Combining scenario planning and multi-criteria decision analysis in practice

The integrated use of scenario planning and multi-criteria decision analysis (MCDA) has been advocated as a powerful combination for providing decision support in strategic decisions. Scenario planning helps decision makers in devising strategies and thinking about possible future scenarios; while MCDA can support an in-depth performance evaluation of each strategy, as well as in the design of more robust and better options. One of the frameworks proposed recently, by Goodwin & Wright, suggests the use of scenario planning with multi-attribute value theory, a mathematically simple, yet extensively researched and widely employed multi-criteria method. However, so far, such framework has been presented only using hypothetical problems. In this paper, we describe two case studies where this approach was used to support real-world strategic decisions. We discuss the challenges and limitations we encountered in applying it and suggest some possible improvements that could be made to such framework. Copyright © 2007 John Wiley & Sons, Ltd.     

Analysis of proposed three-class classification decision rules in terms of the ideal observer decision rule

We analyze recently proposed decision rules for three-class classification from the point of view of ideal observer decision theory. We consider three-class decision rules proposed by Scurfield, by Chan et al., and by Mossman. Scurfield's decision rule is shown to be a special case of the three-class ideal observer decision rule in three different situations. Chan et al. start with an ideal observer model and specify its decision-consequence utility structure in a way that causes two of the decision lines used by the ideal observer to overlap and the third line to become undefined. Finally, we show that, for a particular and obvious choice of ideal-observer-related decision variables, the Mossman decision rule cannot be a special case of the ideal observer decision rule. Despite the considerable difficulties presented by the three-class classification task, the three-class ideal observer provides a useful framework for analyzing a variety of three-class decision strategies.     

Generating interior search directions for multiobjective linear programming

A new multiobjective linear programming (MOLP) algorithm is presented. The algorithm uses a variant of Karmarkar's interior-point algorithm known as the affine-scaling primal algorithm. Using this single-objective algorithm, interior search directions are generated and used to provide an approximation to the gradient of the (implicitly known) utility function. The approximation is guided by assessing locally relevant preference information for the various interior directions through interaction with a decision maker (DM). The resulting algorithm is an interactive approach that makes its progress towards the solution through the interior of the constraints polytope.     

Inducing compensatory information processing through decision aids that facilitate effort reduction: an experimental assessment

This paper examines the role of computer‐based decision aids in reducing cognitive effort and therefore influencing strategy selection. It extends and complements the work reported in the behavioral decision theory literature on the role of effort and accuracy in choice tasks. The central proposition of the research is that if a decision aid makes a strategy that should lead to a more accurate outcome at least as easy to employ as a simpler, but less accurate, heuristic, then the use of a decision aid should induce that more accurate strategy and as a consequence improve decision quality. Otherwise, a decision aid may only influence decision‐making efficiency. This occurs because decision makers use a decision aid in such a way as to minimize their overall level of effort expenditure. Results from a laboratory experiment support this proposition. When a more accurate normative strategy is made less effortful to use, it is used. This result is consistent with the findings of our prior studies, but more clearly demonstrates that decision aids can induce the use of normatively oriented strategies. The key to inducing these strategies is to make the normative strategy easier to execute than competing alternative strategies. Copyright © 2000 John Wiley & Sons, Ltd.     

Future‐focussed thinking: combining scenario planning with decision analysis

This paper first describes current practice in decision analysis and argues that nothing in the technique's application is likely to challenge the strategic decision maker's current worldview of the course of future events that are modelled in the decision tree. By contrast, a scenario planning intervention in an organization has the potential to increase perceived threat and thus lead to a step change in strategic decision making. Strategic decisions are made against a backcloth of the operation of psychological processes that act, it is argued, to reduce the perceived level of environmental threat and result in strategic inertia. For this reason, it is recommended that scenario planning should be adopted as a standard procedure because of its ability to challenge individual and organizational worldviews. The use of scenario planning prior to conventional decision analysis is termed as ‘future‐focussed thinking’, and parallels are drawn between the current advocated approach and that of Keeney's value‐focussed thinking. Both serve to prompt the creation of enhanced options for subsequent evaluation by conventional decision analytic techniques. Copyright © 1999 John Wiley & Sons, Ltd.     

Problem formulation for multi-criteria decision analysis: report of a workshop

This paper reports on a workshop on Problem Formulation in Multi-Criteria Decision Analysis held at SPUDM97. The focus of the workshop was the problem formulation phase which occurs between the analyst meeting a person with a ‘mess’ and the time he or she begins to analyse a structured problem with several alternatives scored against several attributes or criteria. The objectives were: to share experience on procedures which might be transferable between the methodologies; to demonstrate different skills used by the analyst in structuring decision problems; and to catalyse a discussion on the problem formulation phase of an analysis. Three analysts, who generally approach problems using multiattribute methods, addressed the same problem. The problem used was constructed to be realistic to three decision makers, who had been trained in the issues of concern. There were two sessions. Each analyst was assigned a decision maker and formulated the problem independently in the first session, held in parallel. They were each observed by two observers and many of the audience at the workshop. The three formulations were presented along with the comments of the observers and discussed at a second plenary session. This paper reports the three formulations and observations, remarking on the ‘tricks of the trade’ employed by the analysts in formulating the problem. The analysts also describe their thinking and their aims in adopting their approach and style of interaction. More general remarks on the process of decision analysis are also offered. © 1998 John Wiley & Sons, Ltd.     

Self–other differences in multiattribute decision making: Compensatory versus noncompensatory decision strategies

When making decisions where options involve multiple attributes, a person can choose to use a compensatory, utility maximizing strategy, which involves consideration and integration of all available attributes. Alternatively, a person can choose a noncompensatory strategy that extracts only the most important and reliable attributes. The present research examined whether other‐oriented decisions would involve greater reliance on a noncompensatory, lexicographic decision strategy than self‐oriented decisions. In three studies (Mturk workers and college students), the difference in other‐oriented versus self‐oriented decisions in a medical decision context was explained by a subsample of participants that chose the death minimizing operation on all 10 decisions (Study 1) and a subsample of participants who self‐reported that they used a strategy that minimized the chance of death on every decision (i.e., a lexicographic mortality heuristic; Study 2). In Study 2, tests of mediation found that self‐reported use of the mortality heuristic completely accounted for the self–other effect on decisions. In Study 3, participants were more likely to report prospectively that they would adopt the mortality heuristic when making decisions for others than for themselves, suggesting that participants were not mistakenly inferring a lexicographic decision strategy from their past behavior. The results suggest that self–other effects in multiattribute choice involve differential use of compensatory versus noncompensatory decision strategies and that beyond this group difference, individual differences in the use of these strategies also exist within self‐oriented and other‐oriented decisions.     

Instance‐based learning in dynamic decision making

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.     

一种新决策模型——量子决策模型

近10年以来, 一些学者基于量子理论研究思想与方法探索出了量子决策模型。由于该模型独特的理论结构, 它可被用于解释传统决策理论所难以解释的问题, 尤其是个体在不确定情况下的决策行为。该模型解释了诸如分离效应、分类决策的干涉效应以及合取谬误等传统决策模型很难解释的问题, 研究者已经证实其中一种称为量子问题等式的决策模型可以精确地预测决策中的顺序效应。作为一个有助于分析心理学中决策现象的新研究领域, 量子决策模型具有深入研究的理论意义和实践意义。     

任务重要性对决策策略有效性的影响

研究以选择决策中决策任务的主观性为关注点,通过现场和实验室两项实验考察决策任务重要性对启发式和分析式策略有效性的影响。结果发现,在感知任务重要的情况下,采用分析式策略的被试决策后满意度高于采用启发式策略的被试;在感知任务不重要情况下,采用启发式策略的被试决策后满意度高于采用分析式策略的被试。研究表明,决策者感知到的任务重要与否会影响启发式和分析式两种决策策略的效果。     

Groups, group members and individual decision processes: the effects of decision strategy, social interaction style and reception of decision-threatening information on post-decision processes

In two experiments, one conducted at an individual level and one at a group level, it was investigated how decision strategies and the reception of decision-threatening information affect the degree of post-decision consolidation for both individual and group decision-makers. In Experiment 1, roughly half the 55 participants made decisions in three-person groups and the other half individually. The type of decision strategies subjects employed (compensatory, non-compensatory, other) was assessed by questionnaire. In two post-decision sessions, consolidation was assessed using a memory task, either decision-supporting or decision-threatening information being provided at the start of the last post-decision session. In Experiment 2, the same design and procedure were used at a group level. In both experiments, the groups (and the single group members) were analyzed with the SYMLOG instrument. The results indicated that individual decision-makers consolidated their own decisions more than members of decision-making groups. There was also greater post-decision consolidation with the use of non-compensatory decision strategies as well as with reception of decision-threatening information, this latter result being seen as providing an explanation for the greater consolidation that individual decision-makers showed. Furthermore, single task-oriented group members and groups with a task-oriented leader consolidated the decision made by their group.     

Approval-directed agency and the decision theory of Newcomb-like problems

Decision theorists disagree about how instrumentally rational agents, i.e., agents trying to achieve some goal, should behave in so-called Newcomb-like problems, with the main contenders being causal and evidential decision theory. Since the main goal of artificial intelligence research is to create machines that make instrumentally rational decisions, the disagreement pertains to this field. In addition to the more philosophical question of what the right decision theory is, the goal of AI poses the question of how to implement any given decision theory in an AI. For example, how would one go about building an AI whose behavior matches evidential decision theory’s recommendations? Conversely, we can ask which decision theories (if any) describe the behavior of any existing AI design. In this paper, we study what decision theory an approval-directed agent, i.e., an agent whose goal it is to maximize the score it receives from an overseer, implements. If we assume that the overseer rewards the agent based on the expected value of some von Neumann–Morgenstern utility function, then such an approval-directed agent is guided by two decision theories: the one used by the agent to decide which action to choose in order to maximize the reward and the one used by the overseer to compute the expected utility of a chosen action. We show which of these two decision theories describes the agent’s behavior in which situations.

     

Prospect relativity: how choice options influence decision under risk

In many theories of decision under risk (e.g., expected utility theory, rank-dependent utility theory, and prospect theory), the utility of a prospect is independent of other options in the choice set. The experiments presented here show a large effect of the available options, suggesting instead that prospects are valued relative to one another. The judged certainty equivalent for a prospect is strongly influenced by the options available. Similarly, the selection of a preferred prospect is strongly influenced by the prospects available. Alternative theories of decision under risk (e.g., the stochastic difference model, multialternative decision field theory, and range frequency theory), where prospects are valued relative to one another, can provide an account of these context effects.     

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.     

“Mindless” Decision Making as a Failure of Contextual Reasoning

People often make decisions without reference to vital information, even when such information is readily available. In the present research, the authors addressed the possibility that this tendency may derive from failure to have pertinent information immediately available in the decision context. Participants rated the utility of decisions in either the presence or absence of simple pertinent information. The information provided required no training and was relatively obvious (e.g., if money is spent on a given item, that money will not be available for other expenditures). Presentation of such information in the immediate context of decisions significantly improved participants' abilities to understand their negative consequences. These results indicate that the presentation of pertinent information in immediate decision contexts, even information that is already available through participants' long-term memory, can improve the understanding of decision situations and reduce “mindlessness” in decision processes.     

On decision rules and information processing strategies for choices among multiattribute alternatives

Abstract.— It is suggested that a decision in a complex situation can be described as a sequential process in which different decision rules and information processing strategies can be used at different points in time. Examples of possible decision rules are presented in an approximate order of complexity. Two ways for processing the information in a decision situation, viz., breadth-first or depth-first strategies, are discussed and suggestions are made about their relationship to particular decision rules. Finally, it is proposed that the order of application of particular rules in a decision process is guided by a tendency to minimize cognitive effort.     

Decision theory and artificial intelligence II: The hungry monkey

This paper describes a problem-solving framework In which aspects of mathematical decision theory are incorporated into symbolic problem-solving techniques currently predominant in artificial intelligence. The utility function of decision theory IS used to reveal tradeoffs among competing strategies for achieving various goals, taking into account such factors as reliability, the complexity of steps in the strategy, and the value of the goal. The utility function on strategies can therefore be used as a guide when searching for good strategies. It is also used to formulate solutions to the problems of how to acquire a world model, how much planning effort is worthwhile, and whether verification tests should be performed. These techniques are illustrated by application to the classic monkey and bananas problem.     

An Interactive Approach for Multicriteria Decision Making Using a Tchebycheff Utility Function Approximation

Multicriteria decision making (MCDM) can provide an efficient mean for considering various and conflicting objectives to reveal the alternative that maximizes the decision maker's (DM) utility. In this paper, we propose a new interactive MCDM method for implicit alternatives to help a DM obtain a most preferred solution. We employ a Tchebycheff function to generate weights for objectives consistent with the DM's responses to pairwise comparisons between alternatives and present a mixed integer linear programming formulation to generate these weights. Thus, we approximate the DM's utility function by a Tchebycheff function and generate weights consistent with the DM's responses. We test our approach with different true utility functions on various sized multiple criteria linear programming problems. The computational results show that even with non‐Tchebycheff true utility functions, our method can generate alternatives very close to the optimal solution with few questions. The comparison of our results with other methods reveals its advantages. Copyright © 2013 John Wiley & Sons, Ltd.