Dynamic multi-criteria decision making

Most medium-and long-term decision making in industry and government can be viewed as dynamic multi-criteria decision making (MCDM), in which the decision makers are free to alter the emphasis placed on each objective in the light of developing circumstances. In this paper the problem of time-dependent weights in MCDM is discussed and an analysis of empirical data associated with dynamic decision making is presented.     

Beyond IQ: A latent state-trait analysis of general intelligence, dynamic decision making, and implicit learning

The present study investigated cognitive performance measures beyond IQ. In particular, we investigated the psychometric properties of dynamic decision making variables and implicit learning variables and their relation with general intelligence and professional success. N = 173 employees from different companies and occupational groups completed two standard intelligence tests, two dynamic decision making tasks, and two implicit learning tasks at two measurement occasions each. We used structural equation models to test latent state-trait measurement models and the relation between constructs. The results suggest that dynamic decision making and implicit learning are substantially related with general intelligence. Furthermore, general intelligence is the best predictor for income, social status, and educational attainment. Dynamic decision making can predict supervisor ratings even beyond general intelligence.     

Group decision process and incrementalism in organizational decision making

In two studies examining resource allocation, support is found for the notion that group decisions are affected in systematic ways depending on whether or not there was individual consideration of the problem before meeting as a group. Specifically, compared to no prior consideration groups, prior consideration groups (1) escalate their commitment more in progress (i.e., ongoing) decisions, and (2) are less willing to concentrate resources on a single project in adoption (i.e., resource utilization) decisions. The findings challenge the blanket assertion that promoting divergent views in a group decision context is always related to better decisions.     

Instance‐based Learning: A General Model of Repeated Binary Choice

A common practice in cognitive modeling is to develop new models specific to each particular task. We question this approach and draw on an existing theory, instance‐based learning theory (IBLT), to explain learning behavior in three different choice tasks. The same instance‐based learning model generalizes accurately to choices in a repeated binary choice task, in a probability learning task, and in a repeated binary choice task within a changing environment. We assert that, although the three tasks are different, the source of learning is equivalent and therefore, the cognitive process elicited should be captured by one single model. This evidence supports previous findings that instance‐based learning is a robust learning process that is triggered in a wide range of tasks from the simple repeated choice tasks to the most dynamic decision making tasks. Copyright © 2010 John Wiley & Sons, Ltd.     

Positive explorers: modeling dynamic control in normal aging

Situations in which there are multiple changes occurring all at once and which demand complex decisions to be made are common throughout life, but little is known about how normal aging influences performance on these types of scenarios. To determine performance differences associated with normal aging, we test older and younger adults in a dynamic control task. The task involves the control of a single output variable over time via multiple and uncertain input controls. The Single Limited Input, Dynamic Exploratory Responses (SLIDER) computational model, is implemented to determine the behavioral characteristics associated with normal aging in a dynamic control task. Model-based analysis demonstrates a unique performance signature profile associated with normal aging. Specifically, older adults exhibit a positivity effect in which they are more influenced by positively valenced feedback, congruent with previous research, as well as enhanced exploratory behavior.     

The roles of planning,learning, and mental models in repeated dynamic decision making

Repeated search and decision making is a common consumer activity that should benefit from advanced planning. In three simulated shopping experiments, we find that people often fail to plan spontaneously or, when they do plan, do not use an appropriate mental model of the search problem. We also manipulate the mental models used by subjects and find that while our manipulation successfully encourages the development of appropriate mental models and improves performance when search costs are low, it does not result in the type of sophisticated mental model required to change strategies based on increased search costs. Finally, we show that the benefits of planning generalize to real world shopping behavior in a field experiment.     

The Characteristic Objects Method: A New Distance‐based Approach to Multicriteria Decision‐making Problems

Multicriteria decision‐making (MCDM) methods are concerned with the ranking of alternatives based on expert judgements made using a number of criteria. In the MCDM field, the distance‐based approach is one popular method for obtaining a final ranking. The technique for order preference by similarity to the ideal solution (TOPSIS) is a commonly used example of this kind of MCDM method. The TOPSIS ranks the alternatives with respect to their geometric distance from the positive and negative ideal solutions. Unfortunately, two reference points are often insufficient, especially for nonlinear problems. As a consequence of this situation, the final result ranking is prone to errors, including the rank reversals phenomenon. This study proposes a new distance‐based MCDM method: the characteristic objects method. In this approach, the preferences of each alternative are obtained on the basis of the distance from the nearest characteristic objects and their values. For this purpose, we have determined the domain and Fuzzy number set for all the considered criteria. The characteristic objects are obtained as the combination of the crisp values of all the Fuzzy numbers. The preference values of all the characteristic object are determined on the basis of the tournament method and the principle of indifference. Finally, the Fuzzy model is constructed and is used to calculate preference values of the alternatives, making it a multicriteria model that is free of rank reversal. The numerical example is used to illustrate the efficiency of the proposed method with respect to results from the TOPSIS method. The characteristic objects method results are more realistic than the TOPSIS results. Copyright © 2014 John Wiley & Sons, Ltd.     

Executive functions in decision making: An individual differences approach

This individual differences study examined the relationships between three executive functions (updating, shifting, and inhibition), measured as latent variables, and performance on two cognitively demanding subtests of the Adult Decision Making Competence battery: Applying Decision Rules and Consistency in Risk Perception. Structural equation modelling showed that executive functions contribute differentially to performance in these two tasks, with Applying Decision Rules being mainly related to inhibition and Consistency in Risk Perception mainly associated to shifting. The results suggest that the successful application of decision rules requires the capacity to selectively focus attention and inhibit irrelevant (or no more relevant) stimuli. They also suggest that consistency in risk perception depends on the ability to shift between judgement contexts.     

Model and interactive algorithm of bi‐level multi‐objective decision‐making with multiple interconnected decision makers

Furthering a prior research on two‐person bi‐level multi‐objective decision‐making problems of the leader‐follower Stackelberg game, we present an extended model of bi‐level multi‐objective decision‐making with multiple interconnected decision makers at the lower level. In the model, the upper level decision maker acts as a leader and the lower level decision makers behave as the followers, and inter‐connections and interactions exist among these followers in decision‐making scenarios. Following the rules of leader‐follower Stackelberg game, we develop an interactive algorithm of the model for solving multi‐objective decision‐making problems and reflecting the interactive natures among the decision makers. Finally, the authors exemplify the model and algorithm, and draw a conclusion on points of contributions and the significance of this study in decision‐making and support. Copyright © 2001 John Wiley & Sons, Ltd.     

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.     

Comparison of decision learning models using the generalization criterion method

It is a hallmark of a good model to make accurate a priori predictions to new conditions ( Busemeyer & Wang, 2000 ). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short-term predictions, and the third method again used a generalization criterion for long-term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short-versus long-term predictions on simple gambling tasks.     

Goal‐Proximity Decision‐Making

Reinforcement learning (RL) models of decision‐making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal‐option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed mechanism, Goal‐Proximity Decision‐making (GPD), is implemented within the ACT‐R cognitive framework. GPD is found to be more efficient than RL in three maze‐navigation simulations. GPD advantages over RL seem to grow as task difficulty is increased. An experiment is presented where participants are asked to make choices in the absence of prior reward. GPD captures human performance in this experiment better than RL.     

Dispositional optimism,self‐framing and medical decision‐making

Self‐framing is an important but underinvestigated area in risk communication and behavioural decision‐making, especially in medical settings. The present study aimed to investigate the relationship among dispositional optimism, self‐frame and decision‐making. Participants (N = 500) responded to the Life Orientation Test‐Revised and self‐framing test of medical decision‐making problem. The participants whose scores were higher than the middle value were regarded as highly optimistic individuals. The rest were regarded as low optimistic individuals. The results showed that compared to the high dispositional optimism group, participants from the low dispositional optimism group showed a greater tendency to use negative vocabulary to construct their self‐frame, and tended to choose the radiation therapy with high treatment survival rate, but low 5‐year survival rate. Based on the current findings, it can be concluded that self‐framing effect still exists in medical situation and individual differences in dispositional optimism can influence the processing of information in a framed decision task, as well as risky decision‐making.     

Shared knowledge and subgroup influence during decision‐making discussions

It was predicted that preference factions within decision‐making groups would have greater influence to the extent that faction‐member preferences are based on a common pool of decision‐relevant information. Such factions are said to exhibit high informational commonality (IC). Four‐person groups decided how much money to invest in each of two pharmaceutical companies developing new cholesterol‐lowering drugs. Prior to discussion, information about these companies and drugs was distributed among members such that two would initially prefer investing in one company and two would initially prefer investing in the other company. Further, whereas half of the information held by members of one preference faction was held in common between them (high IC), almost none of the information held by those in the other faction was held in common between them (low IC). It was found that groups invested more money in a given company when that company was initially preferred by their high‐IC faction. Additionally, high‐IC factions exerted greater influence on members' private allocation preferences. These effects appear to have been due to the ability of members in the high‐IC factions to work together in a more coordinated manner to argue their position. Copyright © 2004 John Wiley & Sons, Ltd.     

Decision support for real-time, dynamic decision-making tasks

By definition, dynamic decision making dictates that multiple and interrelated decisions be made in a continuously changing environment. Such decision making is difficult and often taxes individuals’ cognitive resources. Here I investigated ways in which to support decision making in these environments. I evaluated three forms of decision support: outcome feedback, cognitive feedback, and feedforward that incorporated (to varying degrees) common features of learning theories associated with dynamic tasks. Participants in a laboratory experiment performed a real-time, dynamic decision-making task while receiving one of the different types of decision support. During the first 2 days, individuals received one type of decision support, but on the third day they performed the task without this support. Participants who received feedforward improved their performance considerably and continued to exhibit improved performance even after discontinuation of the decision support on the third day. Neither outcome feedback nor cognitive feedback resulted in improved performance. More research is necessary to conclusively identify the best forms of dynamic decision-making support and their durability when transferred to new tasks.     

The contributions of multi‐criteria decision making to the development of decision support systems subspecialties: an empirical investigation

This study focuses on examining the structure of decision support systems (DSS) research, with a particular emphasis on assessing the contributions of multi‐criteria decision making (MCDM) to the development of each of the DSS subspecialty areas. This study traces how concepts and findings by researchers in the MCDM area have been picked up by DSS researchers to be applied, extended and refined in the development of DSS research subspecialties. In doing so, factor analysis is applied to an author cocitation frequency matrix derived from a large database file of comprehensive DSS literature. This study concludes that researchers in the MCDM area have made crucial contributions to the development of DSS research subspecialties. Copyright © 1999 John Wiley & Sons, Ltd.     

Myopic regret avoidance: Feedback avoidance and learning in repeated decision making

Decision makers can become trapped by myopic regret avoidance in which rejecting feedback to avoid short-term outcome regret (regret associated with counterfactual outcome comparisons) leads to reduced learning and greater long-term regret over continuing poor decisions. In a series of laboratory experiments involving repeated choices among uncertain monetary prospects, participants primed with outcome regret tended to decline feedback, learned the task slowly or not at all, and performed poorly. This pattern was reversed when decision makers were primed with self-blame regret (regret over an unjustified decision). Further, in a final experiment in which task learning was unnecessary, feedback was more often rejected in the self-blame regret condition than in the outcome regret condition. We discuss the findings in terms of a distinction between two regret components, one associated with outcome evaluation, the other with the justifiability of the decision process used in making the choice.     

Hormonal changes after competition predict sex‐differentiated decision‐making

Recent neuroendocrinology research has pointed out that testosterone (T) and cortisol (C) changes after social interactions can predict risk‐taking behavior in decision‐making, depending on the sex of participants. However, previous research has focused on the effects of the changes in only one hormone, rather than the interaction between them, even though C can suppress T activity. Our aim was to test, in men and women, the role of T changes moderated by C changes after competition in decision‐making. Thus, 48 males and 46 females completed the Iowa Gambling Task (IGT) after a laboratory competition or a noncompetitive task (control task). Saliva samples were collected before and after the competition/control task. IGT was employed to measure risk‐taking decision‐making, considering the degree of uncertainty. Our results showed sex‐differentiated effects of T and C changes on risk‐taking behavior. On the one hand, men from both task groups (Competition/Control) who had higher C and T changes after competition showed more risk‐taking decision‐making (higher IG Risk). On the other hand, women from the competitive task who had high C and T showed conservative decision‐making. Therefore, these results show sex‐differentiated decision‐making profiles, which would help to understand how men and women behave after experiencing a competitive social context.