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.     

An Approach for Determining the Mix between Human and Automated Service Providers

With the growing popularity of self‐service technologies (SST), businesses have to determine how to combine traditional human service with SST technologies. These mix problems have not received adequate attention in the research literature. This paper offers a decision model for solving mix problems between human service channels and SSTs. The decision problem cannot be determined as a traditional max or min problem and it is fuzzy. In fuzzy problems, the decision maker (DM) is considered as the focal point with the relevant knowledge and preferences and therefore, the objective is to maximize the DM's preferences. This paper develops a decision support system for eliciting a DM's value function when the service channels are considered to be substitutes, but when the rate of substitution may change and then uses a linear‐fractional model to fit the DM's data and optimize the DM's preferences. A service mix problem for a grocery store with human service providers and SSTs is used to demonstrate how the decision model can be employed to analyze and solve service mix decision making problems. Copyright © 2014 John Wiley & Sons, Ltd.     

Self‐framing of risky choice

Previous research on framing effects has largely focused on how choice information framed by external sources influences the response of a decision maker. This research examined how decision makers framed choice options and how the hedonic tone of self‐framing influenced their risk preference. By using pie charts and a complementary sentence‐completion task in Experiment 1, participants were able to interpret and frame the expected choice outcomes themselves before making a choice between a sure option and a gamble in either a life–death or a monetary problem. Each of these self‐frames (phrases) was then rated by a group of independent judges in terms of its hedonic tone. The hedonic tone of self‐frames was mostly positive and was more positive in the life–death than the monetary context, suggesting a motivational function of self‐framing. However, positive outcomes were still more likely to be framed positively than negative outcomes. In Experiment 2, choice outcomes were depicted with a whole‐pie chart instead of a pie slice in order to emphasize positive and negative outcomes equally. The results showed that the hedonic tone of self‐framing was still largely positive and more positive in the life domain than the monetary domain. However, compared to Experiment 1, the risk preference in the life–death domain was reversed, showing an outcome salience effect: when the pie‐slice chart emphasized only survival outcomes, participants were more risk taking under positive hedonic frames whereas when the whole‐pie chart depicted both survival and mortality outcomes, they became risk averse under positive frames. In sum, self‐framing reflected a positive bias in encoding risk information and affected the risk preference of the decision maker. Like the tone of voice used in communication, the hedonic tone of self‐framing, either positive or negative, can affect risk perception of a choice problem. Copyright © 2003 John Wiley & Sons, Ltd.     

Generating consensus priority interval vectors for group decision‐making in the AHP

Many practical and important decision‐making problems are complicated by at least two factors: (1) the qualitative/subjective nature of some criteria often results in uncertainty in the individual ratings; and (2) group decision‐making is involved and some means of aggregating individual ratings is required. Traditionally, both individual and group priorities have been represented as point estimates, but this approach presents severe limitations for accommodating imprecision in the decision‐making process. This paper examines the group decision‐making problem in the context where priorities are represented as numeric intervals. A set of techniques that could be used at some of the phases of an analytic hierarchy process (AHP)‐based group decision‐making process, which has the objective of generating a ‘consensus’ priority that represents the group's opinion with regards to the relative importance of a set of N objects (e.g. criteria, alternatives), is presented. Copyright © 2000 John Wiley & Sons, Ltd.     

Cognitive Reflection Predicts Real‐Life Decision Outcomes,but Not Over and Above Personality and Decision‐Making Styles

The Cognitive Reflection Test (CRT), designed to assess the ability to inhibit intuition to process a problem analytically, predicts people's performance in many normative judgement and decision‐making tasks (e.g., Bayesian reasoning, conjunction fallacy and ratio bias). However, how the CRT predicts normative decision‐making performance is unclear, and little is known about the extent to which the CRT predicts real‐life decision outcomes. We investigate the role of the CRT in predicting real‐life decision outcomes and examine whether the CRT predicts real‐life decision outcomes after controlling for two related individual differences: the Big Five personality traits and decision‐making styles. Our results show that greater CRT scores predict positive real‐life decision outcomes measured by the Decision Outcome Inventory. However, the effect size was small, and the relationship became non‐significant after statistically controlling for personality and decision‐making styles. We discuss the limited predictive role of cognitive reflection in real‐life decision‐making outcomes, along with the roles of personality and decision‐making styles. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

Navigation in multiobjective optimization methods

Building on previous work of the authors, this paper formally defines and reviews the first approach, referred to as navigation , towards a common understanding of search and decision‐making strategies to identify the most preferred solution among the Pareto set for a multiobjective optimization problem. In navigation methods, the decision maker interactively learns about the problem, whereas the decision support system learns about the preferences of the decision maker. This work introduces a detailed view on navigation leading to the identification of integral components and features. A number of different existing navigation methods are reviewed and characterized. Finally, an overview of applications involving navigation is given, and promising future research direction are discussed.     

Decision Support System and Multi‐Criteria Decision Aid: A State of the Art and Perspectives

The aim of this paper is to highlight the role of the Decision Support System within the field of multi‐criteria decision aid (MCDA). The MCDA tools have been incorporated into systems to create Multi‐Criteria Decision Support Systems (MCDSSs). In our literature review, we noticed that more than 100 papers have been written over a 20‐year period in which MCDSS was used as a decision‐making tool. The present paper describes some real applications of MCDSS in different fields, harmoniously combined with decision‐making methods such as analytic hierarchy process, Utility Additive, and Goal Programming. The present study proposes an integrative MCDSS evaluation through guidance on the tools most useful for a specific user with a particular decision problem. Copyright © 2014 John Wiley & Sons, Ltd.     

Preference‐based Learning of Ideal Solutions in TOPSIS‐like Decision Models

Combining established modelling techniques from multiple‐criteria decision aiding with recent algorithmic advances in the emerging field of preference learning, we propose a new method that can be seen as an adaptive version of TOPSIS, the technique for order preference by similarity to ideal solution decision model (or at least a simplified variant of this model). On the basis of exemplary preference information in the form of pairwise comparisons between alternatives, our method seeks to induce an ‘ideal solution’ that, in conjunction with a weight factor for each criterion, represents the preferences of the decision maker. To this end, we resort to probabilistic models of discrete choice and make use of maximum likelihood inference. First experimental results on suitable preference data suggest that our approach is not only intuitively appealing and interesting from an interpretation point of view but also competitive to state‐of‐the‐art preference learning methods in terms of prediction accuracy. Copyright © 2014 John Wiley & Sons, Ltd.     

Multi‐Criteria Evaluation for Collaborative Circulating Farming with Collective Operations Between Arable and Cattle Farmers

Tokachi sub‐prefecture in Hokkaido is one of the most famous dairy and crop farming regions in Japan. It is known that Tokachi is faced with various difficult problems such as soil degradation, water contamination and unpleasant odours because of the excessive use of chemical fertilizers and inappropriate treatment of livestock excretion. In this paper, we focus on Shihoro town where agricultural outputs are relatively large in Tokachi, and propose collaborative circulating farming with collective operations between arable and cattle farmers. Under the assumption that the decision‐maker in this problem is a representative of a farming organization who hopes for sustainable agricultural development and values the intentions of local residents including arable and cattle farmers in this region, we employ multi‐attribute utility theory in order to evaluate multiple alternatives of the farming management problem. Copyright © 2012 John Wiley & Sons, Ltd.     

Group‐analytic network process for balancing stakeholder views on fire protection of steel‐framed buildings

To select appropriate fire protection options for buildings during their design stage, economic, safety, environmental, and societal criteria need to be accounted for. The divergent and sometimes conflictual desires from different fire design stakeholders involved in the process present a multicriteria decision problem. Design decision criteria and fire protection options can be interdependent, and so there is a need to manage these desires with an advanced decision analysis technique, thereby reducing uncertainties in the complex decision‐making process. The aim of this paper is to use the weighted/geometric mean method‐analytic network process (W/GMM‐ANP) to balance the opinions of fire design stakeholders extracted from 42 structured stakeholder interviews on selecting the most suitable fire protection option for buildings constructed of steel frames. Different categories of interdependent decision elements were developed from 22 design decision criteria and 5 proposed fire protection options to produce a network of decision clusters for multicriteria decision analysis. In the synthesis and ranking of fire protection options, the W/GMM‐ANP accounted for the multiple interdependencies of weighted and unweighted stakeholder desires and managed the complexity of the decision‐making problem. The technique is proposed for approaching suitable group decisions in structural fire design of steel‐framed buildings as well as other performance‐based engineering decision making that may involve multidisciplinary stakeholders.     

Self‐other decision‐making differences in loss aversion: A regulatory focus perspective

Drawing on a cultural perspective, we examined whether differences in self‐other decision making documented in the West can be replicated in an Eastern context. Study 1 revealed that Chinese participants showed self‐other decision‐making differences in loss aversion similar to those observed in Western participants; loss aversion differed depending on the type of decision recipient, and trait regulatory focus moderated the self‐other decision‐making difference. Study 2 found a mediating effect of situational prevention but not promotion focus on the relationship between decision makers’ roles and loss aversion. Furthermore, both studies revealed that participants did not show a preference for loss aversion, suggesting that the baseline of self‐other decision‐making differences has shifted in China compared to the West.     

Trade‐off ranking method for multi‐criteria decision analysis

Multi‐criteria decision analysis presumes trade‐off between different criteria. As a result, the optimal solution is not unique and can be represented by the Pareto frontier in the objective space. Each Pareto solution is a compromise between different objectives. Despite a limited number of Pareto optimal solutions, the decision‐maker eventually has to choose only one option. Such a choice has to be made with the use of additional preferences not included in the original formulation of the optimization problem. The paper represents a new approach to an automatic ranking that can help the decision‐maker. In contrast to the other methodologies, the proposed method is based on the minimization of trade‐off between different Pareto solutions. To be realized, the approach presumes the existence of a well‐distributed Pareto set representing the entire Pareto frontier. In the paper, such a set is generated with the use of the directed search domain algorithm. The method is applied to a number of test cases and compared against two existing alternative approaches.     

Individual strategy preferences and decisional fit

The present research introduces the concept of decisional fit. A decision maker experiences decisional fit when the individually preferred decision strategy fits the actually applied strategy. In accordance to other fit‐concepts in psychology (e.g., person–environment fit), we expected positive effects of decisional fit. Five studies examine the effects of a fit between the individual preference for intuition and deliberation (PID) and the actually used decision strategy (intuition or deliberation). A comparison of extreme types (according to participants' values on the PID scale) revealed that decisional fit enhances the perceived value of the chosen or evaluated object (Studies 1–3). In Studies 4 and 5, participants experienced less regret after decisional fit. The findings highlight the importance of considering individual differences when comparing intuitive and deliberate decision making, because strategy preferences interact with applied strategies. Copyright © 2008 John Wiley & Sons, Ltd.     

On the Decision to Explore New Alternatives: The Coexistence of Under‐ and Over‐exploration

The decision whether to explore new alternatives or to choose from familiar ones is implicit in many of our daily activities. How is this decision made? When will deviation from optimal exploration be observed? The current paper examines exploration decisions in the context of a multi‐alternative “decisions from experience” task. In each trial, participants could choose a familiar option (the status quo) or a new alternative (risky exploration). The observed exploration rates were more sensitive to the frequent outcome from choosing new alternatives than to the average outcome. That is, the implicit decision whether to explore a new alternative reflects underweighting of rare events: Over‐exploration was documented in “Rare Disasters” environments, and insufficient exploration was evident in “Rare Treasures” environments. In addition, the results reveal a decrease in exploration of new alternatives over time even when it is always optimal and some exploration even when it is never reinforcing. These results can be captured with models that share a distinction between “data collection” and “outcome‐driven” decision modes. Under the data collection mode, the decision maker collects information about the environment, to be used in future choices. Under the outcome‐driven mode, the decision maker relies on small samples of previous experiences with familiar versus unfamiliar alternatives, before the selection of a specific alternative. The predictive value of a two‐parameter “explorative sampler” quantification of these assumptions is demonstrated. Copyright © 2013 John Wiley & Sons, Ltd.     

Experimental studies of sequential selection and assignment with relative ranks

We study a class of sequential selection and assignment problems in which a decision maker (DM) must sequentially assign applicants to positions with the objective of minimizing expected cost. In modeling this class of problems, we assume that on each period the DM is only informed of the rank of the present applicant relative to the applicants that she previously observed and assigned. We first present the optimal decision policy that we subsequently use as a normative benchmark, and then report results from three experiments designed to study sequential assignment behavior. In comparing the aggregate results from all three experiments to the optimal decision policy, we identify a systematic bias, called the middleness bias, to over‐assign applicants to intermediate positions. The results also reveal a strong bias for early applicants to be over‐assigned to important positions. Copyright © 2006 John Wiley & Sons, Ltd.     

Reduction of pairwise comparisons in decision making via a duality approach

Although pairwise comparisons have been seen by many as an effective and intuitive way for eliciting qualitative data for multi‐criteria decision making problems, a major drawback is that the number of the required comparisons increases quadratically with the number of the entities to be compared. Thus, often even data for medium size decision problems may be impractical to be elicited via pairwise comparisons. The more the comparisons are, the higher is the likelihood that the decision maker will introduce erroneous data. This paper introduces a dual formulation to a given multi‐criteria decision making problem, which can significantly alleviate the previous problems. Some theoretical results establish that this is possible when the number of alternatives is greater than the number of decision criteria plus one. Copyright © 1999 John Wiley & Sons, Ltd.     

Early‐Entrant Advantage,Word‐of‐Mouth Communication,Brand Similarity,and the Consumer Decision‐Making Process

To better understand the competitive dynamics between an early and a later entrant, in this study we examined the extent to which word of mouth (WOM) regarding the later entrant and the later entrant's similarity to the early entrant influences the consumer decision process. We hypothesized that the influence of WOM and similarity depends on the nature of the decision‐making task, which is theorized as either a stimuli‐based or memory‐based task. A 3‐stage, sequential‐logit model with 2 focal brands (the early and later entrants) was developed to test the influence of independent measures on the likelihood of (a) retrieval; (b) consideration, given retrieval; and (c) choice, given consideration for both the early entrant and the focal follower. Data from 2 experiments provides support for the multistage conceptualization of the consumer decision process and demonstrates that the effects of WOM communication and similarity depend on the nature of the decision‐making task.