Multi-attribute Decision Tasks Research Articles

How do insects choose flowers? A review of multi‐attribute flower choice and decoy effects in flower‐visiting insects

Understanding why animals (including humans) choose one thing over another is one of the key questions underlying the fields of behavioural ecology, behavioural economics and psychology. Most traditional studies of food choice in animals focus on simple, single-attribute decision tasks. However, animals in the wild are often faced with multi-attribute choice tasks where options in the choice set vary across multiple dimensions. Multi-attribute decision-making is particularly relevant for flower-visiting insects faced with deciding between flowers that may differ in reward attributes such as sugar concentration, nectar volume and pollen composition as well as non-rewarding attributes such as colour, symmetry and odour. How do flower-visiting insects deal with complex multi-attribute decision tasks? Here we review and synthesise research on the decision strategies used by flower-visiting insects when making multi-attribute decisions. In particular, we review how different types of foraging frameworks (classic optimal foraging theory, nutritional ecology, heuristics) conceptualise multi-attribute choice and we discuss how phenomena such as innate preferences, flower constancy and context dependence influence our understanding of flower choice. We find that multi-attribute decision-making is a complex process that can be influenced by innate preferences, flower constancy, the composition of the choice set and economic reward value. We argue that to understand and predict flower choice in flower-visiting insects, we need to move beyond simplified choice sets towards a view of multi-attribute choice which integrates the role of non-rewarding attributes and which includes flower constancy, innate preferences and context dependence. We further caution that behavioural experiments need to consider the possibility of context dependence in the design and interpretation of preference experiments. We conclude with a discussion of outstanding questions for future research. We also present a conceptual framework that incorporates the multiple dimensions of choice behaviour.

Information search in everyday decisions: The generalizability of the attraction search effect

AbstractThe recently proposedintegrated coherence-based decisions and search model(iCodes) makes predictions for search behavior in multi-attribute decision tasks beyond those of classic decision-making heuristics. More precisely, it predicts theAttraction Search Effectthat describes a tendency to search for information for the option that is already attractive given the available evidence. To date, the Attraction Search Effect has been successfully tested using a hypothetical stock-market game that was highly stylized and specifically designed to be highly diagnostic. In three experiments, we tested whether the Attraction Search Effect generalizes to different semantic contexts, different cue-value patterns, and a different presentation format than the classic matrix format. Across all experiments, we find evidence for information-search behavior that matches iCodes’s information-search prediction. Therefore, our results corroborate not only the generalizability of the Attraction Search Effect in various contexts but also the inherent process assumptions of iCodes.

The effect of mood on integration of information in a multi-attribute decision task

The exact effect of different moods on choosing strategies in multi-attribute decision tasks is yet unknown since previous work has found apparently contradicting results. Furthermore, different theoretical accounts lead to opposite expectations. While the “mood-as-information” theory states that a positive mood leads to heuristic processing of information and application of non-compensatory strategies, the “broaden-and-build” theory expects more non-compensatory decision-making in a negative mood. To test the predictions of those two theories, we conducted two experimental studies, in which both the mood and the type of information search were manipulated. The results rather support “mood-as-information” theory, so participants in the positive mood made non-compensatory choices more often than participants in the negative mood. The effect was only present in the open information board, where the information was presented simultaneously, but not in the closed Mouselab, where the information had to be searched in a sequential manner.

Effects of ego-depletion on choice behaviour in a multi-attribute decision task

ABSTRACTWhen a choice has to be made between two options and decision-relevant information about the options is completely available, the Take-The-Best (TTB) heuristic only considers the most important information that discriminates between the options and bases its choice on it. Choices in line with TTB thus allow a decision maker to save time and effort, and they may become more likely therefore under conditions of limited self-control strength (ego-depletion). Ego-depletion was manipulated prior to making a series of choices in a multi-attribute decision task. Choices could have been in line with either TTB or more effortful compensatory decision strategies. As predicted, compared with non-depleted participants, ego-depleted participants were more likely to make TTB-consistent choices.

Method of Inducing Attention using Coefficient of Variation in Decision Making Task

Many findings of behavioral decision research suggested that attention had a crucial role in decision-making. We proposed a method to induce attention toward particular attribute such as money using control by coefficient of variation in multi-attribute decision task. The experiment consisted of two steps: 1) arousing attention using the proposed method and 2) testing the effect of the method. We hypothesized that the participants would pay more attention to high, relative to low, CV attributes. The results for the first step of the experiment showed that the participants paid more attention to high, relative to low CV probability and low, relative to high, CV outcome. Results for the second step of the experiment were similar to those observed in the first step. This finding indicates that the proposed method was effective in arousing attention during the decision-making process.

Do people learn option or strategy routines in multi-attribute decisions? The answer depends on subtle factors

Various studies have shown that established decision routines may become detrimental in changing environments. Routines can be formed at the level of options or at the level of strategies which has been demonstrated in different lines of research. It is unclear, however, which routinization level is spontaneously preferred if both are possible and equally successful. The first experiment demonstrates that a typical multi-attribute decision task using the MouseLab procedure clearly invites strategy routinization. However, the second experiment shows that this level of routinization is massively context-dependent and will be shifted in line with subtle context cues that direct attention to different aspects of the feedback during learning. Theoretical implications for learning models of decision making are discussed.

Identifying decision strategies: A process- and outcome-based classification method

This article presents a classification method that makes possible the detection of important decision strategies that people use in nonrisky multiattribute decision tasks. The classification method incorporates a multimethod approach that combines elements of both structural modeling and process tracing. We begin with a discussion of nine common characteristics of decision strategies and then classify 13 decision strategies on the basis of these characteristics. Next, we demonstrate that six of the characteristics can be used to develop one outcome-based and three process-based metrics. In addition, we present an algorithm that shows how to use the metrics. We demonstrate that using our metrics in conjunction with a measure for direction of search allows identification of most of the 13 decision strategies. Finally, we outline the potential of our research for the design of decision support systems such as online shops.

Influence of Analytically and Intuitively Framed Instructions upon Multi-Attribute Decision Task Approach

Individuals may rely upon a number of decision making strategies in their approach to a complex decision making environment. For example, people may have a predisposition to rely upon intuitive and analytical decision making styles during task performance. These decision making styles, as measured by a Decision Making Styles Inventory (DMI), have been found to predict performance on a multi-attribute decision making task. It follows that manipulating the manner in which task instructions are framed, either analytically or intuitively, may have an influence upon task approach and performance as well. Influence of analytic and intuitive instructions for a multi-attribute decision making task are examined in this paper.

Take The Best, Dawes' Rule, and Compensatory Decision Strategies: A Regression-based Classification Method

Strategy descriptions like the ``Take The Best''-heuristic (G. Gigerenzer et al., 1991), the weighted additive rule, and the equal weight decision rule are competing theories about information integration in multi-attribute decision tasks. Behavioral decision research is confronted with the problem of drawing conclusions about unobservable decision strategies from behavioral data. Although there has been considerable progress due to methodical traditions like `Structural Modeling' and `Process Tracing', these paradigms have certain limitations in testing specific hypotheses about individual strategies. Some of these problems are summarized briefly. A deductive method for classifying individual response patterns is introduced. Predictions about regression coefficients are deduced from competing substantial hypotheses about strategies for decision making. These can be tested at the level of individual participants. The validity of this classification procedure is demonstrated with a Monte Carlo simulation. Some useful applications of the method are described, limitations of the method and potential generalizations are discussed.

Time pressure and the application of decision rules: Choices and judgments among multiattribute alternatives

The effects of time pressure on decisions and judgments were studied and related to the use of different decision rules in a multiattribute decision task. The decision alternatives were students described by their high school grades in Swedish, Psychology and Natural Science. The subjects were asked to choose the student they thought would be most able to follow a university program and graduate as a school psychologist. On the basis of earlier findings using the same kind of decision task (Svenson et al., 1990) it was hypothesised that subjects under time pressure would prefer candidates having the maximum grade across all attributes to a greater extent than subjects under no time pressure. Furthermore, it was hypothesised that subjects under time pressure would also focus more on the most important attribute and choose the alternatives being best on that attribute. The results supported these hypotheses.