job decisions. We review research on vocational choice in light of this model, discuss normative implications for counseling, and outline a research agenda for studying vocational choice from a behavioral decision making perspective.     

Some probabilistic models of best, worst, and best-worst choices

Over the past decade or so, a choice design in which a person is asked to select both the best and the worst option in an available set of options has been gaining favor over more traditional designs, such as where the person is asked, for instance, to: select the best option; select the worst option; rank the options; or rate the options. In this paper, we develop theoretical results for three overlapping classes of probabilistic models for best, worst, and best-worst choices, with the models in each class proposing specific ways in which such choices might be related. The models in these three classes are called random ranking and random utility, joint and sequential, and ratio scale. We include some models that belong to more than one class, with the best known being the maximum-difference (maxdiff) model, summarize estimation issues related to the models, and formulate a number of open theoretical problems.     

Ignoring information in binary choice with continuous variables: When is less “more”?

When can a single variable be more accurate in binary choice than multiple sources of information? We derive analytically the probability that a single variable (SV) will correctly predict one of two choices when both criterion and predictor are continuous variables. We further provide analogous derivations for multiple regression (MR) and equal weighting (EW) and specify the conditions under which the models differ in expected predictive ability. Key factors include variability in cue validities, intercorrelation between predictors, and the ratio of predictors to observations in MR. Theory and simulations are used to illustrate the differential effects of these factors. Results directly address why and when “one-reason” decision making can be more effective than analyses that use more information. We thus provide analytical backing to intriguing empirical results that, to date, have lacked theoretical justification. There are predictable conditions for which one should expect “less to be more.”     

The meaning of indifference in choice behavior: Asymmetries in adjustments embodied in matching

Measuring and assessing people’s preferences is an intricate task that can be realized by different methods. Evidently, the different methods do not necessarily yield consistent results. In the present paper we examine the matching procedure and demonstrate inconsistencies within the method itself. It is shown that the inconsistencies stem from: (1) asymmetry in upward and downward matching (i.e., matching options by increasing or lowering values do not yield the same tradeoffs) and (2) participants’ tendency to match (adjust) the options such that they become more similar, eventually facilitating the choice process. Matching is the most straightforward method of eliciting indifference. Hence, the observed inconsistencies, encapsulated in the matching procedure, have direct implications for the construction of indifference curves (equal-utility contours) that are examined in the final section.     

Negative-based prominence: the role of negative features in matching and choice

Previous research related to the prominence effect (e.g., Tversky, Sattath, & Slovic, 1988) has mainly focused on the causes and boundary conditions of the effect. This article investigates the determinants of prominence and explores a negative-based prominence effect in which the negative attribute becomes the prominent one. Using a matching-choice procedure, Experiments 1 and 2 show that the negative dimension became the prominent one under two different cover stories, suggesting that the negative feature looms larger in choice than in matching. The robustness of negative-based prominence was further demonstrated in a direct choice task without matching (Experiment 3) and was shown to be resistant to the impact of added positive features (Experiment 4). Finally, Experiments 5 and 6 distinguish between two determinants of prominence: intrinsic prominence caused by attribute importance and negative-based prominence caused by negative attribute values. By lowering the values on the positive dimension and enhancing the values on the negative dimension, one can reverse the prominence effect. The results suggest that, compared to a matching task, choice leads to enhanced sensitivity to negative features. The relations between negative characteristics of an attribute and other factors that determine prominence are discussed in the final section.     

The simplest complete model of choice response time: linear ballistic accumulation

We propose a linear ballistic accumulator (LBA) model of decision making and reaction time. The LBA is simpler than other models of choice response time, with independent accumulators that race towards a common response threshold. Activity in the accumulators increases in a linear and deterministic manner. The simplicity of the model allows complete analytic solutions for choices between any number of alternatives. These solutions (and freely-available computer code) make the model easy to apply to both binary and multiple choice situations. Using data from five previously published experiments, we demonstrate that the LBA model successfully accommodates empirical phenomena from binary and multiple choice tasks that have proven difficult for other theoretical accounts. Our results are encouraging in a field beset by the tradeoff between complexity and completeness.     

Social cognitive career theory and the prediction of interests and choice goals in the computing disciplines

We tested the fit of the social cognitive choice model [Lent, R.W., Brown, S.D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance [Monograph]. Journal of Vocational Behavior, 45, 79-122] to the data across gender, educational level, and type of university among students in a variety of computing disciplines. Participants were 1208 students at 21 historically Black and 21 predominantly White universities. They completed measures of self-efficacy, outcome expectations, interests, goals, and social supports and barriers with respect to computing majors. The SCCT model yielded adequate fit to the data across each of the grouping variables. Implications for future research on SCCT’s choice hypotheses in the context of science and engineering-related fields are discussed.     

Free Choice

In everyday language, the central question raised by “free choice” is not causation but whether I do what I want. We can, however, grant that our wants are caused. We commonly fail to appreciate this because there is no consciousness of these causal processes. Nevertheless a question about the causation of my want may on occasion become relevant to the freedom of my choice. Thus one may ask if my want is caused by my knowledge and values, or by causes alien to these. This is not a question about the truth, or implications, of the theory of universal determinism.     

决策中的图形框架效应

决策框架效应作为一种违背不变性原则的非理性偏差, 已经得到研究的广泛证实。本研究突破了传统研究中主要由语言描述引发框架效应的限制, 从图形表征这一新的视角对图形框架效应做了深入探讨。本研究共包括6个实验, 通过操纵选项在不同图形表征版本中物理属性差异的突出性, 发现在表达信息不变的情况下, 人们判断和决策的偏好会受到图形表征的影响, 即出现了图形框架效应。研究结果表明, 图形框架效应普遍存在于各种决策情境以及各种图形表征方式中。基于属性替代理论和齐当别原则, 我们提出了一种解释图形框架效应内部作用机制的两阶段心理加工模型--图形编辑的齐当别模型(The Graph-edited Equate-to-differentiate Model, GEM)。